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1. Synergistic PET/CT Reconstruction Using a Joint Generative Model

Author: Pinton, Noel Jeffrey, Bousse, Alexandre, Wang, Zhihan, Cheze-Le-Rest, Catherine, Maxim, Voichita, Comtat, Claude, Sureau, Florent, and Visvikis, Dimitris
Subjects: Physics - Medical Physics
Abstract: We propose in this work a framework for synergistic positron emission tomography (PET)/computed tomography (CT) reconstruction using a joint generative model as a penalty. We use a synergistic penalty function that promotes PET/CT pairs that are likely to occur together. The synergistic penalty function is based on a generative model, namely $\beta$-variational autoencoder ($\beta$-VAE). The model generates a PET/CT image pair from the same latent variable which contains the information that is shared between the two modalities. This sharing of inter-modal information can help reduce noise during reconstruction. Our result shows that our method was able to utilize the information between two modalities. The proposed method was able to outperform individually reconstructed images of PET (i.e., by maximum likelihood expectation maximization (MLEM)) and CT (i.e., by weighted least squares (WLS)) in terms of peak signal-to-noise ratio (PSNR). Future work will focus on optimizing the parameters of the $\beta$-VAE network and further exploration of other generative network models., Comment: 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D Conference) arXiv:2310.16846
Published: 2024

2. Euclid. I. Overview of the Euclid mission

Author: Euclid Collaboration, Mellier, Y., Abdurro'uf, Barroso, J. A. Acevedo, Achúcarro, A., Adamek, J., Adam, R., Addison, G. E., Aghanim, N., Aguena, M., Ajani, V., Akrami, Y., Al-Bahlawan, A., Alavi, A., Albuquerque, I. S., Alestas, G., Alguero, G., Allaoui, A., Allen, S. W., Allevato, V., Alonso-Tetilla, A. V., Altieri, B., Alvarez-Candal, A., Alvi, S., Amara, A., Amendola, L., Amiaux, J., Andika, I. T., Andreon, S., Andrews, A., Angora, G., Angulo, R. E., Annibali, F., Anselmi, A., Anselmi, S., Arcari, S., Archidiacono, M., Aricò, G., Arnaud, M., Arnouts, S., Asgari, M., Asorey, J., Atayde, L., Atek, H., Atrio-Barandela, F., Aubert, M., Aubourg, E., Auphan, T., Auricchio, N., Aussel, B., Aussel, H., Avelino, P. P., Avgoustidis, A., Avila, S., Awan, S., Azzollini, R., Baccigalupi, C., Bachelet, E., Bacon, D., Baes, M., Bagley, M. B., Bahr-Kalus, B., Balaguera-Antolinez, A., Balbinot, E., Balcells, M., Baldi, M., Baldry, I., Balestra, A., Ballardini, M., Ballester, O., Balogh, M., Bañados, E., Barbier, R., Bardelli, S., Baron, M., Barreiro, T., Barrena, R., Barriere, J. -C., Barros, B. J., Barthelemy, A., Bartolo, N., Basset, A., Battaglia, P., Battisti, A. J., Baugh, C. M., Baumont, L., Bazzanini, L., Beaulieu, J. -P., Beckmann, V., Belikov, A. N., Bel, J., Bellagamba, F., Bella, M., Bellini, E., Benabed, K., Bender, R., Benevento, G., Bennett, C. L., Benson, K., Bergamini, P., Bermejo-Climent, J. R., Bernardeau, F., Bertacca, D., Berthe, M., Berthier, J., Bethermin, M., Beutler, F., Bevillon, C., Bhargava, S., Bhatawdekar, R., Bianchi, D., Bisigello, L., Biviano, A., Blake, R. P., Blanchard, A., Blazek, J., Blot, L., Bosco, A., Bodendorf, C., Boenke, T., Böhringer, H., Boldrini, P., Bolzonella, M., Bonchi, A., Bonici, M., Bonino, D., Bonino, L., Bonvin, C., Bon, W., Booth, J. T., Borgani, S., Borlaff, A. S., Borsato, E., Bose, B., Botticella, M. T., Boucaud, A., Bouche, F., Boucher, J. S., Boutigny, D., Bouvard, T., Bouwens, R., Bouy, H., Bowler, R. A. A., Bozza, V., Bozzo, E., Branchini, E., Brando, G., Brau-Nogue, S., Brekke, P., Bremer, M. N., Brescia, M., Breton, M. -A., Brinchmann, J., Brinckmann, T., Brockley-Blatt, C., Brodwin, M., Brouard, L., Brown, M. L., Bruton, S., Bucko, J., Buddelmeijer, H., Buenadicha, G., Buitrago, F., Burger, P., Burigana, C., Busillo, V., Busonero, D., Cabanac, R., Cabayol-Garcia, L., Cagliari, M. S., Caillat, A., Caillat, L., Calabrese, M., Calabro, A., Calderone, G., Calura, F., Quevedo, B. Camacho, Camera, S., Campos, L., Canas-Herrera, G., Candini, G. P., Cantiello, M., Capobianco, V., Cappellaro, E., Cappelluti, N., Cappi, A., Caputi, K. I., Cara, C., Carbone, C., Cardone, V. F., Carella, E., Carlberg, R. G., Carle, M., Carminati, L., Caro, F., Carrasco, J. M., Carretero, J., Carrilho, P., Duque, J. Carron, Carry, B., Carvalho, A., Carvalho, C. S., Casas, R., Casas, S., Casenove, P., Casey, C. M., Cassata, P., Castander, F. J., Castelao, D., Castellano, M., Castiblanco, L., Castignani, G., Castro, T., Cavet, C., Cavuoti, S., Chabaud, P. -Y., Chambers, K. C., Charles, Y., Charlot, S., Chartab, N., Chary, R., Chaumeil, F., Cho, H., Chon, G., Ciancetta, E., Ciliegi, P., Cimatti, A., Cimino, M., Cioni, M. -R. L., Claydon, R., Cleland, C., Clément, B., Clements, D. L., Clerc, N., Clesse, S., Codis, S., Cogato, F., Colbert, J., Cole, R. E., Coles, P., Collett, T. E., Collins, R. S., Colodro-Conde, C., Colombo, C., Combes, F., Conforti, V., Congedo, G., Conseil, S., Conselice, C. J., Contarini, S., Contini, T., Conversi, L., Cooray, A. R., Copin, Y., Corasaniti, P. -S., Corcho-Caballero, P., Corcione, L., Cordes, O., Corpace, O., Correnti, M., Costanzi, M., Costille, A., Courbin, F., Mifsud, L. Courcoult, Courtois, H. M., Cousinou, M. -C., Covone, G., Cowell, T., Cragg, C., Cresci, G., Cristiani, S., Crocce, M., Cropper, M., Crouzet, P. E, Csizi, B., Cuby, J. -G., Cucchetti, E., Cucciati, O., Cuillandre, J. -C., Cunha, P. A. C., Cuozzo, V., Daddi, E., D'Addona, M., Dafonte, C., Dagoneau, N., Dalessandro, E., Dalton, G. B., D'Amico, G., Dannerbauer, H., Danto, P., Das, I., Da Silva, A., da Silva, R., Doumerg, W. d'Assignies, Daste, G., Davies, J. E., Davini, S., Dayal, P., de Boer, T., Decarli, R., De Caro, B., Degaudenzi, H., Degni, G., de Jong, J. T. A., de la Bella, L. F., de la Torre, S., Delhaise, F., Delley, D., Delucchi, G., De Lucia, G., Denniston, J., De Paolis, F., De Petris, M., Derosa, A., Desai, S., Desjacques, V., Despali, G., Desprez, G., De Vicente-Albendea, J., Deville, Y., Dias, J. D. F., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Diego, J. M., Di Ferdinando, D., Di Giorgio, A. M., Dimauro, P., Dinis, J., Dolag, K., Dolding, C., Dole, H., Sánchez, H. Domínguez, Doré, O., Dournac, F., Douspis, M., Dreihahn, H., Droge, B., Dryer, B., Dubath, F., Duc, P. -A., Ducret, F., Duffy, C., Dufresne, F., Duncan, C. A. J., Dupac, X., Duret, V., Durrer, R., Durret, F., Dusini, S., Ealet, A., Eggemeier, A., Eisenhardt, P. R. M., Elbaz, D., Elkhashab, M. Y., Ellien, A., Endicott, J., Enia, A., Erben, T., Vigo, J. A. Escartin, Escoffier, S., Sanz, I. Escudero, Essert, J., Ettori, S., Ezziati, M., Fabbian, G., Fabricius, M., Fang, Y., Farina, A., Farina, M., Farinelli, R., Farrens, S., Faustini, F., Feltre, A., Ferguson, A. M. N., Ferrando, P., Ferrari, A. G., Ferré-Mateu, A., Ferreira, P. G., Ferreras, I., Ferrero, I., Ferriol, S., Ferruit, P., Filleul, D., Finelli, F., Finkelstein, S. L., Finoguenov, A., Fiorini, B., Flentge, F., Focardi, P., Fonseca, J., Fontana, A., Fontanot, F., Fornari, F., Fosalba, P., Fossati, M., Fotopoulou, S., Fouchez, D., Fourmanoit, N., Frailis, M., Fraix-Burnet, D., Franceschi, E., Franco, A., Franzetti, P., Freihoefer, J., Frenk, C. . S., Frittoli, G., Frugier, P. -A., Frusciante, N., Fumagalli, A., Fumagalli, M., Fumana, M., Fu, Y., Gabarra, L., Galeotta, S., Galluccio, L., Ganga, K., Gao, H., García-Bellido, J., Garcia, K., Gardner, J. P., Garilli, B., Gaspar-Venancio, L. -M., Gasparetto, T., Gautard, V., Gavazzi, R., Gaztanaga, E., Genolet, L., Santos, R. Genova, Gentile, F., George, K., Gerbino, M., Ghaffari, Z., Giacomini, F., Gianotti, F., Gibb, G. P. S., Gillard, W., Gillis, B., Ginolfi, M., Giocoli, C., Girardi, M., Giri, S. K., Goh, L. W. K., Gómez-Alvarez, P., Gonzalez-Perez, V., Gonzalez, A. H., Gonzalez, E. J., Gonzalez, J. C., Beauchamps, S. Gouyou, Gozaliasl, G., Gracia-Carpio, J., Grandis, S., Granett, B. R., Granvik, M., Grazian, A., Gregorio, A., Grenet, C., Grillo, C., Grupp, F., Gruppioni, C., Gruppuso, A., Guerbuez, C., Guerrini, S., Guidi, M., Guillard, P., Gutierrez, C. M., Guttridge, P., Guzzo, L., Gwyn, S., Haapala, J., Haase, J., Haddow, C. R., Hailey, M., Hall, A., Hall, D., Hamaus, N., Haridasu, B. S., Harnois-Déraps, J., Harper, C., Hartley, W. G., Hasinger, G., Hassani, F., Hatch, N. A., Haugan, S. V. H., Häußler, B., Heavens, A., Heisenberg, L., Helmi, A., Helou, G., Hemmati, S., Henares, K., Herent, O., Hernández-Monteagudo, C., Heuberger, T., Hewett, P. C., Heydenreich, S., Hildebrandt, H., Hirschmann, M., Hjorth, J., Hoar, J., Hoekstra, H., Holland, A. D., Holliman, M. S., Holmes, W., Hook, I., Horeau, B., Hormuth, F., Hornstrup, A., Hosseini, S., Hu, D., Hudelot, P., Hudson, M. J., Huertas-Company, M., Huff, E. M., Hughes, A. C. N., Humphrey, A., Hunt, L. K., Huynh, D. D., Ibata, R., Ichikawa, K., Iglesias-Groth, S., Ilbert, O., Ilić, S., Ingoglia, L., Iodice, E., Israel, H., Israelsson, U. E., Izzo, L., Jablonka, P., Jackson, N., Jacobson, J., Jafariyazani, M., Jahnke, K., Jain, B., Jansen, H., Jarvis, M. J., Jasche, J., Jauzac, M., Jeffrey, N., Jhabvala, M., Jimenez-Teja, Y., Muñoz, A. Jimenez, Joachimi, B., Johansson, P. H., Joudaki, S., Jullo, E., Kajava, J. J. E., Kang, Y., Kannawadi, A., Kansal, V., Karagiannis, D., Kärcher, M., Kashlinsky, A., Kazandjian, M. V., Keck, F., Keihänen, E., Kerins, E., Kermiche, S., Khalil, A., Kiessling, A., Kiiveri, K., Kilbinger, M., Kim, J., King, R., Kirkpatrick, C. C., Kitching, T., Kluge, M., Knabenhans, M., Knapen, J. H., Knebe, A., Kneib, J. -P., Kohley, R., Koopmans, L. V. E., Koskinen, H., Koulouridis, E., Kou, R., Kovács, A., Kovačić, I., Kowalczyk, A., Koyama, K., Kraljic, K., Krause, O., Kruk, S., Kubik, B., Kuchner, U., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lacasa, F., Lacey, C. G., La Franca, F., Lagarde, N., Lahav, O., Laigle, C., La Marca, A., La Marle, O., Lamine, B., Lam, M. C., Lançon, A., Landt, H., Langer, M., Lapi, A., Larcheveque, C., Larsen, S. S., Lattanzi, M., Laudisio, F., Laugier, D., Laureijs, R., Laurent, V., Lavaux, G., Lawrenson, A., Lazanu, A., Lazeyras, T., Boulc'h, Q. Le, Brun, A. M. C. Le, Brun, V. Le, Leclercq, F., Lee, S., Graet, J. Le, Legrand, L., Leirvik, K. N., Jeune, M. Le, Lembo, M., Mignant, D. Le, Lepinzan, M. D., Lepori, F., Reun, A. Le, Leroy, G., Lesci, G. F., Lesgourgues, J., Leuzzi, L., Levi, M. E., Liaudat, T. I., Libet, G., Liebing, P., Ligori, S., Lilje, P. B., Lin, C. -C., Linde, D., Linder, E., Lindholm, V., Linke, L., Li, S. -S., Liu, S. J., Lloro, I., Lobo, F. S. N., Lodieu, N., Lombardi, M., Lombriser, L., Lonare, P., Longo, G., López-Caniego, M., Lopez, X. Lopez, Alvarez, J. Lorenzo, Loureiro, A., Loveday, J., Lusso, E., Macias-Perez, J., Maciaszek, T., Maggio, G., Magliocchetti, M., Magnard, F., Magnier, E. A., Magro, A., Mahler, G., Mainetti, G., Maino, D., Maiorano, E., Malavasi, N., Mamon, G. A., Mancini, C., Mandelbaum, R., Manera, M., Manjón-García, A., Mannucci, F., Mansutti, O., Outeiro, M. Manteiga, Maoli, R., Maraston, C., Marcin, S., Marcos-Arenal, P., Margalef-Bentabol, B., Marggraf, O., Marinucci, D., Marinucci, M., Markovic, K., Marleau, F. R., Marpaud, J., Martignac, J., Martín-Fleitas, J., Martin-Moruno, P., Martin, E. L., Martinelli, M., Martinet, N., Martin, H., Martins, C. J. A. P., Marulli, F., Massari, D., Massey, R., Masters, D. C., Matarrese, S., Matsuoka, Y., Matthew, S., Maughan, B. J., Mauri, N., Maurin, L., Maurogordato, S., McCarthy, K., McConnachie, A. W., McCracken, H. J., McDonald, I., McEwen, J. D., McPartland, C. J. R., Medinaceli, E., Mehta, V., Mei, S., Melchior, M., Melin, J. -B., Ménard, B., Mendes, J., Mendez-Abreu, J., Meneghetti, M., Mercurio, A., Merlin, E., Metcalf, R. B., Meylan, G., Migliaccio, M., Mignoli, M., Miller, L., Miluzio, M., Milvang-Jensen, B., Mimoso, J. P., Miquel, R., Miyatake, H., Mobasher, B., Mohr, J. J., Monaco, P., Monguió, M., Montoro, A., Mora, A., Dizgah, A. Moradinezhad, Moresco, M., Moretti, C., Morgante, G., Morisset, N., Moriya, T. J., Morris, P. W., Mortlock, D. J., Moscardini, L., Mota, D. F., Mottet, S., Moustakas, L. A., Moutard, T., Müller, T., Munari, E., Murphree, G., Murray, C., Murray, N., Musi, P., Nadathur, S., Nagam, B. C., Nagao, T., Naidoo, K., Nakajima, R., Nally, C., Natoli, P., Navarro-Alsina, A., Girones, D. Navarro, Neissner, C., Nersesian, A., Nesseris, S., Nguyen-Kim, H. N., Nicastro, L., Nichol, R. C., Nielbock, M., Niemi, S. -M., Nieto, S., Nilsson, K., Noller, J., Norberg, P., Nouri-Zonoz, A., Ntelis, P., Nucita, A. A., Nugent, P., Nunes, N. J., Nutma, T., Ocampo, I., Odier, J., Oesch, P. A., Oguri, M., Oliveira, D. Magalhaes, Onoue, M., Oosterbroek, T., Oppizzi, F., Ordenovic, C., Osato, K., Pacaud, F., Pace, F., Padilla, C., Paech, K., Pagano, L., Page, M. J., Palazzi, E., Paltani, S., Pamuk, S., Pandolfi, S., Paoletti, D., Paolillo, M., Papaderos, P., Pardede, K., Parimbelli, G., Parmar, A., Partmann, C., Pasian, F., Passalacqua, F., Paterson, K., Patrizii, L., Pattison, C., Paulino-Afonso, A., Paviot, R., Peacock, J. A., Pearce, F. R., Pedersen, K., Peel, A., Peletier, R. F., Ibanez, M. Pellejero, Pello, R., Penny, M. T., Percival, W. J., Perez-Garrido, A., Perotto, L., Pettorino, V., Pezzotta, A., Pezzuto, S., Philippon, A., Pierre, M., Piersanti, O., Pietroni, M., Piga, L., Pilo, L., Pires, S., Pisani, A., Pizzella, A., Pizzuti, L., Plana, C., Polenta, G., Pollack, J. E., Poncet, M., Pöntinen, M., Pool, P., Popa, L. A., Popa, V., Popp, J., Porciani, C., Porth, L., Potter, D., Poulain, M., Pourtsidou, A., Pozzetti, L., Prandoni, I., Pratt, G. W., Prezelus, S., Prieto, E., Pugno, A., Quai, S., Quilley, L., Racca, G. D., Raccanelli, A., Rácz, G., Radinović, S., Radovich, M., Ragagnin, A., Ragnit, U., Raison, F., Ramos-Chernenko, N., Ranc, C., Rasera, Y., Raylet, N., Rebolo, R., Refregier, A., Reimberg, P., Reiprich, T. H., Renk, F., Renzi, A., Retre, J., Revaz, Y., Reylé, C., Reynolds, L., Rhodes, J., Ricci, F., Ricci, M., Riccio, G., Ricken, S. O., Rissanen, S., Risso, I., Rix, H. -W., Robin, A. C., Rocca-Volmerange, B., Rocci, P. -F., Rodenhuis, M., Rodighiero, G., Monroy, M. Rodriguez, Rollins, R. P., Romanello, M., Roman, J., Romelli, E., Romero-Gomez, M., Roncarelli, M., Rosati, P., Rosset, C., Rossetti, E., Roster, W., Rottgering, H. J. A., Rozas-Fernández, A., Ruane, K., Rubino-Martin, J. A., Rudolph, A., Ruppin, F., Rusholme, B., Sacquegna, S., Sáez-Casares, I., Saga, S., Saglia, R., Sahlén, M., Saifollahi, T., Sakr, Z., Salvalaggio, J., Salvaterra, R., Salvati, L., Salvato, M., Salvignol, J. -C., Sánchez, A. G., Sanchez, E., Sanders, D. B., Sapone, D., Saponara, M., Sarpa, E., Sarron, F., Sartori, S., Sartoris, B., Sassolas, B., Sauniere, L., Sauvage, M., Sawicki, M., Scaramella, R., Scarlata, C., Scharré, L., Schaye, J., Schewtschenko, J. A., Schindler, J. -T., Schinnerer, E., Schirmer, M., Schmidt, F., Schmidt, M., Schneider, A., Schneider, M., Schneider, P., Schöneberg, N., Schrabback, T., Schultheis, M., Schulz, S., Schuster, N., Schwartz, J., Sciotti, D., Scodeggio, M., Scognamiglio, D., Scott, D., Scottez, V., Secroun, A., Sefusatti, E., Seidel, G., Seiffert, M., Sellentin, E., Selwood, M., Semboloni, E., Sereno, M., Serjeant, S., Serrano, S., Setnikar, G., Shankar, F., Sharples, R. M., Short, A., Shulevski, A., Shuntov, M., Sias, M., Sikkema, G., Silvestri, A., Simon, P., Sirignano, C., Sirri, G., Skottfelt, J., Slezak, E., Sluse, D., Smith, G. P., Smith, L. C., Smith, R. E., Smit, S. J. A., Soldano, F., Solheim, B. G. B., Sorce, J. G., Sorrenti, F., Soubrie, E., Spinoglio, L., Mancini, A. Spurio, Stadel, J., Stagnaro, L., Stanco, L., Stanford, S. A., Starck, J. -L., Stassi, P., Steinwagner, J., Stern, D., Stone, C., Strada, P., Strafella, F., Stramaccioni, D., Surace, C., Sureau, F., Suyu, S. H., Swindells, I., Szafraniec, M., Szapudi, I., Taamoli, S., Talia, M., Tallada-Crespí, P., Tanidis, K., Tao, C., Tarrío, P., Tavagnacco, D., Taylor, A. N., Taylor, J. E., Taylor, P. L., Teixeira, E. M., Tenti, M., Idiago, P. Teodoro, Teplitz, H. I., Tereno, I., Tessore, N., Testa, V., Testera, G., Tewes, M., Teyssier, R., Theret, N., Thizy, C., Thomas, P. D., Toba, Y., Toft, S., Toledo-Moreo, R., Tolstoy, E., Tommasi, E., Torbaniuk, O., Torradeflot, F., Tortora, C., Tosi, S., Tosti, S., Trifoglio, M., Troja, A., Trombetti, T., Tronconi, A., Tsedrik, M., Tsyganov, A., Tucci, M., Tutusaus, I., Uhlemann, C., Ulivi, L., Urbano, M., Vacher, L., Vaillon, L., Valageas, P., Valdes, I., Valentijn, E. A., Valenziano, L., Valieri, C., Valiviita, J., Broeck, M. Van den, Vassallo, T., Vavrek, R., Vega-Ferrero, J., Venemans, B., Venhola, A., Ventura, S., Kleijn, G. Verdoes, Vergani, D., Verma, A., Vernizzi, F., Veropalumbo, A., Verza, G., Vescovi, C., Vibert, D., Viel, M., Vielzeuf, P., Viglione, C., Viitanen, A., Villaescusa-Navarro, F., Vinciguerra, S., Visticot, F., Voggel, K., von Wietersheim-Kramsta, M., Vriend, W. J., Wachter, S., Walmsley, M., Walth, G., Walton, D. M., Walton, N. A., Wander, M., Wang, L., Wang, Y., Weaver, J. R., Weller, J., Wetzstein, M., Whalen, D. J., Whittam, I. H., Widmer, A., Wiesmann, M., Wilde, J., Williams, O. R., Winther, H. -A., Wittje, A., Wong, J. H. W., Wright, A. H., Yankelevich, V., Yeung, H. W., Yoon, M., Youles, S., Yung, L. Y. A., Zacchei, A., Zalesky, L., Zamorani, G., Vitorelli, A. Zamorano, Marc, M. Zanoni, Zennaro, M., Zerbi, F. M., Zinchenko, I. A., Zoubian, J., Zucca, E., and Zumalacarregui, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'
Published: 2024

3. Healthcare workers’ opinions on non-medical criteria for prioritisation of access to care during the pandemic

Author: Haaser, Thibaud, Maternowski, Paul-Jean, Marty, Sylvie, Duc, Sophie, Mollier, Olivier, Poullenot, Florian, Sureau, Patrick, and Avérous, Véronique
Published: 2024
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4. Healthcare workers’ opinions on non-medical criteria for prioritisation of access to care during the pandemic

Author: Thibaud Haaser, Paul-Jean Maternowski, Sylvie Marty, Sophie Duc, Olivier Mollier, Florian Poullenot, Patrick Sureau, and Véronique Avérous
Subjects: Prioritisation, Triage, Covid-19, Ethics committee, Vaccination status, Medical philosophy. Medical ethics, R723-726
Abstract: Abstract Introduction The COVID-19 pandemic generated overflow of healthcare systems in several countries. As the ethical debates focused on prioritisation for access to care with scarce medical resources, numerous recommendations were created. Late 2021, the emergence of the Omicron variant whose transmissibility was identified but whose vaccine sensitivity was still unknown, reactivated debates. Fears of the need to prioritise patients arose, particularly in France. Especially, a debate began about the role of vaccination status in the prioritisation strategy. Material and methods The Ethics Committee (EC) of the University Hospital of Bordeaux (UHB), France, identified prioritisation criteria in the literature (some recommended, such as being a healthcare worker (HCW) or having consented to research, while others were discouraged, such as age with a threshold effect or vaccination status). A survey was sent within the institution in January 2022 to explore frontline physicians' adherence to these prioritisation criteria. The decision making conditions were also surveyed. Results In 15 days, 78/165 (47.3%) frontline physicians responded, and more widely 1286/12946 (9.9%) professionals. A majority of frontline physicians were opposed to prioritising HCWs (54/75, 72%) and even more opposed to participating in research (69/76, 89.6%). Conversely, the results were very balanced for non-recommended criteria (respectively 39/77, 50.7% and 34/69 49.3% in favour for age with a threshold effect and for vaccination status). Decisions were considered to be multi-professional and multi-disciplinary for 65/76, 85.5% and 53/77, 68.8% of frontline physicians. Responders expressed opposition to extending decision-making to representatives of patients, civil society or HCWs not involved in care. Discussion Prioritisation recommendations in case of scarce medical resources were not necessarily approved by the frontline physicians, or by the other HCWs. This questions the way ethical recommendations should be communicated and discussed at a local scale, but it also questions these recommendations themselves. The article also reports the experience of seeking HCWs opinions on a sensitive ethical debate in a period of crisis.
Published: 2024
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5. Convergent ADMM Plug and Play PET Image Reconstruction

Author: Sureau, Florent, Latreche, Mahdi, Savanier, Marion, and Comtat, Claude
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: In this work, we investigate hybrid PET reconstruction algorithms based on coupling a model-based variational reconstruction and the application of a separately learnt Deep Neural Network operator (DNN) in an ADMM Plug and Play framework. Following recent results in optimization, fixed point convergence of the scheme can be achieved by enforcing an additional constraint on network parameters during learning. We propose such an ADMM algorithm and show in a realistic [18F]-FDG synthetic brain exam that the proposed scheme indeed lead experimentally to convergence to a meaningful fixed point. When the proposed constraint is not enforced during learning of the DNN, the proposed ADMM algorithm was observed experimentally not to converge.
Published: 2023

6. Analysis of Combinational Circuit Failure Rate based on Graph Partitioning and Probabilistic Binomial Approach

Author: Goudet, Esther, Sureau, Fabio, Breuil, Paul, Peña Treviño, Luis, Naviner, Lirida, Daveau, Jean-Marc, and Roche, Philippe
Published: 2024
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7. Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning

Author: Euclid Collaboration, Humphrey, A., Bisigello, L., Cunha, P. A. C., Bolzonella, M., Fotopoulou, S., Caputi, K., Tortora, C., Zamorani, G., Papaderos, P., Vergani, D., Brinchmann, J., Moresco, M., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bonino, D., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Gomez-Alvarez, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kummel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Nightingale, J., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Scaramella, R., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespi, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Farinelli, R., Gracia-Carpio, J., Maino, D., Mauri, N., Mei, S., Morisset, N., Sureau, F., Tenti, M., Tramacere, A., Zucca, E., Baccigalupi, C., Balaguera-Antolinez, A., Biviano, A., Blanchard, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Cooray, A. R., Coupon, J., Courtois, H. M., Cucciati, O., Davini, S., De Lucia, G., Dole, H., Escartin, J. A., Escoffier, S., Fabricius, M., Farina, M., Finelli, F., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hook, I., Huertas-Company, M., Joachimi, B., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Nucita, A. A., Patrizii, L., Peel, A., Pollack, J. E., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sanchez, A. G., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., Viel, M., Calura, F., and Hildebrandt, H.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in combination with multiwavelength photometry from other surveys. The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has `sparsity-awareness', so that missing photometry values are still informative for the classification. Our pipeline derives photometric redshifts for galaxies selected as quiescent, aided by the `pseudo-labelling' semi-supervised method. After application of the outlier filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03 and a fraction of catastrophic outliers of ~< 0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our classification pipeline outperforms UVJ selection, in addition to the Euclid I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in completeness and the F1-score of up to a factor of 2. (Abridged), Comment: 37 pages (including appendices), 26 figures; accepted for publication in Astronomy & Astrophysics
Published: 2022
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8. Synergistic PET/MR Reconstruction with VAE Constraint.

Author: Valentin Gautier, Claude Comtat, Florent Sureau, Alexandre Bousse, Voichita Maxim, and Bruno Sixou
Published: 2024

9. Drift of Combinational Circuits Failure Rates with a Probabilistic Model Approximated by Partitioning.

Author: Esther Goudet, Luis Peña Treviño, Gutemberg G. dos Santos Júnior, Sayah El Hajji, Fabio Sureau, Lirida Naviner, Jean-Marc Daveau, and Philippe Roche
Published: 2024
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10. Learning with Fixed Point Condition for Convergent PNP PET Reconstruction.

Author: Marion Savanier, Claude Comtat, and Florent Sureau
Published: 2024
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11. On The Local Geometry of the Pet Reconstruction Problem.

Author: Ramy Merabet, Florent Sureau, and Alain Trouvé
Published: 2024
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12. Euclid preparation: XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

Author: Euclid Collaboration, Bisigello, L., Conselice, C. J., Baes, M., Bolzonella, M., Brescia, M., Cavuoti, S., Cucciati, O., Humphrey, A., Hunt, L. K., Maraston, C., Pozzetti, L., Tortora, C., van Mierlo, S. E., Aghanim, N., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rix, H. -W., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Boucaud, S. Bardelli A., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Lindholm, V., Maino, D., Mei, S., Scottez, V., Sureau, F., Tenti, M., Zucca, E., Borlaff, A. S., Ballardini, M., Biviano, A., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A., Coupon, J., Courtois, H. M., Cuby, J., Davini, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Farina, M., Fotopoulou, S., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Huertas-Company, M., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macías-Pérez, J. F., Magliocchetti, M., Mainetti, G., Marcin, S., Martinelli, M., Martinet, N., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Peel, A., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., and Viel, M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFR) can be measured with deep learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that Deep Learning Neural Networks and Convolutional Neutral Networks (CNN), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multi-band magnitudes together with $H_{\scriptscriptstyle\rm E}$-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving i) the redshift within a normalised error of less than 0.15 for 99.9$\%$ of the galaxies with S/N>3 in the $H_{\scriptscriptstyle\rm E}$-band; ii) the stellar mass within a factor of two ($\sim0.3 \rm dex$) for 99.5$\%$ of the considered galaxies; iii) the SFR within a factor of two ($\sim0.3 \rm dex$) for $\sim$70$\%$ of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning., Comment: accepted for publication in MNRAS, 21 pages, 22 figures, 6 tables
Published: 2022

13. Euclid preparation. XXI. Intermediate-redshift contaminants in the search for $z>6$ galaxies within the Euclid Deep Survey

Author: Euclid Collaboration, van Mierlo, S. E., Caputi, K. I., Ashby, M., Atek, H., Bolzonella, M., Bowler, R. A. A., Brammer, G., Conselice, C. J., Cuby, J., Dayal, P., Díaz-Sánchez, A., Finkelstein, S. L., Hoekstra, H., Humphrey, A., Ilbert, O., McCracken, H. J., Milvang-Jensen, B., Oesch, P. A., Pello, R., Rodighiero, G., Schirmer, M., Toft, S., Weaver, J. R., Wilkins, S. M., Willott, C. J., Zamorani, G., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Surace, C., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Graciá-Carpio, J., Maino, D., Mauri, N., Mei, S., Sureau, F., Zucca, E., Aussel, H., Baccigalupi, C., Balaguera-Antolínez, A., Biviano, A., Blanchard, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Calura, F., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Cooray, A. R., Coupon, J., Courtois, H. M., Crocce, M., Cucciati, O., Davini, S., Dole, H., Escartin, J. A., Escoffier, S., Fabricius, M., Farina, M., Ganga, K., García-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Gwyn, S., Hook, I., Huertas-Company, M., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Maoli, R., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Monaco, P., Morgante, G., Nucita, A. A., Patrizii, L., Peel, A., Pollack, J., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sánchez, A. G., Scottez, V., Sefusatti, E., Stadel, J., Teyssier, R., Valiviita, J., and Viel, M.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: (Abridged) The Euclid mission is expected to discover thousands of z>6 galaxies in three Deep Fields, which together will cover a ~40 deg2 area. However, the limited number of Euclid bands and availability of ancillary data could make the identification of z>6 galaxies challenging. In this work, we assess the degree of contamination by intermediate-redshift galaxies (z=1-5.8) expected for z>6 galaxies within the Euclid Deep Survey. This study is based on ~176,000 real galaxies at z=1-8 in a ~0.7 deg2 area selected from the UltraVISTA ultra-deep survey, and ~96,000 mock galaxies with 25.3$\leq$H<27.0, which altogether cover the range of magnitudes to be probed in the Euclid Deep Survey. We simulate Euclid and ancillary photometry from the fiducial, 28-band photometry, and fit spectral energy distributions (SEDs) to various combinations of these simulated data. Our study demonstrates that identifying z>6 with Euclid data alone will be very effective, with a z>6 recovery of 91(88)% for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z=1-5.8 contaminants amongst apparent z>6 galaxies as observed with Euclid alone is 18%, which is reduced to 4(13)% by including ultra-deep Rubin (Spitzer) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimized to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid (I-Y)>2.8 and (Y-J)<1.4 colour criteria can separate contaminants from true z>6 galaxies, although these are applicable to only 54% of the contaminants, as many have unconstrained (I-Y) colours. In the most optimistic scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z>6 sample. For the faint mock sample, colour cuts are infeasible., Comment: 27 pages, 14 figures, 9 tables; version accepted for publication by A&A
Published: 2022
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14. Accueillir l’enfant suivant un deuil périnatal : l’examen de Brazelton comme intervention thérapeutique précoce

Author: Sureau, Y., Garez, V., and Le Roux, N.
Published: 2024
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15. ShapeNet: Shape Constraint for Galaxy Image Deconvolution

Author: Nammour, F., Akhaury, U., Girard, J. N., Lanusse, F., Sureau, F., Ali, C. Ben, and Starck, J. -L.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Deep Learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful to deconvolve optical astronomical images (Sureau et al. 2020). Yet, this approach only uses the $\ell_2$ loss, which does not guarantee the preservation of physical information (e.g. flux and shape) of the object reconstructed in the image. In Nammour et al. (2021), a new loss function was proposed in the framework of sparse deconvolution, which better preserves the shape of galaxies and reduces the pixel error. In this paper, we extend Tikhonet to take into account this shape constraint, and apply our new DL method, called ShapeNet, to optical and radio-interferometry simulated data set. The originality of the paper relies on i) the shape constraint we use in the neural network framework, ii) the application of deep learning to radio-interferometry image deconvolution for the first time, and iii) the generation of a simulated radio data set that we make available for the community. A range of examples illustrates the results., Comment: 6 pages, 6 figures
Published: 2022
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16. Analysis of Combinational Circuit Failure Rate based on Graph Partitioning and Probabilistic Binomial Approach.

Author: Esther Goudet, Fabio Sureau, Paul Breuil, Luis Peña Treviño, Lirida A. B. Naviner, Jean-Marc Daveau, and Philippe Roche
Published: 2024
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17. Contaminated freshwater as a Harbinger of tropical disease spread in Europe

Author: Axelle Costa, Hugo Guerrero, Aurore Sureau, Morgane Tassaint, and Ronaldo de Carvalho Augusto
Subjects: water quality, aquatic ecosystems, environmental parasitology, Schistosoma spp., chemical pollutants, Environmental sciences, GE1-350
Abstract: Human-induced environmental changes, including climate change and pollution, significantly affect host-parasite interactions, potentially altering the geographical spread and severity of various parasitic diseases. These changes may particularly influence the dynamics of diseases like schistosomiasis, posing significant public health concerns. This review explores how pollutants such as organophosphate pesticides, antibiotics, heavy metals, cities’ landfills, and microplastics can affect the development and transmission dynamics of parasites, especially Schistosoma spp. Our researches highlight that pesticides promote parasitic disease development, while pharmaceuticals have mixed effects on the life cycles of these parasites. Similarly, heavy metals found in water systems disrupt host-pathogen interactions, and microplastics are linked to significant changes in snail stressor genes, a critical intermediate host for several parasites. With the rising impacts of anthropogenic activity on the environment, there is an urgent need to reassess and adjust regulatory policies to minimize these threats. By studying the implications of pollution on host-parasite interactions, we can develop better strategies for disease control and improve the preservation of our ecosystem’s health.
Published: 2024
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18. Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields

Author: Moneti, Andrea, McCracken, H. J., Shuntov, M., Kauffmann, O. B., Capak, P., Davidzon, I., Ilbert, O., Scarlata, C., Toft, S., Weaver, J., Chary, R., Cuby, J., Faisst, A. L., Masters, D. C., McPartland, C., Mobasher, B., Sanders, D. B., Scaramella, R., Stern, D., Szapudi, I., Teplitz, H., Zalesky, L., Amara, A., Auricchio, N., Bodendorf, C., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Garilli, B., Gillis, B., Giocoli, C., Granett, B. R., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kuemmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Rix, H., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespi, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Camera, S., Gracia-Carpio, J., Medinaceli, E., Mei, S., Polenta, G., Romelli, E., Sureau, F., Tenti, M., Vassallo, T., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antolinez, A., Bernardeau, F., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Di Ferdinando, D., Farina, M., Finelli, F., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Maoli, R., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Morisset, N., Nucita, A., Patrizii, L., Potter, D., Renzi, A., Riccio, G., Sanchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Teyssier, R., Tubio, O., Tutusaus, I., Valiviita, J., Viel, M., and Hildebrandt, H.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer's Infrared Array Camera (IRAC). We have combined these new observations with all relevant IRAC archival data of these fields in order to produce the deepest possible mosaics of these regions. In total, these observations represent nearly 11% of the total Spitzer mission time. The resulting mosaics cover a total of approximately 71.5deg$^2$ in the 3.6 and 4.5um bands, and approximately 21.8deg$^2$ in the 5.8 and 8um bands. They reach at least 24 AB magnitude (measured to sigma, in a 2.5 arcsec aperture) in the 3.6um band and up to ~ 5 mag deeper in the deepest regions. The astrometry is tied to the Gaia astrometric reference system, and the typical astrometric uncertainty for sources with 16<[3.6]<19 is <0.15 arcsec. The photometric calibration is in excellent agreement with previous WISE measurements. We have extracted source number counts from the 3.6um band mosaics and they are in excellent agreement with previous measurements. Given that the Spitzer Space Telescope has now been decommissioned these mosaics are likely to be the definitive reduction of these IRAC data. This survey therefore represents an essential first step in assembling multi-wavelength data on the Euclid deep fields which are set to become some of the premier fields for extragalactic astronomy in the 2020s., Comment: 15 pages with 11 figures, approved by Euclid Consortium Publication Board and submitted to Astronomy and Astrophysics. Data products will become available via the IRSA website once the paper is accepted. This paper is a companion to "COSMOS2020: A panchromatic view of the Universe to z~10 from two complementary catalogs" by John Weaver et al., which is being posted in parallel
Published: 2021
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19. Euclid preparation: XIX. Impact of magnification on photometric galaxy clustering

Author: Lepori, F., Tutusaus, I., Viglione, C., Bonvin, C., Camera, S., Castander, F. J., Durrer, R., Fosalba, P., Jelic-Cizmek, G., Kunz, M., Adamek, J., Casas, S., Martinelli, M., Sakr, Z., Sapone, D., Amara, A., Auricchio, N., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Ferriol, S., Franceschi, E., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kümmel, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Fabbian, G., Graciá-Carpio, J., Maino, D., Medinaceli, E., Mei, S., Renzi, A., Romelli, E., Sureau, F., Vassallo, T., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Bernardeau, F., Biviano, A., Blanchard, A., Bolzonella, M., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J. -G., Davini, S., de la Torre, S., Di Ferdinando, D., Farina, M., Ferreira, P. G., Finelli, F., Galeotta, S., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I. M., Ilić, S., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maoli, R., Martinet, N., Maturi, M., Metcalf, R. B., Monaco, P., Morgante, G., Nightingale, J., Nucita, A., Patrizii, L., Popa, V., Potter, D., Riccio, G., Sánchez, A. G, Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Tramacere, A., Valiviita, J., Viel, M., and Hildebrandt, H.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We investigate the importance of lensing magnification for estimates of galaxy clustering and its cross-correlation with shear for the photometric sample of Euclid. Using updated specifications, we study the impact of lensing magnification on the constraints and the shift in the estimation of the best fitting cosmological parameters that we expect if this effect is neglected. We follow the prescriptions of the official Euclid Fisher matrix forecast for the photometric galaxy clustering analysis and the combination of photometric clustering and cosmic shear. The slope of the luminosity function (local count slope), which regulates the amplitude of the lensing magnification, and the galaxy bias have been estimated from the Euclid Flagship simulation.We find that magnification significantly affects both the best-fit estimation of cosmological parameters and the constraints in the galaxy clustering analysis of the photometric sample. In particular, including magnification in the analysis reduces the 1$\sigma$ errors on $\Omega_{\text{m},0}, w_{0}, w_a$ at the level of 20-35%, depending on how well we will be able to independently measure the local count slope. In addition, we find that neglecting magnification in the clustering analysis leads to shifts of up to 1.6$\sigma$ in the best-fit parameters. In the joint analysis of galaxy clustering, cosmic shear, and galaxy-galaxy lensing, magnification does not improve precision, but it leads to an up to 6$\sigma$ bias if neglected. Therefore, for all models considered in this work, magnification has to be included in the analysis of galaxy clustering and its cross-correlation with the shear signal ($3\times2$pt analysis) for an accurate parameter estimation., Comment: 26 pages, 11 figures. Version accepted for publication in A&A
Published: 2021
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20. Euclid preparation: XVI. Exploring the ultra low-surface brightness Universe with Euclid/VIS

Author: Borlaff, A. S., Gómez-Alvarez, P., Altieri, B., Marcum, P. M., Vavrek, R., Laureijs, R., Kohley, R., Buitrago, F., Cuillandre, J. C., Duc, P. A., Venancio, L. M. Gaspar, Amara, A., Andreon, S., Auricchio, N., Azzollini, R., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Bozzo, E., Branchini, E., Brescia, M., Brinchmann, J., Burigana, C., Cabanac, R., Camera, S., Candini, G. P., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Coupon, J., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Ferdinando, D., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Fabricius, M., Farina, M., Farrens, S., Ferreira, P. G., Ferriol, S., Finelli, F., Flose-Reimberg, P., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Ganga, K., Garilli, B., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Keihanen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kirkpatrick, C. C., Kitching, T., Knapen, J. H., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Liebing, P., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Martínez-Delgado, D., Marulli, F., Massey, R., Maturi, M., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Merlin, E., Metcalf, R. B., Meylan, G., Moresco, M., Morgante, G., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S. M., Nightingale, J. W., Nucita, A., Padilla, C., Paltani, S., Pasian, F., Patrizii, L., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Saglia, R., Sánchez, A. G., Sapone, D., Sauvage, M., Schneider, P., Scottez, V., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J. L., Sureau, F., Tallada-Crespí, P., Taylor, A. N., Tenti, M., Tereno, I., Teyssier, R., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Weller, J., Whittaker, L., Zacchei, A., Zamorani, G., and Zucca, E.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: While Euclid is an ESA mission specifically designed to investigate the nature of Dark Energy and Dark Matter, the planned unprecedented combination of survey area ($\sim15\,000$ deg$^2$), spatial resolution, low sky-background, and depth also make Euclid an excellent space observatory for the study of the low surface brightness Universe. Scientific exploitation of the extended low surface brightness structures requires dedicated calibration procedures yet to be tested. We investigate the capabilities of Euclid to detect extended low surface brightness structure by identifying and quantifying sky background sources and stray-light contamination. We test the feasibility of generating sky flat-fields to reduce large-scale residual gradients in order to reveal the extended emission of galaxies observed in the Euclid Survey. We simulate a realistic set of Euclid/VIS observations, taking into account both instrumental and astronomical sources of contamination, including cosmic rays, stray-light, zodiacal light, ISM, and the CIB, while simulating the effects of the presence of background sources in the FOV. We demonstrate that a combination of calibration lamps, sky flats and self-calibration would enable recovery of emission at a limiting surface brightness magnitude of $\mu=29.5^{+0.08}_{-0.27} $ mag arcsec$^{-2}$ ($3\sigma$, $10\times10$ arcsec$^2$) in the Wide Survey, reaching regions 2 magnitudes deeper in the Deep Surveys. Euclid/VIS has the potential to be an excellent low surface brightness observatory. Covering the gap between pixel-to-pixel calibration lamp flats and self-calibration observations for large scales, the application of sky flat-fielding will enhance the sensitivity of the VIS detector at scales of larger than 1 degree, up to the size of the FOV, enabling Euclid to detect extended surface brightness structures below $\mu=31$ mag arcsec$^{-2}$ and beyond., Comment: 23 pages, 13 figures, Euclid Consortium Key Project, accepted for publication in Astronomy & Astrophysics
Published: 2021
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21. Euclid preparation: I. The Euclid Wide Survey

Author: Scaramella, R., Amiaux, J., Mellier, Y., Burigana, C., Carvalho, C. S., Cuillandre, J. -C., Da Silva, A., Derosa, A., Dinis, J., Maiorano, E., Maris, M., Tereno, I., Laureijs, R., Boenke, T., Buenadicha, G., Dupac, X., Venancio, L. M. Gaspar, Gómez-Álvarez, P., Hoar, J., Alvarez, J. Lorenzo, Racca, G. D., Saavedra-Criado, G., Schwartz, J., Vavrek, R., Schirmer, M., Aussel, H., Azzollini, R., Cardone, V. F., Cropper, M., Ealet, A., Garilli, B., Gillard, W., Granett, B. R., Guzzo, L., Hoekstra, H., Jahnke, K., Kitching, T., Meneghetti, M., Miller, L., Nakajima, R., Niemi, S. M., Pasian, F., Percival, W. J., Sauvage, M., Scodeggio, M., Wachter, S., Zacchei, A., Aghanim, N., Amara, A., Auphan, T., Auricchio, N., Awan, S., Balestra, A., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Candini, G. P., Capobianco, V., Carbone, C., Carlberg, R. G., Carretero, J., Casas, R., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Courbin, F., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hudelot, P., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Melchior, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Morin, B., Moscardini, L., Munari, E., Nichol, R. C., Padilla, C., Paltani, S., Peacock, J., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Rix, H. -W., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J. L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Toledo-Moreo, R., Torradeflot, F., Trifoglio, M., Valentijn, E. A., Valenziano, L., Kleijn, G. A. Verdoes, Wang, Y., Welikala, N., Weller, J., Wetzstein, M., Zamorani, G., Zoubian, J., Andreon, S., Baldi, M., Bardelli, S., Boucaud, A., Camera, S., Fabbian, G., Farinelli, R., Graciá-Carpio, J., Maino, D., Medinaceli, E., Mei, S., Neissner, C., Polenta, G., Renzi, A., Romelli, E., Rosset, C., Sureau, F., Tenti, M., Vassallo, T., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Battaglia, P., Biviano, A., Borgani, S., Bozzo, E., Cabanac, R., Cappi, A., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J., de la Torre, S., Desai, S., Di Ferdinando, D., Dole, H., Fabricius, M., Farina, M., Ferreira, P. G., Finelli, F., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Gozaliasl, G., Hook, I. M., Keihanen, E., Kirkpatrick, C. C., Liebing, P., Lindholm, V., Mainetti, G., Martinelli, M., Martinet, N., Maturi, M., McCracken, H. J., Metcalf, R. B., Morgante, G., Nightingale, J., Nucita, A., Patrizii, L., Potter, D., Riccio, G., Sánchez, A. G., Sapone, D., Schewtschenko, J. A., Schultheis, M., Scottez, V., Teyssier, R., Tutusaus, I., Valiviita, J., Viel, M., Vriend, W., and Whittaker, L.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Euclid is an ESA mission designed to constrain the properties of dark energy and gravity via weak gravitational lensing and galaxy clustering. It will carry out a wide area imaging and spectroscopy survey (EWS) in visible and near-infrared, covering roughly 15,000 square degrees of extragalactic sky on six years. The wide-field telescope and instruments are optimized for pristine PSF and reduced straylight, producing very crisp images. This paper presents the building of the Euclid reference survey: the sequence of pointings of EWS, Deep fields, Auxiliary fields for calibrations, and spacecraft movements followed by Euclid as it operates in a step-and-stare mode from its orbit around the Lagrange point L2. Each EWS pointing has four dithered frames; we simulate the dither pattern at pixel level to analyse the effective coverage. We use up-to-date models for the sky background to define the Euclid region-of-interest (RoI). The building of the reference survey is highly constrained from calibration cadences, spacecraft constraints and background levels; synergies with ground-based coverage are also considered. Via purposely-built software optimized to prioritize best sky areas, produce a compact coverage, and ensure thermal stability, we generate a schedule for the Auxiliary and Deep fields observations and schedule the RoI with EWS transit observations. The resulting reference survey RSD_2021A fulfills all constraints and is a good proxy for the final solution. Its wide survey covers 14,500 square degrees. The limiting AB magnitudes ($5\sigma$ point-like source) achieved in its footprint are estimated to be 26.2 (visible) and 24.5 (near-infrared); for spectroscopy, the H$_\alpha$ line flux limit is $2\times 10^{-16}$ erg cm$^{-2}$ s$^{-1}$ at 1600 nm; and for diffuse emission the surface brightness limits are 29.8 (visible) and 28.4 (near-infrared) mag arcsec$^{-2}$., Comment: 43 pages, 51 figures, submitted to A&A
Published: 2021
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22. A Mystery of Mysteries: The Death and Life of Edgar Allan Poe by Mark Dawidziak (review)

Author: Sureau, Eloise C.
Published: 2023

23. Extracellular vesicles from activated platelets possess a phospholipid-rich biomolecular profile and enhance prothrombinase activity

Author: Guerreiro, Eduarda M., Kruglik, Sergei G., Swamy, Samantha, Latysheva, Nadezhda, Østerud, Bjarne, Guigner, Jean-Michel, Sureau, Franck, Bonneau, Stephanie, Kuzmin, Andrey N., Prasad, Paras N., Hansen, John-Bjarne, Hellesø, Olav Gaute, and Snir, Omri
Published: 2024
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24. Euclid Preparation: XIV. The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Data Release 3

Author: Euclid Collaboration, Stanford, S. A., Masters, D., Darvish, B., Stern, D., Cohen, J. G., Capak, P., Hernitschek, N., Davidzon, I., Rhodes, J., Sanders, D. B., Mobasher, B., Castander, F. J., Paltani, S., Aghanim, N., Amara, A., Auricchio, N., Balestra, A., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Casas, R., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Conselice, C. J., Corcione, L., Costille, A., Cropper, M., Degaudenzi, H., Douspis, M., Dubath, F., Dusini, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Giocoli, C., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kummel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Massey, R., Meneghetti, M., Meylan, G., Moscardini, L., Niemi, S. M., Padilla, C., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Roncarelli, M., Rossetti, E., Saglia, R., Scaramella, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Valentijn, E. A., Valenziano, L., Kleijn, G. A. Verdoes, Wang, Y., Zamorani, G., Zoubian, J., Brescia, M., Congedo, G., Conversi, L., Copin, Y., Kermiche, S., Kohley, R., Medinaceli, E., Mei, S., Moresco, M., Morin, B., Munari, E., Polenta, G., Sureau, F., Crespí, P. Tallada, Vassallo, T., Zacchei, A., Andreon, S., Aussel, H., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Biviano, A., Borsato, E., Bozzo, E., Burigana, C., Cabanac, R., Camera, S., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J. -G., Da Silva, A., de la Torre, S., Di Ferdinando, D., Duncan, C. A. J., Dupac, X., Fabricius, M., Farina, M., Farrens, S., Ferreira, P. G., Finelli, F., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Gillard, W., Gozaliasl, G., Graciá-Carpio, J., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Martinet, N., Marulli, F., Maturi, M., Maurogordato, S., Metcalf, R. B., Nakajima, R., Neissner, C., Nightingale, J. W., Nucita, A. A., Patrizii, L., Potter, D., Renzi, A., Riccio, G., Romelli, E., Sanchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Stanco, L., Tenti, M., Teyssier, R., Torradeflot, F., Valiviita, J., Viel, M., Whittaker, L., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Complete Calibration of the Color-Redshift Relation (C3R2) survey is obtaining spectroscopic redshifts in order to map the relation between galaxy color and redshift to a depth of i ~ 24.5 (AB). The primary goal is to enable sufficiently accurate photometric redshifts for Stage IV dark energy projects, particularly Euclid and the Roman Space Telescope, which are designed to constrain cosmological parameters through weak lensing. We present 676 new high-confidence spectroscopic redshifts obtained by the C3R2 survey in the 2017B-2019B semesters using the DEIMOS, LRIS, and MOSFIRE multi-object spectrographs on the Keck telescopes. Combined with the 4454 redshifts previously published by this project, the C3R2 survey has now obtained and published 5130 high-quality galaxy spectra and redshifts. If we restrict consideration to only the 0.2 < z(phot) < 2.6 range of interest for the Euclid cosmological goals, then with the current data release C3R2 has increased the spectroscopic redshift coverage of the Euclid color space from 51% (as reported by Masters et al. 2015) to the current 91%. Once completed and combined with extensive data collected by other spectroscopic surveys, C3R2 should provide the spectroscopic calibration set needed to enable photometric redshifts to meet the cosmology requirements for Euclid, and make significant headway toward solving the problem for Roman., Comment: 14 pages, 7 figures, 4 tables. Accepted for publication in The Astrophysical Journal Supplement. Survey website with links to the C3R2 redshift catalog and the spectroscopic data hosted by the Keck Observatory Archive can be found at https://sites.google.com/view/c3r2-survey/home
Published: 2021
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25. $Euclid$ preparation: XV. Forecasting cosmological constraints for the $Euclid$ and CMB joint analysis

Author: Euclid Collaboration, Ilić, S., Aghanim, N., Baccigalupi, C., Bermejo-Climent, J. R., Fabbian, G., Legrand, L., Paoletti, D., Ballardini, M., Archidiacono, M., Douspis, M., Finelli, F., Ganga, K., Hernández-Monteagudo, C., Lattanzi, M., Marinucci, D., Migliaccio, M., Carbone, C., Casas, S., Martinelli, M., Tutusaus, I., Natoli, P., Ntelis, P., Pagano, L., Wenzl, L., Gruppuso, A., Kitching, T., Langer, M., Mauri, N., Patrizii, L., Renzi, A., Sirri, G., Stanco, L., Tenti, M., Vielzeuf, P., Lacasa, F., Polenta, G., Yankelevich, V., Blanchard, A., Sakr, Z., Pourtsidou, A., Camera, S., Cardone, V. F., Kilbinger, M., Kunz, M., Markovic, K., Pettorino, V., Sánchez, A. G., Sapone, D., Amara, A., Auricchio, N., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kohley, R., Kubik, B., Kümmel, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Marulli, F., Massey, R., Maurogordato, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Scaramella, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Starck, J. L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Kleijn, G. A. Verdoes, Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Medinaceli, E., Mei, S., Rosset, C., Sureau, F., Vassallo, T., Zacchei, A., Andreon, S., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Biviano, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J., de la Torre, S., Di Ferdinando, D., Dole, H., Farina, M., Ferreira, P. G., Flose-Reimberg, P., Galeotta, S., Gozaliasl, G., Graciá-Carpio, J., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Neissner, C., Nightingale, J., Nucita, A. A., Potter, D., Riccio, G., Romelli, E., Schirmer, M., Schultheis, M., Scottez, V., Teyssier, R., Tramacere, A., Valiviita, J., Viel, M., Whittaker, L., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The combination and cross-correlation of the upcoming $Euclid$ data with cosmic microwave background (CMB) measurements is a source of great expectation since it will provide the largest lever arm of epochs, ranging from recombination to structure formation across the entire past light cone. In this work, we present forecasts for the joint analysis of $Euclid$ and CMB data on the cosmological parameters of the standard cosmological model and some of its extensions. This work expands and complements the recently published forecasts based on $Euclid$-specific probes, namely galaxy clustering, weak lensing, and their cross-correlation. With some assumptions on the specifications of current and future CMB experiments, the predicted constraints are obtained from both a standard Fisher formalism and a posterior-fitting approach based on actual CMB data. Compared to a $Euclid$-only analysis, the addition of CMB data leads to a substantial impact on constraints for all cosmological parameters of the standard $\Lambda$-cold-dark-matter model, with improvements reaching up to a factor of ten. For the parameters of extended models, which include a redshift-dependent dark energy equation of state, non-zero curvature, and a phenomenological modification of gravity, improvements can be of the order of two to three, reaching higher than ten in some cases. The results highlight the crucial importance for cosmological constraints of the combination and cross-correlation of $Euclid$ probes with CMB data., Comment: 20 pages, 8 figures, 5 tables, 1 appendix; updated to match version accepted by journal
Published: 2021
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26. Euclid preparation: XII. Optimizing the photometric sample of the Euclid survey for galaxy clustering and galaxy-galaxy lensing analyses

Author: Euclid Collaboration, Pocino, A., Tutusaus, I., Castander, F. J., Fosalba, P., Crocce, M., Porredon, A., Camera, S., Cardone, V., Casas, S., Kitching, T., Lacasa, F., Martinelli, M., Pourtsidou, A., Sakr, Z., Andreon, S., Auricchio, N., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Balestra, A., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Boucaud, A., Bozzo, E., Branchini, E., Brescia, M., Brinchmann, J., Burigana, C., Cabanac, R., Capobianco, V., Cappi, A., Carvalho, C. S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Coupon, J., Courtois, H. M., Cropper, M., Cuby, J. -G., Da Silva, A., de la Torre, S., Di Ferdinando, D., Dubath, F., Duncan, C., Dupac, X., Dusini, S., Farrens, S., Ferreira, P. G., Ferrero, I., Finelli, F., Fotopoulou, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Holmes, W., Hormuth, F., Jahnke, K., Keihanen, E., Kermiche, S., Kiessling, A., Kirkpatrick, C. C., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Metcalf, R. Benton, Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Nichol, R. C., Niemi, S., Nightingale, J., Padilla, C., Paltani, S., Pasian, F., Patrizii, L., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sánchez, A. G., Sapone, D., Scaramella, R., Schneider, P., Scottez, V., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Sureau, F., Taylor, A. N., Tenti, M., Tereno, I., Teyssier, R., Toledo-Moreo, R., Tramacere, A., Valentijn, E. A., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Welikala, N., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The accuracy of photometric redshifts (photo-zs) particularly affects the results of the analyses of galaxy clustering with photometrically-selected galaxies (GCph) and weak lensing. In the next decade, space missions like Euclid will collect photometric measurements for millions of galaxies. These data should be complemented with upcoming ground-based observations to derive precise and accurate photo-zs. In this paper, we explore how the tomographic redshift binning and depth of ground-based observations will affect the cosmological constraints expected from Euclid. We focus on GCph and extend the study to include galaxy-galaxy lensing (GGL). We add a layer of complexity to the analysis by simulating several realistic photo-z distributions based on the Euclid Consortium Flagship simulation and using a machine learning photo-z algorithm. We use the Fisher matrix formalism and these galaxy samples to study the cosmological constraining power as a function of redshift binning, survey depth, and photo-z accuracy. We find that bins with equal width in redshift provide a higher Figure of Merit (FoM) than equipopulated bins and that increasing the number of redshift bins from 10 to 13 improves the FoM by 35% and 15% for GCph and its combination with GGL, respectively. For GCph, an increase of the survey depth provides a higher FoM. But the addition of faint galaxies beyond the limit of the spectroscopic training data decreases the FoM due to the spurious photo-zs. When combining both probes, the number density of the sample, which is set by the survey depth, is the main factor driving the variations in the FoM. We conclude that there is more information that can be extracted beyond the nominal 10 tomographic redshift bins of Euclid and that we should be cautious when adding faint galaxies into our sample, since they can degrade the cosmological constraints., Comment: 21 pages, 13 figures. Abstract abridged
Published: 2021
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27. Euclid preparation: XI. Mean redshift determination from galaxy redshift probabilities for cosmic shear tomography

Author: Euclid Collaboration, Ilbert, O., de la Torre, S., Martinet, N., Wright, A. H., Paltani, S., Laigle, C., Davidzon, I., Jullo, E., Hildebrandt, H., Masters, D. C., Amara, A., Conselice, C. J., Andreon, S., Auricchio, N., Azzollini, R., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Balestra, A., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Borgani, S., Boucaud, A., Bozzo, E., Branchini, E., Brescia, M., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Costille, A., Coupon, J., Courtois, H. M., Cropper, M., Cuby, J., Da Silva, A., Degaudenzi, H., Di Ferdinando, D., Dubath, F., Duncan, C., Dupac, X., Dusini, S., Ealet, A., Fabricius, M., Farrens, S., Ferreira, P. G., Finelli, F., Fosalba, P., Fotopoulou, S., Franceschi, E., Franzetti, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Keihanen, E., Kermiche, S., Kiessling, A., Kirkpatrick, C. C., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maturi, M., Mauri, N., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Metcalf, R. Benton, Moresco, M., Morin, B., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S., Nightingale, J., Padilla, C., Pasian, F., Patrizii, L., Pedersen, K., Pello, R., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sánchez, A. G., Sapone, D., Schneider, P., Schrabback, T., Scottez, V., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Sureau, F., Crespí, P. Tallada, Tenti, M., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tramacere, A., Valentijn, E. A., Valenziano, L., Valiviita, J., Vassallo, T., Wang, Y., Welikala, N., Weller, J., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The analysis of weak gravitational lensing in wide-field imaging surveys is considered to be a major cosmological probe of dark energy. Our capacity to constrain the dark energy equation of state relies on the accurate knowledge of the galaxy mean redshift $\langle z \rangle$. We investigate the possibility of measuring $\langle z \rangle$ with an accuracy better than $0.002\,(1+z)$, in ten tomographic bins spanning the redshift interval $0.299.8\%$. The zPDF approach could also be successful if we debias the zPDF using a spectroscopic training sample. This approach requires deep imaging data, but is weakly sensitive to spectroscopic redshift failures in the training sample. We improve the debiasing method and confirm our finding by applying it to real-world weak-lensing data sets (COSMOS and KiDS+VIKING-450)., Comment: 21 pages, 10 figures, accepted in A&A
Published: 2021
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28. Euclid: Forecasts for $k$-cut $3 \times 2$ Point Statistics

Author: Taylor, P. L., Kitching, T., Cardone, V. F., Ferté, A., Huff, E. M., Bernardeau, F., Rhodes, J., Deshpande, A. C., Tutusaus, I., Pourtsidou, A., Camera, S., Carbone, C., Casas, S., Martinelli, M., Pettorino, V., Sakr, Z., Sapone, D., Yankelevich, V., Auricchio, N., Balestra, A., Bodendorf, C., Bonino, D., Boucaud, A., Branchini, E., Brescia, M., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Corcione, L., Cropper, M., Franceschi, E., Garilli, B., Gillis, B., Giocoli, C., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kermiche, S., Kilbinger, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Marggraf, O., Markovic, K., Massey, R., Medinaceli, E., Mei, S., Meneghetti, M., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Niemi, S., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pires, S., Polenta, G., Poncet, M., Popa, L., Raison, F., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secrou, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Sureau, F., Crespí, P. Tallada, Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., and Zoubian, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Modelling uncertainties at small scales, i.e. high $k$ in the power spectrum $P(k)$, due to baryonic feedback, nonlinear structure growth and the fact that galaxies are biased tracers poses a significant obstacle to fully leverage the constraining power of the {\it Euclid} wide-field survey. $k$-cut cosmic shear has recently been proposed as a method to optimally remove sensitivity to these scales while preserving usable information. In this paper we generalise the $k$-cut cosmic shear formalism to $3 \times 2$ point statistics and estimate the loss of information for different $k$-cuts in a $3 \times 2$ point analysis of the {\it Euclid} data. Extending the Fisher matrix analysis of~\citet{blanchard2019euclid}, we assess the degradation in constraining power for different $k$-cuts. We work in the idealised case and assume the galaxy bias is linear, the covariance is Gaussian, while neglecting uncertainties due to photo-z errors and baryonic feedback. We find that taking a $k$-cut at $2.6 \ h \ {\rm Mpc} ^{-1}$ yields a dark energy Figure of Merit (FOM) of 1018. This is comparable to taking a weak lensing cut at $\ell = 5000$ and a galaxy clustering and galaxy-galaxy lensing cut at $\ell = 3000$ in a traditional $3 \times 2$ point analysis. We also find that the fraction of the observed galaxies used in the photometric clustering part of the analysis is one of the main drivers of the FOM. Removing $50 \% \ (90 \%)$ of the clustering galaxies decreases the FOM by $19 \% \ (62 \%)$. Given that the FOM depends so heavily on the fraction of galaxies used in the clustering analysis, extensive efforts should be made to handle the real-world systematics present when extending the analysis beyond the luminous red galaxy (LRG) sample., Comment: 10 pages, 5 figures. Accepted by the Open Journal of Astrophysics
Published: 2020

29. Euclid: impact of nonlinear prescriptions on cosmological parameter estimation from weak lensing cosmic shear

Author: Martinelli, M., Tutusaus, I., Archidiacono, M., Camera, S., Cardone, V. F., Clesse, S., Casas, S., Casarini, L., Mota, D. F., Hoekstra, H., Carbone, C., Ilić, S., Kitching, T. D., Pettorino, V., Pourtsidou, A., Sakr, Z., Sapone, D., Auricchio, N., Balestra, A., Boucaud, A., Branchini, E., Brescia, M., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C., Conversi, L., Corcione, L., Costille, A., Douspis, M., Dubath, F., Dusini, S., Fabbian, G., Fosalba, P., Frailis, M., Franceschi, E., Gillis, B., Giocoli, C., Grupp, F., Guzzo, L., Holmes, W., Hormuth, F., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Massey, R., Meneghetti, M., Meylan, G., Morin, B., Moscardini, L., Niemi, S., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pires, S., Polenta, G., Poncet, M., Popa, L., Raison, F., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Starck, J. -L., Sureau, F., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Welikala, N., Zacchei, A., and Zoubian, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Upcoming surveys will map the growth of large-scale structure with unprecented precision, improving our understanding of the dark sector of the Universe. Unfortunately, much of the cosmological information is encoded by the small scales, where the clustering of dark matter and the effects of astrophysical feedback processes are not fully understood. This can bias the estimates of cosmological parameters, which we study here for a joint analysis of mock Euclid cosmic shear and Planck cosmic microwave background data. We use different implementations for the modelling of the signal on small scales and find that they result in significantly different predictions. Moreover, the different nonlinear corrections lead to biased parameter estimates, especially when the analysis is extended into the highly nonlinear regime, with both the Hubble constant, $H_0$, and the clustering amplitude, $\sigma_8$, affected the most. Improvements in the modelling of nonlinear scales will therefore be needed if we are to resolve the current tension with more and better data. For a given prescription for the nonlinear power spectrum, using different corrections for baryon physics does not significantly impact the precision of Euclid, but neglecting these correction does lead to large biases in the cosmological parameters. In order to extract precise and unbiased constraints on cosmological parameters from Euclid cosmic shear data, it is therefore essential to improve the accuracy of the recipes that account for nonlinear structure formation, as well as the modelling of the impact of astrophysical processes that redistribute the baryons., Comment: 18 pages, 6 figures. Prepared for submission to Astronomy & Astrophysics
Published: 2020
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30. Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

Author: Euclid Collaboration, Knabenhans, M., Stadel, J., Potter, D., Dakin, J., Hannestad, S., Tram, T., Marelli, S., Schneider, A., Teyssier, R., Andreon, S., Auricchio, N., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Battaglia, P., Bender, R., Biviano, A., Bodendorf, C., Bozzo, E., Branchini, E., Brescia, M., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, R., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Coupon, J., Courtois, H. M., Da Silva, A., de la Torre, S., Di Ferdinando, D., Duncan, C. A. J., Dupac, X., Fabbian, G., Farrens, S., Ferreira, P. G., Finelli, F., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Holmes, W., Hormuth, F., Israel, H., Jahnke, K., Keihanen, E., Kermiche, S., Kirkpatrick, C. C., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Mauri, N., Maurogordato, S., Medinaceli, E., Meneghetti, M., Metcalf, B., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Neissner, C., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Patrizii, L., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sánchez, A. G., Sapone, D., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Sureau, F., Crespí, P. Tallada, Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Welikala, N., Whittaker, L., Zacchei, A., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. Percent-level accurate emulation is now supported in the eight-dimensional parameter space of $w_0w_a$CDM$+\sum m_\nu$models between redshift $z=0$ and $z=3$ for spatial scales within the range 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc. In order to achieve this level of accuracy, we have had to improve the quality of the underlying N-body simulations used as training data: (1) we use self-consistent linear evolution of non-dark matter species such as massive neutrinos, photons, dark energy and the metric field, (2) we perform the simulations in the so-called N-body gauge, which allows one to interpret the results in the framework of general relativity, (3) we run over 250 high-resolution simulations with $3000^3$ particles in boxes of 1 (Gpc/$h$)${}^3$ volumes based on paired-and-fixed initial conditions and (4) we provide a resolution correction that can be applied to emulated results as a post-processing step in order to drastically reduce systematic biases on small scales due to residual resolution effects in the simulations. We find that the inclusion of the dynamical dark energy parameter $w_a$ significantly increases the complexity and expense of creating the emulator. The high fidelity of EuclidEmulator2 is tested in various comparisons against N-body simulations as well as alternative fast predictors like Halofit, HMCode and CosmicEmu. A blind test is successfully performed against the Euclid Flagship v2.0 simulation. Nonlinear correction factors emulated with EuclidEmulator2 are accurate at the level of 1% or better for 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc and $z\leq3$ compared to high-resolution dark matter only simulations. EuclidEmulator2 is publicly available at https://github.com/miknab/EuclidEmulator2 ., Comment: 28 pages, 19 figures, submitted to MNRAS
Published: 2020
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31. Euclid preparation: X. The Euclid photometric-redshift challenge

Author: Euclid Collaboration, Desprez, G., Paltani, S., Coupon, J., Almosallam, I., Alvarez-Ayllon, A., Amaro, V., Brescia, M., Brodwin, M., Cavuoti, S., De Vicente-Albendea, J., Fotopoulou, S., Hatfield, P. W., Hartley, W. G., Ilbert, O., Jarvis, M. J., Longo, G., Saha, R., Speagle, J. S., Tramacere, A., Castellano, M., Dubath, F., Galametz, A., Kuemmel, M., Laigle, C., Merlin, E., Mohr, J. J., Pilo, S., Salvato, M., Rau, M. M., Andreon, S., Auricchio, N., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Bozzo, E., Branchini, E., Brinchmann, J., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, R., Casas, S., Castander, F. J., Castignani, G., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courtois, H. M., Cuby, J. -G., Da Silva, A., de la Torre, S., Degaudenzi, H., Di Ferdinando, D., Douspis, M., Duncan, C. A. J., Dupac, X., Ealet, A., Fabbian, G., Fabricius, M., Farrens, S., Ferreira, P. G., Finelli, F., Fosalba, P., Fourmanoit, N., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Hailey, M., Haugan, S. V. H., Holmes, W., Hormuth, F., Humphrey, A., Jahnke, K., Keihanen, E., Kermiche, S., Kilbinger, M., Kirkpatrick, C. C., Kitching, T. D., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maturi, M., Mauri, N., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Metcalf, R. Benton, Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S., Padilla, C., Pasian, F., Patrizii, L., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rossetti, E., Saglia, R., Sapone, D., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Stern, D., Sureau, F., Crespí, P. Tallada, Tavagnacco, D., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Welikala, N., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-$z$s at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2--2.6 redshift range that the Euclid mission will probe. We design a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data are divided into two samples: one calibration sample for which photometry and redshifts are provided to the participants; and the validation sample, containing only the photometry, to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates sources they consider unfit for use in cosmological analyses. The performance of each method is assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, sources for which the photo-z deviates by more than 0.15(1+z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. [abridged], Comment: Accepted in A&A, 25 pages, 13 figures, 7 tables
Published: 2020
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32. Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes

Author: Martinelli, M., Martins, C. J. A. P., Nesseris, S., Sapone, D., Tutusaus, I., Avgoustidis, A., Camera, S., Carbone, C., Casas, S., Ilić, S., Sakr, Z., Yankelevich, V., Auricchio, N., Balestra, A., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conversi, L., Corcione, L., Dubath, F., Ealet, A., Frailis, M., Franceschi, E., Fumana, M., Garilli, B., Gillis, B., Giocoli, C., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kermiche, S., Kilbinger, M., Kitching, T. D., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Marggraf, O., Markovic, K., Massey, R., Mei, S., Meneghetti, M., Meylan, G., Moscardini, L., Niemi, S., Padilla, C., Paltani, S., Pasian, F., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rhodes, J., Roncarelli, M., Saglia, R., Schneider, P., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Sureau, F., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valenziano, L., Vassallo, T., Wang, Y., Welikala, N., Weller, J., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: In metric theories of gravity with photon number conservation, the luminosity and angular diameter distances are related via the Etherington relation, also known as the distance-duality relation (DDR). A violation of this relation would rule out the standard cosmological paradigm and point at the presence of new physics. We quantify the ability of Euclid, in combination with contemporary surveys, to improve the current constraints on deviations from the DDR in the redshift range $0
Published: 2020
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33. Euclid preparation: VIII. The Complete Calibration of the Colour-Redshift Relation survey: VLT/KMOS observations and data release

Author: Euclid Collaboration, Guglielmo, V., Saglia, R., Castander, F. J., Galametz, A., Paltani, S., Bender, R., Bolzonella, M., Capak, P., Ilbert, O., Masters, D. C., Stern, D., Andreon, S., Auricchio, N., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Biviano, A., Bodendorf, C., Bonino, D., Bozzo, E., Branchini, E., Brau-Nogue, S., Brescia, M., Burigana, C., Cabanac, R. A., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, R., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Coupon, J., Courtois, H. M., Cropper, M., Da Silva, A., de la Torre, S., Di Ferdinando, D., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Fabricius, M., Farrens, S., Ferreira, P. G., Fotopoulou, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Hildebrandt, H., Hoekstra, H., Hormuth, F., Israel, H., Jahnke, K., Keihanen, E., Kermiche, S., Kilbinger, M., Kirkpatrick, C. C., Kitching, T., Kubik, B., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Maraston, C., Marggraf, O., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Metcalf, R. Benton, Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S., Nucita, A. A., Padilla, C., Pasian, F., Patrizii, L., Pocino, A., Poncet, M., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Sanchez, A. G., Sapone, D., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Sureau, F., Tallada-Crespi, P., Tavagnacco, D., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tramacere, A., Valenziano, L., Vassallo, T., Wang, Y., Welikala, N., Wetzstein, M., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Complete Calibration of the Colour-Redshift Relation survey (C3R2) is a spectroscopic effort involving ESO and Keck facilities designed to empirically calibrate the galaxy colour-redshift relation - P(z|C) to the Euclid depth (i_AB=24.5) and is intimately linked to upcoming Stage IV dark energy missions based on weak lensing cosmology. The aim is to build a spectroscopic calibration sample that is as representative as possible of the galaxies of the Euclid weak lensing sample. In order to minimise the number of spectroscopic observations to fill the gaps in current knowledge of the P(z|C), self-organising map (SOM) representations of the galaxy colour space have been constructed. Here we present the first results of an ESO@ VLT Large Programme approved in the context of C3R2, which makes use of the two VLT optical and near-infrared multi-object spectrographs, FORS2 and KMOS. This paper focuses on high-quality spectroscopic redshifts of high-z galaxies observed with the KMOS spectrograph in the H- and K-bands. A total of 424 highly-reliable z are measured in the 1.3<=z<=2.5 range, with total success rates of 60.7% in the H-band and 32.8% in the K-band. The newly determined z fill 55% of high and 35% of lower priority empty SOM grid cells. We measured Halpha fluxes in a 1."2 radius aperture from the spectra of the spectroscopically confirmed galaxies and converted them into star formation rates. In addition, we performed an SED fitting analysis on the same sample in order to derive stellar masses, E(B-V), total magnitudes, and SFRs. We combine the results obtained from the spectra with those derived via SED fitting, and we show that the spectroscopic failures come from either weakly star-forming galaxies (at z<1.7, i.e. in the H-band) or low S/N spectra (in the K-band) of z>2 galaxies., Comment: 21 pages, 12 figures
Published: 2020
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34. Shear measurement bias II: a fast machine learning calibration method

Author: Pujol, Arnau, Bobin, Jerome, Sureau, Florent, Guinot, Axel, and Kilbinger, Martin
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We present a new shear calibration method based on machine learning. The method estimates the individual shear responses of the objects from the combination of several measured properties on the images using supervised learning. The supervised learning uses the true individual shear responses obtained from copies of the image simulations with different shear values. On simulated GREAT3data, we obtain a residual bias after the calibration compatible with 0 and beyond Euclid requirements for a signal-to-noise ratio > 20 within ~15 CPU hours of training using only ~10^5 objects. This efficient machine-learning approach can use a smaller data set because the method avoids the contribution from shape noise. The low dimensionality of the input data also leads to simple neural network architectures. We compare it to the recently described method Metacalibration, which shows similar performances. The different methods and systematics suggest that the two methods are very good complementary methods. Our method can therefore be applied without much effort to any survey such as Euclid or the Vera C. Rubin Observatory, with fewer than a million images to simulate to learn the calibration function., Comment: 16 pages, 1 table, 13 figures
Published: 2020
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35. Euclid: The importance of galaxy clustering and weak lensing cross-correlations within the photometric Euclid survey

Author: Tutusaus, I., Martinelli, M., Cardone, V. F., Camera, S., Yahia-Cherif, S., Casas, S., Blanchard, A., Kilbinger, M., Lacasa, F., Sakr, Z., Ilić, S., Kunz, M., Carbone, C., Castander, F. J., Dournac, F., Fosalba, P., Kitching, T., Markovic, K., Mangilli, A., Pettorino, V., Sapone, D., Yankelevich, V., Auricchio, N., Bender, R., Bonino, D., Boucaud, A., Brescia, M., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conversi, L., Corcione, L., Costille, A., Cropper, M., Dubath, F., Dusini, S., Fabbian, G., Frailis, M., Franceschi, E., Garilli, B., Grupp, F., Guzzo, L., Hoekstra, H., Hormuth, F., Israel, H., Jahnke, K., Kermiche, S., Kubik, B., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Massey, R., Mei, S., Merlin, E., Meylan, G., Moscardini, L., Ntelis, P., Padilla, C., Paltani, S., Pasian, F., Percival, W. J., Pires, S., Poncet, M., Raison, F., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Starck, J., Sureau, F., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valenziano, L., Wang, Y., Welikala, N., Weller, J., Zacchei, A., and Zoubian, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The data from the Euclid mission will enable the measurement of the photometric redshifts, angular positions, and weak lensing shapes for over a billion galaxies. This large dataset will allow for cosmological analyses using the angular clustering of galaxies and cosmic shear. The cross-correlation (XC) between these probes can tighten constraints and it is therefore important to quantify their impact for Euclid. In this study we carefully quantify the impact of XC not only on the final parameter constraints for different cosmological models, but also on the nuisance parameters. In particular, we aim at understanding the amount of additional information that XC can provide for parameters encoding systematic effects, such as galaxy bias or intrinsic alignments (IA). We follow the formalism presented in Euclid Collaboration: Blanchard et al. (2019) and make use of the codes validated therein. We show that XC improves the dark energy Figure of Merit (FoM) by a factor $\sim 5$, whilst it also reduces the uncertainties on galaxy bias by $\sim 17\%$ and the uncertainties on IA by a factor $\sim 4$. We observe that the role of XC on the final parameter constraints is qualitatively the same irrespective of the galaxy bias model used. We also show that XC can help in distinguishing between different IA models, and that if IA terms are neglected then this can lead to significant biases on the cosmological parameters. We find that the XC terms are necessary to extract the full information content from the data in future analyses. They help in better constraining the cosmological model, and lead to a better understanding of the systematic effects that contaminate these probes. Furthermore, we find that XC helps in constraining the mean of the photometric-redshift distributions, but it requires a more precise knowledge of this mean in order not to degrade the final FoM. [Abridged], Comment: 15 pages, 8 figures
Published: 2020
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36. Euclid: The selection of quiescent and star-forming galaxies using observed colours

Author: Bisigello, L., Kuchner, U., Conselice, C. J., Andreon, S., Bolzonella, M., Duc, P. -A., Garilli, B., Humphrey, A., Maraston, C., Moresco, M., Pozzetti, L., Tortora, C., Zamorani, G., Auricchio, N., Brinchmann, J., Capobianco, V., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Corcione, L., Cropper, M. S., Dusini, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Hormuth, F., Israel, H., Jahnke, K., Kermiche, S., Kitching, T., Kohley, R., Kubik, B., Kunz, M., Fèvre, O. Le, Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Massey, R., Masters, D. C., Mei, S., Mellier, Y., Meylan, G., Padilla, C., Paltani, S., Pasian, F., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Raison, F., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Sauvage, M., Schneider, P., Secroun, A., Serrano, S., Sureau, F., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valenziano, L., Wang, Y., Wetzstein, M., and Zoubian, J.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The Euclid mission will observe well over a billion galaxies out to $z\sim6$ and beyond. This will offer an unrivalled opportunity to investigate several key questions for understanding galaxy formation and evolution. The first step for many of these studies will be the selection of a sample of quiescent and star-forming galaxies, as is often done in the literature by using well known colour techniques such as the `UVJ' diagram. However, given the limited number of filters available for the Euclid telescope, the recovery of such rest-frame colours will be challenging. We therefore investigate the use of observed Euclid colours, on their own and together with ground-based u-band observations, for selecting quiescent and star-forming galaxies. The most efficient colour combination, among the ones tested in this work, consists of the (u-VIS) and (VIS-J) colours. We find that this combination allows users to select a sample of quiescent galaxies complete to above $\sim70\%$ and with less than 15$\%$ contamination at redshifts in the range $0.7565\%$ completeness level and contamination below 20$\%$ at $1
Published: 2020
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37. Dynamics of mitochondrial membranes under photo-oxidative stress with high spatiotemporal resolution

Author: Vincent Loriette, Alexandra Fragola, Sergei G. Kruglik, Susmita Sridhar, Antoine Hubert, François Orieux, Eduardo Sepulveda, Franck Sureau, and Stephanie Bonneau
Subjects: structured illumination microscopy (SIM), live cell imaging, shape changes, dynamics, mitochondria, sub-organelles structures, Biology (General), QH301-705.5
Abstract: In our study, we harnessed an original Enhanced Speed Structured Illumination Microscopy (Fast-SIM) imaging setup to explore the dynamics of mitochondrial and inner membrane ultrastructure under specific photo-oxidation stress induced by Chlorin-e6 and light irradiation. Notably, our Fast-SIM system allowed us to observe and quantify a distinct remodeling and shortening of the mitochondrial structure after 60–80 s of irradiation. These changes were accompanied by fusion events of adjacent inner membrane cristae and global swelling of the organelle. Preceding these alterations, a larger sequence was characterized by heightened dynamics within the mitochondrial network, featuring events such as mitochondrial fission, rapid formation of tubular prolongations, and fluctuations in cristae structure. Our findings provide compelling evidence that, among enhanced-resolution microscopy techniques, Fast-SIM emerges as the most suitable approach for non-invasive dynamic studies of mitochondrial structure in living cells. For the first time, this approach allows quantitative and qualitative characterization of successive steps in the photo-induced oxidation process with sufficient spatial and temporal resolution.
Published: 2023
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38. Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Author: Deshpande, A. C., Kitching, T. D., Cardone, V. F., Taylor, P. L., Casas, S., Camera, S., Carbone, C., Kilbinger, M., Pettorino, V., Sakr, Z., Sapone, D., Tutusaus, I., Auricchio, N., Bodendorf, C., Bonino, D., Brescia, M., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conversi, L., Corcione, L., Cropper, M., Dubath, F., Dusini, S., Fabbian, G., Franceschi, E., Fumana, M., Garilli, B., Grupp, F., Hoekstra, H., Hormuth, F., Israel, H., Jahnke, K., Kermiche, S., Kubik, B., Kunz, M., Lacasa, F., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Massey, R., Mei, S., Meneghetti, M., Meylan, G., Moscardini, L., Padilla, C., Paltani, S., Pasian, F., Pires, S., Polenta, G., Poncet, M., Raison, F., Rhodes, J., Roncarelli, M., Saglia, R., Schneider, P., Secroun, A., Serrano, S., Sirri, G., Starck, J. L., Sureau, F., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valenziano, L., Wang, Y., and Zoubian, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Stage IV weak lensing experiments will offer more than an order of magnitude leap in precision. We must therefore ensure that our analyses remain accurate in this new era. Accordingly, previously ignored systematic effects must be addressed. In this work, we evaluate the impact of the reduced shear approximation and magnification bias, on the information obtained from the angular power spectrum. To first-order, the statistics of reduced shear, a combination of shear and convergence, are taken to be equal to those of shear. However, this approximation can induce a bias in the cosmological parameters that can no longer be neglected. A separate bias arises from the statistics of shear being altered by the preferential selection of galaxies and the dilution of their surface densities, in high-magnification regions. The corrections for these systematic effects take similar forms, allowing them to be treated together. We calculated the impact of neglecting these effects on the cosmological parameters that would be determined from Euclid, using cosmic shear tomography. To do so, we employed the Fisher matrix formalism, and included the impact of the super-sample covariance. We also demonstrate how the reduced shear correction can be calculated using a lognormal field forward modelling approach. These effects cause significant biases in Omega_m, sigma_8, n_s, Omega_DE, w_0, and w_a of -0.53 sigma, 0.43 sigma, -0.34 sigma, 1.36 sigma, -0.68 sigma, and 1.21 sigma, respectively. We then show that these lensing biases interact with another systematic: the intrinsic alignment of galaxies. Accordingly, we develop the formalism for an intrinsic alignment-enhanced lensing bias correction. Applying this to Euclid, we find that the additional terms introduced by this correction are sub-dominant., Comment: 16 pages, 6 figures, submitted to Astronomy & Astrophysics on 16/12/2019, accepted on 04/03/2020. SSC Fisher procedure corrected
Published: 2019
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39. Deep Learning for space-variant deconvolution in galaxy surveys

Author: Sureau, Florent, Lechat, Alexis, and Starck, Jean-Luc
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Methodology, Statistics - Machine Learning
Abstract: Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PSF) and have to be at the same time accurate and fast. We investigate in this paper how Deep Learning (DL) could be used to perform this task. We employ a U-Net Deep Neural Network (DNN) architecture to learn in a supervised setting parameters adapted for galaxy image processing and study two strategies for deconvolution. The first approach is a post-processing of a mere Tikhonov deconvolution with closed form solution and the second one is an iterative deconvolution framework based on the Alternating Direction Method of Multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and PSFs show that our two approaches outperforms standard techniques based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on Tikhonov deconvolution leads to the most accurate results except for ellipticity errors at high signal to noise ratio where the ADMM approach performs slightly better, is also more computation-time efficient to process a large number of galaxies, and is therefore recommended in this scenario.
Published: 2019
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40. Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments

Author: Euclid Collaboration, Paykari, P., Kitching, T. D., Hoekstra, H., Azzollini, R., Cardone, V. F., Cropper, M., Duncan, C. A. J., Kannawadi, A., Miller, L., Aussel, H., Conti, I. F., Auricchio, N., Baldi, M., Bardelli, S., Biviano, A., Bonino, D., Borsato, E., Bozzo, E., Branchini, E., Brau-Nogue, S., Brescia, M., Brinchmann, J., Burigana, C., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Charles, Y., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C., Conversi, L., Copin, Y., Coupon, J., Courtois, H. M., Da Silva, A., Dupac, X., Fabbian, G., Farrens, S., Ferreira, P. G., Fosalba, P., Fourmanoit, N., Frailis, M., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B. R., Giocoli, C., Gracia-Carpio, J., Grupp, F., Hormuth, F., Ilic, S., Israel, H., Jahnke, K., Keihanen, E., Kermiche, S., Kilbinger, M., Kirkpatrick, C. C., Kubik, B., Kunz, M., Kurki-Suonio, H., Lacasa, F., Laureijs, R., Mignant, D. Le, Ligori, S., Lilje, P. B., Lloro, I., Maciaszek, T., Maiorano, E., Marggraf, O., Martinelli, M., Martinet, N., Massey, F. Marulli R., Mauri, N., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Metcalf, R. B., Moresco, M., Moscardini, L., Munari, E., Neissner, C., Nichol, R. C., Niemi, S., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pettorino, V., Pires, S., Polenta, G., Pourtsidou, A., Raison, F., Renzi, A., Rhodes, J., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sánchez, A. G., Sapone, D., Scaramella, R., Schneider, P., Schrabback, T., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Sureau, F., Tallada-Crespí, P., Taylor, A., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vannier, M., Vassallo, T., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Our aim is to quantify the impact of systematic effects on the inference of cosmological parameters from cosmic shear. We present an end-to-end approach that introduces sources of bias in a modelled weak lensing survey on a galaxy-by-galaxy level. Residual biases are propagated through a pipeline from galaxy properties (one end) through to cosmic shear power spectra and cosmological parameter estimates (the other end), to quantify how imperfect knowledge of the pipeline changes the maximum likelihood values of dark energy parameters. We quantify the impact of an imperfect correction for charge transfer inefficiency (CTI) and modelling uncertainties of the point spread function (PSF) for Euclid, and find that the biases introduced can be corrected to acceptable levels., Comment: 18 pages. Submitted to A&A. Comments Welcome
Published: 2019
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41. Euclid preparation: VII. Forecast validation for Euclid cosmological probes

Author: Euclid Collaboration, Blanchard, A., Camera, S., Carbone, C., Cardone, V. F., Casas, S., Clesse, S., Ilić, S., Kilbinger, M., Kitching, T., Kunz, M., Lacasa, F., Linder, E., Majerotto, E., Markovič, K., Martinelli, M., Pettorino, V., Pourtsidou, A., Sakr, Z., Sánchez, A. G., Sapone, D., Tutusaus, I., Yahia-Cherif, S., Yankelevich, V., Andreon, S., Aussel, H., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Bender, R., Biviano, A., Bonino, D., Boucaud, A., Bozzo, E., Branchini, E., Brau-Nogue, S., Brescia, M., Brinchmann, J., Burigana, C., Cabanac, R., Capobianco, V., Cappi, A., Carretero, J., Carvalho, C. S., Casas, R., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Coupon, J., Courtois, H. M., Cropper, M., Da Silva, A., de la Torre, S., Di Ferdinando, D., Dubath, F., Ducret, F., Duncan, C. A. J., Dupac, X., Dusini, S., Fabbian, G., Fabricius, M., Farrens, S., Fosalba, P., Fotopoulou, S., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Graciá-Carpio, J., Grupp, F., Guzzo, L., Hoekstra, H., Hormuth, F., Israel, H., Jahnke, K., Keihanen, E., Kermiche, S., Kirkpatrick, C. C., Kohley, R., Kubik, B., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Marggraf, O., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Mellier, Y., Metcalf, B., Metge, J. J., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nichol, R. C., Niemi, S., Nucita, A. A., Padilla, C., Paltani, S., Pasian, F., Percival, W. J., Pires, S., Polenta, G., Poncet, M., Pozzetti, L., Racca, G. D., Raison, F., Renzi, A., Rhodes, J., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Scottez, V., Secroun, A., Sirri, G., Stanco, L., Starck, J. -L., Sureau, F., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Vassallo, T., Kleijn, G. A. Verdoes, Viel, M., Wang, Y., Zacchei, A., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Euclid space telescope will measure the shapes and redshifts of galaxies to reconstruct the expansion history of the Universe and the growth of cosmic structures. Estimation of the expected performance of the experiment, in terms of predicted constraints on cosmological parameters, has so far relied on different methodologies and numerical implementations, developed for different observational probes and for their combination. In this paper we present validated forecasts, that combine both theoretical and observational expertise for different cosmological probes. This is presented to provide the community with reliable numerical codes and methods for Euclid cosmological forecasts. We describe in detail the methodology adopted for Fisher matrix forecasts, applied to galaxy clustering, weak lensing and their combination. We estimate the required accuracy for Euclid forecasts and outline a methodology for their development. We then compare and improve different numerical implementations, reaching uncertainties on the errors of cosmological parameters that are less than the required precision in all cases. Furthermore, we provide details on the validated implementations that can be used by the reader to validate their own codes if required. We present new cosmological forecasts for Euclid. We find that results depend on the specific cosmological model and remaining freedom in each setup, i.e. flat or non-flat spatial cosmologies, or different cuts at nonlinear scales. The validated numerical implementations can now be reliably used for any setup. We present results for an optimistic and a pessimistic choice of such settings. We demonstrate that the impact of cross-correlations is particularly relevant for models beyond a cosmological constant and may allow us to increase the dark energy Figure of Merit by at least a factor of three., Comment: 74 pages, 13 figures, 18 tables. Acknowledgements include Authors' contributions. Abstract abridged. Version accepted for publication in A&A
Published: 2019
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42. Euclid preparation: V. Predicted yield of redshift 7<z<9 quasars from the wide survey

Author: Euclid Collaboration, Barnett, R., Warren, S. J., Mortlock, D. J., Cuby, J. -G., Conselice, C., Hewett, P. C., Willott, C. J., Auricchio, N., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Bellagamba, F., Bender, R., Biviano, A., Bonino, D., Bozzo, E., Branchini, E., Brescia, M., Brinchmann, J., Burigana, C., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Carvalho, C. S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Clédassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Coupon, J., Courtois, H. M., Cropper, M., Da Silva, A., Duncan, C. A. J., Dusini, S., Ealet, A., Farrens, S., Fosalba, P., Fotopoulou, S., Fourmanoit, N., Frailis, M., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B. R., Graciá-Carpio, J., Grupp, F., Hoekstra, H., Hormuth, F., Israel, H., Jahnke, K., Kermiche, S., Kilbinger, M., Kirkpatrick, C. C., Kitching, T., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Martinet, N., Marulli, F., Massey, R., Mauri, N., Medinaceli, E., Mei, S., Mellier, Y., Metcalf, R. B., Metge, J. J., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Neissner, C., Niemi, S. M., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Paykari, P., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Rix, H. -W., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Scaramella, R., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirri, G., Stanco, L., Sureau, F., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We provide predictions of the yield of $78$ may be selected from Euclid $OYJH$ photometry alone, but selection over the redshift interval $77.5$ should be found in the DR1 data release, expected in 2024. It will be possible to determine the bright-end slope of the QLF, $7
Published: 2019
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43. Representation learning for automated spectroscopic redshift estimation

Author: Frontera-Pons, Joana, Sureau, Florent, Moraes, Bruno, Bobin, Jérôme, and Abdalla, Filipe
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Determining the radial positions of galaxies up to a high accuracy depends on the correct identification of salient features in their spectra. Classical techniques for spectroscopic redshift estimation make use of template matching with cross-correlation. These templates are usually constructed from empirical spectra or simulations based on the modeling of local galaxies. We propose two new spectroscopic redshift estimation schemes based on new learning techniques for galaxy spectra representation, using either a dictionary learning technique for sparse representation or denoising autoencoders. We investigate how these representations impact redshift estimation. These methods have been tested on realistic simulated galaxy spectra, with photometry modelled after the Large Synoptic Survey Telescope (LSST) and spectroscopy reproducing properties of the Sloan Digital Sky Survey (SDSS). They were compared to Darth Fader, a robust technique extracting line features and estimating redshift through eigentemplates cross-correlations. We show that both dictionary learning and denoising autoencoders provide improved accuracy and reliability across all signal-to-noise regimes and galaxy types. The representation learning framework for spectroscopic redshift analysis introduced in this work offers high performance in feature extraction and redshift estimation, improving on a classical eigentemplates approach. This is a necessity for next-generation galaxy surveys, and we demonstrate a successful application in realistic simulated survey data., Comment: 15 pages and 11 figures. To be published in A&A
Published: 2019
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44. Introduction

Author: Sureau, Eloise
Published: 2022
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45. Data-driven templates with dictionary learning and sparse representations for photometric redshift estimation

Author: Frontera-Pons, J., Sureau, F., Bobin, J., and Kilbinger, M.
Published: 2023
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46. Modulation of the crystallization of rapeseed oil using lipases and the impact on ice cream properties

Author: Monié, Aurélie, Habersetzer, Thomas, Sureau, Léa, David, Annabelle, Clemens, Karine, Malet-Martino, Myriam, Perez, Emile, Franceschi, Sophie, Balayssac, Stéphane, and Delample, Mathieu
Published: 2023
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47. HepG2BD: A Novel and Versatile Cell Line with Inducible HDV Replication and Constitutive HBV Expression

Author: Matthieu Blanchet, Léna Angelo, Yasmine Tétreault, Marwa Khabir, Camille Sureau, Andrew Vaillant, and Patrick Labonté
Subjects: HBV, HDV, cell line, CRISPR-Cas9, antiviral, Microbiology, QR1-502
Abstract: Individuals chronically infected with hepatitis B virus (HBV) and hepatitis Delta virus (HDV) present an increased risk of developing cirrhosis and hepatocellular carcinoma in comparison to HBV mono-infected individuals. Although HDV only replicates in individuals coinfected or superinfected with HBV, there is currently no in vitro model that can stably express both viruses simultaneously, mimicking the chronic infections seen in HBV/HDV patients. Here, we present the HepG2BD cell line as a novel in vitro culture system for long-term replication of HBV and HDV. HepG2BD cells derive from HepG2.2.15 cells in which a 2 kb HDV cDNA sequence was inserted into the adeno-associated virus safe harbor integration site 1 (AAVS1) using CRISPR-Cas9. A Tet-Off promoter was placed 5′ of the genomic HDV sequence for reliable initiation/repression of viral replication and secretion. HBV and HDV replication were then thoroughly characterized. Of note, non-dividing cells adopt a hepatocyte-like morphology associated with an increased production of both HDV and HBV virions. Finally, HDV seems to negatively interfere with HBV in this model system. Altogether, HepG2BD cells will be instrumental to evaluate, in vitro, the fundamental HBV–HDV interplay during simultaneous chronic replication as well as for antivirals screening targeting both viruses.
Published: 2024
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48. A Bregman Majorization-Minimization Framework for Pet Image Reconstruction.

Author: Claire Rossignol, Florent Sureau, Emilie Chouzenoux, Claude Comtat, and Jean-Christophe Pesquet
Published: 2022
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49. Learning sparse representations on the sphere

Author: Sureau, Florent, Voigtlaender, Felix, Wust, Malte, Starck, Jean-Luc, and Kutyniok, Gitta
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Many representation systems on the sphere have been proposed in the past, such as spherical harmonics, wavelets, or curvelets. Each of these data representations is designed to extract a specific set of features, and choosing the best fixed representation system for a given scientific application is challenging. In this paper, we show that we can learn directly a representation system from given data on the sphere. We propose two new adaptive approaches: the first is a (potentially multi-scale) patch-based dictionary learning approach, and the second consists in selecting a representation among a parametrized family of representations, the {\alpha}-shearlets. We investigate their relative performance to represent and denoise complex structures on different astrophysical data sets on the sphere.
Published: 2018
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50. A highly precise shear bias estimator independent of the measured shape noise

Author: Pujol, Arnau, Kilbinger, Martin, Sureau, Florent, and Bobin, Jerome
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a new method to estimate shear measurement bias in image simulations that significantly improves the precision with respect to current techniques. Our method is based on measuring the shear response for individual images. We generated sheared versions of the same image to measure how the galaxy shape changes with the small applied shear. This shear response is the multiplicative shear bias for each image. In addition, we also measured the individual additive bias. Using the same noise realizations for each sheared version allows us to compute the shear response at very high precision. The estimated shear bias of a sample of galaxies is then the average of the individual measurements. The precision of this method leads to an improvement with respect to previous methods concerned with the precision of estimates of multiplicative bias since our method is not affected by noise from shape measurements, which until now has been the dominant uncertainty. As a consequence, the method does not require shape-noise suppression for a precise estimation of shear multiplicative bias. Our method can be readily used for numerous applications such as shear measurement validation and calibration, reducing the number of necessary simulated images by a few orders of magnitude to achieve the same precision., Comment: 14 pages, 10 figures
Published: 2018
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