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151. Gaia Data Release 3: Stellar multiplicity, a teaser for the hidden treasure

Author

Gaia Collaboration, Arenou, F., Babusiaux, C., Barstow, M. A., Faigler, S., Jorissen, A., Kervella, P., Mazeh, T., Mowlavi, N., Panuzzo, P., Sahlmann, J., Shahaf, S., Sozzetti, A., Bauchet, N., Damerdji, Y., Gavras, P., Giacobbe, P., Gosset, E., Halbwachs, J. -L., Holl, B., Lattanzi, M. G., Leclerc, N., Morel, T., Pourbaix, D., Fiorentin, P. Re, Sadowski, G., Ségransan, D., Siopis, C., Teyssier, D., Zwitter, T., Planquart, L., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Nicolas, C., Nienartowicz, K., Pailler, F., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Altmann, M., Andrae, R., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Haigron, R., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Smith, M., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Bartolomé, S., Bassilana, J. -L., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Blazere, A., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gerlach, E., Geyer, R., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Khanna, S., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Núñez, A. Sáez, Sellés, A. Sagristà, Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M. S., Sciacca, E., Segol, M., Segovia, J. C., Semeux, D., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., and Zucker, S.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Gaia DR3 Catalogue contains for the first time about eight hundred thousand solutions with either orbital elements or trend parameters for astrometric, spectroscopic and eclipsing binaries, and combinations of them. This paper aims to illustrate the huge potential of this large non-single star catalogue. Using the orbital solutions together with models of the binaries, a catalogue of tens of thousands of stellar masses, or lower limits, partly together with consistent flux ratios, has been built. Properties concerning the completeness of the binary catalogues are discussed, statistical features of the orbital elements are explained and a comparison with other catalogues is performed. Illustrative applications are proposed for binaries across the H-R diagram. The binarity is studied in the RGB/AGB and a search for genuine SB1 among long-period variables is performed. The discovery of new EL CVn systems illustrates the potential of combining variability and binarity catalogues. Potential compact object companions are presented, mainly white dwarf companions or double degenerates, but one candidate neutron star is also presented. Towards the bottom of the main sequence, the orbits of previously-suspected binary ultracool dwarfs are determined and new candidate binaries are discovered. The long awaited contribution of Gaia to the analysis of the substellar regime shows the brown dwarf desert around solar-type stars using true, rather than minimum, masses, and provides new important constraints on the occurrence rates of substellar companions to M dwarfs. Several dozen new exoplanets are proposed, including two with validated orbital solutions and one super-Jupiter orbiting a white dwarf, all being candidates requiring confirmation. Beside binarity, higher order multiple systems are also found., Comment: 60 pages, 60 figures. Accepted for publication in Astronomy & Astrophysics (2022-06-09). The catalogue of binary masses is available for download from the ESA Gaia DR3 Archive and will be available from the CDS/VizieR service

Published
2022
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152. Gaia Data Release 3: Chemical cartography of the Milky Way

Author

Gaia Collaboration, Recio-Blanco, A., Kordopatis, G., de Laverny, P., Palicio, P. A., Spagna, A., Spina, L., Katz, D., Fiorentin, P. Re, Poggio, E., McMillan, P. J., Vallenari, A., Lattanzi, M. G., Seabroke, G. M., Casamiquela, L., Bragaglia, A., Antoja, T., Bailer-Jones, C. A. L., Andrae, R., Fouesneau, M., Cropper, M., Cantat-Gaudin, T., Heiter, U., Bijaoui, A., Brown, A. G. A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bastian, U., Drimmel, R., Jansen, F., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Frémat, Y., Galluccio, L., Guerrier, A., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Edvardsson, B., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

Gaia DR3 opens a new era of all-sky spectral analysis of stellar populations thanks to the nearly 5.6 million stars observed by the RVS and parametrised by the GSP-spec module. The all-sky Gaia chemical cartography allows a powerful and precise chemo-dynamical view of the Milky Way with unprecedented spatial coverage and statistical robustness. First, it reveals the strong vertical symmetry of the Galaxy and the flared structure of the disc. Second, the observed kinematic disturbances of the disc -- seen as phase space correlations -- and kinematic or orbital substructures are associated with chemical patterns that favour stars with enhanced metallicities and lower [alpha/Fe] abundance ratios compared to the median values in the radial distributions. This is detected both for young objects that trace the spiral arms and older populations. Several alpha, iron-peak elements and at least one heavy element trace the thin and thick disc properties in the solar cylinder. Third, young disc stars show a recent chemical impoverishment in several elements. Fourth, the largest chemo-dynamical sample of open clusters analysed so far shows a steepening of the radial metallicity gradient with age, which is also observed in the young field population. Finally, the Gaia chemical data have the required coverage and precision to unveil galaxy accretion debris and heated disc stars on halo orbits through their [alpha/Fe] ratio, and to allow the study of the chemo-dynamical properties of globular clusters. Gaia DR3 chemo-dynamical diagnostics open new horizons before the era of ground-based wide-field spectroscopic surveys. They unveil a complex Milky Way that is the outcome of an eventful evolution, shaping it to the present day (abridged)., Comment: Astronomy and Astrophysics (accepted, in press)

Published
2022
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153. Goal-Space Planning with Subgoal Models

Author

Lo, Chunlok, Roice, Kevin, Panahi, Parham Mohammad, Jordan, Scott, White, Adam, Mihucz, Gabor, Aminmansour, Farzane, and White, Martha

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives, such as Double DQN, even though the former uses significantly more memory and computation. The fundamental problem is that learned models can be inaccurate and often generate invalid states, especially when iterated many steps. In this paper, we avoid this limitation by constraining background planning to a set of (abstract) subgoals and learning only local, subgoal-conditioned models. This goal-space planning (GSP) approach is more computationally efficient, naturally incorporates temporal abstraction for faster long-horizon planning and avoids learning the transition dynamics entirely. We show that our GSP algorithm can propagate value from an abstract space in a manner that helps a variety of base learners learn significantly faster in different domains.

Published
2022

154. The Detection of Transiting Exoplanets by Gaia

Author

Panahi, Aviad, Zucker, Shay, Clementini, Gisella, Audard, Marc, Binnenfeld, Avraham, Cusano, Felice, Evans, Dafydd Wyn, Gomel, Roy, Holl, Berry, Ilyin, Ilya, de Fombelle, Grégory Jevardat, Mazeh, Tsevi, Mowlavi, Nami, Nienartowicz, Krzysztof, Rimoldini, Lorenzo, Shahaf, Sahar, and Eyer, Laurent

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Context: The space telescope Gaia is dedicated mainly to performing high-precision astrometry, but also spectroscopy and epoch photometry which can be used to study various types of photometric variability. One such variability type is exoplanetary transits. The photometric data accumulated so far have finally matured enough to allow the detection of some exoplanets. Aims: In order to fully exploit the scientific potential of Gaia, we search its photometric data for the signatures of exoplanetary transits. Methods: The search relies on a version of the Box-Least-Square (BLS) method, applied to a set of stars prioritized by machine-learning classification methods. An independent photometric validation was obtained using the public full-frame images of TESS. In order to validate the first two candidates, radial-velocity follow-up observations were performed using the spectrograph PEPSI of the Large Binocular Telescope (LBT). Results: The radial-velocity measurements confirm that two of the candidates are indeed hot Jupiters. Thus, they are the first exoplanets detected by Gaia - Gaia-1b and Gaia-2b. Conclusions: Gaia-1b and Gaia-2b demonstrate that the approach presented in this paper is indeed effective. This approach will be used to assemble a set of additional exoplanet candidates, to be released in Gaia third data release, ensuring better fulfillment of the exoplanet detection potential of Gaia., Comment: Accepted for publication in A&A, 8 pages, 8 figures

Published
2022
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155. Gaia Early Data Release 3: The celestial reference frame (Gaia-CRF3)

Author

Gaia Collaboration, Klioner, S. A., Lindegren, L., Mignard, F., Hernández, J., Ramos-Lerate, M., Bastian, U., Biermann, M., Bombrun, A., de Torres, A., Gerlach, E., Geyer, R., Hilger, T., Hobbs, D., Lammers, U. L., McMillan, P. J., Steidelmüller, H., Teyssier, D., Raiteri, C. M., Bartolomé, S., Bernet, M., Castañeda, J., Clotet, M., Davidson, M., Fabricius, C., Torres, N. Garralda, González-Vidal, J. J., Portell, J., Rowell, N., Torra, F., Torra, J., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Luri, X., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., De Angeli, F., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Altmann, M., Andrae, R., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Fienga, A., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Rambaux, N., Ramos, P., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Gaia-CRF3 is the celestial reference frame for positions and proper motions in the third release of data from the Gaia mission, Gaia DR3 (and for the early third release, Gaia EDR3, which contains identical astrometric results). The reference frame is defined by the positions and proper motions at epoch 2016.0 for a specific set of extragalactic sources in the (E)DR3 catalogue. We describe the construction of Gaia-CRF3, and its properties in terms of the distributions in magnitude, colour, and astrometric quality. Compact extragalactic sources in Gaia DR3 were identified by positional cross-matching with 17 external catalogues of quasars (QSO) and active galactic nuclei (AGN), followed by astrometric filtering designed to remove stellar contaminants. Selecting a clean sample was favoured over including a higher number of extragalactic sources. For the final sample, the random and systematic errors in the proper motions are analysed, as well as the radio-optical offsets in position for sources in the third realisation of the International Celestial Reference Frame (ICRF3). The Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2 million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million have six-parameter solutions. The sources span the magnitude range G = 13 to 21 with a peak density at 20.6 mag, at which the typical positional uncertainty is about 1 mas. The proper motions show systematic errors on the level of 12 ${\mu}$as yr${}^{-1}$ on angular scales greater than 15 deg. For the 3142 optical counterparts of ICRF3 sources in the S/X frequency bands, the median offset from the radio positions is about 0.5 mas, but exceeds 4 mas in either coordinate for 127 sources. We outline the future of the Gaia-CRF in the next Gaia data releases.

Published
2022
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156. Quantum Semi-Supervised Kernel Learning

Author

Saeedi, Seyran, Panahi, Aliakbar, and Arodz, Tom

Subjects
Computer Science - Machine Learning, Quantum Physics
Abstract

Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of training the model is typically determined by the size of the training dataset. Thus, quantum machine learning methods have the potential to facilitate learning using extremely large datasets. While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels. One of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors. Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines. The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss. Through a theoretical study of the algorithm's computational complexity, we show that it maintains the same speedup as the fully-supervised Quantum LS-SVM.

Published
2022
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157. Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves

Author

Bosch, David, Panahi, Ashkan, Özcelikkale, Ayca, and Dubhash, Devdatt

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, 68T07
Abstract

We compute precise asymptotic expressions for the learning curves of least squares random feature (RF) models with either a separable strongly convex regularization or the $\ell_1$ regularization. We propose a novel multi-level application of the convex Gaussian min max theorem (CGMT) to overcome the traditional difficulty of finding computable expressions for random features models with correlated data. Our result takes the form of a computable 4-dimensional scalar optimization. In contrast to previous results, our approach does not require solving an often intractable proximal operator, which scales with the number of model parameters. Furthermore, we extend the universality results for the training and generalization errors for RF models to $\ell_1$ regularization. In particular, we demonstrate that under mild conditions, random feature models with elastic net or $\ell_1$ regularization are asymptotically equivalent to a surrogate Gaussian model with the same first and second moments. We numerically demonstrate the predictive capacity of our results, and show experimentally that the predicted test error is accurate even in the non-asymptotic regime., Comment: 52 pages, 3 figures

Published
2022

159. Teenagers' Smartphone Use during Homework: An Analysis of Beliefs and Behaviors around Digital Multitasking

Author

Mrazek, Alissa J., Mrazek, Michael D., Ortega, Joshua R., Ji, Rosie R., Karimi, Sana S., Brown, Chelsea S., Alexander, Chelsie A., Khan, Maliha, Panahi, Rhozhen, Sadoff, Madeline, Scott, Ashley, Tyszka, JimiRose E., and Schooler, Jonathan W.

Abstract

Although research suggests that phone usage during academic activities is problematic for learning and performance, little is known about high school students' digital multitasking during homework. This exploratory descriptive study surveyed 135 students from four public U.S. high schools to investigate teenagers' attitudes towards digital distraction, smartphone use during homework, cell phone dependence, and motivations for digital multitasking. Our findings suggested that teens were distracted during homework about 38% of the time, and both mind-wandering and the use of digital devices contributed to this distraction. Of the students surveyed, 64% believed that they should focus more during homework than they currently did, and most were willing to try strategies such as silencing their phone or putting it out of sight. However, many were not currently using such strategies, and our data suggested that students may be spending approximately 204 h per year trying to complete homework but unintentionally distracted from it. We explored their current motivations and beliefs as a necessary first step for the creation of future interventions to help teens reduce their digital multitasking during homework.

Published
2021

184. Reply to comment on 'Failure of the simultaneous block diagonalization technique applied to complete and cluster synchronization of random networks'

Author

Panahi, Shirin, Amaya, Nelson, Klickstein, Isaac, Novello, Galen, and Sorrentino, Francesco

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

We respond briefly to a comment [1, arXiv:2110.15493] recently posted online on our paper [2, arXiv:2108.07893]. Complete and cluster synchronization of random networks is undoubtedly a topic of interest in the Physics, Engineering, and Nonlinear Dynamics literature. In [3] we study both complete and cluster synchronization of networks and introduce indices that measure success (or failure) of application of the SBD technique in decoupling the stability problem into problems of lower dimensionality. Our usage of the word `failure' indicates that the technique does not produce a decomposition which results in a system which is easier to analyze, not that the technique fails in correctly decoupling these problems.

Published
2022

190. Colloid thermophoresis in surfactant solutions: Probing colloid–solvent interactions through microscale experiments.

Author

Pu, Di, Panahi, Amirreza, Natale, Giovanniantonio, and Benneker, Anne M.

Subjects
*NONIONIC surfactants, *SURFACE chemistry, *IONIC solutions, *FUNCTIONAL groups, *THERMOPHORESIS
Abstract

Thermophoresis has emerged as a powerful tool for characterizing and manipulating colloids at the nano- and micro-scales due to its sensitivity to colloid–solvent interactions. The use of surfactants enables the tailoring of surface chemistry on colloidal particles and the tuning of interfacial interactions. However, the microscopic mechanisms underlying thermophoresis in surfactant solutions remain poorly understood due to the complexity of multiscale interaction coupling. To achieve a more fundamental understanding of the roles of surfactants, we investigated the thermophoretic behavior of silica beads in both ionic and nonionic surfactant solutions at various background temperatures. We provide a complete mechanistic picture of the effects of surfactants on interfacial interactions through mode-coupling analysis of both electrophoretic and thermophoretic experiments. Our results demonstrate that silica thermophoresis is predominantly governed by the dissociation of silanol functional groups at silica–water interfaces in nonionic surfactant solutions, while in ionic surfactant solutions, the primary mechanism driving silica thermophoresis is the adsorption of ionic surfactants onto the silica surface. [ABSTRACT FROM AUTHOR]

Published
2024
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192. Superintegrability on the Dunkl oscillator model in three-Dimensional spaces of constant curvature

Author

Dong, Shi-Hai, Najafizade, Amene, Panahi, Hossein, Chung, Won Sang, and Hassanabadi, Hassan

Subjects
Nonlinear Sciences - Exactly Solvable and Integrable Systems, Mathematical Physics, Mathematics - Quantum Algebra, Quantum Physics
Abstract

This paper has studied the three-dimensional Dunkl oscillator models in a generalization of superintegrable Euclidean Hamiltonian systems to curved ones. These models are defined based on curved Hamiltonians, which depend on a deformation parameter of underlying space and involve reflection operators. Their symmetries are obtained by the Jordan-Schwinger representations in the family of the Cayley-Klein orthogonal algebras using the creation and annihilation operators of the dynamical $sl_{-1}(2)$ algebra of the one-dimensional Dunkl oscillator. The resulting algebra is a deformation of $so_{\kappa_1\kappa_2}(4)$ with reflections, which is known as the Jordan-Schwinger-Dunkl algebra $jsd_{\kappa_1\kappa_2}(4)$. Hence, this model is shown to be maximally superintegrable. On the other hand, the superintegrability of the three-dimensional Dunkl oscillator model is studied from the factorization approach viewpoint. The spectrum of this system is derived through the separation of variables in geodesic polar coordinates, and the resulting eigenfunctions are algebraically given in terms of Jacobi polynomials., Comment: 21 pages, to appear in Journal quantum

Published
2021
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193. Generative Models of Brain Dynamics -- A review

Author

Panahi, Mahta Ramezanian, Abrevaya, Germán, Gagnon-Audet, Jean-Christophe, Voleti, Vikram, Rish, Irina, and Dumas, Guillaume

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics., Comment: Updated to two-column format with 15 pages (excluding refs), 3 figs, submitted to Frontiers

Published
2021

194. Matryoshka and Disjoint Cluster Synchronization of Networks

Author

Nazerian, Amirhossein, Panahi, Shirin, Leifer, Ian, Phillips, David, Makse, Hernan, and Sorrentino, Francesco

Subjects
Electrical Engineering and Systems Science - Systems and Control, Nonlinear Sciences - Chaotic Dynamics
Abstract

The main motivation for this paper is to present a definition of network synchronizability for the case of cluster synchronization (CS), in an analogous fashion to Barahona and Pecora for the case of complete synchronization. We find this problem to be substantially more complex than the original one. We distinguish between the two cases of networks with intertwined clusters and no intertwined clusters and between {the two cases that the master stability function is negative either in a bounded range or in an unbounded range of its argument. We first obtain a definition of synchronizability that applies to each individual cluster within a network and then attempt to generalize this definition to the entire network. For CS, the synchronous solution of each cluster may be stable independent of the stability of the other clusters, which results in possibly different ranges in which each cluster synchronizes (isolated CS.) For each pair of clusters, we distinguish between three different cases: Matryoshka Cluster Synchronization (when the range of the stability of the synchronous solution for one cluster is included in that of the other cluster), Partially Disjoint Cluster Synchronization (when the ranges of stability of the synchronous solutions partially overlap), and Complete Disjoint Cluster Synchronization (when the ranges of stability of the synchronous solutions do not overlap.)

Published
2021
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195. Group synchrony, parameter mismatches, and intragroup connections

Author

Panahi, Shirin and Sorrentino, Francesco

Subjects
Nonlinear Sciences - Chaotic Dynamics, Electrical Engineering and Systems Science - Systems and Control
Abstract

Group synchronization arises when two or more synchronization patterns coexist in a network formed of oscillators of different types, with the systems in each group synchronizing on the same time-evolution, but systems in different groups synchronizing on distinct time-evolutions. Group synchronization has been observed and characterized when the systems in each group are identical and the couplings between the systems satisfy specific conditions. By relaxing these constraints and allowing them to be satisfied in an approximate rather than exact way, we observe that stable group synchronization may still occur in the presence of small deviations of the parameters of the individual systems and of the couplings from their nominal values. We analyze this case and provide necessary and sufficient conditions for stability through a master stability function approach, {which also allows us to quantify the synchronization error}. We also investigate the stability of group synchronization in the presence of intra group connections and for this case, extend some of the existing results in the literature. Our analysis points out a broader class of matrices describing intra group connections for which the stability problem can be reduced in a low-dimensional form.

Published
2021
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