Author: "Bordas, P." - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Bordas, P."' showing total 2,046 results

Start Over Author "Bordas, P."

2,046 results on '"Bordas, P."'

1. Exploring the non-thermal physics behind the pulsar wind nebula PSR J2030+4415 through radio observations

Author: Paredes, J. M., Benaglia, P., Bosch-Ramon, V., Tej, A., Saha, A., Martí, J., and Bordas, P.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: PSR J2030+4415 is a gamma-ray pulsar with an X-ray pulsar wind nebula elongated along the north-south direction. The system shows a prominent X-ray filament oriented at an angle of 130{\deg} to the nebula axis. To improve our understanding of the non-thermal processes occurring in the pulsar wind nebula, we attempted to determine the possible existence of a radio counterpart, study its morphology, and obtain restrictive upper limits of the pulsar and filament emission at radio wavelengths. We performed observations of the pulsar PSR J2030+4415 and its surroundings with the upgraded Giant Metrewave Radio Telescope (uGMRT) at two frequency bands, and put the results in context with findings at other wavelengths. We obtained radio images at 736 and 1274 MHz that reveal a structure trailing the pulsar, with a morphology overlapping the X-ray nebula. This radio structure is the radio counterpart of the X-ray pulsar wind nebula. The derived spectral index along this structure shows spatial variation. There are no hints of the pulsar and the filament at any of the explored radio frequencies, but we obtained restrictive upper limits. A physical scenario that combines the radio and the X-ray observations, and consistent with IR data, of the nebula and the filament is presented. We propose that particle acceleration occurs in the nebula tail due to the presence of a re-collimation shock, and the highest energy particles gradually escape from it through energy-dependent diffusion. We also find a lower limit in the energy of the particles escaping along the X-ray filament of ~GeV., Comment: 8 pages, 6 figures. Accepted for publication in A&A
Published: 2025

2. Poromechanical modelling of the time-dependent response of in vivo human skin during extension

Author: Lavigne, Thomas, Urcun, Stéphane, Jacquet, Emmanuelle, Chambert, Jérôme, Elouneg, Aflah, Suarez-Afanador, Camilo A., Bordas, Stéphane P. A., Sciumè, Giuseppe, and Rohan, Pierre-Yves
Subjects: Quantitative Biology - Tissues and Organs
Abstract: This paper proposes a proof of concept application of a biphasic constitutive model to identify the mechanical properties of in vivo human skin under extension. Although poromechanics theory has been extensively used to model other soft biological tissues, only a few studies have been published for skin, and most have been limited to ex vivo or in silico conditions. However, in vivo procedures are crucial to determine the subject-specific properties at different body sites. This study focuses on cyclic uni-axial extension of the upper arm skin, using unpublished data collected by Chambert et al. Our analysis shows that a two-layer finite element model allows representing all relevant features of the observed mechanical response to the imposed external loading, which was composed, in this contribution, of four loading-sustaining-unloading cycles. The Root Mean Square Error (RMSE) between the calibrated model and the measured Force-time response was 8.84e-3 N. Our biphasic model represents a preliminary step toward investigating the mechanical conditions responsible for the onset of injury. It allows for the analysis of changes in Interstitial Fluid (IF) pressure, flow, and osmotic pressure, in addition to the mechanical fields. Future work will focus on the interaction of multiple biochemical factors and the complex network of regulatory signals.
Published: 2024

3. Design of thermal meta-structures made of functionally graded materials using isogeometric density-based topology optimization

Author: Jansari, Chintan, Bordas, Stéphane P. A., Montemurro, Marco, and Atroshchenko, Elena
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: The thermal conductivity of Functionally Graded Materials (FGMs) can be efficiently designed through topology optimization to obtain thermal meta-structures that actively steer the heat flow. Compared to conventional analytical design methods, topology optimization allows handling arbitrary geometries, boundary conditions and design requirements; and producing alternate designs for non-unique problems. Additionally, as far as the design of meta-structures is concerned, topology optimization does not need intuition-based coordinate transformation or the form invariance of governing equations, as in the case of transformation thermotics. We explore isogeometric density-based topology optimization in the continuous setting, which perfectly aligns with FGMs. In this formulation, the density field, geometry and solution of the governing equations are parameterized using non-uniform rational basis spline entities. Accordingly, the heat conduction problem is solved using Isogeometric Analysis. We design various 2D & 3D thermal meta-structures under different design scenarios to showcase the effectiveness and versatility of our approach. We also design thermal meta-structures based on architected cellular materials, a special class of FGMs, using their empirical material laws calculated via numerical homogenization.
Published: 2024

4. A new method of reconstructing images of gamma-ray telescopes applied to the LST-1 of CTAO

Author: Project, CTA-LST, Abe, K., Abe, S., Abhishek, A., Acero, F., Aguasca-Cabot, A., Agudo, I., Alispach, C., Crespo, N. Alvarez, Ambrosino, D., Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Asano, K., Aubert, P., Baktash, A., Balbo, M., Bamba, A., Larriva, A. Baquero, de Almeida, U. Barres, Barrio, J. A., Jiménez, L. Barrios, Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Medrano, J. Bernete, Berti, A., Bezshyiko, I., Bhattacharjee, P., Bigongiari, C., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Borkowski, G., Brunelli, G., Bulgarelli, A., Burelli, I., Burmistrov, L., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Carrasco, M. S., Cassol, F., Castrejón, N., Cauz, D., Cerasole, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chon, G., Chytka, L., Cicciari, G. M., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., Da Vela, P., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Del Burgo, R., Del Peral, L., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrarotto, F., Fiasson, A., Foffano, L., Coromina, L. Freixas, Fröse, S., Fukazawa, Y., López, R. Garcia, Gasbarra, C., Gasparrini, D., Geyer, D., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Günther, P., Hackfeld, J., Hadasch, D., Hahn, A., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Inoue, S., Ioka, K., Iori, M., Iuliano, A., Martinez, I. Jimenez, Quiles, J. Jimenez, Jurysek, J., Kagaya, M., Kalashev, O., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kohri, K., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Lemoigne, L., Linhoff, M., Longo, F., López-Coto, R., López-Oramas, A., Loporchio, S., Lorini, A., Bahilo, J. Lozano, Luciani, H., Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mallamaci, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Marchesi, S., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Mas-Aguilar, A., Maurin, G., Mazin, D., Méndez-Gallego, J., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moya, V., Muraishi, H., Nagataki, S., Nakamori, T., Neronov, A., Nickel, L., Rosillo, M. Nievas, Nikolic, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Orito, R., Otero-Santos, J., Ottanelli, P., Owen, E., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pech, M., Pecimotika, M., Peresano, M., Pfeifle, F., Pietropaolo, E., Pihet, M., Pirola, G., Plard, C., Podobnik, F., Pons, E., Prandini, E., Priyadarshi, C., Prouza, M., Rainò, S., Rando, R., Rhode, W., Ribó, M., Righi, C., Rizi, V., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Ruina, A., Ruiz-Velasco, E., Saito, T., Sakurai, S., Sanchez, D. A., Sano, H., Šarić, T., Sato, Y., Saturni, F. G., Savchenko, V., Schiavone, F., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Siegert, T., Sitarek, J., Sliusar, V., Strišković, J., Strzys, M., Suda, Y., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tavernier, T., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Tutone, A., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vázquez, Ventura, S., Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Visentin, E., Vitale, V., Voitsekhovskyi, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Wan, L., Will, M., Wójtowicz, J., Yamamoto, T., Yamazaki, R., Yeung, P. K. H., Yoshida, T., Yoshikoshi, T., Zhang, W., and Zywucka, N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A
Published: 2024
Full Text: View/download PDF

5. Implementing LLMs in industrial process modeling: Addressing Categorical Variables

Author: Koronaki, Eleni D., Suntaxi, Geremy Loachamin, Papavasileiou, Paris, Giovanis, Dimitrios G., Kathrein, Martin, Boudouvis, Andreas G., and Bordas, Stéphane P. A.
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Important variables of processes are, in many occasions, categorical, i.e. names or labels representing, e.g. categories of inputs, or types of reactors or a sequence of steps. In this work, we use Large Language Models (LLMs) to derive embeddings of such inputs that represent their actual meaning, or reflect the ``distances" between categories, i.e. how similar or dissimilar they are. This is a marked difference from the current standard practice of using binary, or one-hot encoding to replace categorical variables with sequences of ones and zeros. Combined with dimensionality reduction techniques, either linear such as Principal Components Analysis (PCA), or nonlinear such as Uniform Manifold Approximation and Projection (UMAP), the proposed approach leads to a \textit{meaningful}, low-dimensional feature space. The significance of obtaining meaningful embeddings is illustrated in the context of an industrial coating process for cutting tools that includes both numerical and categorical inputs. The proposed approach enables feature importance which is a marked improvement compared to the current state-of-the-art (SotA) in the encoding of categorical variables.
Published: 2024

6. Intrinsic mixed-dimensional beam-shell-solid couplings in linear Cosserat continua via tangential differential calculus

Author: Sky, Adam, Hale, Jack S., Zilian, Andreas, Bordas, Stéphane P. A., and Neff, Patrizio
Subjects: Mathematics - Numerical Analysis, Computer Science - Computational Engineering, Finance, and Science, Mathematical Physics
Abstract: We present an approach to the coupling of mixed-dimensional continua by employing the mathematically enriched linear Cosserat micropolar model. The kinematical reduction of the model to lower dimensional domains leaves its fundamental degrees of freedom intact. Consequently, the degrees of freedom intrinsically agree even at the interface with a domain of a different dimensionality. Thus, this approach circumvents the need for intermediate finite elements or mortar methods. We introduce the derivations of all models of various dimensions using tangential differential calculus. The coupling itself is then achieved by defining a mixed-dimensional action functional with consistent Sobolev trace operators. Finally, we present numerical examples involving a three-dimensional silicone-rubber block reinforced with a curved graphite shell on its lower surface, a three-dimensional silver block reinforced with a graphite plate and beams, and lastly, intersecting silver shells reinforced with graphite beams.
Published: 2024

7. Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

Author: Deshpande, Saurabh, Rappel, Hussein, Hobbs, Mark, Bordas, Stéphane P. A., and Lengiewicz, Jakub
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is still a severely under-investigated problem, especially when input-output relationships are non-linear. To handle this problem, the present work introduces an innovative approach that combines autoencoder deep neural networks with the probabilistic regression capabilities of Gaussian processes. The autoencoder provides a low-dimensional representation of the solution space, while the Gaussian process is a Bayesian method that provides a probabilistic mapping between the low-dimensional inputs and outputs. We validate the proposed framework for its application to surrogate modeling of non-linear finite element simulations. Our findings highlight that the proposed framework is computationally efficient as well as accurate in predicting non-linear deformations of solid bodies subjected to external forces, all the while providing insightful uncertainty assessments.
Published: 2024
Full Text: View/download PDF

8. A short perspective on a posteriori error control and adaptive discretizations

Author: Becker, Roland, Bordas, Stéphane P. A., Chouly, Franz, and Omnes, Pascal
Subjects: Mathematics - Numerical Analysis
Abstract: Error control by means of a posteriori error estimators or indica-tors and adaptive discretizations, such as adaptive mesh refinement, have emerged in the late seventies. Since then, numerous theoretical developments and improvements have been made, as well as the first attempts to introduce them into real-life industrial applications. The present introductory chapter provides an overview of the subject, highlights some of the achievements to date and discusses possible perspectives.
Published: 2024
Full Text: View/download PDF

9. A detailed study of the very-high-energy Crab pulsar emission with the LST-1

Author: Project, CTA-LST, Abe, K., Abe, S., Abhishek, A., Acero, F., Aguasca-Cabot, A., Agudo, I., Crespo, N. Alvarez, Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Artero, M., Asano, K., Aubert, P., Baktash, A., Bamba, A., Larriva, A. Baquero, Baroncelli, L., de Almeida, U. Barres, Barrio, J. A., Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Medrano, J. Bernete, Berti, A., Bhattacharjee, P., Bigongiari, C., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Brunelli, G., Bulgarelli, A., Burelli, I., Burmistrov, L., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Carrasco, M. S., Cassol, F., Castrejón, N., Cauz, D., Cerasole, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chon, G., Chytka, L., Cicciari, G. M., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., Da Vela, P., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Del Peral, L., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrarotto, F., Fiasson, A., Foffano, L., Coromina, L. Freixas, Fröse, S., Fukazawa, Y., López, R. Garcia, Gasbarra, C., Gasparrini, D., Gavira, L., Geyer, D., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Günther, P., Hackfeld, J., Hadasch, D., Hahn, A., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Ioka, K., Iori, M., Martinez, I. Jimenez, Quiles, J. Jiménez, Jurysek, J., Kagaya, M., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kohri, K., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Lemoigne, L., Linhoff, M., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Bahilo, J. Lozano, Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mallamaci, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Marchesi, S., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Mas-Aguilar, A., Maurin, G., Mazin, D., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moya, V., Muraishi, H., Nagataki, S., Nakamori, T., Neronov, A., Nickel, L., Rosillo, M. Nievas, Nikolic, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Orito, R., Otero-Santos, J., Ottanelli, P., Owen, E., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pech, M., Pecimotika, M., Peresano, M., Pfeiffle, F., Pietropaolo, E., Pihet, M., Pirola, G., Plard, C., Podobnik, F., Pons, E., Prandini, E., Priyadarshi, C., Prouza, M., Rando, R., Rhode, W., Ribó, M., Righi, C., Rizi, V., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Saito, T., Sakurai, S., Sanchez, D. A., Sano, H., Šarić, T., Sato, Y., Saturni, F. G., Savchenko, V., Schiavone, F., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Siegert, T., Silvia, R., Sitarek, J., Sliusar, V., Strišković, J., Strzys, M., Suda, Y., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tavernier, T., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Truzzi, S., Tutone, A., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vázquez, Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Visentin, E., Vitale, V., Voitsekhovskyi, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Wan, L., Will, M., Yamamoto, T., Yamazaki, R., Yeung, P. K. H., Yoshida, T., Yoshikoshi, T., Zhang, W., and Zywucka, N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A
Published: 2024

10. TeV $\gamma$-ray emission near globular cluster Terzan 5 as a probe of cosmic ray transport

Author: Krumholz, Mark R., Crocker, Roland M., Bahramian, Arash, and Bordas, Pol
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract: Cosmic rays travelling through interstellar space have their propagation directions repeatedly scattered by fluctuating interstellar magnetic fields. The nature of this scattering is a major unsolved problem in astrophysics, one that has resisted solution largely due to a lack of direct observational constraints on the scattering rate. Here we show that very high-energy $\gamma$-ray emission from the globular cluster Terzan 5, which has unexpectedly been found to be displaced from the cluster, presents a direct probe of this process. We show that this displacement is naturally explained by cosmic rays accelerated in the bow shock around the cluster propagating a finite distance before scattering processes re-orient enough of them towards Earth to produce a detectable $\gamma$-ray signal. The angular distance between the cluster and the signal places tight constraints on the scattering rate, which we show are consistent with a model whereby scattering is primarily due to excitation of magnetic waves by the cosmic rays themselves. The analysis method we develop here will make it possible to use sources with similarly displaced non-thermal X-ray and TeV $\gamma$-ray signals as direct probes of cosmic ray scattering across a range of Galactic environments., Comment: Accepted to Nature Astronomy; 16 pages, 7 figures; this submission contains the main article, supplementary information, and extended data in a single document
Published: 2024

11. Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease

Author: de Lope, Elisa Gómez, Deshpande, Saurabh, Torné, Ramón Viñas, Liò, Pietro, Glaab, Enrico, and Bordas, Stéphane P. A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Biomolecules, Quantitative Biology - Molecular Networks
Abstract: Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and machine learning methods. Graph neural networks have emerged as promising alternatives, yet the optimal strategies for their design and optimization in real-world biomedical challenges remain unclear. This study evaluates various graph representation learning models for case-control classification using high-throughput biological data from Parkinson's disease and control samples. We compare topologies derived from sample similarity networks and molecular interaction networks, including protein-protein and metabolite-metabolite interactions (PPI, MMI). Graph Convolutional Network (GCNs), Chebyshev spectral graph convolution (ChebyNet), and Graph Attention Network (GAT), are evaluated alongside advanced architectures like graph transformers, the graph U-net, and simpler models like multilayer perceptron (MLP). These models are systematically applied to transcriptomics and metabolomics data independently. Our comparative analysis highlights the benefits and limitations of various architectures in extracting patterns from omics data, paving the way for more accurate and interpretable models in biomedical research., Comment: Submitted to Machine Learning in Computational Biology 2024 as an extended abstract, 2 pages + 1 appendix
Published: 2024

12. Uncertainty analysis in acoustics: perturbation methods and isogeometric boundary element methods

Author: Chen, Leilei, Lian, Haojie, Huo, Ruijin, Du, Jing, Liu, Weisong, Meng, Zhuxuan, and Bordas, Stéphane P. A.
Published: 2024
Full Text: View/download PDF

13. Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis

Author: Suntaxi, Geremy Loachamín, Papavasileiou, Paris, Koronaki, Eleni D., Giovanis, Dimitrios G., Gakis, Georgios, Aviziotis, Ioannis G., Kathrein, Martin, Pozzetti, Gabriele, Czettl, Christoph, Bordas, Stéphane P. A., and Boudouvis, Andreas G.
Subjects: Physics - Chemical Physics, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, Statistics - Applications
Abstract: This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interplay of physical mechanism that dominate in each one of them. Through this work, we address three key objectives. Firstly, our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of "outcomes" corresponding to distinct process regimes, wherein the relative influence of input variables undergoes notable shifts. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach. Secondly, we demonstrate the development of an efficient surrogate model, based on Polynomial Chaos Expansion (PCE), that maintains accuracy, facilitating streamlined computational analyses. Finally, as a result of PCE, sensitivity analysis is made possible by means of Sobol' indices, that quantify the impact of process inputs across identified regimes. The insights gained from our analysis contribute to the formulation of hypotheses regarding phenomena occurring beyond the transition regime. Notably, the significance of temperature even in the diffusion-limited regime, as evidenced by the Arrhenius plot, suggests activation of gas phase reactions at elevated temperatures. Importantly, our proposed methods yield insights that align with experimental observations and theoretical principles, aiding decision-making in process design and optimization. By circumventing the need for costly and time-consuming experiments, our approach offers a pragmatic pathway towards enhanced process efficiency. Moreover, this study underscores the potential of data-driven computational methods for innovating reactor design paradigms.
Published: 2024

14. Integrating supervised and unsupervised learning approaches to unveil critical process inputs

Author: Papavasileiou, Paris, Giovanis, Dimitrios G., Pozzetti, Gabriele, Kathrein, Martin, Czettl, Christoph, Kevrekidis, Ioannis G., Boudouvis, Andreas G., Bordas, Stéphane P. A., and Koronaki, Eleni D.
Subjects: Computer Science - Machine Learning
Abstract: This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims to (i) discern critical parameters influencing the output and (ii) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we address the pivotal question of the significance of each input in shaping the process outcome, using an industrial Chemical Vapor Deposition (CVD) process as an example. The initial objective involves merging subject matter expertise and clustering techniques exclusively on the process output, here, coating thickness measurements at various positions in the reactor. This approach identifies groups of production runs that share similar qualitative characteristics, such as film mean thickness and standard deviation. In particular, the differences of the outcomes represented by the different clusters can be attributed to differences in specific inputs, indicating that these inputs are critical for the production outcome. Leveraging this insight, we subsequently implement supervised classification and regression methods using the identified critical process inputs. The proposed methodology proves to be valuable in scenarios with a multitude of inputs and insufficient data for the direct application of deep learning techniques, providing meaningful insights into the underlying processes.
Published: 2024
Full Text: View/download PDF

15. Quantum-informed simulations for mechanics of materials: DFTB+MBD framework

Author: Shen, Zhaoxiang, Sosa, Raúl I., Bordas, Stéphane P. A., Tkatchenko, Alexandre, and Lengiewicz, Jakub
Subjects: Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics, Quantum Physics
Abstract: The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and understanding these behaviors require models that rely on insights from electronic and atomic scales. In such cases, classical simplified approximations at those scales are insufficient, and quantum-based modeling is required. In this paper, we study how quantum effects can modify the mechanical properties of systems relevant to materials engineering. We base our study on a high-fidelity modeling framework that combines two computationally efficient models rooted in quantum first principles: Density Functional Tight Binding (DFTB) and many-body dispersion (MBD). The MBD model is applied to accurately describe non-covalent van der Waals interactions. Through various benchmark applications, we demonstrate the capabilities of this framework and the limitations of simplified modeling. We provide an open-source repository containing all codes, datasets, and examples presented in this work. This repository serves as a practical toolkit that we hope will support the development of future research in effective large-scale and multiscale modeling with quantum-mechanical fidelity.
Published: 2024

16. Dark Matter Line Searches with the Cherenkov Telescope Array

Author: Abe, S., Abhir, J., Abhishek, A., Acero, F., Acharyya, A., Adam, R., Aguasca-Cabot, A., Agudo, I., Aguirre-Santaella, A., Alfaro, J., Alfaro, R., Alvarez-Crespo, N., Batista, R. Alves, Amans, J. -P., Amato, E., Ambrosi, G., Angel, L., Aramo, C., Arcaro, C., Arnesen, T. T. H., Arrabito, L., Asano, K., Ascasibar, Y., Aschersleben, J., Ashkar, H., Backes, M., Baktash, A., Balazs, C., Balbo, M., Larriva, A. Baquero, Martins, V. Barbosa, de Almeida, U. Barres, Barrio, J. A., Batković, I., Batzofin, R., Baxter, J., González, J. Becerra, Beck, G., Benbow, W., Berge, D., Bernardini, E., Bernete, J., Bernlöhr, K., Berti, A., Bertucci, B., Bhattacharjee, P., Bhattacharyya, S., Bigongiari, C., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Blazek, J., Bocchino, F., Boisson, C., Bolmont, J., Bonnoli, G., Bonollo, A., Bordas, P., Bosnjak, Z., Bottacini, E., Böttcher, M., Bringmann, T., Bronzini, E., Brose, R., Brown, A. M., Brunelli, G., Bulgarelli, A., Bulik, T., Burelli, I., Burmistrov, L., Burton, M., Buscemi, M., Bylund, T., Cailleux, J., Campoy-Ordaz, A., Cantlay, B. K., Capasso, G., Caproni, A., Capuzzo-Dolcetta, R., Caraveo, P., Caroff, S., Carosi, A., Carosi, R., Carquin, E., Carrasco, M. -S., Cassol, F., Castaldini, L., Castrejon, N., Castro-Tirado, A. J., Cerasole, D., Cerruti, M., Chadwick, P. M., Chaty, S., Chen, A. W., Chernyakova, M., Chiavassa, A., Chudoba, J., Chytka, L., Cicciari, G. M., Cifuentes, A., Araujo, C. H. Coimbra, Colapietro, M., Conforti, V., Conte, F., Contreras, J. L., Costa, A., Costantini, H., Cotter, G., Cristofari, P., Cuevas, O., Curtis-Ginsberg, Z., D'Amico, G., D'Ammando, F., Dai, S., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Caprio, V., Pino, E. M. de Gouveia Dal, De Lotto, B., De Lucia, M., de Menezes, R., de Naurois, M., de Souza, V., del Peral, L., del Valle, M. V., Giler, A. G. Delgado, Mengual, J. Delgado, Delgado, C., Dell'aiera, M., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Diebold, S., Dinesh, A., Djuvsland, J., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Dörner, J., Doro, M., Dournaux, J. -L., Duangchan, C., Dubos, C., Ducci, L., Dwarkadas, V. V., Ebr, J., Eckner, C., Egberts, K., Einecke, S., Elsässer, D., Emery, G., Errando, M., Escanuela, C., Escarate, P., Godoy, M. Escobar, Escudero, J., Esposito, P., Ettori, S., Falceta-Goncalves, D., Fedorova, E., Fegan, S., Feng, Q., Ferrand, G., Ferrarotto, F., Fiandrini, E., Fiasson, A., Filipovic, M., Fioretti, V., Fiori, M., Foffano, L., Guiteras, L. Font, Fontaine, G., Fröse, S., Fukazawa, Y., Fukui, Y., Furniss, A., Galanti, G., Galaz, G., Galelli, C., Gallozzi, S., Gammaldi, V., Garczarczyk, M., Gasbarra, C., Gasparrini, D., Ghalumyan, A., Gianotti, F., Giarrusso, M., Paiva, J. G. Giesbrecht Formiga, Giglietto, N., Giordano, F., Giuffrida, R., Glicenstein, J. -F., Glombitza, J., Goldoni, P., González, J. M., González, M. M., Coelho, J. Goulart, Gradetzke, T., Granot, J., Grasso, D., Grau, R., Gréaux, L., Green, D., Green, J. G., Grolleron, G., Guedes, L. M. V., Gueta, O., Hackfeld, J., Hadasch, D., Hamal, P., Hanlon, W., Hara, S., Harvey, V. M., Hassan, T., Hayashi, K., Heß, B., Heckmann, L., Heller, M., Cadena, S. Hernández, Hervet, O., Hinton, J., Hiroshima, N., Hnatyk, B., Hnatyk, R., Hofmann, W., Holder, J., Horan, D., Horvath, P., Hovatta, T., Hrabovsky, M., Hrupec, D., Iarlori, M., Inada, T., Incardona, F., Inoue, S., Inoue, Y., Iocco, F., Iori, M., Ishio, K., Jamrozy, M., Janecek, P., Jankowsky, F., Jean, P., Quiles, J. Jimenez, Jin, W., Juramy-Gilles, C., Jurysek, J., Kagaya, M., Kalekin, O., Karas, V., Katagiri, H., Kataoka, J., Kaufmann, S., Kazanas, D., Kerszberg, D., Kieda, D. B., Kleiner, T., Kluge, G., Kobayashi, Y., Kohri, K., Komin, N., Kornecki, P., Kosack, K., Kowal, G., Kubo, H., Kushida, J., La Barbera, A., La Palombara, N., Láinez, M., Lamastra, A., Lapington, J., Laporte, P., Lazarević, S., Lazendic-Galloway, J., Lemoine-Goumard, M., Lenain, J. -P., Leone, F., Leonora, E., Leto, G., Lindfors, E., Linhoff, M., Liodakis, I., Lipniacka, A., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Bahilo, J. Lozano, Luque-Escamilla, P. L., Macias, O., Majumdar, P., Mallamaci, M., Malyshev, D., Mandat, D., Manicò, G., Mariotti, M., Márquez, I., Marquez, P., Marsella, G., Martí, J., Martínez, G. A., Martínez, M., Martinez, O., Marty, C., Mas-Aguilar, A., Mastropietro, M., Mazin, D., Menchiari, S., Mestre, E., Meunier, J. -L., Meyer, D. M. -A., Meyer, M., Miceli, D., Miceli, M., Michailidis, M., Michałowski, J., Miener, T., Miranda, J. M., Mitchell, A., Mizote, M., Mizuno, T., Moderski, R., Molero, M., Molfese, C., Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moulin, E., Zamanillo, V. Moya, Munari, K., Murach, T., Muraczewski, A., Muraishi, H., Nakamori, T., Nayak, A., Nemmen, R., Neto, J. P., Nickel, L., Niemiec, J., Nieto, D., Rosillo, M. Nievas, Nikołajuk, M., Nikolić, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Okumura, A., Olive, J. -F., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, E., Orlando, S., Ostrowski, M., Otero-Santos, J., Oya, I., Pagano, I., Pagliaro, A., Palatiello, M., Panebianco, G., Paneque, D., Pantaleo, F. R., Paredes, J. M., Parmiggiani, N., Patricelli, B., Pe'er, A., Pech, M., Pecimotika, M., Pensec, U., Peresano, M., Pérez-Romero, J., Persic, M., Peters, K. P., Petruk, O., Piano, G., Pierre, E., Pietropaolo, E., Pihet, M., Pinchbeck, L., Pirola, G., Pittori, C., Plard, C., Podobnik, F., Pohl, M., Pollet, V., Ponti, G., Prandini, E., Principe, G., Priyadarshi, C., Produit, N., Prouza, M., Pueschel, E., Pühlhofer, G., Pumo, M. L., Queiroz, F., Quirrenbach, A., Rainò, S., Rando, R., Razzaque, S., Regeard, M., Reimer, A., Reimer, O., Reisenegger, A., Rhode, W., Ribeiro, D., Ribó, M., Ricci, C., Richtler, T., Rico, J., Rieger, F., Riitano, L., Rizi, V., Roache, E., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Rodríguez-Vázquez, J. J., Romano, P., Romeo, G., Rosado, J., de Leon, A. Rosales, Rowell, G., Rudak, B., Ruiter, A. J., Rulten, C. B., Sadeh, I., Saha, L., Saito, T., Salzmann, H., Sánchez-Conde, M., Sandaker, H., Sangiorgi, P., Sano, H., Santander, M., Santos-Lima, R., Sapienza, V., Šarić, T., Sarkar, A., Sarkar, S., Saturni, F. G., Savarese, S., Scherer, A., Schiavone, F., Schipani, P., Schleicher, B., Schovanek, P., Schubert, J. L., Schwanke, U., Arroyo, M. Seglar, Seitenzahl, I. R., Sergijenko, O., Servillat, M., Siegert, T., Siejkowski, H., Siqueira, C., Sliusar, V., Slowikowska, A., Sol, H., Spencer, S. T., Spiga, D., Stamerra, A., Stanič, S., Starecki, T., Starling, R., Stawarz, Ł., Steppa, C., Hatlen, E. Sæther, Stolarczyk, T., Strišković, J., Suda, Y., Świerk, P., Tajima, H., Tak, D., Takahashi, M., Takeishi, R., Tavernier, T., Tejedor, L. A., Terauchi, K., Teshima, M., Testa, V., Tian, W. W., Tibaldo, L., Tibolla, O., Peixoto, C. J. Todero, Torradeflot, F., Torres, D. F., Tosti, G., Tothill, N., Toussenel, F., Tramacere, A., Travnicek, P., Tripodo, G., Trois, A., Truzzi, S., Tutone, A., Vaclavek, L., Vacula, M., Vallania, P., Vallés, R., van Eldik, C., van Scherpenberg, J., Vandenbroucke, J., Vassiliev, V., Acosta, M. Vázquez, Vecchi, M., Ventura, S., Vercellone, S., Verna, G., Viana, A., Viaux, N., Vigliano, A., Vignatti, J., Vigorito, C. F., Villanueva, J., Visentin, E., Vitale, V., Vodeb, V., Voisin, V., Voitsekhovskyi, V., Vorobiov, S., Voutsinas, G., Vovk, I., Vuillaume, T., Wagner, S. J., Walter, R., White, M., White, R., Wierzcholska, A., Will, M., Williams, D. A., Wohlleben, F., Wolter, A., Yamamoto, T., Yang, L., Yoshida, T., Yoshikoshi, T., Zaharijas, G., Zampieri, L., Sanchez, R. Zanmar, Zavrtanik, D., Zavrtanik, M., Zdziarski, A. A., Zech, A., Zhang, W., Zhdanov, V. I., Ziętara, K., Živec, M., and Zuriaga-Puig, J.
Subjects: High Energy Physics - Phenomenology, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract: Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version
Published: 2024
Full Text: View/download PDF

17. Enhancing Biomechanical Simulations Based on A Posteriori Error Estimates: The Potential of Dual Weighted Residual-Driven Adaptive Mesh Refinement

Author: Bui, Huu Phuoc, Duprez, Michel, Rohan, Pierre-Yves, Lejeune, Arnaud, Bordas, Stephane P. A., Bucki, Marek, and Chouly, Franz
Subjects: Mathematics - Numerical Analysis, 65N30, 92C10, 74B20
Abstract: The Finite Element Method (FEM) is a well-established procedure for computing approximate solutions to deterministic engineering problems described by partial differential equations. FEM produces discrete approximations of the solution with a discretisation error that can be an be quantified with \emph{a posteriori} error estimates. The practical relevance of error estimates for biomechanics problems, especially for soft tissue where the response is governed by large strains, is rarely addressed. In this contribution, we propose an implementation of \emph{a posteriori} error estimates targeting a user-defined quantity of interest, using the Dual Weighted Residual (DWR) technique tailored to biomechanics. The proposed method considers a general setting that encompasses three-dimensional geometries and model non-linearities, which appear in hyperelastic soft tissues. We take advantage of the automatic differentiation capabilities embedded in modern finite element software, which allows the error estimates to be computed generically for a large class of models and constitutive laws. First we validate our methodology using experimental measurements from silicone samples, and then illustrate its applicability for patient-specific computations of pressure ulcers on a human heel.
Published: 2024

18. Teraelectronvolt gamma-ray emission near globular cluster Terzan 5 as a probe of cosmic ray transport

Author: Krumholz, Mark R., Crocker, Roland M., Bahramian, Arash, and Bordas, Pol
Published: 2024
Full Text: View/download PDF

19. What is generative in generative artificial intelligence? A design-based perspective

Author: Bordas, Antoine, Le Masson, Pascal, Thomas, Maxime, and Weil, Benoit
Published: 2024
Full Text: View/download PDF

20. NIR spectral classification of the companion in the gamma-ray binary HESS J1832-093 as an O6 V star

Author: van Soelen, Brian, Bordas, Pol, Negueruela, Ignacio, Wilhelmi, Emma de Oña, Papitto, Alessandro, and Ribó, Marc
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: HESS J1832-093 is a member of the rare class of gamma-ray binaries, as recently confirmed by the detection of orbitally modulated X-ray and gamma-ray emission with a period of ~86 d. The spectral type of the massive companion star has been difficult to retrieve as there is no optical counterpart, but the system is coincident with a near-infrared source. Previous results have shown that the infrared counterpart is consistent with an O or B type star, but a clear classification is still lacking. We observed the counterpart twice, in 2019 and 2021, with the X-Shooter spectrograph operating on the VLT. The obtained spectra classify the counterpart as an O6 V type star. We estimate a distance to the source of $6.7 \pm 0.5$ kpc, although this estimate can be severely affected by the high extinction towards the source. This new O6 V classification for the companion star in HESS J1832-093 provides further support to an apparent grouping around a given spectral type for all discovered gamma-ray binaries that contain an O-type star. This may be due to the interplay between the initial mass function and the wind-momentum-luminosity relation., Comment: 6 pages, 2 figures, accepted for publication in MNRAS Letters
Published: 2024

21. Model design in data science: engineering design to uncover design processes and anomalies

Author: Bordas, Antoine, Le Masson, Pascal, and Weil, Benoit
Published: 2025
Full Text: View/download PDF

22. Treating sleep-disordered breathing of idiopathic pulmonary fibrosis patients with CPAP and nocturnal oxygen treatment. A pilot study: Sleep-disordered breathing treatment in IPF

Author: Bordas-Martinez, Jaume, Salord, Neus, Vicens-Zygmunt, Vanesa, Carmezim, João, Pérez, Sandra, Prado, Eliseo, Calvo, María, Blavia, Rosana, Bermudo, Guadalupe, Santos, Salud, Monasterio, Carmen, and Molina-Molina, María
Published: 2024
Full Text: View/download PDF

23. Chasing Gravitational Waves with the Cherenkov Telescope Array

Author: Green, Jarred Gershon, Carosi, Alessandro, Nava, Lara, Patricelli, Barbara, Schüssler, Fabian, Seglar-Arroyo, Monica, Consortium, Cta, Abe, Kazuki, Abe, Shotaro, Acharyya, Atreya, Adam, Remi, Aguasca-Cabot, Arnau, Agudo, Ivan, Alfaro, Jorge, Alvarez-Crespo, Nuria, Batista, Rafael Alves, Amans, Jean-Philippe, Amato, Elena, Ambrosino, Filippo, Angüner, Ekrem Oguzhan, Antonelli, Lucio Angelo, Aramo, Carla, Arcaro, Cornelia, Arrabito, Luisa, Asano, Katsuaki, Aschersleben, Jann, Ashkar, Halim, Stuani, Luiz Augusto, Baack, Dominik, Backes, Michael, Balazs, Csaba, Balbo, Matteo, Larriva, Andres Baquero, Martins, Victor Barbosa, de Almeida, Ulisses Barres, Barrio, Juan Abel, Bastieri, Denis, Batista, Pedro Ivo, Batković, Ivana, Batzofin, Rowan William, Baxter, Joshua Ryo, Beck, Geoffrey, Tjus, Julia Becker, Beiske, Lukas, Belardinelli, Daniele, Benbow, Wystan, Bernardini, Elisa, Medrano, Juan Bernete, Bernlöhr, Konrad, Berti, Alessio, Beshley, Vasyl, Bhattacharjee, Pooja, Bhattacharyya, Saptashwa, Bi, Baiyang, Biederbeck, Noah, Biland, Adrian, Bissaldi, Elisabetta, Blanch, Oscar, Blazek, Jiri, Boisson, Catherine, Bolmont, Julien, Bonnoli, Giacomo, Bordas, Pol, Bošnjak, Željka, Bradascio, Federica, Braiding, Catherine, Bronzini, Ettore, Brose, Robert, Brown, Anthony M., Brun, Francois, Brunelli, Giulia, Bulgarelli, Andrea, Burelli, Irene, Burmistrov, Leonid, Burton, Michael, Bylund, Tomas, Calisse, Paolo Gherardo, Campoy-Ordaz, Anna, Cantlay, Brandon Khan, Capalbi, Milvia, Caproni, Anderson, Capuzzo-Dolcetta, Roberto, Carlile, Colin, Caroff, Sami, Carosi, Roberto, Carrasco, Marie-Sophie, Cascone, Enrico, Cassol, Franca, Castrejon, Noelia, Catalani, Fernando, Cerasole, Davide, Cerruti, Matteo, Chaty, Sylvain, Chen, Andrew W, Chernyakova, Maria, Chiavassa, Andrea, Chudoba, Jiří, Araujo, Carlos Henrique Coimbra, Conforti, Vito, Conte, Francesco, Contreras, Jose Luis, Cossou, Christophe, Costa, Alessandro, Costantini, Heide, Cristofari, Pierre, Cuevas, Omar, Curtis-Ginsberg, Zachary, D'Amico, Giacomo, D'Ammando, Filippo, Dadina, Mauro, Dalchenko, Mykhailo, David, Ludovic, Davids, Isak Delberth, Dazzi, Francesco, De Angelis, Alessandro, de Lavergne, Mathieu de Bony, De Caprio, Vincenzo, De Cesare, Giovanni, Pino, Elisabete M. de Gouveia Dal, De Lotto, Barbara, de Lucia, Mario, de Menezes, Raniere, de Naurois, Mathieu, Wilhelmi, Emma de Oña, De Simone, Nicola, de Souza, Vitor, Del Peral, Luis, del Valle, Maria Victoria, Delagnes, Eric, Giler, Andres Gabriel Delgado, Delgado, Carlos, Dell'aiera, Michael, Della Ceca, Roberto, Della Valle, Massimo, Della Volpe, Domenico, Depaoli, Davide, Dettlaff, Antonios, Di Girolamo, Tristano, Di Piano, Ambra, Di Pierro, Federico, Di Tria, Riccardo, Di Venere, Leonardo, Díaz-Bahamondes, Christian, Dib, Claudio, Diebold, Sebastian, Dima, Razvan, Dinesh, Adithiya, Djannati-Ataï, Arache, Djuvsland, Julia Isabel, Dominguez, Alberto, Dominik, Rune Michael, Donini, Alice, Dorner, Daniela, Dörner, Julien, Doro, Michele, Anjos, Rita de Cassia dos, Dournaux, Jean-Laurent, Dravins, Dainis, Duangchan, Chaimongkol, Dubos, Coline, Ducci, Lozenzo, Dwarkadas, Vikram V., Ebr, Jan, Eckner, Christopher, Egberts, Kathrin, Einecke, Sabrina, Elsässer, Dominik, Emery, Gabriel, Godoy, Miguel Escobar, Escudero, Juan, Esposito, Paolo, Falceta-Gonçalves, Diego, Ramazani, Vandad Fallah, Faure, Alice, Fedorova, Elena, Fegan, Stephen, Feijen, Kirsty, Feng, Qi, Ferrand, Gilles, Ferrarotto, Fabio, Fiandrini, Emanuele, Fiasson, Armand, Fioretti, Valentina, Foffano, Luca, Guiteras, Lluis Font, Fontaine, Gerard, Fröse, Stefan, Fukami, Satoshi, Fukui, Yasuo, Funk, Stefan, Gaggero, Daniele, Galanti, Giorgio, Galaz, Gaspar, Gallant, Yves A., Gallozzi, Stefano, Gammaldi, Viviana, Gasbarra, Claudio, Gaug, Markus, Ghalumyan, Arsen, Gianotti, Fulvio, Giarrusso, Marina, Giglietto, Nicola, Giordano, Francesco, Giuliani, Andrea, Glicenstein, Jean-Francois, Glombitza, Jonas, Goldoni, Paolo, González, José Mauricio, González, Maria Magdalena, Coelho, Jaziel Goulart, Granot, Jonathan, Grasso, Dario, Haro, Roger Grau, Green, David, Greenshaw, Tim, Grolleron, Guillaume, Grube, Jeff, Gueta, Orel, Gunji, Shuichi, Hadasch, Daniela, Hamal, Petr, Hanlon, William, Hara, Satoshi, Harvey, Violet M., Hashiyama, Kazuaki, Hassan, Tarek, Heller, Matthieu, Cadena, Sergio Hernández, Hie, Jonathan, Hiroshima, Nagisa, Hnatyk, Bohdan, Hnatyk, Roman, Hoffmann, Dirk, Hofmann, Werner, Holler, Markus, Horan, Deirdre, Horvath, Pavel, Hovatta, Talvikki, Hrupec, Dario, Hussain, Saqib, Iarlori, Marco, Inada, Tomohiro, Incardona, Federico, Inome, Yusuke, Inoue, Susumu, Iocco, Fabio, Ishio, Kazuma, Jamrozy, Marek, Janecek, Petr, Jankowsky, Felix, Jarnot, Christian, Jean, Pierre, Martínez, Irene Jiménez, Jin, Weidong, Jocou, Laurent, Juramy-Gilles, Claire, Jurysek, Jakub, KALEKIN, Oleg, Kantzas, Dimitrios, Karas, Vladimir, Kaufmann, Sarah, Kerszberg, Daniel, Khelifi, Bruno, Kieda, David B, Kleiner, Tobias Kai, Kluźniak, Włodzimierz, Kobayashi, Yukiho, Kohri, Kazunori, Komin, Nukri, Kornecki, Paula, Kosack, Karl, Kubo, Hidetoshi, Kushida, Junko, La Barbera, Antonino, La Palombara, Nicola, Lainez, Maria, Lamastra, Alessandra, Lapington, Jon S, Lazarevic, Sanja, Lazendic-Galloway, Jasmina, Leach, Steven, Lemoine-Goumard, Marianne, Lenain, Jean-Philippe, Leto, Giuseppe, Leuschner, Fabian, Lindfors, Elina, Linhoff, Maximilian, Liodakis, Ioannis, Loïc, Lozach, Lombardi, Saverio, Longo, Francesco, López-Coto, Rubén, López-Moya, Marcos, López-Oramas, Alicia, Loporchio, Serena, Bahilo, Julio Lozano, Luque-Escamilla, Pedro L., Macias, Oscar, Maier, Gernot, Majumdar, Pratik, Malyshev, Denys, Malyshev, Dmitry, Mandat, Dusan, Manicò, Giulio, Marinos, Peter David, Markoff, Sera, Márquez, Isabel, Marquez, Patricia, Marsella, Giovanni, Martí, Josep, Martin, Pierrick, Martínez, Gustavo Augusto, Martínez, Manel, Martinez, Oibar, Marty, Christophe, Mas-Aguilar, Alvaro, Mastropietro, Michele, Maurin, Gilles, Max-Moerbeck, Walter, Mazin, Daniel, Melkumyan, David, Menchiari, Stefano, Mestre, Enrique, Meunier, Jean-Luc, Meyer, Dominique M. -A., Miceli, Davide, Michailidis, Miltiadis, Michałowski, Jerzy, Miener, Tjark, Miranda, Jose Miguel, Mitchell, Alison, Mizote, Masaya, Mizuno, Tsunefumi, Moderski, Rafal, Mohrmann, Lars, Molero, Miguel, Molfese, Cesare, Molina, Edgar, Montaruli, Teresa, Moralejo, Abelardo, Morcuende, Daniel, Morik, Katharina, Morselli, Aldo, Moulin, Emmanuel, Zamanillo, Victor Moya, Mukherjee, Reshmi, Munari, Kevin, Muraczewski, Adam, Muraishi, Hiroshi, Nakamori, Takeshi, Nayak, Amrit, Nemmen, Rodrigo, Nickel, Lukas, Niemiec, Jacek, Nieto, Daniel, Rosillo, Mireia Nievas, Nikołajuk, Marek, Nishijima, Kyoshi, Noda, Koji, Nosek, Dalibor, Novosyadlyj, Bohdan, Novotný, Vladimír, Nozaki, Seiya, O'Brien, Paul, Ohishi, Michiko, Ohtani, Yoshiki, Okumura, Akira, Olive, Jean-François, Olmi, Barbara, Ong, Rene A., Orienti, Monica, Orito, Reiko, Orlandini, Mauro, Orlando, Elena, Ostrowski, Michal, Otte, Nepomuk, Oya, Igor, Pagano, Isabella, Pagliaro, Antonio, Palatiello, Michele, Panebianco, Gabriele, Paredes, Josep M., Parmiggiani, Nicolò, Patel, Sonal Ramesh, Pavlović, Dijana, Pe'er, Asaf, Pech, Miroslav, Pecimotika, Mario, Peresano, Michele, Pérez-Romero, Judit, Peron, Giada, Persic, Massimo, Petrucci, Pierre-Olivier, Petruk, Oleh, Pfeifle, Felix, Pintore, Fabio, Pirola, Giorgio, Pittori, Carlotta, Plard, Cyann, Podobnik, Franjo, Pohl, Martin, Pons, Estelle, Prandini, Elisa, Prast, Julie, Principe, Giacomo, Priyadarshi, Chaitanya, Produit, Nicolas, Prokhorov, Dmitry, Pueschel, Elisa, Pühlhofer, Gerd, Pumo, Maria Letizia, Punch, Michael, Quirrenbach, Andreas, Raino, Silvia, Randazzo, Nunzio, Rando, Riccardo, Ravel, Thierry, Razzaque, Soebur, Regeard, Maxime, Reichherzer, Patrick, Reimer, Anita, Reimer, Olaf, Reisenegger, Andreas, Reposeur, Thierry, Reville, Brian, Rhode, Wolfgang, Ribó, Marc, Richtler, Tom, Rieger, Frank, Roache, Emmet, Fernandez, Gonzalo Rodriguez, Frías, Maria Dolores Rodríguez, Rodríguez-Vázquez, Juan José, Romano, Patrizia, Romeo, Giuseppe, Rosado, Jaime, Rowell, Gavin P, Rudak, Bronislaw, Ruiter, Ashley J., Rulten, Cameron Boyd, Russo, Federico, Sadeh, Iftach, Saha, Lab, Saito, Takayuki, Sakurai, Shunsuke, Salzmann, Heiko, Sanchez, David, Sanchez-Conde, Miguel, Sangiorgi, Pierluca, Sano, Hidetoshi, Santander, Marcos, Santangelo, Andrea, Santos-Lima, Reinaldo, Sanuy, Andreu, Šarić, Toni, Sarkar, Arkadipta, Sarkar, Subir, Saturni, Francesco Gabriele, Savchenko, Volodymyr, Scherer, Andres, Schipani, Pietro, Schleicher, Bernd, Schovanek, Petr, Schubert, Jan Lukas, Schwanke, Ullrich, Schwefer, Georg, Scuderi, Salvatore, Arroyo, Monica Seglar, Seitenzahl, Ivo, Sergijenko, Olga, Sguera, Vito, Shang, Ruo-Yu, Sharma, Pooja, Sidibe, Guereguin Der Sylvestre, Sidoli, Lara, Siejkowski, Hubert, Siqueira, Clarissa, Sizun, Patrick, Sliusar, Vitalii, Slowikowska, Agnieszka, Sol, Helene, Specovius, Andreas, Spencer, Samuel Timothy, Spiga, Daniele, Stamerra, Antonio, Stanič, Samo, Starecki, Tomasz, Starling, Rhaana, Steppa, Constantin, Stolarczyk, Thierry, Strišković, Jelena, Strzys, Marcel C., Suda, Yusuke, Suomijarvi, Tiina, Tak, Donggeun, Takahashi, Mitsunari, Takeishi, Ryuji, Tam, Pak-Hin Thomas, Tanaka, Shuta J, Tanaka, Takaaki, Terauchi, Kenta, Testa, Vincenzo, Tibaldo, Luigi, Tibolla, Omar, Torradeflot, Francesc, Torres, Diego F., Torresi, Eleonora, Tothill, Nick, Toussenel, Francois, Touzard, Victoria, Tramacere, Andrea, Travnicek, Petr, Tripodo, Giovanni, Truzzi, Stefano, Tsiahina, Adellain, Tutone, Antonio, Vacula, Martin, Vallage, Bertrand, Vallania, Piero, Vallés, Ramon, van Eldik, Christopher, van Scherpenberg, Juliane, Vandenbroucke, Justin, Vassiliev, Vladimir, Venault, Philippe, Ventura, Sofia, Vercellone, Stefano, Verna, Gaia, Viana, Aion, Viaux, Nicolás, Vigliano, Alessandro, Vignatti, Jonatan, Vigorito, Carlo francesco, Vitale, Vincenzo, Vodeb, Veronika, Voisin, Vincent, Vorobiov, Serguei, Voutsinas, Georgios Gerasimos, Vovk, Ievgen, Waegebaert, Vincent, Wagner, Stefan Joachim, Walter, Roland, Ward, Martin, Wechakama, Maneenate, White, Richard, Wierzcholska, Alicja, Will, Martin, Williams, David A., Wohlleben, Frederik, Wolter, Anna, Yamamoto, Tokonatsu, Yamazaki, Ryo, Yang, Lili, Yoshida, Tatsuo, Yoshikoshi, Takanori, Zacharias, Michael, Sanchez, Ricardo Zanmar, Zavrtanik, Danilo, Zavrtanik, Marko, Zdziarski, Andrzej A., Zech, Andreas, Zhdanov, Valery I., Ziȩtara, Krzysztof, Živec, Miha, and Zuriaga-Puig, Jaume
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts of this gravitational wave event, ushered in a new era of multimessenger astronomy, providing the first direct evidence that BNS mergers are progenitors of short gamma-ray bursts (GRBs). Such events may also produce very-high-energy (VHE, > 100GeV) photons which have yet to be detected in coincidence with a gravitational wave signal. The Cherenkov Telescope Array (CTA) is a next-generation VHE observatory which aims to be indispensable in this search, with an unparalleled sensitivity and ability to slew anywhere on the sky within a few tens of seconds. New observing modes and follow-up strategies are being developed for CTA to rapidly cover localization areas of gravitational wave events that are typically larger than the CTA field of view. This work will evaluate and provide estimations on the expected number of of gravitational wave events that will be observable with CTA, considering both on- and off-axis emission. In addition, we will present and discuss the prospects of potential follow-up strategies with CTA., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219)
Published: 2023

24. Performance of the joint LST-1 and MAGIC observations evaluated with Crab Nebula data

Author: Abe, H., Abe, K., Abe, S., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Crespo, N. Alvarez, Aniello, T., Ansoldi, S., Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Artero, M., Asano, K., Aubert, P., Baack, D., Babić, A., Baktash, A., Bamba, A., Larriva, A. Baquero, Baroncelli, L., de Almeida, U. Barres, Barrio, J. A., Batković, I., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernardos, M. I., Medrano, J. Bernete, Berti, A., Besenrieder, J., Bhattacharjee, P., Biederbeck, N., Bigongiari, C., Biland, A., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Bošnjak, Ž., Bulgarelli, A., Burelli, I., Burmistrov, L., Buscemi, M., Busetto, G., Ordaz, A. Campoy, Cardillo, M., Caroff, S., Carosi, A., Carosi, R., Carrasco, M. S., Carretero-Castrillo, M., Cassol, F., Castro-Tirado, A. J., Cauz, D., Cerasole, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chytka, L., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cornelia, A., Cortina, J., Costantini, H., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., De Lucia, M., de Menezes, R., Del Peral, L., Del Popolo, A., Deleglise, G., Delfino, M., Mendez, C. Delgado, Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Depaoli, D., Di Piano, A., Di Pierro, F., Di Pilato, A., Di Tria, R., Di Venere, L., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Díaz, C., Eisenberger, L., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Fariña, L., Fattorini, A., Ferrara, G., Ferrarotto, F., Fiasson, A., Foffano, L., Font, L., Coromina, L. Freixas, Fröse, S., Fukami, S., Fukazawa, Y., López, R. J. Garcia, Garcia, E., Garczarczyk, M., López, R. J. García, Gasbarra, C., Gasparrini, D., Gasparyan, S., Gaug, M., Geyer, D., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinović, N., Grau, R., Green, D., Green, J. G., Gunji, S., Günther, P., Hackfeld, J., Hadasch, D., Hahn, A., Hashiyama, K., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Hütten, M., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Ioka, K., Iori, M., Iotov, R., Ishio, K., Jacquemont, M., Martínez, I. Jiménez, Jobst, E., Jormanainen, J., Jurysek, J., Kagaya, M., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kohri, K., Kong, A., Kouch, P. M., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Flour, T. Le, Leone, F., Lindfors, E., Linhoff, L., Linhoff, M., Lombardi, S., Longo, F., Loporchio, S., Lorini, A., Bahilo, J. Lozano, Luque-Escamilla, P. L., Lyard, E., Lezáun, M. Láinez, López-Coto, R., López-Moya, M., López-Oramas, A., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Mandat, D., Maneva, G., Manganaro, M., Mangano, S., Mang, N., Manicò, G., Mannheim, K., Mariotti, M., Marquez, P., Marsella, G., Martinez, O., Martínez, G., Martínez, M., Martí, J., Mas-Aguilar, A., Maurin, G., Mazin, D., Menchiari, S., Mender, S., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Mićanović, S., González, M. Molero, Molina, E., Mondal, H. A., Montaruli, T., Monteiro, I., Moralejo, A., Morcuende, D., Morselli, A., Moya, V., Muraishi, H., Murase, K., Nagataki, S., Nakamori, T., Nanci, C., Neronov, A., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nosek, D., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Orito, R., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pavletić, L., Pech, M., Pecimotika, M., Peresano, M., Persic, M., Pfeiffle, F., Pietropaolo, E., Pihet, M., Pirola, G., Plard, C., Podobnik, F., Poireau, V., Polo, M., Pons, E., Moroni, P. G. Prada, Prandini, E., Prast, J., Principe, G., Priyadarshi, C., Prouza, M., Rando, R., Rhode, W., Ribó, M., Rico, J., Righi, C., Rizi, V., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Sahakyan, N., Saito, T., Sakurai, S., Sanchez, D. A., Satalecka, K., Sato, M., Sato, Y., Saturni, F. G., Savchenko, V., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Sciaccaluga, A., Siegert, T., Silvia, R., Sitarek, J., Sliusar, V., Sobczynska, D., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Suutarinen, S., Šarić, T., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tavecchio, F., Temnikov, P., Terada, Y., Terauchi, K., Terzić, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Tosti, L., Travnicek, P., Truzzi, S., Tutone, A., Ubach, S., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Verguilov, V., Viale, I., Vigliano, A., Vigorito, C. F., Visentin, E., Vitale, V., Voutsinas, G., Vovk, I., Vuillaume, T., Acosta, M. Vázquez, Walter, R., Wei, Z., Will, M., Yamamoto, T., Yamazaki, R., Yoshida, T., Yoshikoshi, T., and Zywucka, N.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Aims. LST-1, the prototype of the Large-Sized Telescope for the upcoming Cherenkov Telescope Array Observatory, is concluding its commissioning in Observatorio del Roque de los Muchachos on the island of La Palma. The proximity of LST-1 (Large-Sized Telescope 1) to the two MAGIC (Major Atmospheric Gamma Imaging Cherenkov) telescopes permits observations of the same gamma-ray events with both systems. Methods. We describe the joint LST-1+MAGIC analysis pipeline and use simultaneous Crab Nebula observations and Monte Carlo simulations to assess the performance of the three-telescope system. The addition of the LST-1 telescope allows the recovery of events in which one of the MAGIC images is too dim to survive analysis quality cuts. Results. Thanks to the resulting increase in the collection area and stronger background rejection, we find a significant improvement in sensitivity, allowing the detection of 30% weaker fluxes in the energy range between 200 GeV and 3 TeV. The spectrum of the Crab Nebula, reconstructed in the energy range ~60 GeV to ~10 TeV, is in agreement with previous measurements., Comment: Accepted for publication in Astronomy & Astrophysics
Published: 2023
Full Text: View/download PDF

25. Prospects for $\gamma$-ray observations of the Perseus galaxy cluster with the Cherenkov Telescope Array

Author: Consortium, The Cherenkov Telescope Array, Abe, K., Abe, S., Acero, F., Acharyya, A., Adam, R., Aguasca-Cabot, A., Agudo, I., Aguirre-Santaella, A., Alfaro, J., Alfaro, R., Alvarez-Crespo, N., Batista, R. Alves, Amans, J. -P., Amato, E., Angüner, E. O., Antonelli, L. A., Aramo, C., Araya, M., Arcaro, C., Arrabito, L., Asano, K., Ascasíbar, Y., Aschersleben, J., Ashkar, H., Stuani, L. Augusto, Baack, D., Backes, M., Baktash, A., Balazs, C., Balbo, M., Ballester, O., Larriva, A. Baquero, Martins, V. Barbosa, de Almeida, U. Barres, Barrio, J. A., Batista, P. I., Batkovic, I., Batzofin, R., Baxter, J., González, J. Becerra, Beck, G., Tjus, J. Becker, Benbow, W., Medrano, J. Bernete, Bernlöhr, K., Berti, A., Bertucci, B., Beshley, V., Bhattacharjee, P., Bhattacharyya, S., Bi, B., Biederbeck, N., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Blazek, J., Boisson, C., Bolmont, J., Bordas, P., Bosnjak, Z., Bottacini, E., Bradascio, F., Braiding, C., Bronzini, E., Brose, R., Brown, A. M., Brun, F., Brunetti, G., Bucciantini, N., Bulgarelli, A., Burelli, I., Burmistrov, L., Burton, M., Bylund, T., Calisse, P. G., Campoy-Ordaz, A., Cantlay, B. K., Capalbi, M., Caproni, A., Capuzzo-Dolcetta, R., Caraveo, P., Caroff, S., Carosi, R., Carquin, E., Carrasco, M. -S., Cascone, E., Cassol, F., Castro-Tirado, A. J., Cerasole, D., Cerruti, M., Chadwick, P., Chaty, S., Chen, A. W., Chernyakova, M., Chiavassa, A., Chudoba, J., Chytka, L., Cifuentes, A., Araujo, C. H. Coimbra, Conforti, V., Conte, F., Contreras, J. L., Cortina, J., Costa, A., Costantini, H., Cotter, G., Cristofari, P., Cuevas, O., Curtis-Ginsberg, Z., D'Amico, G., D'Ammando, F., Dalchenko, M., Dazzi, F., de Lavergne, M. de Bony, De Caprio, V., Laadim, F. De Frondat, Pino, E. M. de Gouveia Dal, De Lotto, B., De Lucia, M., De Martino, D., de Menezes, R., de Naurois, M., De Simone, N., de Souza, V., del Valle, M. V., Delagnes, E., Giler, A. G. Delgado, Delgado, C., Dell'aiera, M., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Diebold, S., Djannati-Ataï, A., Djuvsland, J., Dominik, R. M., Donini, A., Dorner, D., Dörner, J., Doro, M., Anjos, R. D. C. dos, Dournaux, J. -L., Duangchan, C., Dubos, C., Dumora, D., Dwarkadas, V. V., Ebr, J., Eckner, C., Egberts, K., Einecke, S., Elsässer, D., Emery, G., Godoy, M. Escobar, Escudero, J., Esposito, P., Ettori, S., Evoli, C., Falceta-Goncalves, D., Ramazani, V. Fallah, Fattorini, A., Faure, A., Fedorova, E., Fegan, S., Feijen, K., Feng, Q., Ferrand, G., Ferrarotto, F., Fiandrini, E., Fiasson, A., Filipovic, M., Fioretti, V., Foffano, L., Guiteras, L. Font, Fontaine, G., Fröse, S., Fukazawa, Y., Fukui, Y., Gaggero, D., Galanti, G., Gallozzi, S., Gammaldi, V., Garczarczyk, M., Gasbarra, C., Gasparrini, D., Gaug, M., Ghalumyan, A., Gianotti, F., Giarrusso, M., Giesbrecht, J., Giglietto, N., Giordano, F., Glicenstein, J. -F., Göksu, H., Goldoni, P., González, J. M., González, M. M., Coelho, J. Goulart, Granot, J., Grau, R., Gréaux, L., Green, D., Green, J. G., Grenier, I., Grolleron, G., Grube, J., Gueta, O., Hackfeld, J., Hadasch, D., Hamal, P., Hanlon, W., Hara, S., Harvey, V. M., Hassan, T., Heckmann, L., Heller, M., Cadena, S. Hernández, Hervet, O., Hie, J., Hiroshima, N., Hnatyk, B., Hnatyk, R., Hoang, J., Hoffmann, D., Hofmann, W., Holder, J., Horan, D., Horvath, P., Hrupec122, D., Hütten, M., Iarlori, M., Inada, T., Incardona, F., Inoue, S., Iocco, F., Iori, M., Jamrozy, M., Janecek, P., Jankowsky, F., Jarnot, C., Jean, P., Martínez, I. Jiménez, Jin, W., Juramy-Gilles, C., Jurysek, J., Kagaya, M., Kantzas, D., Karas, V., Katagiri, H., Kataoka, J., Kaufmann, S., Kerszberg, D., Khélifi, B., Kissmann, R., Kleiner, T., Kluge, G., Kluźniak, W., Knödlseder, J., Kobayashi, Y., Kohri, K., Komin, N., Kornecki, P., Kosack, K., Kowal, G., Kubo, H., Kushida, J., La Barbera, A., La Palombara, N., Láinez, M., Lamastra, A., Lapington, J., Laporte, P., Lazarević, S., Leitgeb, F., Lemoine-Goumard, M., Lenain, J. -P., Leone, F., Leto, G., Leuschner, F., Lindfors, E., Linhoff, M., Liodakis, I., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Luque-Escamilla, P. L., Macias, O., Mackey, J., Majumdar, P., Malyshev, D., Mandat, D., Manganaro, M., Manicò, G., Mariotti, M., Markoff, S., Márquez, I., Marquez, P., Marsella, G., Martínez, G. A., Martínez, M., Martinez, O., Marty, C., Mas-Aguilar, A., Mastropietro, M., Maurin, G., Mazin, D., Melkumyan, D., Mello, A. J. T. S., Meunier, J. -L., Meyer, D. M. -A., Meyer, M., Miceli, D., Michailidis, M., Michałowski, J., Miener, T., Miranda, J. M., Mitchell, A., Mizote, M., Mizuno, T., Moderski, R., Molero, M., Molfese, C., Molina, E., Montaruli, T., Morcuende, D., Morik, K., Morlino, G., Morselli, A., Moulin, E., Zamanillo, V. Moya, Munari, K., Murach, T., Muraczewski, A., Muraishi, H., Nagataki, S., Nakamori, T., Nemmen, R., Neyroud, N., Nickel, L., Niemiec, J., Nieto, D., Rosillo, M. Nievas, Nikołajuk, M., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., O'Brien, P., Ohishi, M., Ohtani, Y., Okumura, A., Olive, J. -F., Olmi, B., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, E., Ostrowski, M., Oya, I., Pagliaro, A., Palatiello, M., Panebianco, G., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Parmiggiani, N., Patel, S. R., Patricelli, B., Pavlović, D., Pech, M., Pecimotika, M., Pensec, U., Peresano, M., Pérez-Romero, J., Peron, G., Persic, M., Petrucci, P. -O., Petruk, O., Piano, G., Pierre, E., Pietropaolo, E., Pintore, F., Pirola, G., Pita, S., Plard, C., Podobnik, F., Pohl, M., Polo, M., Pons, E., Ponti, G., Prandini, E., Prast, J., Principe, G., Priyadarshi, C., Produit, N., Pueschel, E., Pühlhofer, G., Pumo, M. L., Punch, M., Queiroz, F., Quirrenbach, A., Rainò, S., Rando, R., Razzaque, S., Recchia, S., Regeard, M., Reichherzer, P., Reimer, A., Reimer, O., Reisenegger, A., Rhode, W., Ribeiro, D., Ribó, M., Richtler, T., Rico, J., Rieger, F., Righi, C., Riitano, L., Rizi, V., Roache, E., Fernandez, G. Rodriguez, Rodríguez-Vázquez, J. J., Romano, P., Romeo, G., Rosado, J., de Leon, A. Rosales, Rowell, G., Rudak, B., Rulten, C. B., Russo, F., Sadeh, I., Saha, L., Saito, T., Salzmann, H., Sanchez, D., Sánchez-Conde, M., Sangiorgi, P., Sano, H., Santander, M., Santangelo, A., Santos-Lima, R., Sanuy, A., Šarić, T., Sarkar, A., Sarkar, S., Satalecka, K., Saturni, F. G., Savchenko, V., Scherer, A., Schipani, P., Schleicher, B., Schubert, J. L., Schussler, F., Schwanke, U., Schwefer, G., Arroyo, M. Seglar, Seiji, S., Semikoz, D., Sergijenko, O., Servillat, M., Sguera, V., Shang, R. Y., Sharma, P., Siejkowski, H., Sinha, A., Siqueira, C., Sliusar, V., Slowikowska, A., Sol, H., Specovius, A., Spencer, S. T., Spiga, D., Stamerra, A., Stanič, S., Starecki, T., Starling, R., Stawarz, Ł., Steppa, C., Stolarczyk, T., Strišković, J., Suda, Y., Suomijärvi, T., Tajima, H., Tak, D., Takahashi, M., Takeishi, R., Tanaka, S. J., Tavernier, T., Tejedor, L. A., Terauchi, K., Terrier, R., Teshima, M., Tian, W. W., Tibaldo, L., Tibolla, O., Torradeflot, F., Torres, D. F., Torresi, E., Tosti, G., Tosti, L., Tothill, N., Toussenel, F., Touzard, V., Tramacere, A., Travnicek, P., Tripodo, G., Truzzi, S., Tsiahina, A., Tutone, A., Vacula, M., Vallage, B., Vallania, P., van Eldik, C., van Scherpenberg, J., Vandenbroucke, J., Vassiliev, V., Acosta, M. Vázquez, Vecchi, M., Ventura, S., Vercellone, S., Verna, G., Viana, A., Viaux, N., Vigliano, A., Vigorito, C. F., Vitale, V., Vodeb, V., Voisin, V., Vorobiov, S., Voutsinas, G., Vovk, I., Vuillaume, T., Wagner, S. J., Walter, R., Wechakama, M., White, R., Wierzcholska, A., Will, M., Williams, D. A., Wohlleben, F., Wolter, A., Yamamoto, T., Yamazaki, R., Yoshida, T., Yoshikoshi, T., Zacharias, M., Zaharijas, G., Zavrtanik, D., Zavrtanik, M., Zdziarski, A. A., Zech, A., Zhdanov, V. I., Živec, M., Zuriaga-Puig, J., and Luque, P. De la Torre
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology
Abstract: Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with $\tau_{\chi}>10^{27}$s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario., Comment: 93 pages (including author list, appendix and references), 143 figures. Submitted to JCAP
Published: 2023
Full Text: View/download PDF

26. Sonics: develop intuition on biomechanical systems through interactive error controlled simulations

Author: Mazier, Arnaud, El Hadramy, Sidaty, Brunet, Jean-Nicolas, Hale, Jack S., Cotin, Stéphane, and Bordas, Stéphane P. A.
Published: 2024
Full Text: View/download PDF

27. Statistically Enhanced Learning: a feature engineering framework to boost (any) learning algorithms

Author: Felice, Florian, Ley, Christophe, Groll, Andreas, and Bordas, Stéphane
Subjects: Statistics - Methodology
Abstract: Feature engineering is of critical importance in the field of Data Science. While any data scientist knows the importance of rigorously preparing data to obtain good performing models, only scarce literature formalizes its benefits. In this work, we will present the method of Statistically Enhanced Learning (SEL), a formalization framework of existing feature engineering and extraction tasks in Machine Learning (ML). The difference compared to classical ML consists in the fact that certain predictors are not directly observed but obtained as statistical estimators. Our goal is to study SEL, aiming to establish a formalized framework and illustrate its improved performance by means of simulations as well as applications on real life use cases.
Published: 2023

28. Observations of the Crab Nebula and Pulsar with the Large-Sized Telescope Prototype of the Cherenkov Telescope Array

Author: Project, CTA-LST, Abe, H., Abe, K., Abe, S., Aguasca-Cabot, A., Agudo, I., Crespo, N. Alvarez, Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Artero, M., Asano, K., Aubert, P., Baktash, A., Bamba, A., Larriva, A. Baquero, Baroncelli, L., de Almeida, U. Barres, Barrio, J. A., Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Bernardos, M. I., Medrano, J. Bernete, Berti, A., Bhattacharjee, P., Biederbeck, N., Bigongiari, C., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Borghese, A., Bulgarelli, A., Burelli, I., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Cassol, F., Cauz, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chytka, L., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., D'Amico, G., Dalchenko, M., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Deleglise, G., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Depaoli, D., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrara, G., Ferrarotto, F., Fiasson, A., Coromina, L. Freixas, Fröse, S., Fukami, S., Fukazawa, Y., Garcia, E., López, R. Garcia, Gasbarra, C., Gasparrini, D., Geyer, F., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Giro, E., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Hackfeld, J., Hadasch, D., Hahn, A., Hashiyama, K., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Hütten, M., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Ioka, K., Iori, M., Ishio, K., Iwamura, Y., Jacquemont, M., Martinez, I. Jimenez, Jurysek, J., Kagaya, M., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Flour, T. Le, Linhoff, M., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Marusevec, P., Mas-Aguilar, A., Maurin, G., Mazin, D., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Monteiro, I., Moralejo, A., Morcuende, D., Morselli, A., Mrakovcic, K., Murase, K., Nagai, A., Nagataki, S., Nakamori, T., Nickel, L., Nievas, M., Nishijima, K., Noda, K., Nosek, D., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okazaki, N., Okumura, A., Orito, R., Otero-Santos, J., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pech, M., Pecimotika, M., Peresano, M., Pérez, A., Pietropaolo, E., Pirola, G., Plard, C., Podobnik, F., Poireau, V., Polo, M., Pons, E., Prandini, E., Prast, J., Principe, G., Priyadarshi, C., Prouza, M., Rando, R., Rhode, W., Ribó, M., Rizi, V., Fernandez, G. Rodriguez, Ruiz, J. E., Saito, T., Sakurai, S., Sanchez, D. A., Šarić, T., Sato, Y., Saturni, F. G., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Silvia, R., Sitarek, J., Sliusar, V., Spolon, A., Strišković, J., Strzys, M., Suda, Y., Sunada, Y., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tejedor, L. A., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Truzzi, S., Tutone, A., Uhlrich, G., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vázquez, Verguilov, V., Viale, I., Vigliano, A., Vigorito, C. F., Vitale, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Will, M., Yamamoto, T., Yamazaki, R., Yoshida, T., Yoshikoshi, T., Zywucka, N., Bernloehr, K., Gueta, O., Kosack, K., Maier, G., and Watson, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: CTA (Cherenkov Telescope Array) is the next generation ground-based observatory for gamma-ray astronomy at very-high energies. The Large-Sized Telescope prototype (LST-1) is located at the Northern site of CTA, on the Canary Island of La Palma. LSTs are designed to provide optimal performance in the lowest part of the energy range covered by CTA, down to $\simeq 20$ GeV. LST-1 started performing astronomical observations in November 2019, during its commissioning phase, and it has been taking data since then. We present the first LST-1 observations of the Crab Nebula, the standard candle of very-high energy gamma-ray astronomy, and use them, together with simulations, to assess the basic performance parameters of the telescope. The data sample consists of around 36 hours of observations at low zenith angles collected between November 2020 and March 2022. LST-1 has reached the expected performance during its commissioning period - only a minor adjustment of the preexisting simulations was needed to match the telescope behavior. The energy threshold at trigger level is estimated to be around 20 GeV, rising to $\simeq 30$ GeV after data analysis. Performance parameters depend strongly on energy, and on the strength of the gamma-ray selection cuts in the analysis: angular resolution ranges from 0.12 to 0.40 degrees, and energy resolution from 15 to 50%. Flux sensitivity is around 1.1% of the Crab Nebula flux above 250 GeV for a 50-h observation (12% for 30 minutes). The spectral energy distribution (in the 0.03 - 30 TeV range) and the light curve obtained for the Crab Nebula agree with previous measurements, considering statistical and systematic uncertainties. A clear periodic signal is also detected from the pulsar at the center of the Nebula., Comment: Accepted in ApJ. v3: updated author list and acknowledgements, fixed typos and other minor issues
Published: 2023
Full Text: View/download PDF

29. A Quick Briefing on the New Version of Eye and Pen (Version 3.01): News and Update

Author: David Chesnet, Clara Solier, Benjamin Bordas, and Cyril Perret
Abstract: To explore the dynamics of processing in manuscript production, it is necessary to possess a system for recording the writer's graphic activity. This work describes the new version of the Eye and Pen program (version 3.01). In addition to the fact that it is now freely available (https://www.eyeandpen.net), the improvements described focus on its use for handwritten word production. First, we give some examples of the new features and improvements included in this new version, e.g., compatibility with previous versions and with other writing dynamics acquisition programs; "in-air" measurements; the choice of driver for timestamping; signal processing tools. The second part is dedicated to the issue of synchronization and temporal precision for the display of stimuli. The material presented here is available on OSF (http://osf.io/afpvz; project Eye and Pen).
Published: 2024
Full Text: View/download PDF

30. Sensitivity of the Cherenkov Telescope Array to spectral signatures of hadronic PeVatrons with application to Galactic Supernova Remnants

Author: Consortium, The Cherenkov Telescope Array, Acero, F., Acharyya, A., Adam, R., Aguasca-Cabot, A., Agudo, I., Aguirre-Santaella, A., Alfaro, J., Aloisio, R., Crespo, N. Álvarez, Batista, R. Alves, Amati, L., Amato, E., Ambrosi, G., Angüner, E. O., Aramo, C., Arcaro, C., Armstrong, T., Asano, K., Ascasibar, Y., Aschersleben, J., Backes, M., Baktash, A., Balazs, C., Balbo, M., Ballet, J., Larriva, A. Baquero, Martins, V. Barbosa, de Almeida, U. Barres, Barrio, J. A., Bastieri, D., Baxter, J. R., Tjus, J. Becker, Benbow, W., Bernardos-Martín, M. I., Bernete, J., Berti, A., Bertucci, B., Beshley, V., Bhattacharjee, P., Bhattacharyya, S., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Bordas, P., Bottacini, E., Bregeon, J., Brose, R., Bucciantini, N., Bulgarelli, A., Capasso, M., Dolcetta, R. A. Capuzzo, Caraveo, P., Cardillo, M., Carosi, R., Casanova, S., Cascone, E., Cassol, F., Catalani, F., Cerruti, M., Chadwick, P., Chaty, S., Chen, A., Chernyakova, M., Chiavassa, A., Chudoba, J., Coimbra-Araujo, C., Conforti, V., Contreras, J. L., Costa, A., Costantini, H., Cristofari, P., Crocker, R., D'Amico, G., D'Ammando, F., De Angelis, A., De Caprio, V., Pino, E. M. de Gouveia Dal, Wilhelmi, E. de Ona, de Souza, V., Delgado, C., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Pierro, F., Di Tria, R., Di Venere, L., Diebold, S., Djuvsland, J. I., Donini, A., Doro, M., Anjos, R. d. C. Dos, Dwarkadas, V. V., Einecke, S., Elsässer, D., Emery, G., Evoli, C., Falceta-Goncalves, D., Fedorova, E., Fegan, S., Ferrand, G., Fiandrini, E., Filipovic, M., Fioretti, V., Fiori, M., Foffano, L., Fontaine, G., Fukami, S., Galanti, G., Galaz, G., Gammaldi, V., Gasbarra, C., Ghalumyan, A., Ghirlanda, G., Giarrusso, M., Giavitto, G., Giglietto, N., Giordano, F., Giroletti, M., Giuliani, A., Giunti, L., Godinovic, N., Coelho, J. Goulart, Gréaux, L., Green, D., Grondin, M-H., Gueta, O., Gunji, S., Hassan, T., Heller, M., Hernández-Cadena, S., Hinton, J., Hnatyk, B., Hnatyk, R., Hoffmann, D., Hofmann, W., Holder, J., Horan, D., Horvath, P., Hrabovsky, M., Hrupec, D., Inada, T., Incardona, F., Inoue, S., Ishio, K., Jamrozy, M., Janecek, P., Martínez, I. Jiménez, Jin, W., Jung-Richardt, I., Jurysek, J., Kaaret, P., Karas, V., Katz, U., Kerszberg, D., Khélifi, B., Kieda, D. B., Kissmann, R., Kleiner, T., Kluge, G., Kluzniak, W., Knödlseder, J., Kobayashi, Y., Kohri, K., Komin, N., Kornecki, P., Kubo, H., La Palombara, N., Láinez, M., Lamastra, A., Lapington, J., Lemoine-Goumard, M., Lenain, J. -P., Leone, F., Leto, G., Leuschner, F., Lindfors, E., Liodakis, I., Lohse, T., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Luque-Escamilla, P. L., Macias, O., Mackey, J., Majumdar, P., Mandat, D., Manganaro, M., Manicò, G., Marconi, M., Martí, J., Martínez, G., Martinez, M., Martinez, O., Mello, A. J. T. S., Menchiari, S., Meyer, D. M. -A., Micanovic, S., Miceli, D., Miceli, M., Michalowski, J., Miener, T., Miranda, J. M., Mitchell, A., Mode, B., Moderski, R., Mohrmann, L., Molina, E., Montaruli, T., Morcuende, D., Morlino, G., Morselli, A., Mosè, M., Moulin, E., Mukherjee, R., Munari, K., Murach, T., Nagai, A., Nagataki, S., Nemmen, R., Niemiec, J., Nieto, D., Rosillo, M. Nievas, Nikolajuk, M., Nishijima, K., Noda, K., Novosyadlyj, B., Nozaki, S., Ohishi, M., Ohm, S., Ohtani, Y., Okumura, A., Olmi, B., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, E., Orlando, S., Ostrowski, M., Oya, I., Pantaleo, F. R., Paredes, J. M., Patricelli, B., Pecimotika, M., Peresano, M., Pérez-Romero, J., Persic, M., Petruk, O., Piano, G., Pietropaolo, E., Pirola, G., Pittori, C., Pohl, M., Ponti, G., Prandini, E., Principe, G., Priyadarshi, C., Pueschel, E., Pühlhofer, G., Pumo, M. L., Quirrenbach, A., Rando, R., Razzaque, S., Reichherzer, P., Reimer, A., Reimer, O., Renaud, M., Reposeur, T., Ribó, M., Richtler, T., Rico, J., Rieger, F., Rigoselli, M., Riitano, L., Rizi, V., Roache, E., Romano, P., Romeo, G., Rosado, J., Rowell, G., Rudak, B., Sadeh, I., Safi-Harb, S., Saha, L., Sailer, S., Sánchez-Conde, M., Sarkar, S., Satalecka, K., Saturni, F. G., Scherer, A., Schovánek, P., Schussler, F., Schwanke, U., Scuderi, S., Seglar-Arroyo, M., Sergijenko, O., Servillat, M., Shang, R-Y., Sharma, P., Siejkowski, H., Sliusar, V., Słowikowska, A., Sol, H., Specovius, A., Spencer, S. T., Spengler, G., Stamerra, A., Stanič, S., Starecki, T., Starling, R., Stolarczyk, T., Pereira, L. A. Stuani, Suda, Y., Suomijarvi, T., Sushch, I., Tajima, H., Tam, P-H. T., Tanaka, S. J., Tavecchio, F., Testa, V., Tian, W., Tibaldo, L., Torres, D. F., Tothill, N., Vallage, B., Vallania, P., van Eldik, C., van Scherpenberg, J., Vandenbroucke, J., Acosta, M. Vazquez, Vecchi, M., Vercellone, S., Verna, G., Viana, A., Vignatti, J., Vitale, V., Vodeb, V., Vorobiov, S., Vuillaume, T., Wagner, S. J., Walter, R., White, M., Wierzcholska, A., Will, M., Williams, D., Yang, L., Yoshida, T., Yoshikoshi, T., Zaharijas, G., Zampieri, L., Zavrtanik, D., Zavrtanik, M., Zhdanov, V. I., and Živec, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3~PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been proposed as PeVatron candidates. The potential to search for hadronic PeVatrons with the Cherenkov Telescope Array (CTA) is assessed. The focus is on the usage of very high energy $\gamma$-ray spectral signatures for the identification of PeVatrons. Assuming that SNRs can accelerate CRs up to knee energies, the number of Galactic SNRs which can be identified as PeVatrons with CTA is estimated within a model for the evolution of SNRs. Additionally, the potential of a follow-up observation strategy under moonlight conditions for PeVatron searches is investigated. Statistical methods for the identification of PeVatrons are introduced, and realistic Monte--Carlo simulations of the response of the CTA observatory to the emission spectra from hadronic PeVatrons are performed. Based on simulations of a simplified model for the evolution for SNRs, the detection of a $\gamma$-ray signal from in average 9 Galactic PeVatron SNRs is expected to result from the scan of the Galactic plane with CTA after 10 hours of exposure. CTA is also shown to have excellent potential to confirm these sources as PeVatrons in deep observations with $\mathcal{O}(100)$ hours of exposure per source., Comment: 34 pages, 16 figures, Accepted for publication in Astroparticle Physics
Published: 2023
Full Text: View/download PDF

31. Volume and Mass Conservation in Lagrangian Meshfree Methods

Author: Suchde, Pratik, Leithäuser, Christian, Kuhnert, Jörg, and Bordas, Stéphane P. A.
Subjects: Physics - Fluid Dynamics, Mathematics - Numerical Analysis
Abstract: Meshfree Lagrangian frameworks for free surface flow simulations do not conserve fluid volume. Meshfree particle methods like SPH are not mimetic, in the sense that discrete mass conservation does not imply discrete volume conservation. On the other hand, meshfree collocation methods typically do not use any notion of mass. As a result, they are neither mass conservative nor volume conservative at the discrete level. In this paper, we give an overview of various sources of conservation errors across different meshfree methods. The present work focuses on one specific issue: unreliable volume and mass definitions. We introduce the concept of representative masses and densities, which are essential for accurate post-processing especially in meshfree collocation methods. Using these, we introduce an artificial compression or expansion in the fluid to rectify errors in volume conservation. Numerical experiments show that the introduced frameworks significantly improve volume conservation behaviour, even for complex industrial test cases such as automotive water crossing.
Published: 2023
Full Text: View/download PDF

32. Level set topology optimization of metamaterial-based heat manipulators using isogeometric analysis

Author: Jansari, Chintan, Bordas, Stéphane P. A., and Atroshchenko, Elena
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: We exploit level set topology optimization to find the optimal material distribution for metamaterial-based heat manipulators. The level set function, geometry, and solution field are parameterized using the non-uniform rational B-spline (NURBS) basis functions in order to take advantage of easy control of smoothness and continuity. In addition, NURBS approximations can produce conic geometries exactly and provide higher efficiency for higher-order elements. The values of the level set function at the control points (called expansion coefficients) are utilized as design variables. For optimization, we use an advanced mathematical programming technique, Sequential Quadratic Programming (SQP). Taking into account a large number of design variables and the small number of constraints associated with our optimization problem, the adjoint method is utilized to calculate the required sensitivities with respect to the design variables. The efficiency and robustness of the proposed method are demonstrated by solving three numerical examples. We have also shown that the current method can handle different geometries and types of objective functions. In addition, regularization techniques such as Tikhonov regularization and volume regularization have been explored to reduce unnecessary complexity and increase the manufacturability of optimized topologies.
Published: 2023

33. Breast simulation pipeline: from medical imaging to patient-specific simulations

Author: Mazier, Arnaud and Bordas, Stéphane P. A.
Subjects: Physics - Medical Physics, Computer Science - Computational Engineering, Finance, and Science
Abstract: Breast-conserving surgery is the most acceptable operation for breast cancer removal from an invasive and psychological point of view. Before the surgical procedure, a preoperative MRI is performed in the prone configuration, while the surgery is achieved in the supine position. This leads to a considerable movement of the breast, including the tumor, between the two poses, complicating the surgeon's task. In this work, a simulation pipeline allowing the computation of patient-specific geometry and the prediction of personalized breast material properties was put forward. Through image segmentation, a finite element model including the subject-specific geometry is established. By first computing an undeformed state of the breast, the geometrico-material model is calibrated by surface acquisition in the intra-operative stance. Using an elastic corotational formulation, the patient-specific mechanical properties of the breast and skin were identified to obtain the best estimates of the supine configuration. The final results are a Mean Absolute Error of 4.00mm for the mechanical parameters E_breast = 0.32 kPa and E_skin = 22.72 kPa, congruent with the current state-of-the-art. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 5 to 30 min depending on the initial parameters, reaching a simulation speed of 20s. To our knowledge, our model offers one of the best compromises between accuracy and speed. Satisfactory results were obtained for the estimation of breast deformation from preoperative to intra-operative configuration. Furthermore, we have demonstrated the clinical feasibility of such applications using a simulation framework that aims at the smallest disturbance of the actual surgical pipeline.
Published: 2023

34. Multi-compartment poroelastic models of perfused biological soft tissues: implementation in FEniCSx

Author: Lavigne, Thomas, Urcun, Stéphane, Rohan, Pierre-Yves, Sciumè, Giuseppe, Baroli, Davide, and Bordas, Stéphane P. A.
Subjects: Quantitative Biology - Tissues and Organs, Physics - Computational Physics
Abstract: Soft biological tissues demonstrate strong time-dependent and strain-rate mechanical behavior, arising from their intrinsic visco-elasticity and fluid-solid interactions (especially at sufficiently large time scales). The time-dependent mechanical properties of soft tissues influence their physiological functions and are linked to several pathological processes. Poro-elastic modeling represents a promising approach because it allows the integration of multiscale/multiphysics data to probe biologically relevant phenomena at a smaller scale and embeds the relevant mechanisms at the larger scale. The implementation of multi-phasic flow poro-elastic models however is a complex undertaking, requiring extensive knowledge. The open-source software FEniCSx Project provides a novel tool for the automated solution of partial differential equations by the finite element method. This paper aims to provide the required tools to model the mixed formulation of poro-elasticity, from the theory to the implementation, within FEniCSx. Several benchmark cases are studied. A column under confined compression conditions is compared to the Terzaghi analytical solution, using the L2-norm. An implementation of poro-hyper-elasticity is proposed. A bi-compartment column is compared to previously published results (Cast3m implementation). For all cases, accurate results are obtained in terms of a normalized Root Mean Square Error (RMSE). Furthermore, the FEniCSx computation is found three times faster than the legacy FEniCS one. The benefits of parallel computation are also highlighted., Comment: https://github.com/Th0masLavigne/Dolfinx_Porous_Media.git
Published: 2023
Full Text: View/download PDF

35. Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

Author: Deshpande, Saurabh, Sosa, Raúl I., Bordas, Stéphane P. A., and Lengiewicz, Jakub
Subjects: Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science
Abstract: Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks--a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.
Published: 2022
Full Text: View/download PDF

36. Discovery of WRN inhibitor HRO761 with synthetic lethality in MSI cancers

Author: Ferretti, Stephane, Hamon, Jacques, de Kanter, Ruben, Scheufler, Clemens, Andraos-Rey, Rita, Barbe, Stephanie, Bechter, Elisabeth, Blank, Jutta, Bordas, Vincent, Dammassa, Ernesta, Decker, Andrea, Di Nanni, Noemi, Dourdoigne, Marion, Gavioli, Elena, Hattenberger, Marc, Heuser, Alisa, Hemmerlin, Christelle, Hinrichs, Jürgen, Kerr, Grainne, Laborde, Laurent, Jaco, Isabel, Núñez, Eloísa Jiménez, Martus, Hans-Joerg, Quadt, Cornelia, Reschke, Markus, Romanet, Vincent, Schaeffer, Fanny, Schoepfer, Joseph, Schrapp, Maxime, Strang, Ross, Voshol, Hans, Wartmann, Markus, Welly, Sarah, Zécri, Frédéric, Hofmann, Francesco, Möbitz, Henrik, and Cortés-Cros, Marta
Published: 2024
Full Text: View/download PDF

37. Current Guidelines for the Implementation of Flywheel Resistance Training Technology in Sports: A Consensus Statement

Author: Beato, Marco, de Keijzer, Kevin L., Muñoz-Lopez, Alejandro, Raya-González, Javier, Pozzo, Marco, Alkner, Björn A., Dello Iacono, Antonio, Vicens-Bordas, Jordi, Coratella, Giuseppe, Maroto-Izquierdo, Sergio, Gonzalo-Skok, Oliver, McErlain-Naylor, Stuart A., Martin-Rivera, Fernando, Hernandez-Davo, Jose L., Arrones, Luis Suarez, Sabido, Rafael, de Hoyo, Moises, Fernandez-Gonzalo, Rodrigo, and Norrbrand, Lena
Published: 2024
Full Text: View/download PDF

38. Oncology and mechanics: landmark studies and promising clinical applications

Author: Urcun, Stéphane, Lorenzo, Guillermo, Baroli, Davide, Rohan, Pierre-Yves, Sciumè, Giuseppe, Skalli, Wafa, Lubrano, Vincent, and Bordas, Stéphane P. A.
Subjects: Quantitative Biology - Tissues and Organs, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Cell Behavior
Abstract: Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to propose to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design and therapeutic targets. Additionally, a specific focus is brought on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling.
Published: 2022
Full Text: View/download PDF

39. MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations

Author: Deshpande, Saurabh, Bordas, Stéphane P. A., and Lengiewicz, Jakub
Subjects: Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science
Abstract: In many cutting-edge applications, high-fidelity computational models prove to be too slow for practical use and are therefore replaced by much faster surrogate models. Recently, deep learning techniques have increasingly been utilized to accelerate such predictions. To enable learning on large-dimensional and complex data, specific neural network architectures have been developed, including convolutional and graph neural networks. In this work, we present a novel encoder-decoder geometric deep learning framework called MAgNET, which extends the well-known convolutional neural networks to accommodate arbitrary graph-structured data. MAgNET consists of innovative Multichannel Aggregation (MAg) layers and graph pooling/unpooling layers, forming a graph U-Net architecture that is analogous to convolutional U-Nets. We demonstrate the predictive capabilities of MAgNET in surrogate modeling for non-linear finite element simulations in the mechanics of solids.
Published: 2022
Full Text: View/download PDF

40. Smart cloud collocation: geometry-aware adaptivity directly from CAD

Author: Jacquemin, Thibault, Suchde, Pratik, and Bordas, Stéphane P. A.
Subjects: Mathematics - Numerical Analysis
Abstract: Computer Aided Design (CAD) is widely used in the creation and optimization of various industrial systems and processes. Transforming a CAD geometry into a computational discretization that be used to solve PDEs requires care and a deep knowledge of the selected computational method. In this article, we present a novel integrated collocation scheme based on smart clouds. It allows us to transform a CAD geometry into a complete point collocation model, aware of the base geometry, with minimum effort. For this process, only the geometry of the domain, in the form of a STEP file, and the boundary conditions are needed. We also introduce an adaptive refinement process for the resultant smart cloud using an \textit{a posteriori} error indication. The scheme can be applied to any 2D or 3D geometry, to any PDE and can be applied to most point collocation approaches. We illustrate this with the meshfree Generalized Finite Difference (GFD) method applied to steady linear elasticity problems. We further show that each step of this process, from the initial discretization to the refinement strategy, is connected and is affected by the approach selected in the previous step, thus requiring an integrated scheme where the whole solution process should be considered at once.
Published: 2022

41. Multi-wavelength study of the galactic PeVatron candidate LHAASO J2108+5157

Author: Abe, S., Aguasca-Cabot, A., Agudo, I., Crespo, N. Alvarez, Antonelli, L. A., Aramo, C., Arbet-Engels, A., Artero, M., Asano, K., Aubert, P., Baktash, A., Bamba, A., Larriva, A. Baquero, Baroncelli, L., de Almeida, U. Barres, Barrio, J. A., Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Bernardos, M. I., Medrano, J. Bernete, Berti, A., Bhattacharjee, P., Biederbeck, N., Bigongiari, C., Bissaldi, E., Blanch, O., Bordas, P., Buisson, C., Bulgarelli, A., Burelli, I., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Cassol, F., Cauz, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chytka, L., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., D'Amico, G., Dalchenko, M., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Deleglise, G., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrara, G., Fiasson, A., Coromina, L. Freixas, Fröse, S., Fukami, S., Fukazawa, Y., Garcia, E., López, R. Garcia, Gasparrini, D., Geyer, D., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Giro, E., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Hackfeld, J., Hadasch, D., Hahn, A., Hashiyama, K., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Hütten, M., Imazawa, R., Inada, T., Inome, Y., Ioka, K., Iori, M., Ishio, K., Iwamura, Y., Jacquemont, M., Martinez, I. Jimenez, Jurysek, J., Kagaya, M., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Flour, T. Le, Linhoff, M., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Marusevec, P., Mas-Aguilar, A., Maurin, G., Mazin, D., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Monteiro, I., Moralejo, A., Morcuende, D., Morselli, A., Mrakovcic, K., Murase, K., Nagai, A., Nakamori, T., Nickel, L., Nievas, M., Nishijima, K., Noda, K., Nosek, D., Nozaki, S., Ohishi, M., Ohtani, Y., Okazaki, N., Okumura, A., Orito, R., Otero-Santos, J., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pavletić, L., Pech, M., Pecimotika, M., Pietropaolo, E., Pirola, G., Podobnik, F., Poireau, V., Polo, M., Pons, E., Prandini, E., Prast, J., Priyadarshi, C., Prouza, M., Rando, R., Rhode, W., Ribó, M., Rizi, V., Fernandez, G. Rodriguez, Saito, T., Sakurai, S., Sanchez, D. A., Šarić, T., Saturni, F. G., Scherpenberg, J., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Sitarek, J., Sliusar, V., Spolon, A., Strišković, J., Strzys, M., Suda, Y., Sunada, Y., Tajima, H., Takahashi, M., Takahashi, H., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Truzzi, S., Tutone, A., Uhlrich, G., Vacula, M., Acosta, M. Vázquez, Verguilov, V., Viale, I., Vigliano, A., Vigorito, C. F., Vitale, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Will, M., Yamamoto, T., Yamazaki, R., Yoshida, T., Yoshikoshi, T., Zywucka, N., Balbo, M., Eckert, D., and Tramacere, A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: LHAASO J2108+5157 is one of the few known unidentified Ultra-High-Energy (UHE) gamma-ray sources with no Very-High-Energy (VHE) counterpart, recently discovered by the LHAASO collaboration. We observed LHAASO J2108+5157 in the X-ray band with XMM-Newton in 2021 for a total of 3.8 hours and at TeV energies with the Large-Sized Telescope prototype (LST-1), yielding 49 hours of good quality data. In addition, we analyzed 12 years of Fermi-LAT data, to better constrain emission of its High-Energy (HE) counterpart 4FGL J2108.0+5155. We found an excess (3.7 sigma) in the LST-1 data at energies E > 3 TeV. Further analysis in the whole LST-1 energy range assuming a point-like source, resulted in a hint (2.2 sigma) of hard emission which can be described with a single power law with photon index Gamma = 1.6 +- 0.2 between 0.3 - 100 TeV. We did not find any significant extended emission which could be related to a Supernova Remnant (SNR) or Pulsar Wind Nebula (PWN) in the XMM-Newton data, which puts strong constraints on possible synchrotron emission of relativistic electrons. The LST-1 and LHAASO observations can be explained as inverse Compton-dominated leptonic emission of relativistic electrons with a cutoff energy of $100^{+70}_{-30}$ TeV. The low magnetic field in the source imposed by the X-ray upper limits on synchrotron emission is compatible with a hypothesis of a PWN or a TeV halo. The lack of a pulsar in the neighborhood of the UHE source is a challenge to the PWN/TeV-halo scenario. The UHE gamma rays can also be explained as $\pi^0$ decay-dominated hadronic emission due to interaction of relativistic protons with one of the two known molecular clouds in the direction of the source. The hard spectrum in the LST-1 band is compatible with protons escaping a shock around a middle-aged SNR because of their high low-energy cut-off., Comment: 26 pages, 9 figures, 5 tables; Astronomy and Astrophysics (accepted on 6 March 2023)
Published: 2022
Full Text: View/download PDF

42. SOniCS: Develop intuition on biomechanical systems through interactive error controlled simulations

Author: Mazier, Arnaud, Hadramy, Sidaty El, Brunet, Jean-Nicolas, Hale, Jack S., Cotin, Stéphane, and Bordas, Stéphane P. A.
Subjects: Computer Science - Mathematical Software, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Software Engineering
Abstract: This new approach allows the user to experiment with model choices easily and quickly without requiring in-depth expertise, as constitutive models can be modified by one line of code only. This ease in building new models makes SOniCS ideal to develop surrogate, reduced order models and to train machine learning algorithms for uncertainty quantification or to enable patient-specific simulations. SOniCS is thus not only a tool that facilitates the development of surgical training simulations but also, and perhaps more importantly, paves the way to increase the intuition of users or otherwise non-intuitive behaviors of (bio)mechanical systems. The plugin uses new developments of the FEniCSx project enabling automatic generation with FFCx of finite element tensors such as the local residual vector and Jacobian matrix. We validate our approach with numerical simulations such as manufactured solutions, cantilever beams, and benchmarks provided by FEBio. We reach machine precision accuracy and demonstrate the use of the plugin for a real-time haptic simulation involving a surgical tool controlled by the user in contact with a hyperelastic liver. We include complete examples showing the use of our plugin for simulations involving Saint Venant-Kirchhoff, Neo-Hookean, Mooney-Rivlin, and Holzapfel Ogden anisotropic models as supplementary material.
Published: 2022

43. Design of metamaterial-based heat manipulators by isogeometric shape optimization

Author: Jansari, Chintan, Bordas, Stéphane P. A., and Atroshchenko, Elena
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: There has been a growing interest in controlled heat flux manipulation to increase the efficiency of thermal apparatus. Heat manipulators control and manipulate heat flow. A key to the effective performance of these heat manipulators is their thermal design. Such designs can be achieved by a periodic assembly of unit cells (known as metamaterials or meta-structure), whose geometry and material properties can be optimized for a specific objective. In this work, we focus on thermal metamaterial-based heat manipulators such as thermal concentrator (which concentrates the heat flux in a specified region of the domain). The main scope of the current work is to optimize the shape of the heat manipulators using Particle Swarm Optimization (PSO) method. The geometry is defined using NURBS basis functions due to the higher smoothness and continuity and the thermal boundary value problem is solved using Isogeometric Analysis (IGA). Often, nodes as design variables (as in Lagrange finite element method) generate the serrate shapes of boundaries which need to be smoothened later. For the NURBS-based boundary with the control points as design variables, the required smoothness can be predefined through knot vectors and smoothening in the post-processing can be avoided. The optimized shape generated by PSO is compared with the other shape exploited in the literature. The effects of the number of design variables, the thermal conductivity of the materials used, as well as some of the geometry parameters on the optimum shapes are also demonstrated.
Published: 2022

44. A graph-based probabilistic geometric deep learning framework with online enforcement of physical constraints to predict the criticality of defects in porous materials

Author: Krokos, Vasilis, Bordas, Stéphane P. A., and Kerfriden, Pierre
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: Stress prediction in porous materials and structures is challenging due to the high computational cost associated with direct numerical simulations. Convolutional Neural Network (CNN) based architectures have recently been proposed as surrogates to approximate and extrapolate the solution of such multiscale simulations. These methodologies are usually limited to 2D problems due to the high computational cost of 3D voxel based CNNs. We propose a novel geometric learning approach based on a Graph Neural Network (GNN) that efficiently deals with three-dimensional problems by performing convolutions over 2D surfaces only. Following our previous developments using pixel-based CNN, we train the GNN to automatically add local fine-scale stress corrections to an inexpensively computed coarse stress prediction in the porous structure of interest. Our method is Bayesian and generates densities of stress fields, from which credible intervals may be extracted. As a second scientific contribution, we propose to improve the extrapolation ability of our network by deploying a strategy of online physics-based corrections. Specifically, we condition the posterior predictions of our probabilistic predictions to satisfy partial equilibrium at the microscale, at the inference stage. This is done using an Ensemble Kalman algorithm, to ensure tractability of the Bayesian conditioning operation. We show that this innovative methodology allows us to alleviate the effect of undesirable biases observed in the outputs of the uncorrected GNN, and improves the accuracy of the predictions in general., Comment: 68 pages; 52 figures
Published: 2022
Full Text: View/download PDF

45. A Discrete Droplet Method for Modelling Thin Film Flows

Author: Bharadwaj, Anand S, Kuhnert, Joerg, Bordas, Stephane P. A., and Suchde, Pratik
Subjects: Physics - Fluid Dynamics
Abstract: In this paper, we present a new model to simulate the formation, evolution, and break up of a thin film of fluid flowing over a curved surface. Referred to as the discrete droplet method (DDM), the model captures the evolution of thin fluid films by tracking individual moving fluid droplets. In contrast to existing thin film models that solve a PDE to determine the film height, here, we compute the film height by numerical integration based on the aggregation of droplets. The novelty of this approach in using droplets makes it suitable for simulating the formation of fluid films, and modelling thin film flows on partially wetted surfaces. The DDM is a Lagrangian approach, with a force balance on each droplet governing the motion, and derivatives approximated using a smoothed particle hydrodynamics (SPH) like approach. The proposed model is thoroughly validated by comparing results against analytical solutions, against the results of the shallow-water equations for thin film flow, and also against results from a full 3D resolved Navier Stokes model. We also present the use of the DDM on an industrial test case. The results highlight the effectiveness of the model for simulations of flows with thin films.
Published: 2022
Full Text: View/download PDF

46. An a posteriori error estimator for the spectral fractional power of the Laplacian

Author: Bulle, Raphaël, Barrera, Olga, Bordas, Stéphane P. A., Chouly, Franz, and Hale, Jack S.
Subjects: Mathematics - Numerical Analysis, 65N15, 65N30
Abstract: We develop a novel a posteriori error estimator for the $L^2$ error committed by the finite element discretization of the solution of the fractional Laplacian. Our a posteriori error estimator takes advantage of the semi-discretization scheme using rational approximations which allow to reformulate the fractional problem into a family of non-fractional parametric problems. The estimator involves applying the implicit Bank-Weiser error estimation strategy to each parametric non-fractional problem and reconstructing the fractional error through the same rational approximation used to compute the solution to the original fractional problem. In addition we propose an algorithm to adapt both the finite element mesh and the rational scheme in order to balance the discretization errors. We provide several numerical examples in both two and three-dimensions demonstrating the effectivity of our estimator for varying fractional powers and its ability to drive an adaptive mesh refinement strategy.
Published: 2022
Full Text: View/download PDF

47. Design of metamaterial-based heat manipulators using isogeometric level-set topology optimization

Author: Jansari, Chintan, Bordas, Stéphane P. A., and Atroshchenko, Elena
Published: 2024
Full Text: View/download PDF

48. An adapted deflated conjugate gradient solver for robust extended/generalised finite element solutions of large scale, 3D crack propagation problems

Author: Agathos, Konstantinos, Dodwell, Tim, Chatzi, Eleni, and Bordas, Stephane P. A.
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: An adapted deflation preconditioner is employed to accelerate the solution of linear systems resulting from the discretization of fracture mechanics problems with well-conditioned extended/generalized finite elements. The deflation space typically used for linear elasticity problems is enriched with additional vectors, accounting for the enrichment functions used, thus effectively removing low frequency components of the error. To further improve performance, deflation is combined, in a multiplicative way, with a block-Jacobi preconditioner, which removes high frequency components of the error as well as linear dependencies introduced by enrichment. The resulting scheme is tested on a series of non-planar crack propagation problems and compared to alternative linear solvers in terms of performance.
Published: 2021
Full Text: View/download PDF

49. Probabilistic Deep Learning for Real-Time Large Deformation Simulations

Author: Deshpande, Saurabh, Lengiewicz, Jakub, and Bordas, Stéphane P. A.
Subjects: Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science
Abstract: For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information about how certain can we be about their predictions. In the present work, we propose a highly efficient deep-learning surrogate framework that is able to accurately predict the response of bodies undergoing large deformations in real-time. The surrogate model has a convolutional neural network architecture, called U-Net, which is trained with force-displacement data obtained with the finite element method. We propose deterministic and probabilistic versions of the framework. The probabilistic framework utilizes the Variational Bayes Inference approach and is able to capture all the uncertainties present in the data as well as in the deep-learning model. Based on several benchmark examples, we show the predictive capabilities of the framework and discuss its possible limitations
Published: 2021
Full Text: View/download PDF

50. Neural-network acceleration of projection-based model-order-reduction for finite plasticity: Application to RVEs

Author: Vijayaraghavan, S., Wu, L., Noels, L., Bordas, S. P. A., Natarajan, S., and Beex, L. A. A.
Subjects: Computer Science - Machine Learning
Abstract: Compared to conventional projection-based model-order-reduction, its neural-network acceleration has the advantage that the online simulations are equation-free, meaning that no system of equations needs to be solved iteratively. Consequently, no stiffness matrix needs to be constructed and the stress update needs to be computed only once per increment. In this contribution, a recurrent neural network is developed to accelerate a projection-based model-order-reduction of the elastoplastic mechanical behaviour of an RVE. In contrast to a neural network that merely emulates the relation between the macroscopic deformation (path) and the macroscopic stress, the neural network acceleration of projection-based model-order-reduction preserves all microstructural information, at the price of computing this information once per increment.
Published: 2021

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Region

Database

Publisher

2,046 results on '"Bordas, P."'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources