Zobrazeno 1 - 10
of 16 104
pro vyhledávání: '"Perreault A"'
Hierarchical Archimedean copulas (HACs) are multivariate uniform distributions constructed by nesting Archimedean copulas into one another, and provide a flexible approach to modeling non-exchangeable data. However, this flexibility in the model stru
Externí odkaz:
http://arxiv.org/abs/2411.10615
We investigate the robustness of Neural Ratio Estimators (NREs) and Neural Posterior Estimators (NPEs) to distributional shifts in the context of measuring the abundance of dark matter subhalos using strong gravitational lensing data. While these dat
Externí odkaz:
http://arxiv.org/abs/2411.05905
Autor:
Legin, Ronan, Isi, Maximiliano, Wong, Kaze W. K., Hezaveh, Yashar, Perreault-Levasseur, Laurence
Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to ``clean''
Externí odkaz:
http://arxiv.org/abs/2410.19956
Autor:
Jin, Zehao, Pasquato, Mario, Davis, Benjamin L., Deleu, Tristan, Luo, Yu, Cho, Changhyun, Lemos, Pablo, Perreault-Levasseur, Laurence, Bengio, Yoshua, Kang, Xi, Maccio, Andrea Valerio, Hezaveh, Yashar
Correlations between galaxies and their supermassive black holes (SMBHs) have been observed, but the causal mechanisms remained unclear. The emerging field of causal inference now enables examining these relationships using observational data. This s
Externí odkaz:
http://arxiv.org/abs/2410.00965
Autor:
Rhea, Carter, Hlavacek-Larrondo, Julie, Adam, Alexandre, Kraft, Ralph, Bogdan, Akos, Perreault-Levasseur, Laurence, Prunier, Marine
Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clust
Externí odkaz:
http://arxiv.org/abs/2409.10711
Computer programming initially required humans to directly translate their goals into machine code. These goals could have easily been expressed as a written (or human) language directive. Computers, however, had no capacity to satisfactorily interpr
Externí odkaz:
http://arxiv.org/abs/2408.11060
Autor:
Bourdin, Antoine, Legin, Ronan, Ho, Matthew, Adam, Alexandre, Hezaveh, Yashar, Perreault-Levasseur, Laurence
Cosmological hydrodynamical simulations, while the current state-of-the art methodology for generating theoretical predictions for the large scale structures of the Universe, are among the most expensive simulation tools, requiring upwards of 100 mil
Externí odkaz:
http://arxiv.org/abs/2408.00839
Autor:
Barco, Gabriel Missael, Adam, Alexandre, Stone, Connor, Hezaveh, Yashar, Perreault-Levasseur, Laurence
Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-level distributions can serve as effective priors for parameters of interest. With the advent of machine learn
Externí odkaz:
http://arxiv.org/abs/2407.17667
Autor:
Marconi, A., Abreu, M., Adibekyan, V., Alberti, V., Albrecht, S., Alcaniz, J., Aliverti, M., Prieto, C. Allende, Gómez, J. D. Alvarado, Alves, C. S., Amado, P. J., Amate, M., Andersen, M. I., Antoniucci, S., Artigau, E., Bailet, C., Baker, C., Baldini, V., Balestra, A., Barnes, S. A., Baron, F., Barros, S. C. C., Bauer, S. M., Beaulieu, M., Bellido-Tirado, O., Benneke, B., Bensby, T., Bergin, E. A., Berio, P., Biazzo, K., Bigot, L., Bik, A., Birkby, J. L., Blind, N., Boebion, O., Boisse, I., Bolmont, E., Bolton, J. S., Bonaglia, M., Bonfils, X., Bonhomme, L., Borsa, F., Bouret, J. -C., Brandeker, A., Brandner, W., Broeg, C. H., Brogi, M., Brousseau, D., Brucalassi, A., Brynnel, J., Buchhave, L. A., Buscher, D. F., Cabona, L., Cabral, A., Calderone, G., Calvo-Ortega, R., Cantalloube, F., Martins, B. L. Canto, Carbonaro, L., Caujolle, Y., Chauvin, G., Chazelas, B., Cheffot, A. -L., Cheng, Y. S., Chiavassa, A., Christensen, L., Cirami, R., Cirasuolo, M., Cook, N. J., Cooke, R. J., Coretti, I., Covino, S., Cowan, N., Cresci, G., Cristiani, S., Parro, V. Cunha, Cupani, G., D'Odorico, V., Dadi, K., Leão, I. de Castro, De Cia, A., De Medeiros, J. R., Debras, F., Debus, M., Delorme, A., Demangeon, O., Derie, F., Dessauges-Zavadsky, M., Di Marcantonio, P., Di Stefano, S., Dionies, F., de Souza, A. Domiciano, Doyon, R., Dunn, J., Egner, S., Ehrenreich, D., Faria, J. P., Ferruzzi, D., Feruglio, C., Fisher, M., Fontana, A., Frank, B. S., Fuesslein, C., Fumagalli, M., Fusco, T., Fynbo, J., Gabella, O., Gaessler, W., Gallo, E., Gao, X., Genolet, L., Genoni, M., Giacobbe, P., Giro, E., Goncalves, R. S., Gonzalez, O. A., Hernández, J. I. González, Gouvret, C., Temich, F. Gracia, Haehnelt, M. G., Haniff, C., Hatzes, A., Helled, R., Hoeijmakers, H. J., Hughes, I., Huke, P., Ivanisenko, Y., Järvinen, A. S., Järvinen, S. P., Kaminski, A., Kern, J., Knoche, J., Kordt, A., Korhonen, H., Korn, A. J., Kouach, D., Kowzan, G., Kreidberg, L., Landoni, M., Lanotte, A. A., Lavail, A., Lavie, B., Lee, D., Lehmitz, M., Li, J., Li, W., Liske, J., Lovis, C., Lucatello, S., Lunney, D., MacIntosh, M. J., Madhusudhan, N., Magrini, L., Maiolino, R., Maldonado, J., Malo, L., Man, A. W. S., Marquart, T., Marques, C. M. J., Marques, E. L., Martinez, P., Martins, A., Martins, C. J. A. P., Martins, J. H. C., Maslowski, P., Mason, C. A., Mason, E., McCracken, R. A., Sousa, M. A. F. Melo e, Mergo, P., Micela, G., Milaković, D., Molliere, P., Monteiro, M. A., Montgomery, D., Mordasini, C., Morin, J., Mucciarelli, A., Murphy, M. T., N'Diaye, M., Nardetto, N., Neichel, B., Neri, N., Niedzielski, A. T., Niemczura, E., Nisini, B., Nortmann, L., Noterdaeme, P., Nunes, N. J., Oggioni, L., Olchewsky, F., Oliva, E., Onel, H., Origlia, L., Ostlin, G., Ouellette, N. N. -Q., Palle, E., Papaderos, P., Pariani, G., Pasquini, L., Castro, J. Peñate, Pepe, F., Peroux, C., Levasseur, L. Perreault, Perruchot, S., Petit, P., Pfuhl, O., Pino, L., Piqueras, J., Piskunov, N., Pollo, A., Poppenhaeger, K., Porru, M., Puschnig, J., Quirrenbach, A., Rauscher, E., Rebolo, R., Redaelli, E. M. A., Reffert, S., Reid, D. T., Reiners, A., Richter, P., Riva, M., Rivoire, S., Rodriguez-López, C., Roederer, I. U., Romano, D., Roth, M., Rousseau, S., Rowe, J., Saccardi, A., Salvadori, S., Sanna, N., Santos, N. C., Diaz, P. Santos, Sanz-Forcada, J., Sarajlic, M., Sauvage, J. -F., Savio, D., Scaudo, A, Schäfer, S., Schiavon, R. P., Schmidt, T. M., Selmi, C., Simoes, R., Simonnin, A., Sivanandam, S., Sordet, M., Sordo, R., Sortino, F., Sosnowska, D., Sousa, S. G., Spang, A., Spiga, R., Stempels, E., Stevenson, J. R. Y., Strassmeier, K. G., Mascareño, A. Suárez, Sulich, A., Sun, X., Tanvir, N. R., Tenegi-Sangines, F., Thibault, S., Thompson, S. J., Tisserand, P., Tozzi, A., Turbet, M., Veran, J. -P., Vallee, P., Vanni, I., Varas, R., Vega-Moreno, A., Venn, K. A., Verma, A., Vernet, J., Viel, M., Wade, G., Waring, C., Weber, M., Weder, J., Wehbe, B., Weingrill, J., Woche, M., Xompero, M., Zackrisson, E., Zanutta, A., Osorio, M. R. Zapatero, Zechmeister, M., Zimara, J.
The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs
Externí odkaz:
http://arxiv.org/abs/2407.14601
Autor:
Cadigan, Noel G., Perreault, Andrea M., Nguyen, Hoang, Chen, Jiaying, Beita-Jimenez, Andres, Fuller, Natalie, Ransier, Krista
We developed a state-space age-structured catch-at-length (ACL) assessment model for redfish in NAFO Divisions 3LN. The model was developed to address limitations in the surplus production model that was previously used to assess this stock. The ACL
Externí odkaz:
http://arxiv.org/abs/2407.04799