Zobrazeno 1 - 10
of 1 239
pro vyhledávání: '"A. PASQUATO"'
We present the first steps toward applying causal representation learning to astronomy. Following up on previous work that introduced causal discovery to the field for the first time, here we solve a long standing conundrum by identifying the directi
Externí odkaz:
http://arxiv.org/abs/2410.14775
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:
Prodan, George P., Pasquato, Mario, Iorio, Giuliano, Ballone, Alessandro, Torniamenti, Stefano, Di Carlo, Ugo Niccolò, Mapelli, Michela
Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the ability to set
Externí odkaz:
http://arxiv.org/abs/2409.10627
Autor:
Mekhaël, Nicolas, Pasquato, Mario, Carenini, Gaia, Braga, Vittorio F., Trevisan, Piero, Bono, Giuseppe, Hezaveh, Yashar
We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variables, in the Galactic globular cluster $\omega$ Centauri. Light curves a
Externí odkaz:
http://arxiv.org/abs/2407.16868
Autor:
Santoliquido, Filippo, Dupletsa, Ulyana, Tissino, Jacopo, Branchesi, Marica, Iacovelli, Francesco, Iorio, Giuliano, Mapelli, Michela, Gerosa, Davide, Harms, Jan, Pasquato, Mario
Publikováno v:
A&A 690, A362 (2024)
Third-generation (3G) gravitational-wave detectors such as the Einstein Telescope (ET) will observe binary black hole (BBH) mergers at redshifts up to $z\sim 100$. However, an unequivocal determination of the origin of high-redshift sources will rema
Externí odkaz:
http://arxiv.org/abs/2404.10048
Autor:
Rhea, Carter Lee, Hlavacek-Larrondo, Julie, Giroux, Justine, Thilloy, Auriane, Choi, Hyunseop, Rousseau-Nepton, Laurie, Gendron-Marsolais, Marie-Lou, Pasquato, Mario, Prunet, Simon
In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying
Externí odkaz:
http://arxiv.org/abs/2404.01175
Autor:
Jin, Zehao, Macciò, Andrea V., Faucher, Nicholas, Pasquato, Mario, Buck, Tobias, Dixon, Keri L., Arora, Nikhil, Blank, Marvin, Vulanović, Pavle
Publikováno v:
MNRAS, 529, 4, (2024)
Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realism of these simulations remains a challenge. The two common approaches
Externí odkaz:
http://arxiv.org/abs/2403.19464
Autor:
Kavanagh, Bradley J., Karydas, Theophanes K., Bertone, Gianfranco, Di Cintio, Pierfrancesco, Pasquato, Mario
Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a detection hinges on the accurate modelling of the dynamical friction, i
Externí odkaz:
http://arxiv.org/abs/2402.13762
Autor:
Cavallo, L., Spina, L., Carraro, G., Magrini, L., Poggio, E., Cantat-Gaudin, T., Pasquato, M., Lucatello, S., Ortolani, S., Schiappacasse-Ulloa, J.
With the unprecedented increase of known star clusters, quick and modern tools are needed for their analysis. In this work, we develop an artificial neural network trained on synthetic clusters to estimate the age, metallicity, extinction, and distan
Externí odkaz:
http://arxiv.org/abs/2311.03009
Autor:
Pasquato, Mario, Trevisan, Piero, Askar, Abbas, Lemos, Pablo, Carenini, Gaia, Mapelli, Michela, Hezaveh, Yashar
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This approach h
Externí odkaz:
http://arxiv.org/abs/2310.18560