BEGIN:VCALENDAR VERSION:2.0 PRODID:-//https://astronomia.uc.cl/2jucext1/institutoastrofisica///NONSGML kigkonsult.se iCalcreator 2.10.15// METHOD:PUBLISH BEGIN:VEVENT UID:20260518T182831-04-2457wmCVPi@https://astronomia.uc.cl/2jucext1/institu toastrofisica/ DTSTAMP:20260518T222831Z DESCRIPTION:One of the main predictions derived from ?-CDM cosmology is tha t structure grows hierarchically as consequence of the gravity-driven asse mbly of dark matter halos and the galaxies they host\, making galaxy merge rs a fundamental element within the current framework of galaxy evolution. Despite this\, the specific role that galaxy mergers play in the differen t processes observed in galaxy evolution along with the rates that are est imated\, from either observations or simulations\, remain not fully unders tood. In that regard\, the development of accurate methods to detect galax y mergers is something extremely relevant in order to improve our comprehe nsion of the merging process\, but also to bring into agreement galaxy for mation models with the available observational data.\nInspired by the rece nt success of multiple deep learning techniques used to address different astrophysical problems\, we present a novel method based on the utilizatio n of Convolutional Neural Networks (CNNs) and the Horizon-AGN cosmological simulation for identifying galaxy mergers in an automated and accurate ma nner. The main idea behind this method consists on generating large traini ng sets of HST-like multiband mock observations of mergers\, isolated gala xies (no-mergers) and projection effects (fake-mergers) employing as input the virtual galaxies of the Horizon-AGN simulation\, which then are used to train a CNN that is able of identifying galaxy mergers. These mock obse rvations are generated considering massive galaxies (M_star <\;= 10^10 M _sun) in the redshift range 0.5 <\; z <\; 3.5\, and in the case of gal axy mergers\, only those that correspond to major ones (stellar mass ratio <\;= 1:4) are taken into account.\nThe performance exhibited by this ne w method outperforms not only the classic approaches based on non-parametr ic morphologies employed for identifying galaxy mergers\, such as CAS and G-M20\, but also the extended and improved versions of these methods that rely on the utilization of machine learning. Moreover\, the use of a simul ated training set allowed us to relate the observability timescales of the different methods analyzed with the actual timescales of the galaxy merge rs selected from the simulation. https://astronomia.uc.cl/2jucext1/institu toastrofisica/es/instituto-2/seminars/evento/347-automated-detection-of-ga laxy-mergers-using-cosmological-simulations-and-deep-learning-dr-fernando- caro-lerma-observatoire-de-paris DTSTART:20190401T171500Z DTEND:20190401T174500Z LOCATION:Seminar Room (Instituto de Astrofísica\, Pontificia Universidad Ca tólica\, Vicuña Mackenna 4860\, Santiago\, Chile) SUMMARY:Automated Detection of Galaxy Mergers using Cosmological Simulation s and Deep Learning (Dr. Fernando Caro\; LERMA\, Observatoire de Paris) URL:https://astronomia.uc.cl/2jucext1/institutoastrofisica/es/instituto-2/s eminars/evento/347-automated-detection-of-galaxy-mergers-using-cosmologica l-simulations-and-deep-learning-dr-fernando-caro-lerma-observatoire-de-par is END:VEVENT END:VCALENDAR