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BEGIN:VEVENT
DTSTART;TZID=Atlantic/Canary:20240201T103000
DTEND;TZID=Atlantic/Canary:20240201T113000
UID:iactalks-1743
X-WR-CALNAME: IAC Talks: Open Astronomy Seminars
X-ORIGINAL-URL: /iactalks/Talks/view/1743
CREATED:2024-02-01T10:30:00+00:00
X-WR-CALDESC: IAC Talks upcomming talks
SUMMARY:Cosmology with Machine Learning: new tools for data and for theory
DESCRIPTION:Cosmology with Machine Learning: new tools for data and for the
 ory\nProf. Raul Abramo\n\n \nCosmology today is facing two fundamenta
 l problems: we do not know  for certain what is the present expansion rate
  of the Universe, or why  that expansion seems to be accelerating. In
  order to address these  challenges, new telescopes and instruments are be
 coming available. One  of these instruments is J-PAS, which at the end of 
 2023 started to map  the Universe using 56 narrow optical bands, detecting
  $\sim 4. \times  10^4$ extragalactic objects in each square degree. Massi
 ve new data sets  such as J-PAS can be quite challenging to digest, and Ma
 chine Learning  (ML) has become central in our efforts to detect, classify
  and to  extract properties of astrophysical sources. I will discuss how M
 L has  allowed us to advance in source classification, determining which o
 nes  are stars, galaxies or quasars, paving the way for J-PAS to become on
 e  of the most complete galaxy surveys at $z \lesssim 1$, and the best  qu
 asar survey at $z \lesssim 4$. I will also show that, on the  theoretical 
 side, numerical simulations together with ML techniques have  allowed us t
 o reproduce the intricate relationships between halos and  galaxies with u
 nprecedented accuracy, which is what will ensure that we  can realize the 
 potential of these amazing new observations in terms of a  new understandi
 ng about the evolution of our Universe.\nZoom:  https://rediris.zoom.
 us/j/94907029675?pwd=amVxUmxRSldweXZLaGFkYWdMdU9JUT09\nID: 949 0702 9675\n
 Password:  254951\nYoutube: https://youtube.com/live/JE-rCBMhpVQ
 ?feature=share\n\n \n
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