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BEGIN:VEVENT
DTSTART;TZID=Atlantic/Canary:20210907T120000
DTEND;TZID=Atlantic/Canary:20210907T130000
UID:iactalks-1502
X-WR-CALNAME: IAC Talks: Open Astronomy Seminars
X-ORIGINAL-URL: /iactalks/Talks/view/1502
CREATED:2021-09-07T12:00:00+01:00
X-WR-CALDESC: IAC Talks upcomming talks
SUMMARY:Pushing automated classifications to their limits: the largest gala
 xy morphological catalog up to date
DESCRIPTION:Pushing automated classifications to their limits: the largest 
 galaxy morphological catalog up to date\nDr. Jesús Vega-Ferrero\n\nGalaxy
  morphologies are one of the key diagnostics of galaxy evolutionary tracks
 , but visual classifications are extremely time-consuming. The sheer size 
 of Big Data surveys, containing millions of galaxies, make this approach c
 ompletely impractical. Deep Learning (DL) algorithms, where no image pre-p
 rocessing is required, have already come to the rescue for image analysis 
 of large data surveys. In this seminar, I will present the largest multi-b
 and catalog of automated galaxy morphologies to date containing morphologi
 cal classifications of ∼27 million galaxies from the Dark Energy Surve
 y. The classification separates: (a) early-type galaxies (ETGs) from late-
 types (LTGs); and (b) face-on galaxies from edge-on. These classifications
  have been obtained using a supervised DL algorithm. Our Convolutional Neu
 ral Networks (CNNs) are trained on a small subset of DES objects with prev
 iously known classifications, but hese typically have mr < 17.7 mag. We
  overcome the lack ofa training sample by modeling fainter objects up to m
 r < 21.5 mag, i.e., by simulating what thebrighter objects with well-de
 termined classifications would look like if they were at higher redshifts.
 The CNNs reach a 97% accuracy to mr < 21.5 on their training sets, sugg
 esting that they are ableto recover features more accurately than the huma
 n eye. We obtain secure classifications for 87%and 73% of the catalog for 
 the ETG vs. LTG and edge-on vs. face-on models, respectively.\n 
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