Found 21 talks width keyword Deep Learning

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Tuesday September 7, 2021
Dr. Jesús Vega-Ferrero
IAC

Abstract

Galaxy 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 completely impractical. Deep Learning (DL) algorithms, where no image pre-processing is required, have already come to the rescue for image analysis of large data surveys. In this seminar, I will present the largest multi-band catalog of automated galaxy morphologies to date containing morphological classifications of ∼27 million galaxies from the Dark Energy Survey. 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 Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications, but hese typically have mr < 17.7 mag. We overcome the lack ofa training sample by modeling fainter objects up to mr < 21.5 mag, i.e., by simulating what thebrighter objects with well-determined classifications would look like if they were at higher redshifts.The CNNs reach a 97% accuracy to mr < 21.5 on their training sets, suggesting that they are ableto recover features more accurately than the human 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.

 


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Friday November 9, 2018
Prof. Michael Biehl
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Supervised learning: classification and regression
Lecture 4


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Thursday November 8, 2018
Prof. Michael Biehl
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Supervised learning: classification and regression
Lecture 3


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Thursday November 8, 2018
Prof. Marc Huertas-Company
Université Paris-Diderot - Observatoire de Paris

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Deep learning
Lecture 4


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Thursday November 8, 2018
Prof. Mario Juric
University of Washington

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Data challenges and solutions in forthcoming surveys
Lecture 4


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Thursday November 8, 2018
Prof. George Djorgovski
Caltech, Division of Physics, Mathematics and Astronomy

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: General overview on the use of machine learning techniques in astronomy
Lecture 4


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Wednesday November 7, 2018
Mrs. Dalya Baron
School of Physics and Astronomy, Tel-Aviv University

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Machine learning methods for non-supervised classification and dimension reduction techniques
Lecture 4


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Wednesday November 7, 2018
Prof. Mario Juric
University of Washington

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Data challenges and solutions in forthcoming surveys
Lecture 3


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Wednesday November 7, 2018
Prof. George Djorgovski
Caltech, Division of Physics, Mathematics and Astronomy

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: General overview on the use of machine learning techniques in astronomy
Lecture 3


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Tuesday November 6, 2018
Prof. Marc Huertas-Company
Université Paris-Diderot - Observatoire de Paris

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Deep learning
Lecture 3

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