Found 24 talks width keyword Machine learning

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Thursday September 16, 2021
Dr. Siddharth Mishra-Sharme
NYU

Abstract

The next decade will see a deluge of new cosmological data that will enable us to accurately map out the distribution of matter in the local Universe, image billions of stars and galaxies to unprecedented precision, and create high-resolution maps of the Milky Way. Signatures of new physics as well as astrophysical processes of interest may be hiding in these observations, offering significant discovery potential. At the same time, the complexity of astrophysical data provides significant challenges to carrying out these searches using conventional methods. I will describe how overcoming these issues will require a qualitative shift in how we approach modeling and inference in cosmology, bringing together several recent advances in machine learning and simulation-based (or likelihood-free) inference. I will ground the talk through examples of proposed analyses that use machine learning-enabled simulation-based inference with an aim to uncover the identity of dark matter, while at the same time emphasizing the generality of these techniques to a broad range of problems in astrophysics, cosmology, and beyond.

 

https://rediris.zoom.us/j/83193959785?pwd=TExXSDJ6UDg5a24yWDM1TnlOWkNTZz09

Meeting ID: 831 9395 9785
Passcode: 343950O

YouTube: https://youtu.be/1Nkzn-cGaIo


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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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Tuesday June 22, 2021
Dr. Christophe Morisset
Universidad Autónoma Nacional de México

Abstract

Artificial intelligence techniques are increasingly used in our daily lives. They also play an important role in science, including astrophysics. I am particularly interested in the use of machine learning regressors. I will present an overview of the current situation and some recent uses of these methods in the study of planetary nebulae or HII regions.



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Tuesday June 8, 2021
Dr. Hector Socas-Navarro
IAC

Abstract

In this talk I'll present results from a recent paper in which we have developed a new analysis technique for solar spectra based on artificial neural networks. Our first test applications yielded some unexpected and interesting results. The fine-scale network of temperature enhancements in the quiet middle and upper photosphere have a reversed pattern. Hot pixels in the middle photosphere, possibly associated with small-scale magnetic elements, appear cool at higher levels (log(tau)=-3 and -4), and vice versa. We also find hot arcs on the limb side of magnetic pores, which we interpret as the first direct observational evidence of the "hot wall" effect. Hot walls are a prediction of theoretical models from the 1970s which had not been observed until now.

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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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