1. Introduction of Machine Learning for Astronomy (Hands-on Workshop)
- Author
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Wang, Yu, Moradi, Rahim, Haghighi, Mohammad H. Zhoolideh, and Rastegarnia, Fatemeh
- Subjects
High Energy Astrophysical Phenomena (astro-ph.HE) ,Cosmology and Nongalactic Astrophysics (astro-ph.CO) ,FOS: Physical sciences ,Astronomy and Astrophysics ,General Relativity and Quantum Cosmology (gr-qc) ,Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - High Energy Astrophysical Phenomena ,Instrumentation ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,General Relativity and Quantum Cosmology ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then demonstrate this process with an example of inferring redshifts from SDSS spectra. To emphasize that machine learning for astronomy is easy to get started, we demonstrate that the most basic CNN network can be used to obtain high accuracy, we also show that with simple modifications, the network can be converted for classification problems and also to processing gravitational wave data., Comment: Proceedings based on the lectures given at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting, to be published in Astronomical and Astrophysical Transactions
- Published
- 2023
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