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    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version Nima Sedaghat, Martino Romaniello, Jonathan E Carrick, François-Xavier Pineau, Machines learn to infer stellar parameters just by looking at a large number of spectra, Monthly Notices of the Royal Astronomical Society, Volume 501, Issue 4, March 2021, Pages 6026–6041, https://doi.org/10.1093/mnras/staa3540 is available online at: https://academic.oup.com/mnras/article-abstract/501/4/6026/6121645

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Machines Learn to Infer Stellar Parameters Just by Looking at a Large Number of Spectra

Research output: Contribution to journalJournal articlepeer-review

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  • Nima Sedaghat
  • Martino Romaniello
  • Jon Carrick
  • François-Xavier Pineau
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<mark>Journal publication date</mark>1/03/2021
<mark>Journal</mark>Monthly Notices of the Royal Astronomical Society
Issue number4
Volume501
Number of pages16
Pages (from-to)6026-6041
Publication StatusPublished
Early online date29/01/21
<mark>Original language</mark>English

Abstract

Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad hypothesis behind our work is that letting the abundant real astrophysical data speak for itself, with minimal supervision and no labels, can reveal interesting patterns that may facilitate discovery of novel physical relationships. Here, as the first step, we seek to interpret the representations a deep convolutional neural network chooses to learn, and find correlations in them with current physical understanding. We train an encoder–decoder architecture on the self-supervised auxiliary task of reconstruction to allow it to learn general representations without bias towards any specific task. By exerting weak disentanglement at the information bottleneck of the network, we implicitly enforce interpretability in the learned features. We develop two independent statistical and information-theoretical methods for finding the number of learned informative features, as well as measuring their true correlation with astrophysical validation labels. As a case study, we apply this method to a data set of ∼270 000 stellar spectra, each of which comprising ∼300 000 dimensions. We find that the network clearly assigns specific nodes to estimate (notions of) parameters such as radial velocity and effective temperature without being asked to do so, all in a completely physics-agnostic process. This supports the first part of our hypothesis. Moreover, we find with high confidence that there are ∼4 more independently informative dimensions that do not show a direct correlation with our validation parameters, presenting potential room for future studies.

Bibliographic note

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version Nima Sedaghat, Martino Romaniello, Jonathan E Carrick, François-Xavier Pineau, Machines learn to infer stellar parameters just by looking at a large number of spectra, Monthly Notices of the Royal Astronomical Society, Volume 501, Issue 4, March 2021, Pages 6026–6041, https://doi.org/10.1093/mnras/staa3540 is available online at: https://academic.oup.com/mnras/article-abstract/501/4/6026/6121645