PREPRINT
3BA0A65F-23A2-4C20-B4A3-ACAE01A447D2

Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows

Ioana Ciuca, Yuan-Sen Ting
arXiv:2207.02785

Submitted on 6 July 2022

Abstract

Stellar spectra encode detailed information about the stars. However, most machine learning approaches in stellar spectroscopy focus on supervised learning. We introduce Mendis, an unsupervised learning method, which adopts normalizing flows consisting of Neural Spline Flows and GLOW to describe the complex distribution of spectral space. A key advantage of Mendis is that we can describe the conditional distribution of spectra, conditioning on stellar parameters, to unveil the underlying structures of the spectra further. In particular, our study demonstrates that Mendis can robustly capture the pixel correlations in the spectra leading to the possibility of detecting unknown atomic transitions from stellar spectra. The probabilistic nature of Mendis also enables a rigorous determination of outliers in extensive spectroscopic surveys without the need to measure elemental abundances through existing analysis pipelines beforehand.

Preprint

Comment: 6 pages, 3 figures, accepted to the ICML 2022 Machine Learning for Astrophysics workshop

Subjects: Astrophysics - Solar and Stellar Astrophysics; Astrophysics - Instrumentation and Methods for Astrophysics

URL: https://arxiv.org/abs/2207.02785