Stellar parameter prediction and spectral simulation using machine learning

A systematic comparison of methods with HARPS observational data^★

¹ Department of Cybernetics, Czech Technical University in Prague, Czech Republic
² European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching, Germany

^★★ Corresponding author; cvrcevo1@fel.cvut.cz

Received: 12 June 2024
Accepted: 4 November 2024

Abstract

Aims. We applied machine learning to the entire data history of ESO’s High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on simulating spectra. We systematically investigated the impact of various factors on the accuracy and fidelity of the results, including the use of simulated data, the effect of varying amounts of real training data, network architectures, and learning paradigms.

Methods. Our approach integrates supervised and unsupervised learning techniques within autoencoder frameworks. Our methodology leverages an existing simulation model that utilizes a library of existing stellar spectra in which the emerging flux is computed from first principles rooted in physics and a HARPS instrument model to generate simulated spectra comparable to observational data. We trained standard and variational autoencoders on HARPS data to predict spectral parameters and generate spectra. Convolutional and residual architectures were compared, and we decomposed autoencoders in order to assess component impacts.

Results. Our models excel at predicting spectral parameters and compressing real spectra, and they achieved a mean prediction error of ~50 K for effective temperatures, making them relevant for most astrophysical applications. Furthermore, the models predict metallicity ([M/H]) and surface gravity (log g) with an accuracy of ~0.03 dex and ~0.04 dex, respectively, underscoring their broad applicability in astrophysical research. Moreover, the models can generate new spectra that closely mimic actual observations, enriching traditional simulation techniques. Our variational autoencoder-based models achieve short processing times: 779.6 ms on a CPU and 3.97 ms on a GPU. These results demonstrate the benefits of integrating high-quality data with advanced model architectures, as it significantly enhances the scope and accuracy of spectroscopic analysis. With an accuracy comparable to the best classical analysis method but requiring a fraction of the computation time, our methods are particularly suitable for high-throughput observations such as massive spectroscopic surveys and large archival studies.