Consistent and accurate estimation of stellar parameters from HARPS-N Spectroscopy using Deep Learning

Frederik Boe Hüttel*, Line Katrine Harder Clemmensen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Consistent and accurate estimation of stellar parameters is of great importance for information retrieval in astrophysical research. The parameters
span a wide range from effective temperature to rotational velocity. We propose to estimate the stellar parameters directly from spectral signals coming from the HARPS-N spectrograph pipeline before any spectrum-processing steps are applied to extract the 1D spectrum. We use residual networks
and an attention-based model to estimate the stellar parameters. The models estimate both mean and uncertainty of the stellar parameters through
the parameters of a Gaussian distribution. The estimated distributions create a basis to generate data-driven Gaussian confidence intervals for the
estimated stellar parameters. We show that residual networks and attention-based models can estimate the stellar parameters with high accuracy for
low Signal-to-noise ratio (SNR) compared to previous methods. With an observation of the Sun from the HARPS-N spectrograph, we show that the models can estimate stellar parameters from real observational data.
Original languageEnglish
Title of host publicationProceedings of Northern Lights Deep Learning Workshop 2021
Number of pages7
Publication date2021
DOIs
Publication statusPublished - 2021
EventNorthern Lights Deep Learning Workshop 2021
- Virtual event, Tromsø , Norway
Duration: 18 Jan 202120 Jan 2021
http://www.nldl.org

Workshop

WorkshopNorthern Lights Deep Learning Workshop 2021
LocationVirtual event
CountryNorway
CityTromsø
Period18/01/202120/01/2021
Internet address

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