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MELCHIORS: The Mercator Library of High Resolution Stellar Spectroscopy

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arxiv 2311.02705 v1 pith:VCILRWTF submitted 2023-11-05 astro-ph.SR astro-ph.IM

classification astro-ph.SRastro-ph.IM
keywords spectraspectralhighlibrarycorrectioneffectivegivenhemisphere
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Over the past decades, libraries of stellar spectra have been used in a large variety of science cases, including as sources of reference spectra for a given object or a given spectral type. Despite the existence of large libraries and the increasing number of projects of large-scale spectral surveys, there is to date only one very high-resolution spectral library offering spectra from a few hundred objects from the southern hemisphere (UVES-POP) . We aim to extend the sample, offering a finer coverage of effective temperatures and surface gravity with a uniform collection of spectra obtained in the northern hemisphere. Between 2010 and 2020, we acquired several thousand echelle spectra of bright stars with the Mercator-HERMES spectrograph located in the Roque de Los Muchachos Observatory in La Palma, whose pipeline offers high-quality data reduction products. We have also developed methods to correct for the instrumental response in order to approach the true shape of the spectral continuum. Additionally, we have devised a normalisation process to provide a homogeneous normalisation of the full spectral range for most of the objects. We present a new spectral library consisting of 3256 spectra covering 2043 stars. It combines high signal-to-noise and high spectral resolution over the entire range of effective temperatures and luminosity classes. The spectra are presented in four versions: raw, corrected from the instrumental response, with and without correction from the atmospheric molecular absorption, and normalised (including the telluric correction).

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From stellar light to astrophysical insight: automating variable star research with machine learning

    astro-ph.IM 2025-07 unverdicted

    An invited review of machine learning for automated variable star research, covering data cleaning, variability classification, stellar parameter inference, and foundation models.

  2. Basic stellar observables

    astro-ph.SR 2024-12 unverdicted

    A pedagogical review chapter summarizing the standard physical and observational methods for deriving basic stellar properties from their light.

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