11institutetext: Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D
bus 2401, 3001 Leuven, Belgium
11email<EMAIL_ADDRESS>22institutetext: Royal Observatory of
Belgium, Avenue Circulaire 3, 1180 Brussels, Belgium 33institutetext: Institut
d’Astronomie et d’Astrophysique, Université Libre de Bruxelles, CP 226
Boulevard du Triomphe, 1050 Bruxelles, Belgium 44institutetext: INAF -
Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125, Firenze, Italy
55institutetext: Centre for eResearch, The University of Auckland, Auckland
1010, New Zealand 66institutetext: Royal Belgian Institute for Space Aeronomy
(BIRA-IASB), 3 av. circulaire, B-1180 Bruxelles, Belgium 77institutetext:
Space sciences, Technologies and Astrophysics Research (STAR) Institute,
Université de Liège, Allée du 6 Août 19C, Bat. B5C, B-4000 Liège, Belgium
88institutetext: ACRI-ST, Centre d’Etudes et de Recherche de Grasse (CERGA),
10 Av. Nicolas Copernic, 06130 Grasse, France 99institutetext: Recogito AS,
Storgaten 72, 8200 Fauske, Norway 1010institutetext: Department of
Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The
Netherlands 1111institutetext: Centre for Extragalactic Astronomy, Department
of Physics, Durham University, South Road, Durham, DH1 3LE, UK
1212institutetext: Max-Planck-Institut für Sonnensystemforschung, 37077
Göttingen, Germany 1313institutetext: Astronomical Institute, Czech Academy of
Sciences, 251 65 Ondřejov, Czech Republic 1414institutetext: Astroserver.org,
Fő tér 1, 8533 Malomsok, Hungary 1515institutetext: Redwire Space,
Hogenakkerhoekstraat 9, 9150 Kruibeke, Belgium 1616institutetext: Instituto de
Astrofísica de Canarias, E-38200 La Laguna, Tenerife, Spain 1717institutetext:
Departamento de Astrofísica, Universidad de La Laguna, E-38206 La Laguna,
Tenerife, Spain 1818institutetext: Institut für Physik, Karl-Franzens
Universität Graz, Universitätsplatz 5/II, NAWI Graz, 8010 Graz, Austria
1919institutetext: Instituut voor Theoretische Fysica, KU Leuven,
Celestijnenlaan 200D bus 2415, 3001 Leuven, Belgium 2020institutetext:
European Southern Observatory, Alonso de Córdova 3107, Vitacura, Casilla
19001, Santiago de Chile, Chile 2121institutetext: School of Physics and
Astronomy, University of Leeds, Leeds LS2 9JT, UK

# MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.

The Mercator Library of High Resolution Stellar Spectroscopy

P. Royer0000-0001-9341-2546 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. T. Merle0000-0001-8253-1603
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. K. Dsilva0000-0002-1476-9772 MELCHIORS††thanks: The dataset
is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. S. Sekaran
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. H. Van Winckel0000-0001-5158-9327 MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. Y.
Frémat0000-0002-4645-6017 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. M. Van der
Swaelmen0000-0002-1765-3521 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. S.
Gebruers0000-0001-8426-4158 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. A.
Tkachenko0000-0003-0842-2374 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. M.
Laverick0000-0002-9220-2982 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. M. Dirickx
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. G. Raskin0000-0002-1323-9788 MELCHIORS††thanks: The dataset
is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. H. Hensberge
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. M. Abdul-Masih0000-0001-6566-7568 MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. B. Acke MELCHIORS††thanks:
The dataset is described in Appendix A. It can be visualised and downloaded
from www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. M.L. Alonso
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. S. Bandhu Mahato0000-0002-1677-6474 MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. P. G.
Beck0000-0003-4745-2242 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. N. Behara
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. S. Bloemen0000-0002-6636-921X MELCHIORS††thanks: The dataset
is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. B. Buysschaert
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. N. Cox0000-0002-7926-4492 MELCHIORS††thanks: The dataset is
described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. J. Debosscher
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. P. De Cat MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. P. Degroote
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. R. De Nutte0009-0009-5807-5770 MELCHIORS††thanks: The dataset
is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. K. De Smedt
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. B. de Vries MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. L. Dumortier
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. A. Escorza0000-0003-3833-2513 MELCHIORS††thanks: The dataset
is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. K. Exter MELCHIORS††thanks:
The dataset is described in Appendix A. It can be visualised and downloaded
from www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. S. Goriely
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. N. Gorlova MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. M. Hillen
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. W. Homan MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. A. Jorissen
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. D. Kamath MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. M.
Karjalainen0000-0003-0751-3231 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. R.
Karjalainen0000-0002-2656-909X MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. P.
Lampens0000-0002-7034-4912 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. A. Lobel0000-0001-5030-019X
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. R. Lombaert MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. P. Marcos-
Arenal0000-0003-1549-9396 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. J. Menu0000-0001-6905-1840
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. F. Merges MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. E. Moravveji
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. P. Nemeth0000-0003-0963-0239 MELCHIORS††thanks: The dataset
is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. P. Neyskens
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. R. Ostensen MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. P. I. Pápics
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. J. Perez MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. S. Prins0000-0002-6695-7759
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. S. Royer  A. Samadi-Ghadim MELCHIORS††thanks: The dataset is
described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. H. Sana0000-0001-6656-4130
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. A. Sans Fuentes MELCHIORS††thanks: The dataset is described
in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. S.
Scaringi0000-0001-5387-7189 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. V. Schmid
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. L. Siess MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. C. Siopis
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. K. Smolders MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. S. Sodor MELCHIORS††thanks:
The dataset is described in Appendix A. It can be visualised and downloaded
from www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. A. Thoul0000-0002-8107-118X
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. S. Triana MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. B.
Vandenbussche0000-0002-1368-3109 MELCHIORS††thanks: The dataset is described
in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. M. Van de
Sande0000-0001-9298-6265 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. G. Van De Steene
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. S. Van Eck0000-0003-0499-8608 MELCHIORS††thanks: The dataset
is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. P.A.M. van
Hoof0000-0001-7490-0739 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. A.J. Van
Marle0000-0002-2387-4515 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. T. Van
Reeth0000-0003-2771-1745 MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. L. Vermeylen
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. D. Volpi MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/. J. Vos0000-0001-6172-1272
MELCHIORS††thanks: The dataset is described in Appendix A. It can be
visualised and downloaded from www.royer.se/melchiors.html and from the CDS,
via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.MELCHIORS††thanks: The
dataset is described in Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/. C. Waelkens MELCHIORS††thanks: The dataset is described in
Appendix A. It can be visualised and downloaded from
www.royer.se/melchiors.html and from the CDS, via anonymous ftp to
cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/cgi-
bin/qcat?J/A+A/.MELCHIORS††thanks: The dataset is described in Appendix A. It
can be visualised and downloaded from www.royer.se/melchiors.html and from the
CDS, via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/cgi-bin/qcat?J/A+A/.

(Received; accepted)

###### Abstract

Aims. 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.

Methods. 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.

Results. 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).

Conclusions.

###### Key Words.:

spectral library – spectral reference – high resolution spectroscopy – HRD –
instrumental response – normalisation

##  1 Introduction

Theoretical libraries of stellar spectra offer noiseless data with a virtually
infinite spectral resolution and an excellent coverage of the stellar
parameter space. However, such libraries suffer from model-dependent
systematic uncertainties, and they depend on the existence of reliable
databases of atomic and molecular line lists and opacities (Jofré et al.,
2019).

Observational libraries, in contrast, do not rely on any assumption but often
suffer from observational limitations in terms of spectral resolution,
coverage of the parameter space, or signal-to-noise ratio. In Table 1, we list
an overview of optical libraries that currently exist or are in the process of
being acquired. (For an extended list covering older references, see Sánchez-
Blázquez et al. (2006) and references therein. For a list of near-infrared
libraries, see Rayner et al. (2009).)

Table 1: Optical spectral libraries. The term $N_{*}$ refers to the number of
targets. The term $N_{sp}$ indicates the number of spectra when it is
different from the number of targets. The wavelengths are expressed in
nanometers.

Library | $N_{*}$ | $N_{sp}$ | $\lambda_{min}$ | $\lambda_{max}$ | Resolution | Reference
---|---|---|---|---|---|---
Gunn & Strycker | 175 |  | 313 | 1 080 | 200 | Gunn & Stryker (1983)
Silva & Cornell | 72 |  | 351 | 893 | 550 | Silva & Cornell (1992)
James | 83 |  | 350 | 750 | 800 | James (2013)
Kitt-Peak | 161 |  | 351 | 742 | 1 000 | Jacoby et al. (1984)
Picklesaa$a$Pickles: Spectra assembled from various sources. | 131 |  | 115 | 2 500 | 1 000 | Pickles (1998)
NGSLbb$b$NGSL, Gaia-FGK: High signal-to-noise. | 378 |  | 167 | 1 025 | 1 000 | Gregg et al. (2006)
SDSS-MaStar | 3 321 | 8 646 | 362 | 1 035 | 1 800 | Yan et al. (2019)
STELIB | 249 |  | 320 | 930 | 2 000 | Le Borgne et al. (2003)
MILEScc$c$MILES: Spectrophotometry. | 985 |  | 352 | 750 | 2 000 | Falcón-Barroso et al. (2011)
SDSS-BOSSdd$d$SDSS-BOSS: Spectral templates assembled from low-resolution spectra extending down to spectral type L. | 324 |  | 365 | 1 020 | 2 000 | Kesseli et al. (2017)
Diaz et al. | 106 |  | 790 | 910 | 2 300 | Diaz et al. (1989)
MUSE library | 35 |  | 480 | 930 | 3 000 | Ivanov et al. (2019)
Indo-US | 1 273 |  | 346 | 946 | 5 000 | Valdes et al. (2004)
XSLee$e$X-shooter Spectral Library: Data release 3. | 683 | 830 | 350 | 2 480 | 10 000 | Gonneau et al. (2020); Verro et al. (2022)
ELODIE | 1 388 | 1 962 | 390 | 680 | 42 000 | Prugniel & Soubiran (2001, 2004); Prugniel et al. (2007)
FGKM library | 404 |  | 499 | 641 | 60 000 | Yee et al. (2017)
Gaia-FGKb,fb,ffootnotemark: $b,f$ | 34 | 71 | 300 | 1 020 | 80 000 | Blanco-Cuaresma et al. (2014)
UVES-POPgg$g$UVES-POP’s spectral resolution reaches 120 000 over part of the range. | 394 |  | 304 | 1 040 | 80 000 | Bagnulo et al. (2003)
MELCHIORS | 2 043 | 3 256 | 380 | 900 | 85 000 | This work

111 $f$$f$footnotetext: Gaia-FGK: Limited to spectral types F, G, K.

Different spectral surveys target different goals, and this is reflected in
the properties of the final libraries. Despite its limited size (161 spectra),
resolution ($\sim\\!\\!1000$), S/N ($\sim\\!\\!100$), and wavelength coverage
(351–742 nm), the library of Jacoby et al. (1984), for instance, is still
highly cited nowadays, especially thanks to its large coverage of the
Hertzsprung-Russel Diagram (HRD).

Over the past decades, much larger and systematic efforts have been developed
on the basis of much more extensive spectral libraries. As an example, SDSS-
APOGEE aims at studying the galactic structure and evolution via the chemical
peculiarities of various populations of stars. The survey therefore covered
100 000 high S/N spectra in the H-Band at a resolution of 20 000 (Allende
Prieto et al., 2008). In the visible, another example is the Gaia-ESO Survey
(Gilmore et al., 2012, 2022; Randich et al., 2013, 2022), which targeted 100
000 stars in all the components of the Milky Way at medium and high resolution
with GIRAFFE and UVES spectrographs. Other surveys, such as the Gaia FGK
Benchmark Stars project (Blanco-Cuaresma et al., 2014), use the high-quality
spectra to provide the community with benchmarks for parameter determinations
and abundance derivations (Jofré et al., 2017). Projects like BRASS (Laverick
et al., 2018, 2019) and Ruffoni et al. (2014) exploit high-quality data to
attack the uncertainties in atomic data parameters (i.e. part of the
systematic uncertainties underlying the production of theoretical spectra).

While huge spectral libraries are becoming available and offer invaluable
input for statistical analyses, they come at the price of mandatory trade-offs
in the observable parameter space. Huge numbers of targets can only be
observed with reduced spectral resolution or (but often and) reduced S/N.
Moreover, the individual targets in these types of observations cannot benefit
from the same care in data reduction as is possible for smaller-scale
projects.

While this illustrates the need for such spectral libraries, a quick look at
Table 1 also shows that a large collection of spectra that has high-resolution
(significantly larger than 30 000), has a high S/N, widely covers the HRD and
offers high-quality data products is still missing. A noticeable exception is
UVES-POP (Bagnulo et al., 2003), a filler programme on the ESO-VLT instrument
UVES, which delivered a database consisting of 394 spectra of high-spectral
resolution and high S/N. The data reduction pipeline applied to these echelle
spectra was unfortunately sub-optimal in some aspects (e.g. order matching),
resulting in imperfect representations of the spectral continua. These spectra
were nevertheless instrumental in studies covering a wide range of
applications (see the 350 citations to the UVES-POP Messenger article), re-
enforcing the need for such high-quality, high-resolution, and high S/N
spectral libraries.

From 2010 to 2020, we conducted a spectral survey of bright stars from the
northern hemisphere as a filler programme with the Mercator-HERMES
spectrograph (Raskin et al., 2011) attached to the Mercator telescope at the
Roque de Los Muchachos Observatory on the island of La Palma, Spain. The
resulting library combines high-spectral resolution (85 000) and high S/Ns. It
was also designed for densely sampling the HRD using optically bright objects.

As our aim is to also offer high-quality data products, including
automatically normalised spectra for quick-look analyses (while being science
ready in most circumstances), the present database gathers some unique
properties when compared to the other libraries reported in Table 1 in terms
of coverage of the HRD, spectral resolution, S/N, high-quality data reduction,
and sample size. The potential uses of such an empirical library include
population synthesis for galactic and extra-galactic studies, references for
stellar atmosphere models, spectral classification, and studies of binaries
and outreach. It may also constitute a nice reference sample to train neural
networks for data-driven machine learning applications.

We present the target selection and observations in Section 2 and the data
processing steps in Section 3, including the principle of the instrumental
response correction and of the automatic spectral normalisation. The resulting
database is presented in Section 4, along with the distribution on the sky, in
the HRD or in S/N. We present some brief conclusions in Section 5.

##  2 Target selection and observations

Our prime goal was to sample the HRD as densely as possible while minimising
the required exposure time. It was also clear from the beginning that
assembling a large spectral database could only be possible in the context of
a filler programme. Therefore, our targets would have to be bright and
observable even in poor seeing conditions as well as through thin clouds.
Finally, we had to ascertain a good distribution in right ascension in order
to offer possibilities for short but useful observations whenever gaps in the
regular observing programmes would appear. Consequently, the original target
selection was based on simple criteria.

We started compiling our sample by collecting all objects at a declination
higher than $-30$ degrees, due to the latitude of the observatory, and with an
apparent magnitude of $V<8$, given the filler nature of the programme. We
retrieved the corresponding 34263 objects from Simbad and rejected the non-
stellar objects (360) as well as those bearing a bad V-band magnitude quality
flag (flag ‘E’; 402 objects), leaving 33511 objects (10 entries were rejected
for both criteria).

We then sorted the objects by spectral type, and out of the 2006 unique
spectral type strings, we selected the well-behaved strings (i.e. rejecting
classifications such as ‘K2/K3IIICN…’ or ‘$\sim$’). Our goal was to build a
library of reference spectra from which spectral templates could potentially
be derived, but we did not aim at maximising diversity. Hence, this selection
automatically removed the stars known as emission line stars as well as those
known to be magnetic and/or chemically peculiar. This first selection left us
with 28247 objects. In order to bring this number down to a reasonable
requirement in terms of telescope time, we then limited the input catalogue to
the ten brightest objects for each type, sub-type, and luminosity class (e.g.
F8V or A0III), providing a selection of 2178 targets.

The exposure time calculator of the HERMES spectrograph was used in
combination with Simbad’s B and V magnitudes in order to estimate the required
exposure times, targeting a S/N of 200. The filler nature of the programme
made it so that the final S/N was often slightly inferior when the observer
was not tracking the S/N in real time or when the exposure times were so short
that this was not possible in practice.

The observations were acquired over 513 different nights between April 7,
2010, and February 12, 2020. The observations acquired prior to September 28,
2010, were later excluded, as no reliable meteorological data exist for that
period, which hampered the correction for the molecular absorption in the
corresponding 125 spectra (section 3.2).

In April 2013, at the request of the BRITE consortium (K. Zwintz, priv.
comm.), 95 objects brighter than $m_{v}=4$ and missing from the original list
were added to the sample. Seventy four O-type stars were also re-introduced at
the same time to compensate for their scarcity in the original catalogue as
much as possible.

In May 2014, a majority of the targets had been observed already, and the
complete catalogue was re-submitted to the scheduler of Mercator-HERMES. This
would restore the availability of filler targets all year long while providing
interesting information on the variability or binarity for our targets. For
this resubmission, a few entries had to be manually edited, to correct for
inaccurate information from Simbad. In particular, three duplicate entries
were removed, leaving a total of 2360 objects.

Not all objects in the catalogue were actually observed. A bias exists against
the faintest targets. These are indeed more difficult to schedule, especially
in the context of a filler programme. We find it is worth noting, however,
that a handful of objects that were not in the original catalogue were
actually observed as part of this programme at the discretion of the observers
on duty during the observation shift.

Table 2: Wavelength ranges and the respective molecules included in the fitting process with molecfit. Wavelength start (Å) | Wavelength end (Å) | Molecule
---|---|---
6274 | 6324 | O2
6850 | 7074 | O2 (B band)
7227 | 7269 | H2O
7589 | 7714 | O2 (A band)
8101 | 8344 | H2O
8940 | 8971 | H2O
Figure 1: Effect of the telluric correction on a spectrum from HD 19356 (B8V).
In red: the spectrum before the correction. In black: the spectrum after
correction with molecfit. The insert shows the region around $H\alpha$.
Whereas weak lines are usually very well corrected, large correction residuals
do appear at the level of saturated telluric lines.

##  3 Data processing

At the entrance of the telescope, any stellar spectrum S can be decomposed in
a number of factors:

$S=C\cdot\ N\cdot\ A^{e}\cdot\ A^{m},$ (1)

where $A^{e}$ is the atmospheric extinction, $A^{m}$ is the molecular
absorption by the Earth’s atmosphere (telluric lines), and $S^{0}=C\,\cdot\,N$
is the stellar spectrum (including the interstellar reddening). Finally, $C$
represents the stellar continuum, that is, the ratio between $S^{0}$ and the
normalised spectrum $N$. The spectrum $S$ contains the effect of the
interstellar absorption, which we do not correct and is consequently included
in $C$. In the present work, we aim at providing three types of data products:
the response-corrected spectra before and after correction for the telluric
lines (i.e. $S^{atm}_{OBJ}\triangleq C\,\cdot\,N\,\cdot\,A^{m}$ and
$S^{0}_{OBJ}\triangleq C\,\cdot\,N$, respectively) and the normalised spectra
$N$ of the telluric-corrected spectrum.

To this aim, we considered three types of measurements: observations of
scientific targets, observations of spectrophotometric calibrators, and flat-
field exposures. After subtraction of the electronic offset, these
measurements can be expressed as:

$\displaystyle f_{obj}$ $\displaystyle=S_{OBJ}\,\cdot\,\Re^{\prime},$ (2)
$\displaystyle f_{cal}$ $\displaystyle=S_{CAL}\cdot\,\Re^{\prime},$ (3)
$\displaystyle ff_{raw}$ $\displaystyle=FF_{SED}\cdot\,\Re^{\prime}.$ (4)

The left-hand sides of these equations represent the three types of
measurements, and $FF_{SED}$ expresses the global shape of the flat-field
signal imposed by the spectrum of the lamps and the filters used for these
measurements. $\Re^{\prime}=\Re\,\cdot\,B\,\cdot\,P$ describes the
instrumental response in which $B$ represents the blaze function driving the
response inside every order of the echelle spectrometer, $P$ is the pixel
response non-uniformity (PRNU), and $\Re$ (hereafter called ‘the response’)
captures the remaining instrumental effects. The complex shape of the response
$\Re$ is determined by the flat-field procedure in the HERMES instrument,
which relies on the combination of a high-powered tungsten lamp with red
blocking filters to obtain enough blue photons and a low-powered tungsten lamp
for the red spectral orders (Raskin et al., 2011).

Table 3: Calibration stars used for the instrumental response correction,
including their observation frequency and the astrophysical parameters used as
input to generate our SED models. Errors on the astrophysical parameters
derived from atmospheric modelling in gssp are on the order of 100 K in
$T_{\text{eff}}$, 0.1 dex in $\log{g}$, 1 km/s in $v\sin{i}$, 0.1 km/s in
$v_{\text{mic}}$, and 0.1 dex in $\mathrm{[M/H]}$.

Target |  $T_{\text{eff}}$ [K] | $\log{g}\,[\text{dex}]$ | $v\sin{i}\,[\text{km/s}]$ |  $v_{\text{mic}}$ [km/s] | $\mathrm{[M/H]}$ | #nights
---|---|---|---|---|---|---
HD 36267 | 15250 | 4.2 | 127 | 0.6 | 0.0 | 29
HD 152614 | 11750 | 3.9 | 109 | 1.5 | 0.0 | 40
HD 149212 | 10500 | 3.8 | 157 | 0.0 | 0.4 | 10
HD 14055 | 10250 | 4.3 | 235 | 2.6 | $-0.1$ | 58
HD 87887 | 10250 | 3.7 | 22 | 1.2 | 0.1 | 26
HD 42818 | 10000 | 4.2 | 275 | 2.5 | 0.1 | 18
HD 214994 | 9500 | 3.6 | 15 | 2.1 | 0.2 | 6
HD 46300aa$a$The model for this object was taken from the synthetic library of Munari et al. (2005). | 9500 | 2.0 | 100 | 2.0 | 0.0 | 36
HD 118098 | 8300 | 3.7 | 213 | 2.3 | $-0.2$ | 66
GSC4293-0432 | 8000 | 4.0 | 40 | 3.1 | 0.3 | 2
HD 56169 | 7900 | 2.7 | 216 | 3.3 | $-0.6$ | 27
HD 184006 | 7800 | 3.0 | 245 | 3.6 | $-0.4$ | 46
HD 147449 | 7100 | 4.0 | 79 | 2.9 | 0.0 | 17
HD 84937 | 6400 | 3.8 | 5 | 1.7 | $-2.1$ | 12
222

The response $\Re$ is both wavelength and time dependent. The wavelength
dependence describes the relative spectral response function (RSRF), and the
time dependence takes into account possible variations of the environmental
conditions (temperature, atmospheric pressure, and humidity), instrument
ageing, and (in our case) residuals with respect to the average atmospheric
extinction (see 3.2 below). Formally, the response encapsulates the flux
calibration coefficient, which we disregard in this work, as our spectra are
not calibrated in flux.

The flat-field exposures are usually acquired at the beginning and the end of
the night. The celestial calibrators are objects whose spectra are known from
accurate modelling. One such object was observed every night whenever
possible. We used the celestial calibrators to correct for the instrumental
response (Section 3.3).

###  3.1 Pipeline

The raw 2D echelle spectra were first processed with the standard HERMES
pipeline, which includes bias correction, background subtraction, flat-field
correction, cosmic-ray clipping, order merging, and wavelength calibration,
including barycentric correction (Raskin et al., 2011). The wavelength
calibration is based on a spectral mask containing 1600 carefully selected
isolated Th, Ar, and Ne lines. This mask contains the 2D pixel coordinates and
identification of all lines. During the wavelength calibration, the positions
of these lines were carefully measured in the 2D extracted pixel-order space.
Except for the bluest and the two reddest orders, the wavelength calibration
per order is based on 20 to 50 spectral lines. A 2D polynomial fit of the
order (8, 8) in pixel-order space determines the wavelength calibration. The
inter-order calibration is typically better than 50 m s-1, as evaluated from a
cross-correlation measurement using a ThArNe spectral mask on a ThArNe
extracted file. For order 40, which is the reddest spectral order, we had to
derive the calibration from only one Ne line, and the wavelength calibration
is significantly poorer.

We measured wavelength calibration frames typically just before and after the
astronomical night, with one additional calibration in the middle of the
night. Since the spectrograph is not mounted in a vacuum tank, it is subject
to atmospheric pressure variations. Nevertheless, thanks to the control and
stability of the temperature within the spectrograph room, the velocity
stability is good, with a 1 $\sigma$ level of 60 m s-1 on 3432 measured radial
velocity standards over a period of 10 years. For the purpose of line
identification and the spectral characterisation of stellar sources, this
wavelength stability and calibration homogeneity is excellent.

The flat-field correction is a mandatory step to remove the PRNU and the
effect of the blaze function in every order prior to order merging. Dividing
by the flat-field nevertheless artificially imposes the shape of $1/FF_{SED}$
onto every spectrum thus corrected. The output of the pipeline hence consists
of 1D spectra still influenced by the effects of the Earth’s atmosphere and
the newly introduced shape of the flat-field.

###  3.2 Atmospheric extinction and telluric correction

To correct for the atmospheric extinction $A^{e}$, a wavelength and airmass
dependent atmospheric correction was applied following the tabulated values
for the Roque de Los Muchachos Observatory on the island of La
Palma.333https://www.ing.iac.es/Astronomy/observing/manuals/html_manuals
/general/obs_guide/node293.html This resulted in an underestimation of the
extinction for part of the observations (e.g. those performed under thin
clouds). We note however that despite the filler nature of our programme, a
fair fraction of the observations were executed under average to good weather
conditions, as the short exposure times and extensive coverage of the sky
offered easy-to-schedule solutions to fill short gaps in the regular observing
programmes.

To correct for the telluric contamination $A^{m}$, we used the publicly
available tool molecfit (Smette et al., 2015; Kausch et al., 2015). By
combining essential input from the Mercator weather station with a molecular
line database and a radiative transfer code, molecfit models the instrument
line spread function and atmospheric content at the time of the
observation.444Due to the absence of meteorological data records prior to
September 28, 2010, the spectra obtained before that date were excluded to
ensure the homogeneity of the dataset. The wavelength ranges and molecules
included in the fitting procedure are listed in Table 2. For every region
included in the fit, the continuum is estimated locally using a first-order
polynomial. The molecfit tool also corrects for any minor wavelength shifts by
fitting the wavelength with a second-order Chebyshev polynomial. The
differences with the original wavelength calibration were found to be
negligible ($\leq\\!100$ m s-1). Hence, we kept the original wavelength
calibration in the final products.

After correction, the spectra showed residuals of $\sim\\!\\!5\%$ of the
continuum in the telluric bands, with the exception of the saturated lines
(Fig. 1). While fitting saturated lines, the division of two small numbers
often results in large residuals (the oxygen band around $7600\,$Å is often
impacted this way). In the region above $8950\,$Å , we observed ‘P
Cygni’-shaped residuals due to a small imperfection of the wavelength
calibration solution at the very edge of the spectra. In order for the user to
estimate the impact of molecfit on the data, we provide both versions of the
spectra, that is, before and after correction.

Figure 2: Best-fitting normalised spectroscopic model (in black) of the
highest-S/N observed spectrum of HD 149212 (in blue), focussing on the
wavelength region between the Hγ and Hβ.

###  3.3 Response correction

At this stage, the spectra could be expressed as:

$\displaystyle f_{obj}^{0,FF}$
$\displaystyle=\frac{S^{0}_{OBJ}\,\cdot\,\Re}{ff},$ (5) $\displaystyle
f_{cal}^{0,FF}$ $\displaystyle=\frac{S^{0}_{CAL}\,\cdot\,\Re}{ff},$ (6)

where ‘$FF$’ and ‘$0$’ indicate that the flat-field and atmospheric
corrections have been applied and $ff\triangleq FF_{SED}\,\cdot\,\Re$. If we
define an effective instrumental response $\hat{\Re}$ combining the system
response with the effect of the flat-field process, that is,

$\hat{\Re}\triangleq\frac{\Re}{ff}=\frac{f^{0,FF}_{cal}}{S^{0}_{CAL}},$ (7)

we can express the final spectrum corrected from the instrumental response,
the effects induced by the flat-field correction, and the effects from the
atmosphere as:

$S^{0}_{OBJ}=\frac{f^{0,FF}_{obj}}{\hat{\Re}}.$ (8)

Figure 3: The derivation of the effective response for a night (eqn. 7). The
spectrophotometric calibrator star used in this example is HD 42818. Top: the
spectrum corrected for flat-field and atmospheric effects ($f_{cal}^{0,FF}$).
Middle: Model SED used to derive the response ($S^{0}_{CAL}$). Bottom: The
derived effective response in red which is fit with a spline in black
($\hat{\Re}$). The knot points used to fit the spline are shown with vertical
lines.  Figure 4: Instrumental response correction for a spectrum of the
scientific target HD 3379 (B2.5IV). Top: Spectrum corrected for flat-field and
atmospheric effects ($f_{obj}^{0,FF}$). Middle: Effective response
($\hat{\Re}$) for the same night. Bottom: Response-corrected spectrum
($S^{0}_{OBJ}$).

One calibration star was observed every night when possible as part of the
normal calibration set of HERMES, aiming at a daily determination of
$\hat{\Re}$. These targets were carefully selected for this purpose, as they
are well documented in the literature and have reliable models established by
a series of independent publications. All of these targets were analysed with
the LTE spectral-fitting code gssp (Tkachenko, 2015). The gssp code generates
synthetic spectra using the SynthV radiative transfer code (Tsymbal, 1996)
combined with a grid of atmospheric models from the LLmodels code (Shulyak et
al., 2004). These synthetic spectra were then fitted to the highest-S/N
observed spectrum for each star in the HERMES archive. The atmospheric
parameters $T_{\text{eff}}$, log $g$, projected rotational velocity ($v$ sin
$i$), microturbulent velocity ($v_{\text{mic}}$), and the metallicity
($\mathrm{[M/H]}$) as well as their corresponding uncertainties were then
determined from the distribution of $\chi^{2}$ values of the fit of each
synthetic spectrum to the observed spectrum. We then used the parameters in
the LLmodels grid closest to our best-fitting values as the input to generate
the spectral energy distribution models of our calibrators. An example of the
fit is shown in Fig. 2. The list of calibrators, the astrophysical parameters
of the selected models, and the number of nights they were observed are
presented in Table 3.

Figure 5: Variability of the instrumental response. Red: Standard deviation of
the ratio between subsequent responses derived for our programme indicative of
the additional uncertainty on the low-frequency components of the continuum
for the stars with stdnight=0. Grey: Standard deviation computed over all
responses, regardless of their acquisition time. (See text for details.)

Figure 6: Illustration of the iteration process in a given Chebyshev
polynomial order for an early-type star (top) and a late-type star (bottom),
focussing on the wavelength range [3750, 5000] Å. Top: Order 26 for HD 3901
with spectral type B2V. Bottom: Order 16 for HD 41116 with spectral type
G5III. The successive polynomial fits start with the thick black line,
representing the first iteration, and progress towards the green line,
representing the iteration with the best $d$ score. For the sake of
completeness, the iterations corresponding to the best $p$ and $n$ scores are
represented as well, in red and blue respectively. The final selection is
based on $d$, combining $p$ and $n$.

We first smoothed the result of the division of the calibrator spectrum by the
model in Eq. 7 with a median filter and then fitted it with a spline in order
to produce a smooth yet accurate representation of the effective response
before injecting it in Eq. 8. This was necessary in order to avoid introducing
the noise resulting from the observation of the calibration star or local
residuals from its model into the corrected spectra. The density of the knot
points used to fit the spline was chosen locally, depending on the complexity
of the response. Due to the intrinsic width of H$\alpha$, we noted that the
spline occasionally overfitted that spectral line, introducing some local
features at the few percent level around it in the response. Hence, we
recommend some caution for this line, especially for nights where the response
was derived from GSC4293-0432 or HD 46300 (identifiable from the fits header
entry stdname; see Appendix A).

Once the effective response was established for the night, all spectra
obtained over the corresponding night were divided by it, resulting in a
reliable representation of the continuum shape. The derivation of the
instrumental response for a night is illustrated in Fig. 3, and an example of
response-correction of a science-spectrum is displayed in Fig. 4.

At this stage, three of the coldest objects accepted as calibration stars in
the observation planning had to be rejected because some low-frequency
component of their continuum was not properly reproduced by the models, thus
inducing systematic residual features in the instrument response model (HD
220657, HD 226826, and HD 185395, with $T_{\mathrm{eff}}=6000$, 6500, and 6900
K, respectively). The corresponding nights were considered not to have any
reference spectrum for the response derivation. Including those nights, no
calibration star could be observed for over 129 of the 479 nights encompassed
by this programme, corresponding to 1007 spectra in total (31%). The effective
response derived for the nearest night in time was then used instead. We find
it important to note that the shape of the flat-field affects the effective
response. This change in the $FF_{SED}$ depends on the temperature of the
lamps, which is not monitored. Hence, we cannot control the applicability of a
given response (derived from another night) to a given night. As a result, for
such nights, most of the response corrections have a higher degree of
inaccuracy, which may lead to some low-frequency signatures in the instrument-
response corrected spectra. In order to easily identify the corresponding
spectra, we introduced a dedicated Boolean keyword called ‘stdnight’ in the
fits headers, and set it to zero when the response could not be computed from
calibration observations obtained on the same night (see Appendix A).

To quantify the uncertainty introduced by correcting spectra with a response
established from another night, we first re-scaled all responses to an average
of one over the range [5400, 5600] Å. We then computed the ratio of each
response by that of the next night in our programme. The standard deviation of
the resulting dataset is presented in red in Fig. 5. The additional
uncertainty is limited to less than 5% over the range between 4100 and 6750 Å.
Beyond 7000 Å, the variability compared to our reference range is
significantly larger, peaking at almost 17% at 7950 Å. This is expected
because, as stated above, that part of the spectrum is illuminated with a
different lamp during the flat-field measurements, hence inducing larger
differences between the bluest and reddest parts of the response as the two
lamps may react differently to the various measurement conditions.

For the sake of completeness, we also analysed the variability of the response
over the entire programme. Therefore, we computed the median response and
divided all of the responses by it. We then computed the standard deviation of
the result as a function of the wavelength. The result is displayed in Fig. 5
and indicated by the grey dashed line. Although the variability over the
entire programme is significantly larger than between subsequent observing
nights, hence suggesting some kind of monotonic evolution of the response, we
observed a more irregular behaviour. Our efforts to identify the driving
parameters for that variability (e.g. environmental parameters or ageing of
the lamps) nevertheless remained unsuccessful.

Figure 7: Evolution of the $d$ scores with the number of iterations for a
given polynomial order applied to the same spectra as in Fig. 6. Left: Order
26 for HD 3901 with spectral type B2V Right: Order 16 for HD 41116 with
spectral type G5III. The numbers given below each point represent the number
of the selected wavelength points (over the full wavelength range) in the
polynomial fitting.

Figure 8: Pseudo-normalised version of the observed spectra presented in Fig.
6 but over the full wavelength range. Top: HD 3901 (B2V). Bottom: HD 41116
(G5III). Top sub-panel: Response-corrected spectrum (black dots) and the
normalisation function (green line). Bottom sub-panel: Normalised spectrum.

###  3.4 Normalisation

The procedure to pseudo-normalise the HERMES spectra was eased by the previous
steps of the data reduction, but it remained challenging since the spectral
range to normalise runs over more than 5 000 Å. Manual pseudo-normalisation
tools exist, but they render the normalisation process highly time-consuming
and do not ensure the repeatability of the result. Recent development to make
automatic and tunable normalisation processes has emerged based on the convex-
hull and alpha-shape theories, such as AFS (Xu et al., 2019) and RASSINE
(Cretignier et al., 2020), and even based on machine learning techniques, for
example, using deep neural networks (Różański et al., 2022). In this work, we
rely on a more classical approach based on a iterative filtering method
similar to the ones that use a sigma-clipping process, as done, for example,
on a smaller wavelength extent in Muñoz Bermejo et al. (2013); Sana et al.
(2013); Paletou et al. (2015). Given that our approach to normalisation relies
on the pseudo-continuum as measured rather than on the true continuum of the
object, it underestimates the continuum in the blue, especially for cold
objects, and we hence refer to it as ‘pseudo-normalisation’.

It is often argued that high-order polynomials are not flexible enough. This
is true when the normalisation process occurs directly on the merged échelle
spectra (top panel of Fig. 4), which is not corrected from the instrumental
response function (middle panel of Fig. 4). In our case, the polynomials are
fitted to the response-corrected spectra, whose shapes are much smoother
(bottom panel of Fig. 4), except for late M-type stars, where strong molecular
bands render this approach inappropriate (and the normalisation is skipped;
see below).

Our normalisation process can be divided into five steps: First, we smooth the
response-corrected spectrum using a Savitzky–Golay filter with a third-order
polynomial and a window length of 5 pixels. This allows for the smoothing of
any possibly remaining cosmics or noisy parts of the spectrum (mainly below 4
000 Å). Second, for computational reasons, we undersample the smoothed version
of the response-corrected spectrum by a factor 20, resulting in spectra with
about 8 400 samples evenly spaced in log-wavelength. Third, we compute a grid
of normalising functions. Those functions are Chebyshev polynomials of
different degrees between zero and 40. Each function results from an iterative
process described in the next paragraph. Fourth, a score is attributed to each
of the normalisation functions reflecting their quality. Fifth, the score is
used to select the best normalisation function in the grid.

The iterative process in the third step goes as follows: After the polynomial
fit, we filter out all samples from the spectrum falling under the fit and
proceed to fit the same polynomial on the remaining samples. At each
iteration, a normalised spectrum is computed whose quality is assessed by a
score ‘$d$’:

$d=p-n,$ (9)

where $p$ is the median of all flux samples above one and $n$ is the median of
all samples below one. As the best estimate of the normalised spectrum, we
accept the result realising the smallest score $d$ over all iterations and all
polynomial orders ($d$ is positive by construction).

This process nominally runs over ten iterations but is occasionally halted
before by an additional stop-criterion that imposes a minimum sampling density
over the entire wavelength range. We indeed imposed that at least 100
wavelength samples remain in the filtered spectrum per range of 1000 Å.
Without this, the filtering, which is increasingly aggressive as iterations
progress, may result in gaps in the wavelength coverage, leading to ill-
behaved solutions to the fits.

An example of the selection of the best function for a given polynomial order
is illustrated in Fig. 6 for an early-type (top panel) and a late-type
spectrum (bottom panel). Fig. 7 shows the behaviour of the $d$ score computed
in the course of the fitting process of the same spectra, that is, the
successive iterations for a given polynomial order.

The $d$ score is closer to zero when fewer absorption lines are present in the
spectra (compare the left and right panels of Fig. 7 for a B2V-type star with
fewer absorption features than the G5III-type star). High $d$ scores reflect a
poorer normalisation but may also be correlated with the decreasing sampling
in wavelengths due to the filtering, as explained above. Of course, the error
bars on the $d$ score are also an indication of the quality of the normalising
functions. The error bars on $d$ are based on the median absolute deviation
for scores $p$ and $n$, as this is more robust to outliers than the standard
deviation, and propagated to $d$ via Eq. 9.

Figure 8 illustrates the final normalising functions and normalised spectra
for the stars presented in Fig. 6. A polynomial degree 26 at the third
iteration gives the best $d$ score for HD3901 (B2V), and a polynomial degree
28 at the fourth iteration gives the best $d$ score for HD 41116 (G5III).

Figure 9: Best $d$ score as a function of $B-V$. We provide normalised spectra
when the best $d$ score is lower than 0.24. The spectra excluded from the
normalisation process represent about 6% of the sample and are made of M-, S-,
and C-type stars. See text for details.

The presence of telluric lines at the red end of the spectra resulted in poor
access to and hence a poor representation of the continuum longwards of 8950
Å. This lead us to discard the last 50 Å of the normalised spectra.

Finally, we decided not to provide normalised spectra for the latest spectral
types presenting significant molecular bands. We used a criterion based on the
$d$ score for the cut-off. The score indeed shows a positive trend with $B-V$,
as displayed in Fig. 9. A turn-off appears around $B-V=1.6$. It is produced by
long-period variables, which are all (asymptotic) red giants of spectral types
M to C (Lebzelter et al., 2023). For the giants (luminosity class III), which
make up the bulk of our sample of M-type stars, the $B-V$ color index reaches
a maximum value of 1.65 for M5, then decreases to 1.50 for M8, as shown in
Table 1 of Fitzgerald (1970). The main sequence does not show this turnover,
but our sample only includes three main-sequence M-type stars, explaining the
depletion at larger $B-V$. Hence, to define the cut-off for the normalisation,
we adjusted a quadratic $B-V$ function with respect to the
$\log{d_{\mathrm{best}}}$ score. This provided a maximum value of the
$d_{\mathrm{best}}=0.24$ for which we provide normalised versions of the
spectra. We confirmed that the criterion is appropriate by visual inspection.
The median $B-V$ of the spectra with larger $d_{\mathrm{best}}$ is 1.6. The
excluded spectra represent about 6% of the MELCHIORS database (201 spectra
corresponding to 156 stars) and are mainly made of M giant and sub-giant stars
as well as S- and C-type stars, with significant expression of molecular
bands.

##  4 Results

Figure 10: Sky distribution of the objects from the MELCHIORS catalogue. The size of the symbols is related to their apparent V magnitude. Figure 11:  V-band magnitude distribution of the MELCHIORS stars compared to the stars listed in Simbad with declinations larger than $-30^{\circ}$. The median $V$-band magnitude of MELCHIORS stars is 5.9. Figure 12: Distribution of the MELCHIORS stars per spectral type and luminosity class (excluding the Sun, as in all other subsequent figures). The number above each bar is the total number of star per spectral type. About 11% of targets do not have a luminosity class on Simbad (grey histogram). Early-type stars and giants are over-represented in the database. Table 4: Number of stars per spectral type and luminosity class (excluding the Sun). | WR | O | B | A | F | G | K | M | S/C | Total
---|---|---|---|---|---|---|---|---|---|---
V | 0 | 29 | 129 | 123 | 151 | 90 | 50 | 3 | 0 | 575
IV | 0 | 14 | 103 | 107 | 67 | 73 | 25 | 0 | 0 | 389
III | 0 | 20 | 109 | 72 | 60 | 107 | 132 | 107 | 1 | 608
II | 0 | 9 | 46 | 24 | 24 | 37 | 50 | 9 | 0 | 199
I | 0 | 19 | 15 | 5 | 4 | 3 | 5 | 2 | 0 | 53
N/A | 4 | 4 | 24 | 18 | 37 | 23 | 47 | 54 | 7 | 218
Total | 4 | 95 | 426 | 349 | 343 | 333 | 309 | 175 | 8 | 2042

###  4.1 Stellar content

The MELCHIORS database is made of 2043 stars (excluding the Sun) all above a
declination of $\delta=-30^{\circ}$ since they were observed from the island
of La Palma. Figure 10 shows the distribution of the MELCHIORS targets on the
sky (in equatorial coordinates). An overdensity of early-type stars traces the
disc of the Milky Way. The targets are colour-coded with their colour index
$B-V$. The lowest values, representing the hottest stars, mainly trace the
disc and bulge, while the highest values, representing the coolest, reddest,
and the most likely to be giant stars, are rather located in the halo. The
distribution of the targets in the $V$-band magnitude is displayed in Fig. 11
and includes the brightest stars of the northern hemisphere. The median $V$
magnitude of the targets is $5.9$. We compared our sample with the absolute
numbers of stars in the Simbad database with a declination larger than
$-30^{\circ}$, in bins of one magnitude.555These numbers were obtained by
using the ‘Simbad: query by criteria’ with the search expression Vmag >= X &
Vmag < X + 1 & dec > -30 & otypes = ’*’, where X varies between $-1$ and $12$
per step of 1 magnitude. The latest condition on otypes ensures the exclusion
of the non-stellar objects from the query. The completeness of the MELCHIORS
database compared to Simbad is $\sim 80$% for $V\leq 4$ but drops to 55% for
$V\leq 6$. Excluding the Sun, the apparent brightest target is Arcturus
($\alpha$ Boo), while the faintest star is HD~286340, which is an SC7/8 star.

Most of the MELCHIORS stars have spectral types and $V$-band magnitudes
available in Simbad. We cross-matched targets without a spectral type or
$V$-band magnitude with the catalogue of Stellar Spectral Classifications
(Skiff, 2014) in order to gather the missing information. For HD~88133, we
adopted the Gaia magnitude because the information was not available in Skiff
(2014). For HD~114762, we adopted the V magnitude of the primary object
($V=7.3$; removed from Simbad in 2018) rather than the most recent reference
in Skiff (2014), corresponding to the very faint M9 companion ($V=15$;
Patience et al., 2002; Bowler et al., 2009).

Figure 12 presents the number of MELCHIORS stars per spectral type (OBAFGKM)
also including a few Wolf-Rayet (WR) and evolved S-type and C-type (S/C)
stars. Due to the bias of our sample towards low apparent magnitudes, this
distribution does not reflect the stellar initial mass function. Instead, it
is biased towards the brightest and hottest stars, while about 99% of all
stars are of a spectral type later than A. The luminosity classes are also
biased. While we expected about 10% of the stars to have evolved off the main
sequence, giants and supergiants (luminosity classes I, II, and III) represent
between one-quarter to more than one-half of the number of stars in our
sample, as a function of the spectral type. About 11% of the targets do not
have a luminosity class in the Simbad database and are represented in grey in
Fig. 12. The summary of the MELCHIORS database per spectral type and
luminosity class is given in Table 4.

We display the distribution of MELCHIORS stars per Simbad type in Fig. 13.
About 29% of the objects are normal single stars, and 27% are flagged as high
proper motion stars. We stress that about 24% of the library is related to
stellar multiplicity (227 spectroscopic binaries, 196 double or multiple
stars, 42 eclipsing binaries, 22 variable star of $\beta$ Cep type, 1 high-
mass X-ray binary, 1 symbiotic star, 1 composite object, and 1 ellipsoidal
variable star). This gives a general overview of the type of objects in the
MELCHIORS database as reported in Simbad. (Nonetheless, we advise the reader
to keep in mind that the Simbad database is constantly being updated and that
the distribution of object types reflected in Fig. 13 provides but a snapshot
of the knowledge gathered in the Simbad database around March 2022.)

Figure 13:  Simbad types of stars in the MELCHIORS database. Figure 14:
Colour-absolute magnitude diagram of the MELCHIORS stars using astrometric and
photometric Gaia DR3 data. Apparent visual magnitudes and S/N of the best
spectrum per star are also encoded. The intersection of the dotted lines shows
the position of the Sun. Figure 15: Distribution of the spectra as a function
of S/N in the U, B, V, R, and I wavelength ranges. Figure 16: Number of
spectra per star on a logarithmic scale. The distribution of known Simbad
binaries is overplotted in red. The name of the stars with more than eight
spectra are indicated. The MELCHIORS star with the highest number of spectra,
HD188001 (9 Sge),is an O-type runaway blue supergiant, that could also be a
spectroscopic binary (see text for details).

Figure 17: Examples of normalised spectra. Top: Spectral type sequence –
OBAFGKM. Bottom: Luminosity class sequence – V, III, I. We use a black
background for a better visualisation of the colours of the corresponding
stars, following M. Charity’s work, who derived chromaticity and rgb-pixel
colour from spectra for various stellar types and/or classes, providing
physically motivated colours.

###  4.2 Gaia colour-absolute magnitude diagram

We cross-matched the MELCHIORS database with Gaia DR3 using a cone search
radius of 1 arcsec that was iteratively increased if no match was obtained
until 3 arcsec at maximum. We obtained a positive cross-match for a sub-sample
of 1690 of the 2043 stars. Among them, one has a negative parallax, and 97 do
not have any parallax at all. These mainly correspond to very bright targets.
A colour-absolute magnitude diagram of the MELCHIORS stars is displayed on
Fig. 14 for the sub-sample of 1592 stars for which parallaxes and photometry
are available from Gaia DR3. The main sequence is poorly populated in G, K,
and M stars compared to the giant branch for those types, due to a combination
of two factors. First, the magnitude-limited selection imposes a bias against
cool main-sequence stars with respect to their giant counterparts (see Fig. 12
and Table 4). Second, the fraction of high proper-motion stars is important
(80 of the $\sim$140 G, K, and M main-sequence stars), and those stars were
filtered out by the cone search radius. The error bars on the colour and G
magnitude were computed according to Equation 12 of Merle et al. (2020) since
it is not directly provided in the Gaia DR3. The uncertainties on the absolute
magnitudes were obtained by propagating errors on parallaxes and G magnitudes.
The position of the Sun is represented with the dotted line at
$G_{\mathrm{BP}}-G_{\mathrm{RB}}=0.82$ and $\mathcal{M}_{G}=4.67$ (Casagrande
& VandenBerg, 2018). The asymptotic giant branch (AGB) reaches a saturation in
absolute $G$ magnitude due to strong molecular absorptions in this wavelength
range, explaining the horizontal shape of the AGB in the Gaia colour-absolute
magnitude diagram.

###  4.3 The spectra

The MELCHIORS database is made of 3256 high S/N spectra of the 2043 selected
stars covering the wavelength range [3800, 9000] Å with the spectral
resolution of $R=85\,000$ of the HERMES spectrograph on the Mercator
telescope. Figure 15 shows the S/N distribution of the spectra at the
effective wavelengths of the U, B, V, R, and I filters, as estimated from the
counts in the corresponding wavelength bins of the original 2D spectra (spot
checks in the final products indicate this to be conservative). Except in the
U band, the median S/N is larger than 110 in all bands, and it reaches 200 in
the V and R bands.

Figure 16 represents the number of spectra per star, and shows that 50% of the
stars have more than one spectrum. The binaries, overplotted in red in the
figure, were selected from the main Simbad object types. They represent about
one-fourth of the database. The stars with more than eight spectra are
individually identified in the figure.

We rejected HD 205767 ($\xi$ Aqr) from the catalogue. Though it was seemingly
acquired at the correct celestial coordinates, our spectrum did not match the
spectral type of that object. Also, we did not investigate the stellar binary
content of the MELCHIORS database in detail but relied on the main Simbad
type. Consequently, we could have missed some binaries, or we may provide a
simple view on debated objects. As an example, HD 188001 (9 Sge), a runaway
O-type star, is the star with the highest number of spectra (23) in the
MELCHIORS database. Its main object type in Simbad is blue supergiant, while
it is also known as a spectroscopic binary in the SB9 database (Pourbaix et
al., 2004), with orbital parameters from Aslanov & Barannikov (1992).
Nevertheless, this star could also be a single star, according to McSwain et
al. (2007), while Aldoretta et al. (2015), suggesting constant RV, mentioned
the star as having an unresolved companion. We also mention that there are 56
non-single stars identified from the Gaia-DR3 cross-match: 24 astrometric
binaries, 24 spectroscopic binaries, 7 astro+spectro binaries, and one
eclipsing binary (the blue supergiant HD 167971). They can be retrieved using
the meta-data described in Appendix A.4.3. We note that an individual analysis
of every object is nevertheless beyond the scope of the present paper.
Finally, we illustrate the variety of spectra in our database by presenting a
sample of spectra over a sequence of spectral types (OBAFGKM) and luminosity
classes (V, III, I) in Fig. 17.

Figure 18: MELCHIORS solar reference. Top: MELCHIORS solar spectrum. The
response and telluric corrected spectrum is shown in black and the normalised
spectrum is shown in red. Bottom: Excerpt around H$\alpha$ compared to the IAG
(Reiners et al., 2016) and PEPSI (Strassmeier et al., 2018) solar spectra
degraded to the resolution of HERMES. Note the correction for the telluric
lines performed in the MELCHIORS solar spectrum, in contrast with the other
spectra.

###  4.4 The MELCHIORS solar reference

The provided MELCHIORS solar spectrum reference was taken as a reflection
spectrum on Ganymede on June 22, 2018. It has been reduced using the same
procedure as all the other spectra (see Sect. 3). The spectrum is set in the
barycentric restframe of the Solar System and corrected from the radial
velocity of Ganymede. The S/N is about 560 at 5000 Å. Figure 18 presents the
solar spectrum corrected from instrumental response and from tellurics in the
top panel. As already mentioned for the other stars, we noticed an imperfect
normalisation at both ends of the spectrum. In the blue, this is due to the
presence of many metallic lines, including strong ones (the Ca II H and K
lines at 3968.47 and 3933.66 Å, the Fe I L line at 3820.44 Å, and others,
including strong lines of Mg I). In the red, this is due to the presence of
telluric lines. In the bottom panel of Fig. 18, we show a focus around
H$\alpha$ at 6562.81 Å. We compared it with two recent observed solar spectra:
(i) the IAG solar flux atlas from a Fourier transform spectrograph with a
resolution of $R=\lambda/\Delta\lambda\sim 10^{6}$ (Reiners et al., 2016) and
(ii) the PEPSI solar spectrum at the LBT with a resolution of $R=270\,000$
(Strassmeier et al., 2018). The two comparison spectra were degraded at the
resolution of HERMES by convolving them with a Gaussian profile of width
$\sigma=3.5$ km/s, which corresponds to a resolution of $R=85\,000$. The
comparison shows that the radial velocity correction was well performed. We
note that the MELCHIORS reference spectrum is well corrected for the
tellurics, which are clearly visible in the two other spectra at different
intensity levels. Our solar reference spectrum also shows excellent agreement
with the solar atlas from Beck et al. (2016), also produced with HERMES, as
well as with the high-resolution spectrum from Wallace et al. (2011).
Interestingly, and with the exception of the ELODIE (Prugniel et al., 2007)
and Gaia-FGK libraries (Blanco-Cuaresma et al., 2014), no solar spectrum is
provided in the optical spectral libraries reported in Table 1.

##  5 Summary

The spectral library presented in this work gathers 3256 spectra of 2043 stars
of all spectral types and luminosity classes, including the Sun as a star
(with a spectrum of Ganymede). The spectra cover the full visible spectral
range [3 800, 9 000] Å with high resolution ($R=85\,000$) and with a median
S/N reaching 200 in the V band. In addition, the spectra were acquired in a
consistent way and with a single instrument. This is, to our knowledge, a
unique combination in comparison to existing optical empirical libraries.

Special care was taken in the data reduction, allowing us to provide the
spectra in four different flavours: raw, corrected from the instrumental
response, with and without correction from the atmospheric molecular
absorption, and normalised (including the telluric correction). As with any
automatic processing, we recommend using the final products with caution,
especially for the analysis of wide spectral features such as H$\alpha$.

The sample is $\sim 80\%$ complete up to $V=4$ for declinations
$\delta\,>-30^{\circ}$ and has a median magnitude of $V=5.9$. The spectral
library contains two or more spectra for about 50% of the targets. All spectra
are available in CDS as well as via an online repository:
www.royer.se/melchiors.html.

###### Acknowledgements.

This work is based on observations made with the Mercator Telescope, operated
on the island of La Palma by the Flemmish Community, at the Spanish
Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de
Canarias. Mercator operates thanks to the IRI-FWO project with code I000521N.
This work is also based on observations obtained with the HERMES spectrograph,
which is supported by the Research Foundation of Flanders (FWO), Belgium, the
Research Council of KU Leuven, Belgium, the Fonds National de la Recherche
Scientifique (FNRS), Belgium, the Royal Observatory of Belgium, the
Observatoire de Genève, Switzerland and the Thüringer Landessternwarte
Tautenburg, Germany. The research for the present results has been subsidised
by the Belgian Federal Science policy Office under contract No.
BR/143/A2/BRASS. It has also received funding from the KU Leuven Research
Council (grant C16/18/005: PARADISE), from the Research Foundation Flanders
(FWO) under grant agreement G089422N as well as from the BELgian federal
Science Policy Office (BELSPO) through PRODEX grant PLATO. TM is supported by
a grant from the Fondation ULB and by the BELSPO Belgian federal research
programme FED-tWIN under the research profile Prf-2020-033_BISTRO. JV
acknowledges support from the Grant Agency of the Czech Republic (GAČR
22-34467S). MVdS acknowledges support from the European Union’s Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie grant
agreement No 882991. M.K. acknowledges the support from ESA-PRODEX
PEA4000127913. RK acknowledges the support by Inter-transfer grant no
LTT-20015. PGB acknowledges support by the Spanish Ministry of Science and
Innovation with the Ramón y Cajal fellowship number RYC-2021-033137-I and the
number MRR4032204. SG acknowledges support from FWO by means of a PhD Aspirant
mandate under contract No. 11E5620N.

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##  Appendix A Products and formats

The MELCHIORS database is accessible in CDS and can be found at
www.royer.se/melchiors.html, where simple selection tools and quick-look
images are available as well. The library contains the following datasets:

  1. 1.

The spectral library itself

  2. 2.

The calibration spectra used to derive the instrumental response for every
observing night

  3. 3.

The models of the calibration spectra

  4. 4.

The meta-data information, including

     * •

Parameters describing the spectra in the library and the corresponding targets

     * •

Parameters linking the spectra with their associated calibration counterpart

     * •

Parameters issued from the cross-match with Gaia-DR3, where applicable

  5. 5.

Quick-look images

###  A.1 The spectral library

The spectra are presented individually and in FITS format, and they are
identified by a unique observation identifier (hereafter ‘obsid’), which is a
six-character integer occasionally pre-pended with ‘00’. The fits files have a
primary header and one extension, which is a binary table with ten columns:

  1. 1.

wave_log: Natural logarithm of the wavelength

  2. 2.

wave: Wavelength

  3. 3.

flux_norm: Normalised flux density

  4. 4.

err_norm: Uncertainty on ‘flux_norm’

  5. 5.

flux_tac: Telluric absorption and instrument response-corrected flux density

  6. 6.

err_tac: Uncertainty on ‘flux_tac’

  7. 7.

flux_irc: Instrument response-corrected flux density

  8. 8.

err_irc: Uncertainty on ‘flux_icr’

  9. 9.

flux_raw: Raw flux density; output of the HERMES pipeline

  10. 10.

err_raw: Uncertainty on ‘flux_raw’

The wavelengths are expressed in angstroms, with an equidistant sampling in
natural logarithm of the wavelength (of about $5.210^{-6}$), which is the
baseline for the HERMES spectrograph, in order to facilitate velocity
corrections without re-binning the data. The WCS keywords in the primary
header describe that logarithmic wavelength scale.

Except in their normalised version, the spectra were scaled to an average flux
of 1.0 over the wavelength range between 5400 and 5600 Å. The barycentric
correction was applied to all spectra, allowing for direct measurement of the
stellar radial velocity. The solar spectrum (obsid 884752) is the only one
that has been corrected further, based on the radial velocity of Ganymede.

All spectra obey the same format, including those which were not normalised.
In the latter cases, the corresponding columns are filled with NaNs. Beyond
8950 Å, all normalised spectra are filled with NaNs. Two small wavelength
ranges not covered by the HERMES spectrograph are also padded with NaNs in all
spectra: $\sim$ 3Å around 8578 Å and $\sim$ 8Å around 8790 Å.

In the primary header, normlzd is set to ‘True’ if the spectrum was
normalised, and the keywords ‘dscore’, ‘dscmad’, ‘degree’, and ‘it’ refer to
the selected solution for the normalisation and its quality (see text). The
following keywords describe the instrument response correction (linking to the
corresponding meta-data file):

  1. 1.

unseq: Unique sequence number = obsid

  2. 2.

stdname: Calibration target used for the instrument response correction

  3. 3.

stdunseq: Observation identifier of the calibration measurement

  4. 4.

obsnight: Observation date of unseq

  5. 5.

rspnight: Observation date of stdunseq

  6. 6.

stdnight: Boolean — true if unseq and stdunseq were acquired the same night
(i.e. if obsnight = rspnight)

  7. 7.

stdstar: Boolean — true for the calibration spectra; false for the primary
targets

The format for the dates is yyyymmdd.0, and corresponds to the evening,
applied to the entire night.

###  A.2 Calibration spectra

This dataset contains all the calibration spectra used to derive the
instrument response on any given night of the programme. The spectra are
presented in a similar format as presented in Section A.1 except that the
binary table is limited to the two wavelength related columns plus the
‘flux_raw’ and ‘err_raw’ columns.

###  A.3 Theoretical models

This dataset gathers the theoretical models of the 15 calibration stars
presented in Table 3 and used for the instrumental response corrections. The
models are presented in ascii with two columns, namely, wavelength (in Å) and
flux density (in erg s-1 cm-2 Å-1).

###  A.4 Meta-data

The meta-data files are human-readable, pipe-separated ascii files with a
heading and a trailing ‘—’ character (written in ‘fixed_width’ format with
astropy.io.ascii).

####  A.4.1 Meta-data: spectral library

The file melchiors_meta_spectra.txt provides the main parameters describing
the spectra and the corresponding targets (the files have different names in
CDS, but the correspondence is trivial). It contains the following fields:

  1. 1.

obsid: Unique observation identifier.

  2. 2.

target: Target name. This corresponds to the HD number, if present, and
otherwise to the Hipparcos identifier or the Simbad name taken from the
‘starname’ entry.

  3. 3.

starname: Simbad name of the target.

  4. 4.

HD: HD target identifier.

  5. 5.

HIP: HIP target identifier.

  6. 6.

V: Apparent V magnitude.

  7. 7.

BV: B-V colour index (unavailable values are set to 90).

  8. 8.

stype: Spectral type.

  9. 9.

comments: Simbad code for the object type.

  10. 10.

ra: Right ascension (J2000).

  11. 11.

dec: Declination (J2000).

  12. 12.

pmra: Proper motion in right ascension.

  13. 13.

pmdec: Proper motion in declination.

  14. 14.

airmass: Airmass.

  15. 15.

exptime: Exposure time.

  16. 16.

usn, bsn, vsn, rsn, isn: Approximate S/N in the U, B, V, R, and I bands.

  17. 17.

bvcor: Barycentric velocity correction applied.

  18. 18.

bjd: Acquisition date (julian).

  19. 19.

date-avg: Acquisition date.

  20. 20.

filename: Name of the file containing the spectrum.

  21. 21.

stdnight: stdnight fits keyword.

  22. 22.

stype_ref: Reference for the spectral type.

  23. 23.

dscore: Normalisation quality index, in [0.00 - 0.24].

Most of the fields are either filled from the fits headers or from a Simbad
query. We refer to the text for more details. For the solar spectrum (obsid
884752), some of the meta-data are related to Ganymede and not the Sun itself
(coordinates, barycentric correction).

####  A.4.2 Meta-data: calibration spectra

The file melchiors_calibration_match.txt provides the links between the
spectral library and the calibration observations used for the instrument
response correction. The information provided in this file was also reflected
in the primary header of the spectra. This file contains the following five
columns, obsid, cal_obsid, cal_name, night, and cal_night, which respectively
correspond to the following fits header entries, unseq, stdunseq, stdname,
obsnight, and rspnight (see above).

####  A.4.3 Meta-data: Gaia-DR3

The file melchiors_gaiadr3.txt contains the result of the cross-matching of
our dataset with the table gaia_source of Gaia-DR3 using a cone search of 3
arcsec for 1592 MELCHIORS stars. It reports all fields from the Gaia-DR3, and
we therefore direct the reader to the corresponding Gaia documentation. The
field target is common with melchiors_meta_spectra.txt. Concerning binary
stars, the field non_single_star identifies them with values of 1, 2, 3, and 4
for astrometric, spectroscopic, astro+spectroscopic, and eclipsing binaries,
respectively.

###  A.5 Quick-look

A quick-look image is available for every spectrum in the library in .png
format. Each image shows the entire spectrum before and after telluric
absorption correction as well as a zoom in $H\alpha$ before and after
normalisation. These images are accessible individually on the website and are
included in the bulk download of the auxiliary data.