The Gaia EDR3 view of Johnson-Kron-Cousins standard stars: the curated Landolt and Stetson collections. (arXiv:2205.06186v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Pancino_E/0/1/0/all/0/1">E. Pancino</a> (INAF-OAA, SSDC), <a href="http://arxiv.org/find/astro-ph/1/au:+Marrese_P/0/1/0/all/0/1">P. M. Marrese</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Marinoni_S/0/1/0/all/0/1">S. Marinoni</a> (INAF-OARM, SSDC), <a href="http://arxiv.org/find/astro-ph/1/au:+Sanna_N/0/1/0/all/0/1">N. Sanna</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Turchi_A/0/1/0/all/0/1">A. Turchi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tsantaki_M/0/1/0/all/0/1">M. Tsantaki</a> (INAF-OAA), <a href="http://arxiv.org/find/astro-ph/1/au:+Rainer_M/0/1/0/all/0/1">M. Rainer</a> (INAF-OAA, INAF-OAMI), <a href="http://arxiv.org/find/astro-ph/1/au:+Altavilla_G/0/1/0/all/0/1">G. Altavilla</a> (INAF-OARM, SSDC), <a href="http://arxiv.org/find/astro-ph/1/au:+Monelli_M/0/1/0/all/0/1">M. Monelli</a> (IAC), <a href="http://arxiv.org/find/astro-ph/1/au:+Monaco_L/0/1/0/all/0/1">L. Monaco</a> (Andres Bello)
(Shortened). In the era of large surveys and space missions, it is necessary
to rely on large samples of well-characterized stars for inter-calibrating and
comparing measurements from different sources. Among the most employed
photometric systems, the Johnson-Kron-Cousins has been used for decades and for
a large amount of important datasets. Using Gaia DR3 as a reference, as well as
data from reddening maps, spectroscopic surveys, and variable stars monitoring
surveys, we curated and characterized the widely used Landolt and Stetson
collections of more than 200 000 secondary standards, removing binaries,
blends, and variable stars, and we classified and parametrized them, employing
classical as well as machine learning techniques. In particular, our
atmospheric parameters agree significantly better with spectroscopic ones,
compared to other catalogues obtained by means of machine learning. We also
cross-matched the collections with the major photometric surveys to provide a
comprehensive table with the magnitudes of the secondary standards in the most
widely used photometric systems (ugriz, grizy, Gaia, Hipparcos, Tycho, 2MASS).
We finally provide a set of 167 polynomial transformations, valid for dwarfs
and giants, metal-poor and metal-rich stars, to transform UBVRI magnitudes into
the above photometric systems and vice-versa.
(Shortened). In the era of large surveys and space missions, it is necessary
to rely on large samples of well-characterized stars for inter-calibrating and
comparing measurements from different sources. Among the most employed
photometric systems, the Johnson-Kron-Cousins has been used for decades and for
a large amount of important datasets. Using Gaia DR3 as a reference, as well as
data from reddening maps, spectroscopic surveys, and variable stars monitoring
surveys, we curated and characterized the widely used Landolt and Stetson
collections of more than 200 000 secondary standards, removing binaries,
blends, and variable stars, and we classified and parametrized them, employing
classical as well as machine learning techniques. In particular, our
atmospheric parameters agree significantly better with spectroscopic ones,
compared to other catalogues obtained by means of machine learning. We also
cross-matched the collections with the major photometric surveys to provide a
comprehensive table with the magnitudes of the secondary standards in the most
widely used photometric systems (ugriz, grizy, Gaia, Hipparcos, Tycho, 2MASS).
We finally provide a set of 167 polynomial transformations, valid for dwarfs
and giants, metal-poor and metal-rich stars, to transform UBVRI magnitudes into
the above photometric systems and vice-versa.
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