Multivariate Time-series Analysis of Variable Objects in the Gaia Mission. (arXiv:1911.09114v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Eyer_L/0/1/0/all/0/1">Laurent Eyer</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Suveges_M/0/1/0/all/0/1">Maria Süveges</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ridder_J/0/1/0/all/0/1">Joris De Ridder</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Regibo_S/0/1/0/all/0/1">Sara Regibo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mowlavi_N/0/1/0/all/0/1">Nami Mowlavi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Holl_B/0/1/0/all/0/1">Berry Holl</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Rimoldini_L/0/1/0/all/0/1">Lorenzo Rimoldini</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bouchy_F/0/1/0/all/0/1">Francois Bouchy</a>
In astronomy, we are witnessing an enormous increase in the number of source
detections, precision, and diversity of measurements. Additionally, multi-epoch
data is becoming the norm, making time-series analyses an important aspect of
current astronomy. The Gaia mission is an outstanding example of a multi-epoch
survey that provides measurements in a large diversity of domains, with its
broad-band photometry; spectrophotometry in blue and red (used to derive
astrophysical parameters); spectroscopy (employed to infer radial velocities, v
sin(i), and other astrophysical parameters); and its extremely precise
astrometry. Most of all that information is provided for sources covering the
entire sky. Here, we present several properties related to the Gaia time
series, such as the time sampling; the different types of measurements; the
Gaia G, G BP and G RP-band photometry; and Gaia-inspired studies using the
CORrelation-RAdial-VELocities data to assess the potential of the information
on the radial velocity, the FWHM, and the contrast of the cross-correlation
function. We also present techniques (which are used or are under development)
that optimize the extraction of astrophysical information from the different
instruments of Gaia, such as the principal component analysis and the
multi-response regression. The detailed understanding of the behavior of the
observed phenomena in the various measurement domains can lead to richer and
more precise characterization of the Gaia data, including the definition of
more informative attributes that serve as input to (our) machine-learning
algorithms.
In astronomy, we are witnessing an enormous increase in the number of source
detections, precision, and diversity of measurements. Additionally, multi-epoch
data is becoming the norm, making time-series analyses an important aspect of
current astronomy. The Gaia mission is an outstanding example of a multi-epoch
survey that provides measurements in a large diversity of domains, with its
broad-band photometry; spectrophotometry in blue and red (used to derive
astrophysical parameters); spectroscopy (employed to infer radial velocities, v
sin(i), and other astrophysical parameters); and its extremely precise
astrometry. Most of all that information is provided for sources covering the
entire sky. Here, we present several properties related to the Gaia time
series, such as the time sampling; the different types of measurements; the
Gaia G, G BP and G RP-band photometry; and Gaia-inspired studies using the
CORrelation-RAdial-VELocities data to assess the potential of the information
on the radial velocity, the FWHM, and the contrast of the cross-correlation
function. We also present techniques (which are used or are under development)
that optimize the extraction of astrophysical information from the different
instruments of Gaia, such as the principal component analysis and the
multi-response regression. The detailed understanding of the behavior of the
observed phenomena in the various measurement domains can lead to richer and
more precise characterization of the Gaia data, including the definition of
more informative attributes that serve as input to (our) machine-learning
algorithms.
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