A census of $rho$-Oph candidate members from gaia DR2. (arXiv:1902.07600v1 [astro-ph.EP])
<a href="http://arxiv.org/find/astro-ph/1/au:+Canovas_H/0/1/0/all/0/1">H. Cánovas</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cantero_C/0/1/0/all/0/1">C. Cantero</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cieza_L/0/1/0/all/0/1">L. Cieza</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bombrun_A/0/1/0/all/0/1">A. Bombrun</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lammers_U/0/1/0/all/0/1">U. Lammers</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Merin_B/0/1/0/all/0/1">B. Merín</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mora_A/0/1/0/all/0/1">A. Mora</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ribas_A/0/1/0/all/0/1">Á. Ribas</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ruiz_Rodriguez_D/0/1/0/all/0/1">D. Ruíz-Rodríguez</a>
Rho-Ophiuchus (Rho-Oph), one of the nearest star forming regions, is an ideal
laboratory to study the earlier stages of the stellar and protoplanetary disc
evolution. We aim to find potential new members of the Rho-Oph region and
identify those surrounded by a circumstellar disc. We constructed a control
sample composed by 188 bona fide Rho-Oph members. Using this sample as a
reference we applied three different density-based Machine Learning clustering
algorithms (DBSCAN, OPTICS, and HDBSCAN) to a sample drawn from the Gaia DR2
catalogue centred on the Rho-Oph cloud. The clustering analysis was applied in
the five astrometric dimensions defined by the 3-dimensional Cartesian space
and the proper motions in right ascension and declination. The three clustering
algorithms systematically identify a main cluster with astrometric properties
consistent with those of the control sample. Joining their outcome we
constructed a common sample containing 391 member candidates including 166 new
(not yet discussed in the literature) objects. Combining the Gaia data with
2MASS and WISE photometry we built the spectral energy distributions from $0.5$
to $22microm$ for the subset of 48 objects with high quality photometry,
finding a total of 41 discs (including 11 Class II and 1 Class III new discs).
Density-based clustering algorithms are a promising tool to identify candidate
members of star forming regions in large astrometric databases. Combining the
Gaia DR2 data with infrared catalogues it is possible to discover
protoplanetary discs that escaped detection by previous surveys. The objects
here presented conform an interesting sample to be followed-up with
sub-millimetre (and longer wavelengths) observatories and future infrared
facilities. If confirmed, the candidate members here discussed would represent
an increment of roughly $40-50%$ of the current census of the Rho-Oph region.
Rho-Ophiuchus (Rho-Oph), one of the nearest star forming regions, is an ideal
laboratory to study the earlier stages of the stellar and protoplanetary disc
evolution. We aim to find potential new members of the Rho-Oph region and
identify those surrounded by a circumstellar disc. We constructed a control
sample composed by 188 bona fide Rho-Oph members. Using this sample as a
reference we applied three different density-based Machine Learning clustering
algorithms (DBSCAN, OPTICS, and HDBSCAN) to a sample drawn from the Gaia DR2
catalogue centred on the Rho-Oph cloud. The clustering analysis was applied in
the five astrometric dimensions defined by the 3-dimensional Cartesian space
and the proper motions in right ascension and declination. The three clustering
algorithms systematically identify a main cluster with astrometric properties
consistent with those of the control sample. Joining their outcome we
constructed a common sample containing 391 member candidates including 166 new
(not yet discussed in the literature) objects. Combining the Gaia data with
2MASS and WISE photometry we built the spectral energy distributions from $0.5$
to $22microm$ for the subset of 48 objects with high quality photometry,
finding a total of 41 discs (including 11 Class II and 1 Class III new discs).
Density-based clustering algorithms are a promising tool to identify candidate
members of star forming regions in large astrometric databases. Combining the
Gaia DR2 data with infrared catalogues it is possible to discover
protoplanetary discs that escaped detection by previous surveys. The objects
here presented conform an interesting sample to be followed-up with
sub-millimetre (and longer wavelengths) observatories and future infrared
facilities. If confirmed, the candidate members here discussed would represent
an increment of roughly $40-50%$ of the current census of the Rho-Oph region.
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