Extended stellar systems in the solar neighborhood. IV. Meingast 1: the most massive stellar stream in the solar neighborhood. (arXiv:2002.05728v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Ratzenbock_S/0/1/0/all/0/1">S. Ratzenböck</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Meingast_S/0/1/0/all/0/1">S. Meingast</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Alves_J/0/1/0/all/0/1">J. Alves</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Moller_T/0/1/0/all/0/1">T. Möller</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bomze_I/0/1/0/all/0/1">I. Bomze</a>
Nearby stellar streams carry unique information on the dynamical evolution
and disruption of stellar systems in the Galaxy, the mass distribution in the
disk, and provide unique targets for planet formation and evolution studies. We
revisit the stream discovered in Meingast et al (2019) to search for new
members, using Gaia DR2 data and a machine learning approach. We use a bagging
classifier of one-class Support Vector Machines to perform a search in
positions and proper motions for new stream members. We use the variable
prediction frequency resulting from the multitude of classifiers to estimate a
stream membership criterion which we use to select high fidelity sources. We
use the HR diagram and the Cartesian velocity distribution as test and
validation tools. We find about 2000 stream members with high-fidelity, or
about an order of magnitude more than previously known, unveiling the stream’s
population across the entire stellar mass spectrum, from B-stars to M-stars,
including white dwarfs. We find that, apart from being slightly more
metal-poor, the HRD of the stream is indistinguishable from that of the
Pleiades cluster. For the mass range at which we are mostly complete, $sim$0.2
M$_odot$ $ < $ M $ < $ $sim$4 M$_odot$, we find a normal IMF, allowing us to
estimate the total mass of stream to be about 2000 M$_odot$, making this
relatively young stream by far the most massive known. In addition, we identify
several white dwarfs as potential stream members. The nearby Meingast 1 stream,
due to its richness, age, and distance, is a new fundamental laboratory for
star and planet formation and evolution studies for the poorly studied
gravitationally unbound star-formation mode. We also demonstrate that One-Class
Support Vector Machines can be effectively used to unveil the full stellar
populations of nearby stellar systems with Gaia data.
Nearby stellar streams carry unique information on the dynamical evolution
and disruption of stellar systems in the Galaxy, the mass distribution in the
disk, and provide unique targets for planet formation and evolution studies. We
revisit the stream discovered in Meingast et al (2019) to search for new
members, using Gaia DR2 data and a machine learning approach. We use a bagging
classifier of one-class Support Vector Machines to perform a search in
positions and proper motions for new stream members. We use the variable
prediction frequency resulting from the multitude of classifiers to estimate a
stream membership criterion which we use to select high fidelity sources. We
use the HR diagram and the Cartesian velocity distribution as test and
validation tools. We find about 2000 stream members with high-fidelity, or
about an order of magnitude more than previously known, unveiling the stream’s
population across the entire stellar mass spectrum, from B-stars to M-stars,
including white dwarfs. We find that, apart from being slightly more
metal-poor, the HRD of the stream is indistinguishable from that of the
Pleiades cluster. For the mass range at which we are mostly complete, $sim$0.2
M$_odot$ $ < $ M $ < $ $sim$4 M$_odot$, we find a normal IMF, allowing us to
estimate the total mass of stream to be about 2000 M$_odot$, making this
relatively young stream by far the most massive known. In addition, we identify
several white dwarfs as potential stream members. The nearby Meingast 1 stream,
due to its richness, age, and distance, is a new fundamental laboratory for
star and planet formation and evolution studies for the poorly studied
gravitationally unbound star-formation mode. We also demonstrate that One-Class
Support Vector Machines can be effectively used to unveil the full stellar
populations of nearby stellar systems with Gaia data.
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