Description:
<jats:title>Abstract</jats:title>
<jats:p>Our understanding of the brown dwarf population in star-forming regions is dependent on knowing distances and proper motions and therefore will be improved through the <jats:italic>Gaia</jats:italic> space mission. In this paper, we select new samples of very low-mass objects (VLMOs) in Upper Scorpius using UKIDSS colors and optimized proper motions calculated using <jats:italic>Gaia</jats:italic> DR1. The scatter in proper motions from VLMOs in Upper Scorpius is now (for the first time) dominated by the kinematic spread of the region itself, not by the positional uncertainties. With age and mass estimates updated using <jats:italic>Gaia</jats:italic> parallaxes for early-type stars in the same region, we determine masses for all VLMOs. Our final most complete sample includes 453 VLMOs of which ∼125 are expected to be brown dwarfs. The cleanest sample is comprised of 131 VLMOs, with ∼105 brown dwarfs. We also compile a joint sample from the literature that includes 415 VLMOs, out of which 152 are likely brown dwarfs. The disk fraction among low-mass brown dwarfs (<jats:inline-formula>
<jats:tex-math>
<?CDATA $M\lt 0.05\,{M}_{\odot }$?>
</jats:tex-math>
<jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ajaa9751ieqn1.gif" xlink:type="simple" />
</jats:inline-formula>) is substantially higher than in more massive objects, indicating that disks around low-mass brown dwarfs survive longer than in low-mass stars overall. The mass function for <jats:inline-formula>
<jats:tex-math>
<?CDATA $0.01\lt M\lt 0.1\,{M}_{\odot }$?>
</jats:tex-math>
<jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ajaa9751ieqn2.gif" xlink:type="simple" />
</jats:inline-formula> is consistent with the Kroupa Initial Mass Function. We investigate the possibility that some “proper motion outliers” have undergone a dynamical ejection early in their evolution. Our analysis shows that the color–magnitude cuts used when selecting samples introduce strong bias into the population statistics due to varying levels of contamination and completeness.</jats:p>