Predicted microlensing events by nearby very-low-mass objects: Pan-STARRS DR1 vs. Gaia DR2

Martin Bo Nielsen, D. M. Bramich

Research output: Contribution to journalArticle

Abstract

Microlensing events can be used to directly measure the masses of single field stars to a precision of ≈ 1−10% . The majority of direct mass measurements for stellar and sub-stellar objects typically only come from observations of binary systems. Hence microlensing provides an important channel for direct mass measurements of single stars. The Gaia satellite has observed ≈ 1.7 billion objects, and analysis of the second data release has recently yielded numerous event predictions for the next few decades. However, the Gaia catalog is incomplete for nearby very-low-mass objects such as brown dwarfs for which mass measurements are most crucial. We employ a catalog of very-low-mass objects from Pan-STARRS data release 1 (PDR1) as potential lens stars, and we use the objects from Gaia data release 2 (GDR2) as potential source stars. We then search for future microlensing events up to the year 2070. The Pan-STARRS1 objects are first cross-matched with GDR2 to remove any that are present in both catalogs. This leaves a sample of 1718 possible lenses. We fit MIST isochrones to the Pan-STARRS1, AllWISE and 2MASS photometry to estimate their masses. We then compute their paths on the sky, along with the paths of the GDR2 source objects, until the year 2070, and search for potential microlensing events. Source-lens pairs that will produce a microlensing signal with an astrometric amplitude of greater than 0.131 mas, or a photometric amplitude of greater than 0.4 mmag, are retained.

Original languageEnglish (US)
Pages (from-to)351-370
Number of pages20
JournalActa Astronomica
Volume68
Issue number4
DOIs
StatePublished - Jan 1 2018

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Keywords

  • Astrometry
  • Catalogs
  • Gravitational lensing: micro
  • Methods: data analysis
  • Stars: fundamental parameters

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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