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Gravitational Microlensing Events Due to Stellar Mass Black Holes
- Publication Year :
- 2001
-
Abstract
- We present an analysis of the longest timescale microlensing events discovered by the MACHO Collaboration during a 7 year survey of the Galactic bulge. We find 6 events that exhibit very strong microlensing parallax signals due, in part, to accurate photometric data from the GMAN and MPS collaborations. The microlensing parallax fit parameters are used in a likelihood analysis, which is able to estimate the distance and masses of the lens objects based upon a standard model of the Galactic velocity distribution. This analysis indicates that the most likely masses of 5 of the 6 lenses are > 1 Msun, which suggests that a substantial fraction of the Galactic lenses may be massive stellar remnants. This could explain the observed excess of long timescale microlensing events. The lenses for events MACHO-96-BLG-5 and MACHO-98-BLG-6 are the most massive, with mass estimates of M/Msun = 6 +10/-3 and M/Msun = 6 +7/-3, respectively. The observed upper limits on the absolute brightness of main sequence stars for these lenses are < 1 Lsun, so both lenses are black hole candidates. The black hole interpretation is also favored by a likelihood analysis with a Bayesian prior using a conventional model for the lens mass function. We consider the possibility that the source stars for some of these 6 events may lie in the foreground or background of the Galactic bulge, but we find that this is unlikely. Future HST observations of these events can either confirm the black hole lens hypothesis or detect the lens stars and provide a direct measurement of their masses. Future observations of similar events by SIM or the Keck or VLTI interferometers will allow direct measurements of the lens masses for stellar remnant lenses as well.<br />Comment: 47 pages, with 26 included postscript figures. Includes a new likelihood analysis with a mass function prior
Details
- Database :
- OAIster
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1312110213
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1086.342225