ON THE PROSPECT OF INFERRING THE HALO STRUCTURE AND THE MASSES OF DARK OBJECTS THROUGH PARALLAX MICROLENSING

Authors
Citation
D. Markovic, ON THE PROSPECT OF INFERRING THE HALO STRUCTURE AND THE MASSES OF DARK OBJECTS THROUGH PARALLAX MICROLENSING, Monthly Notices of the Royal Astronomical Society, 299(4), 1998, pp. 929-941
Citations number
19
Categorie Soggetti
Astronomy & Astrophysics
ISSN journal
00358711
Volume
299
Issue
4
Year of publication
1998
Pages
929 - 941
Database
ISI
SICI code
0035-8711(1998)299:4<929:OTPOIT>2.0.ZU;2-1
Abstract
We study the proposed use of parallax microlensing in the direction of the Large Magellanic Cloud (LMC) to separate the effects of the mass function of dark massive halo objects (MHOs or 'machos') on the one ha nd, and their spatial distribution and kinematics on the other. This d isentanglement is supposed to allow a much better determination of the two than could be achieved entirely on the basis of the durations of events. We restrict our treatment to the same class of power-law spher ical models for the halo of MHOs studied in a previous paper by Markov ic & Sommer-Larsen, and assume that one can eliminate microlensing eve nts caused by massive objects outside the halo (e.g., the LMC halo). W hereas the duration-based error in the average MHO mass, <(mu)over bar > = (M) over bar/M-., exceeds (at N = 100 events) <(mu)over bar> by a factor of 2 or more, parallax microlensing remarkably brings it down t o 15-20 per cent of <(mu)over bar>, regardless of the shape of the mas s function. In addition, the slope alpha of the mass function, dn/d mu proportional to mu(alpha), can be inferred relatively accurately (sig ma(alpha) < 0.4) for a broader range, -3 < alpha < 0. The improvement in the inference of the halo structure is also significant: the index gamma of the density profile (rho similar to R-gamma) can be obtained with the error sigma(gamma) < 0.4. While in a typical situation the er rors for the parameters specifying the velocity dispersion profile are of about the same magnitude as the parameters themselves, virtually a ll the uncertainty is 'concentrated' in linear combinations of the par ameters that may have little influence on the profile, thus allowing i ts reasonably accurate inference.