
On the prospect of inferring the halo structure and the masses of dark objects through parallax microlensing
Author(s) -
Draza Markovic
Publication year - 1998
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1046/j.1365-8711.1998.01627.x
Subject(s) - gravitational microlensing , physics , halo , astrophysics , parallax , galactic halo , large magellanic cloud , astronomy , dark matter , stars , galaxy
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 hand, and their spatial distribution and kinematics on the other. This disentanglement 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 spherical models for the halo of MHOs studied in a previous paper by Marković 38 Sommer‐Larsen, and assume that one can eliminate microlensing events caused by massive objects outside the halo (e.g., the LMC halo). Whereas the duration‐based error in the average MHO mass, μ¯ ≡ M ¯/M, exceeds (at N = 100 events) μ¯ by a factor of 2 or more, parallax microlensing remarkably brings it down to 15–20 per cent of μ¯, regardless of the shape of the mass function. In addition, the slope α of the mass function, d n /dμ ∝ μ α , can be inferred relatively accurately (σ α < 0.4) for a broader range, −3 < α < 0. The improvement in the inference of the halo structure is also significant: the index γ of the density profile ( ρ ∼ R −γ ) can be obtained with the error σ γ < 0.4. While in a typical situation the errors for the parameters specifying the velocity dispersion profile are of about the same magnitude as the parameters themselves, virtually all the uncertainty is ‘concentrated’ in linear combinations of the parameters that may have little influence on the profile, thus allowing its reasonably accurate inference.