2020
DOI: 10.1016/j.dark.2020.100567
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On simple analytic models of microlensing amplification statistics

Abstract: Gravitational microlensing is a key probe of the nature of dark matter and its distribution on the smallest scales. For many practical purposes, confronting theory to observation requires to model the probability that a light source is highly amplified by many-lens systems. This article reviews four simple analytic models of the amplification probability distribution, based on different approximations: (i) the strongest-lens model; (ii) the multiplicative model, where the total amplification is assumed to be t… Show more

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Cited by 12 publications
(6 citation statements)
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“…In such situations, characterized by low optical depth, accounting only for the PBH that is the closest to the l.o.s. is sufficient to model the net lensing effect [41].…”
Section: Microlensing Searches For Machosmentioning
confidence: 99%
“…In such situations, characterized by low optical depth, accounting only for the PBH that is the closest to the l.o.s. is sufficient to model the net lensing effect [41].…”
Section: Microlensing Searches For Machosmentioning
confidence: 99%
“…In a more modern language, we may say that the calculation was made at first order in the micro-lensing optical depth τ, which coincides with the convergence κ if the density of the lenses were smoothed out; see e.g Fleury & García-Bellido (2020). …”
mentioning
confidence: 91%
“…However, even for large mean magnifications where the lensing enters the complex regime associated with an intricate caustic network, the approximation by Rauch provides a simple way to capture broad behaviour of the magnification probability distributions found by numerical simulations (e.g. Fleury & García-Bellido 2020). Given the relative uncertainties associated with both FRB and GRB luminosity functions (James et al 2021;Banerjee et al 2021) particularly FRBs as their progenitor/s remain unknown, we will make use of this simple empirical model as opposed to vastly more computationally intensive numerical simulations.…”
Section: Magnification Probability Density Functionmentioning
confidence: 99%