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Determining the parameters of high amplification microlensing events by means of statistical machine learning techniques

Published online by Cambridge University Press:  30 May 2017

Elena Fedorova
Elena Fedorova*
Affiliation:
Astronomical Observatory of National Taras Shevchenko University of Kyiv, Observatorna str.3, Kiev 04053, Ukraine email: efedorova@ukr.net
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Abstract

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Strong gravitational microlensing (GM) events provide us a possibility to determine both the parameters of microlensed source and microlens. GM can be an important clue to understand the nature of dark matter on comparably small spatial and mass scales (i.e. substructure), especially when speaking about the combination of astrometrical and photometrical data about high amplification microlensing events (HAME). In the same time, fitting of HAME lightcurves of microlensed sources is quite time-consuming process. That is why we test here the possibility to apply the statistical machine learning techniques to determine the source and microlens parameters for the set of HAME lightcurves, using the simulated set of amplification curves of sources microlensed by point masses and clumps of DM with various density profiles.

Keywords

gravitational microlensinghigh amplification microlensing eventsstatistical machine learningetc.
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 213 - 216
Copyright
Copyright © International Astronomical Union 2017 

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