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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: [email protected]
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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 

References

Ade, P. A. R., et al. 2013, A&A, 1303, 5062.Google Scholar
Collobert, R., Bengio, S., 2004. Proc. Int’l Conf. on Machine Learning (ICML).Google Scholar
Contini, E., De Lucia, G., & Borgani, S. 2012, MNRAS, 420, 2978.Google Scholar
Diemand, J., Moore, B., & Stadel, J. 2005, Nature, 433, 389–391.Google Scholar
Fedorova, E., Del Popolo, A., Zhdanov, V. I., et al. 2014, Renc. Moriond 49th. Cosmology, eds: Auge, E., Dumarchez, J. and Tran Thanh Van, J. al. Procs, 407.Google Scholar
Fedorova, E., Sliusar, V. M., Zhdanov, V. I., et al. 2016, MNRAS, 457, 4147.CrossRefGoogle Scholar
Hastie, T., Tibshirani, R., Friedman, J., Elements of Statistical Learning, Springer, 2009.CrossRefGoogle Scholar
Hisano, J., Inoue, K. T., & Takahashi, T. 2006, Phys. Lett. B., 643, 141.Google Scholar
Knebe, A., Arnold, B., Power, C., & Gibson, B. K. 2008, MNRAS, 386 (2), 1029.Google Scholar
Kormendy, J. & Freeman, K. C. 2003, IAU Symp. 220 Eds: Ryder, S.D. et al. San Francisco: Astron. Soc. Pacific., 377; Astro-Ph/0407321.Google Scholar
Mao, S., Jing, Y., Ostriker, J. P., & Weller, J. 2004, ApJ, 604, L5.CrossRefGoogle Scholar
McKean, J. P., et al. 2007, MNRAS, 378 (1), 109.CrossRefGoogle Scholar
Navarro, J., Frenk, C., & White, S. 1996, ApJ, 462, 563.CrossRefGoogle Scholar
Oguri, M. 2005, MNRAS, 361 (1), L38.Google Scholar
Rocha, M. E., et al. 2012, MNRAS, 430 (1), 81.Google Scholar
Shulga, V., et al. Dark energy and dark matter in the Universe. V.2, 2014, Akademperiodics, 357p.Google Scholar
Stadel, J., Potter, D., Moore, B., et al. 2009, MNRAS, 398, L21.Google Scholar
Tisserand, P., Le Guillou, L., Afonso, C., et al. 2007, A&A, 469, 387.Google Scholar
Wyrzykowski, Ł., Kozłowski, S., Skowron, J., et al., 2009, MNRAS, 397, 1228.Google Scholar
Vogelsberger, M., et al. 2012, MNRAS, 423, 3740.CrossRefGoogle Scholar
Zhdanov, V., Alexandrov, A., Fedorova, E., et al. 2012, ISRN A&A, 2012, ID 906951.Google Scholar
Zhang, D. 2011, MNRAS, 418 (3), 1850.Google Scholar