Identifying Microlensing Events Using Neural Networks

• Autorzy: Mróz P.

Identyfikatory

DOI

10.32023/0001-5237/70.3.1

• Języki publikacji: EN

Abstrakty

EN

Current gravitational microlensing surveys are observing hundreds of millions of stars in the Galactic bulge - which makes finding rare microlensing events a challenging tasks. In almost all previous works, microlensing events have been detected either by applying very strict selection cuts or manually inspecting tens of thousands of light curves. However, the number of microlensing events expected in the future space-based microlensing experiments forces us to consider fully-automated approaches. They are especially important for selecting binary-lens events that often exhibit complex light curve morphologies and are otherwise difficult to find. There are no dedicated selection algorithms for binary-lens events in the literature, which hampers their statistical studies. Here, we present two simple neural-network-based classifiers for detecting single and binary microlensing events. We demonstrate their robustness using OGLE-III and OGLE-IV data sets and show they perform well on microlensing events detected in data from the Zwicky Transient Facility (ZTF). Classifiers are able to correctly recognize ≈98% of single-lens events and 80-85% of binary-lens events.

Słowa kluczowe

EN

gravitational lensing: micro; methods: data analysis

• Wydawca: Copernicus Foundation for Polish Astronomy
• Czasopismo: Acta Astronomica
• Rocznik: 2020
• Tom: Vol. 70, No. 3
• Strony: 169--180
• Opis fizyczny: Bibliogr. 31 poz., rys., tab., wykr.

Twórcy

autor

Mróz P.

• Division of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA

Bibliografia

• Abadi, M., et al. 2016, arXiv:1603.04467.
• Alcock, C., et al. 2000, ApJ, 541, 734.
• Bond, I.A., et al. 2001, MNRAS, 327, 868.
• Chu, S., Wagstaff, K., Bryden, G., and Shvartzvald, Y. 2019, ASPC, 523, 127.
• Gaudi, B.S. 2012, Ann. Rev. Astron. Astrophys., 50, 411.
• Géron, A. 2019, in: “Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems”, O’Reilly Media.
• Godines, D., Bachelet, E., Narayan, G., and Street, R.A. 2019, Astronomy and Computing, 28, 100298.
• Gould, A., and Loeb, A. 1992, ApJ, 396, 104.
• Hamadache, C., et al. 2006, A&A, 454, 185.
• Kim, D.-J., et al. 2018, AJ, 155, 76.
• Kim, S.-L., et al. 2016, Journal of the Korean Astronomical Society, 49, 37.
• Kingma, D.P., and Ba, J. 2014, arXiv:1412.6980.
• Liebig, C., D’Ago, G., Bozza, V., and Dominik, M. 2015, MNRAS, 450, 1565.
• Mróz, P., et al. 2017, Nature, 548, 183.
• Mróz, P., et al. 2019, ApJS, 244, 29.
• Mróz, P., et al. 2020, Research Notes of the American Astronomical Society, 4, 13.
• Paczyński, B. 1986, ApJ, 304, 1.
• Penny, M.T., et al. 2019, ApJS, 241, 3.
• Popowski, P., et al. 2001, ASPC, 239, 244.
• Shvartzvald, Y., et al. 2016, MNRAS, 457, 4089.
• Skowron, J., et al. 2016, Acta Astron., 66, 1.
• Sumi, T., et al. 2003, ApJ, 591, 204.
• Sumi, T., et al. 2011, Nature, 473, 349.
• Tisserand, P., et al. 2007, A&A, 469, 387.
• Udalski, A., Szymański,M., Kałużny, J., Kubiak,M., Krzemiński,W.,Mateo,M., Preston, G.W., and Paczyński, B. 1993, Acta Astron., 43, 289.
• Udalski, A., Szymański, M., Kaluzny, J., Kubiak, M., Mateo, M., Krzeminski, W., and Paczynski, B. 1994, Acta Astron., 44, 227.
• Udalski, A., Szymański, M.K., and Szymański, G. 2015, Acta Astron., 65, 1.
• Wyrzykowski, Ł., et al. 2009, MNRAS, 397, 1228.
• Wyrzykowski, Ł., et al. 2015, ApJS, 216, 12.
• Wyrzykowski, Ł., et al. 2016, MNRAS, 458, 3012.
• Yee, J.C., et al. 2012, ApJ, 755, 102.