A machine learning classifier for microlensing in wide-field surveys
Abstract
While microlensing is very rare, occurring on average once per million stars observed, current and near-future surveys are coming online with the capability of providing photometry of almost the entire visible sky to depths up to R ∼22 mag or fainter every few days, which will contribute to the detection of black holes and exoplanets through follow-up observations of microlensing events. Based on galactic models, we can expect microlensing events across a vastly wider region of the galaxy, although the cadence of these surveys (2-3 d-1) is lower than traditional microlensing surveys, making efficient detection a challenge. Rapid advances are being made in the utility of time-series data to detect and classify transient events in real-time using very high data-rate surveys, but limited work has been published regarding the detection of microlensing events, particularly for when the data streams are of relatively low-cadence. In this research, we explore the utility of a Random Forest algorithm for identifying microlensing signals using time-series data, with the goal of creating an efficient machine learning classifier that can be applied to search for microlensing in wide-field surveys even with low-cadence data. We have applied and optimized our classifier using the OGLE-II microlensing dataset, in addition to testing with PTF/iPTF survey data and the currently operating ZTF, which applies the same data handling infrastructure that is envisioned for the upcoming LSST.
- Publication:
-
Astronomy and Computing
- Pub Date:
- July 2019
- DOI:
- 10.1016/j.ascom.2019.100298
- arXiv:
- arXiv:2004.14347
- Bibcode:
- 2019A&C....2800298G
- Keywords:
-
- Gravitational microlensing;
- Classification;
- Random forest;
- Machine learning;
- PTF;
- ZTF;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Astrophysics - Earth and Planetary Astrophysics;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Solar and Stellar Astrophysics
- E-Print:
- doi:10.1016/j.ascom.2019.100298