TY - JOUR

T1 - How to Find Variable Active Galactic Nuclei with Machine Learning

AU - Faisst, Andreas L.

AU - Prakash, Abhishek

AU - Capak, Peter L.

AU - Lee, Bomee

PY - 2019/8/10

Y1 - 2019/8/10

N2 - Machine-learning (ML) algorithms will play a crucial role in studying the large data sets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with brightness-variable active galactic nuclei (AGNs). Specifically, we focus on an unsupervised method based on self-organizing maps (SOM) that we apply to a set of nonparametric variability estimators. This technique allows us to maintain domain knowledge and systematics control while using all the advantages of ML. Using simulated light curves that match the noise properties of observations, we verify the potential of this algorithm in identifying variable light curves. We then apply our method to a sample of similar to 8300 WISE color-selected AGN candidates in Stripe 82, in which we have identified variable light curves by visual inspection. We find that with ML we can identify these variable classified AGN with a purity of 86% and a completeness of 66%, a performance that is comparable to that of more commonly used supervised deep-learning neural networks. The advantage of the SOM framework is that it enables not only a robust identification of variable light curves in a given data set, but it is also a tool to investigate correlations between physical parameters in multidimensional space-such as the link between AGN variability and the properties of their host galaxies. Finally, we note that our method can be applied to any time-sampled light curve (e. g., supernovae, exoplanets, pulsars, and other transient events).

AB - Machine-learning (ML) algorithms will play a crucial role in studying the large data sets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with brightness-variable active galactic nuclei (AGNs). Specifically, we focus on an unsupervised method based on self-organizing maps (SOM) that we apply to a set of nonparametric variability estimators. This technique allows us to maintain domain knowledge and systematics control while using all the advantages of ML. Using simulated light curves that match the noise properties of observations, we verify the potential of this algorithm in identifying variable light curves. We then apply our method to a sample of similar to 8300 WISE color-selected AGN candidates in Stripe 82, in which we have identified variable light curves by visual inspection. We find that with ML we can identify these variable classified AGN with a purity of 86% and a completeness of 66%, a performance that is comparable to that of more commonly used supervised deep-learning neural networks. The advantage of the SOM framework is that it enables not only a robust identification of variable light curves in a given data set, but it is also a tool to investigate correlations between physical parameters in multidimensional space-such as the link between AGN variability and the properties of their host galaxies. Finally, we note that our method can be applied to any time-sampled light curve (e. g., supernovae, exoplanets, pulsars, and other transient events).

KW - galaxies: active

KW - galaxies: evolution

KW - galaxies: photometry

KW - methods: data analysis

KW - SELF-ORGANIZING MAPS

KW - FUZZY ARCHETYPES

KW - CLASSIFICATION

KW - VARIABILITY

KW - STARS

U2 - 10.3847/2041-8213/ab3581

DO - 10.3847/2041-8213/ab3581

M3 - Letter

VL - 881

JO - The Astrophysical Journal Letters

JF - The Astrophysical Journal Letters

SN - 2041-8205

IS - 1

M1 - 9

ER -