A recurrent neural network for classification of unevenly sampled variable stars

Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time ("light curves"). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to intranight cadence choices as well as diurnal and seasonal constraints. With nightly observations of millions of variable stars and transients from upcoming surveys, efficient and accurate discovery and classification techniques on noisy, irregularly sampled data must be employed with minimal human-in-the-loop involvement. Machine learning for inference tasks on such data traditionally requires the laborious hand-coding of domain-specific numerical summaries of raw data ("features"). Here we present a novel unsupervised autoencoding recurrent neural network (RNN) that makes explicit use of sampling times and known heteroskedastic noise properties. When trained on optical variable star catalogs, this network produces supervised classification models that rival other best-in-class approaches. We find that autoencoded features learned on one time-domain survey perform nearly as well when applied to another survey. These networks can continue to learn from new unlabeled observations and may be used in other unsupervised tasks such as forecasting and anomaly detection.Comment: 23 pages, 14 figures. The published version is at Nature Astronomy (https://www.nature.com/articles/s41550-017-0321-z). Source code for models, experiments, and figures at https://github.com/bnaul/IrregularTimeSeriesAutoencoderPaper (Zenodo Code DOI: 10.5281/zenodo.1045560

Identifying Explosive Transients and Implications for Gravitational Wave Followup

abstract: High-energy explosive phenomena, Gamma-Ray Bursts (GRBs) and Supernovae (SNe), provide unique laboratories to study extreme physics and potentially open up the new discovery window of Gravitational-wave astronomy. Uncovering the intrinsic variability of GRBs constrains the size of the GRB emission region, and ejecta velocity, in turn provides hints on the nature of GRBs and their progenitors. We develop a novel method which ties together wavelet and structure-function analyses to measure, for the first time, the actual minimum variability timescale, Delta t_min, of GRB light curves. Implementing our technique to the largest sample of GRBs collected by Swift and Fermi instruments reveals that only less than 10% of GRBs exhibit evidence for variability on timescales below 2 ms. Investigation on various energy bands of the Gamma-ray Burst Monitor (GBM) onboard Fermi shows that the tightest constraints on progenitor radii derive from timescales obtained from the hardest energy channel of light curves (299--1000 keV). Our derivations for the minimum Lorentz factor, Gamma_min, and the minimum emission radius, R = 2c Gamma_min^2 Delta t_min / (1+z), find Gamma < 400 which imply typical emission radii R ~ 1 X 10^14 cm for long-duration GRBs and R ~ 3 X 10^13 cm for short-duration GRBs (sGRBs). I present the Reionization and Transients InfraRed (RATIR) followup of LIGO/Virgo Gravitational-wave events especially for the G194575 trigger. I show that expanding our pipeline to search for either optical riZ or near-infrared YJH detections (3 or more bands) should result in a false-alarm-rate ~1% (one candidate in the vast 100 deg^2 LIGO error region) and an efficiency ~90%. I also present the results of a 5-year comprehensive SN search by the Palomar Transient Factory aimed to measure the SN rates in the local Luminous Infrared Galaxies. We find that the SN rate of the sample, 0.05 +/- 0.02 1/yr (per galaxy), is consistent with that expected from the theoretical prediction, 0.060 +/- 0.002 1/yr (per galaxy).Dissertation/ThesisDoctoral Dissertation Astrophysics 201