Methods: Statistical

A Machine-learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves

A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10$^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to åisebox-0.5ex few× 10$^6$ targets. As we …

A mid-infrared study of RR Lyrae stars with the Wide-field Infrared Survey Explorer all-sky data release

We present a group of 3740 previously identified RR Lyrae variables well observed with the Wide-field Infrared Survey Explorer (WISE). We explore how the shape of the generic RR Lyrae mid-infrared light curve varies over period-space, comparing light …

Mid-infrared period-luminosity relations of RR Lyrae stars derived from the AllWISE Data Release.

We use photometry from the recent AllWISE Data Release of the Wide-field Infrared Survey Explorer (WISE) of 129 calibration stars, combined with prior distances obtained from the established M$_V$-[Fe/H] relation and Hubble Space Telescope …

Using machine learning for discovery in synoptic survey imaging data

Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are highly …

Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that …

Rapid, Machine-learned Resource Allocation: Application to High-redshift Gamma-Ray Burst Follow-up

As the number of observed gamma-ray bursts (GRBs) continues to grow, follow-up resources need to be used more efficiently in order to maximize science output from limited telescope time. As such, it is becoming increasingly important to rapidly …

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often …

Mid-infrared Period-luminosity Relations of RR Lyrae Stars Derived from the WISE Preliminary Data Release

Interstellar dust presents a significant challenge to extending parallax-determined distances of optically observed pulsational variables to larger volumes. Distance ladder work at mid- infrared wavebands, where dust effects are negligible and …

On Machine-learned Classification of Variable Stars with Sparse and Noisy Time-series Data

With the coming data deluge from synoptic surveys, there is a need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly observed variables based on small numbers of time-series measurements. In …

Optimal Time-series Selection of Quasars

We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al., we parameterize the ensemble quasar structure function in Sloan …