Methods: Data Analysis

The volumetric rate of normal type Ia supernovae in the local Universe discovered by the Palomar Transient Factory

We present the volumetric rate of normal type Ia supernovae (SNe Ia) discovered by the Palomar Transient Factory (PTF). Using strict data-quality cuts, and considering only periods when the PTF maintained a regular cadence, PTF discovered 90 SNe Ia …

Mapping the Interstellar Reddening and Extinction toward Baadetextquoterights Window Using Minimum Light Colors of ab-type RR Lyrae Stars: Revelations from the De-reddened Color-Magnitude Diagrams

We have obtained repeated images of six fields toward the Galactic bulge in five passbands (u, g, r, i, z) with the DECam imager on the Blanco 4 m telescope at CTIO. From more than 1.6 billion individual photometric measurements in the field centered …

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 …

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 …

Millions of Multiples: Detecting and Characterizing Close-separation Binary Systems in Synoptic Sky Surveys

The direct detection of binary systems in wide-field surveys is limited by the size of the stars' point-spread functions (PSFs). A search for elongated objects can find closer companions, but is limited by the precision to which the PSF shape can be …

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 …

Three New Eclipsing White-dwarf-M-dwarf Binaries Discovered in a Search for Transiting Planets around M-dwarfs

We present three new eclipsing white-dwarf/M-dwarf binary systems discovered during a search for transiting planets around M-dwarfs. Unlike most known eclipsing systems of this type, the optical and infrared emission is dominated by the M-dwarf …

Discovery of Bright Galactic R Coronae Borealis and DY Persei Variables: Rare Gems Mined from ACVS

We present the results of a machine-learning (ML)-based search for new R Coronae Borealis (RCB) stars and DY Persei-like stars (DYPers) in the Galaxy using cataloged light curves from the All-Sky Automated Survey (ASAS) Catalog of Variable Stars …

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 …