# stars: variables: general

## Classification of periodic variable stars with novel cyclic-permutation invariant neural networks

We present Cyclic-Permutation Invariant Neural Networks, a novel class of neural networks (NNs) designed to be invariant to phase shifts of period-folded periodic sequences by means of 'symmetry padding'. In the context of periodic variable star …

## A Near-infrared Period-Luminosity Relation for Miras in NGC 4258, an Anchor for a New Distance Ladder

We present year-long, near-infrared (NIR) Hubble Space Telescope (HST) WFC3 observations of Mira variables in the water megamaser host galaxy NGC 4258. Miras are asymptotic giant branch variables that can be divided into oxygen- (O-) and carbon- (C-) …

## 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 …

## 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 …

## 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 …

## 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 …

## 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 …

## PTF10nvg: An Outbursting Class I Protostar in the Pelican/North American Nebula

During a synoptic survey of the North American Nebula region, the Palomar Transient Factory (PTF) detected an optical outburst (dubbed PTF10nvg) associated with the previously unstudied flat or rising spectrum infrared source IRAS 20496+4354. The PTF …

## Discovery of Precursor Luminous Blue Variable Outbursts in Two Recent Optical Transients: The Fitfully Variable Missing Links UGC 2773-OT and SN 2009ip

We present progenitor-star detections, light curves, and optical spectra of supernova (SN) 2009ip and the 2009 optical transient in UGC 2773 (U2773-OT), which were not genuine SNe. Precursor variability in the decade before outburst indicates that …