# Characterizing Variable Stars in a Single Night with LSST

## TL;DR

This paper proposes a continuous 15-second exposure observational method for a single night to create a comprehensive dataset of stellar lightcurves, enabling detailed variability analysis and machine learning classification of variable stars.

## Contribution

It introduces a novel observational strategy and analytical framework to characterize variable stars with high temporal resolution, addressing limitations of LSST's sparse cadence.

## Key findings

- Creates a dataset of ~1 million lightcurves from a single night.
- Enables detailed variability analysis of dM stars.
- Provides features for machine learning classification of variable stars.

## Abstract

Stars exhibit a bewildering variety of variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotations. The principal LSST surveys will have cadences too sparse and irregular to capture most of these phenomena. A novel idea is proposed here to observe a single Galactic field, rich in unobscured stars, in a continuous sequence of $\sim 15$ second exposures for one long winter night in a single photometric band. The result will be a unique dataset of $\sim 1$ million regularly spaced stellar lightcurves. The lightcurves will gives a particularly comprehensive collection of dM star variability. A powerful array of statistical procedures can be applied to the ensemble of lightcurves from the long-standing fields of time series analysis, signal processing and econometrics. Dozens of `features' describing the variability can be extracted and subject to machine learning classification, giving a unique authoritative objective classification of rapidly variable stars. The most effective features can then inform the wider LSST community on the best approaches to variable star identification and classification from the sparse, irregular cadences that dominate the LSST project.