Metrics for Optimization of Large Synoptic Survey Telescope Observations of Stellar Variables and Transients

TL;DR

This paper introduces three quantitative metrics within the LSST Metric Analysis Framework to optimize survey strategies for detecting and characterizing stellar transients and variables, enhancing LSST's scientific return.

Contribution

The paper develops and demonstrates three new metrics tailored to evaluate LSST's effectiveness in observing stellar transients and variables, aiding survey optimization.

Findings

Metrics effectively quantify LSST's transient detection capabilities.

Metrics inform optimal survey cadence and coverage.

Implications for LSST survey design are discussed.

Abstract

The Large Synoptic Survey Telescope (LSST) will be the largest time-domain photometric survey ever. In order to maximize the LSST science yield for a broad array of transient stellar phenomena, it is necessary to optimize the survey cadence, coverage, and depth via quantitative metrics that are specifically designed to characterize the time-domain behavior of various types of stellar transients. In this paper we present three such metrics built on the LSST Metric Analysis Framework (MAF) model (Jones et al. 2014). Two of the metrics quantify the ability of LSST to detect non-periodic and/or non-recurring transient events, and the ability of LSST to reliably measure periodic signals of various timescales. The third metric provides a way to quantify the range of stellar parameters in the stellar populations that LSST will probe. We provide example uses of these metrics and discuss some implications based on these metrics for optimization of the LSST survey for observations of stellar variables and transients.