Rubin Observatory's Survey Strategy Performance for Tidal Disruption Events

TL;DR

This paper evaluates the effectiveness of Rubin Observatory's survey strategies in detecting and identifying Tidal Disruption Events (TDEs), emphasizing the importance of u-band observations and optimal sampling for scientific analysis.

Contribution

It introduces a framework for assessing survey strategies' performance in TDE detection and identifies optimal strategies that enhance TDE identification without compromising other science goals.

Findings

Baseline strategy detects ~1.5% of TDEs meeting criteria.

Longer u-band exposures improve TDE detection rates.

Strategies with more blue-band observations and frequent sampling are preferred.

Abstract

Tidal Disruption Events (TDEs) are rare transients, which are considered as promising tools in probing supermassive black holes in quiescent galaxies. The majority of $\approx 60$ known TDEs has been discovered with time-domain surveys in the last two decades. Currently, $\approx 10$ TDEs are discovered per year, and this number will increase with the Legacy Survey of Space and Time (LSST) at Rubin Observatory. This work evaluates LSST survey strategies in view of their performance in identifying TDEs. We assume that TDEs can be identified photometrically based on their colors, particularly $u$-band, and will be scientifically useful if we can detect the light curve peak to derive physical quantities. We define requirements for the Rubin light curves needed to achieve this (detections pre-peak, post-peak, in different bands to measure colour). We then inject model light curves into the Operations Simulator, and calculate the fraction of TDEs passing our requirements for several strategies. We find that under the baseline strategy, $\approx 1.5$\% of simulated TDEs fulfil our detection criteria, while this number increases when more time is devoted to $u$-band observations. An ideal observing strategy for photometric identification of TDEs would have longer $u$-band exposures, which should not come at the expense of fewer $u$-band visits. A filter distribution weighted towards more observing time in bluer bands, intra-night visits in different filters, and strategies with frequent sampling leading to higher quality light curves are preferred. We find that these strategies benefiting TDE science do not impact significantly other science cases.