Predicting Quasar Counts Detectable in the LSST Survey

TL;DR

This study predicts the number of quasars LSST will detect using simulated survey data, highlighting how different strategies influence detection counts and aiding in optimizing survey plans.

Contribution

It provides the first detailed prediction of quasar detections in LSST across various survey strategies using the latest simulations.

Findings

Over 70% of quasars detected within the first year in all bands

Expected detection of 184 million AGNs in the z-band over 10 years

Variations in u-band strategy can change detections by up to 15%

Abstract

The Legacy Survey of Space and Time (LSST), being conducted by the Vera C. Rubin Observatory, is a wide-field multi-band survey that will revolutionize our understanding of extragalactic sources through its unprecedented combination of area and depth. While the LSST survey strategy is still being finalized, the Rubin Observatory team has generated a series of survey simulations using the LSST Operations Simulator to explore the optimal survey strategy that best accommodates the majority of scientific goals. In this study, we utilize the latest simulated data to predict the number of detectable quasars by LSST in each band and evaluate the impact of different survey strategies. We find that the number of quasars and lower luminosity AGNs detected in the baseline strategy (v4.3.1) in the redshift range z=0.3-6.7 will be highest in the i-band and lowest in the u-band. Over 70% of quasars are expected to be detected within the first year in all bands, as LSST will have already reached the break of the luminosity function at most redshifts. With a limiting magnitude of 25.7 mag, we expect to detect 184 million AGNs in the z-band over the 10-year survey, with quasars constituting only 6% of the total AGNs in each band. This arises because, considering that the luminosities of most low-luminosity AGNs are affected by contamination from their host galaxies, we set a magnitude threshold when predicting the number of quasars. We find that variations in the u-band strategy can impact the number of quasar detections. Specifically, the difference between the baseline strategy and that with the largest total exposure in u is 15%. In contrast, changes in rolling strategies, DDF strategies, weather conditions, and Target of Opportunity observations result in variations below 2%. These results provide valuable insights for optimizing approaches to maximize the scientific output of quasar studies.