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Predicting Quasar Counts Detectable in the LSST Survey
Guodong Li2026
Roberto J. AssefWn Brandt
Few Citations
0 citations · Astronomy and Astrophysics
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TLDR

This study shows that a new sky survey will find millions of bright objects called quasars, and that small changes in how the survey is done will not make a big difference in how many are found.

Summary

1 Study Aim

The main goal of this paper is to predict how many quasars (very bright, distant objects powered by black holes) the Legacy Survey of Space and Time (LSST) will detect. The authors also want to see how different ways of running the survey might change the number of quasars found, and to help guide the best survey strategy for future studies of these objects. Simply put: The study aims to figure out how many quasars the LSST will find and whether changing the survey plan matters.

2 Study Design

The researchers used computer simulations of the LSST survey, created with the LSST Operations Simulator, to model how the telescope will observe the sky over ten years. They applied the Metrics Analysis Framework (MAF), a software tool that analyzes simulated survey data, to estimate the number of quasars and lower-luminosity active galactic nuclei (AGNs, which are galaxies with bright centers powered by black holes) that could be detected in each filter band. The study tested different survey strategies, including changes in exposure time, number of visits, and observing patterns, to see how these affect quasar counts. The predictions are based on established models of quasar brightness and distribution, and the results are compared across several possible survey plans. Simply put: The study uses computer models to see how many quasars LSST will find under different survey plans.

3 Findings

The study demonstrates that, using the baseline LSST survey plan, about 6 to 12 million quasars will be detected, with the most found in the i band and the fewest in the u band. Over 70% of these quasars are expected to be found in the first year. The total number of AGNs detected could reach up to 199 million, but only about 6% of these are bright enough to be called quasars. Changing the survey strategy in the u band can increase quasar detections by up to 15%, but other changes—like rolling cadence, deep field focus, weather, or special event follow-ups—change the total by less than 2%. The results suggest that the current survey plan is already close to optimal for finding quasars, and only major changes in the u band would have a noticeable effect. The authors recommend that future surveys focus on refining the models of quasar brightness to improve predictions. Simply put: The survey will find millions of quasars, and only big changes in the plan for one filter would change that number much.

Abstract

Abstract The Legacy Survey of Space and Time (LSST), being conducted by the Vera C. Rubin Observatory, is a wide-field multiband 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 and the Metrics Analysis Framework to predict the number of quasars detectable by LSST in each band and evaluate the impact of different survey strategies. We find that the number of quasars and lower-luminosity active galactic nuclei (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 (about 12 million) and lowest in the u band (about 6 million). Over 70% of quasars are expected to be detected within the first year in all bands, as LSST will have already reached the break in the luminosity function at most redshifts. With a limiting magnitude of 25.7 (26.9) mag, we expect to detect 184 (199) million AGNs in the z band ( r band) over the 10 yr 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, Deep Drilling Field 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.

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