We present a phenomenological forward Monte Carlo model for forecasting the population of active galactic nuclei (AGNs) in dwarf galaxies observable via their optical variability. Our model accounts for expected changes in the spectral energy distribution of AGNs in the intermediate-mass black hole (IMBH) mass range and uses observational constraints on optical variability as a function of black hole (BH) mass to generate mock light curves. Adopting several different models for the BH occupation function, including one for off-nuclear IMBHs, we quantify differences in the predicted local AGN mass and luminosity functions in dwarf galaxies. As a result, we are able to model the fraction of variable AGNs as a function of important galaxy host properties, such as host galaxy stellar mass, in the presence of selection effects. We find that our adopted occupation fractions for the ‘heavy’ and ‘light’ initial BH seeding scenarios can be distinguished with variability at the 2–3σ level for galaxy host stellar masses below ∼108M⊙ with data from the upcoming Vera C. Rubin Observatory. We also demonstrate the prevalence of a selection bias whereby recovered IMBH masses fall, on average, above the predicted value from the local host galaxy–BH mass scaling relation with the strength of this bias dependent on the survey sensitivity. Our methodology can be used more broadly to calibrate AGN demographic studies in synoptic surveys. Finally, we show that a targeted ∼ hourly cadence program over a few nights with the Rubin Observatory can provide strong constraints on IMBH masses given their expected rapid variability time-scales.
- Award ID(s):
- 1909641
- NSF-PAR ID:
- 10357843
- Date Published:
- Journal Name:
- The Astrophysical Journal Supplement Series
- Volume:
- 260
- Issue:
- 1
- ISSN:
- 0067-0049
- Page Range / eLocation ID:
- 2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3847/1538-4365/ac5969
