Thesis Supervisor: Paula Sanchez Saez

Abstract

Supermassive black holes (SMBHs; 10⁶–10¹⁰ M_⊙) sit at the centres of massive galaxies and play a central role in galaxy evolution, yet their origin remains one of the most fundamental open questions in astrophysics. Competing seeding models, such as light seeds (Population III remnants) versus heavy seeds (direct gas collapse), predict different initial BH masses, host environments, and growth pathways. Intermediate-mass black holes (IMBHs; 10³–10⁶ M_⊙) are key to distinguishing these scenarios, as their demographics and low-mass BH–host scaling place strong constraints on the dominant SMBH formation channel.

Despite hundreds of spectroscopic IMBH identifications in SDSS and DESI, selection remains incomplete because low metallicity, star-formation dilution, and intrinsic faintness can hide them from classical line diagnostics. Optical variability provides a complementary route to uncover accreting IMBHs. Combining ZTF/LSST optical light curves with spectroscopy from SDSS, DESI, and 4MOST will enable a statistically robust census of actively accreting IMBHs.

In this project, the PhD student will (i) carry out a comprehensive variability analysis of all known IMBHs using ZTF/LSST light curves, to quantify how often they show AGN-like optical variability and analyse their X-ray properties using public surveys such as eROSITA; (ii) develop machine-learning classifiers based on optical light curves to identify new IMBH candidates, validating them with archival spectroscopy and 4MOST-ChANGES fibre Target-of-Opportunity observations; and (iii) estimate BH masses and host-galaxy properties for both known and newly identified IMBHs to determine occupation fractions and map BH–host scaling relations across the low-mass range, and from this, place empirical limits on SMBH seeding models. Through this project, the student will become an active member of the ALeRCE broker and 4MOST-ChANGES collaboration and, by the end of their PhD, will have developed expertise at the intersection of observational astrophysics and data science, skills that are increasingly important in the new era of large astronomical surveys.

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