Relevant Projects:

Current Federal grants: National Science Foundation, Astronomy and Astrophysics Grant 2019-2023 “Raising the bar for black hole mass measurements in lower mass galaxies”; NASA, Astrophysics Theory Program 2019-2023 “Exploring the nature of dark matter with Gaia”; NASA JWST: 2018-2023: Nuclear Dynamics of a Nearby Seyfert with NIRSpec Integral; NASA JWST 2020-2024 “Do massive black holes come in small packages?”, NASA HST 2020-2024 ‘High-resolution ACS/WFC3 Imaging of Compact Stellar Systems in the Virgo Cluster in Support of JWST Cycle 1 Science”

Monica Valluri

Research Professor

Research Professor, Department of Astronomy

The focus of the research in our group is two fold. 1) We use stellar kinematics in galactic nuclei to infer the masses of super massive black holes using dynamical modeling. This includes a combination of statistical and optimization methods to fit data from telescopes such as the James Webb Space telescope. My group also uses galactic dynamical simulations to study the effects that black holes have on their host galaxies and statistical methods to compare observed galaxies with simulations. 2) We construct dynamical models of the Milky Way to understand its structure, dynamics and formation history. We construct models with 9-dimensional data (3D positions, 3D velocities, iron-abundance, alpha-element abundance, age) for tens of millions of individual stars in the Milky Way to constrain the properties of dark matter. Dark matter is the substance that constitute 85-90% of the mass budget in the Universe but is still undetected. The data science methods used here including unsupervised learning to identify clusters in 6D-9D phase space – which enables us to identify the satellites that built up the Milky Way and the nature of the dark matter in these satellites, Bayesian inference to constrain the nature of dark matter by characterizing the structure of tidal streams, deep learning to infer properties like ages and distances of stars from their spectra.

Two dimensional projections in the space of conserved quantities called orbital actions {Jr,Jz,Jφ} for stars that originally belonged to several satellites in 3 MW-like galaxies from the FIRE-2 simulations. The three columns correspond to each of the three different galaxies in FIRE-2 simulations: m12f, m12i and m12m, from left to right. The satellites that fell into the Milky Way like galaxy more recently or had greater mass accreted are readily identified by unsupervised learning (clustering) algorithms based on density in phase-space. Each satellite is indicated by a different color.