(Jay) Digvijay Wadekar

Research:
Here is a brief description of a few selected projects.

1. Gravitational wave searches and parameter estimation
Nearly all of the previous gravitational wave (GW) searches in the LIGO-Virgo data include GW waveforms with only the dominant quadrupole mode, i.e., omitting higher-order harmonics which are predicted by general relativity. We detected new black hole mergers in the LIGO-Virgo O3 data from a novel search pipeline that includes the higher-order harmonics. The black holes in some of the new detections have astrophysically interesting properties such as occupation of the pair-instability mass gap, high-redshift (1 < z < 2), and positive effective spins. Apart from the search with higher harmonics, I also performed a GW search for exotic objects with large tidal deformabilities (e.g., boson stars and black holes with axion clouds).

Properties of new black hole mergers found in the LIGO-Virgo O3 data by our search which includes GW higher harmonics beyond the quadrupole mode. Colored contours show our new events, while all the previously reported events from O1-O3 runs (combining the GWTC-3, OGC and previous IAS catalogs) are shown in transparent gray.

Here is the link to the PDF of a recent talk

The video below is from one of my online talks

2. Interpretable ML techniques for cosmology and astrophysics
Finding low-scatter relationships in properties of complex systems (e.g., stars, supernovae, galaxies) is important to gain physical insights into them and/or to estimate their distances/masses. As the size of simulation/observational datasets grow, finding low-scatter relationships in the data becomes extremely arduous using manual data analysis methods. We used machine learning techniques to expeditiously search for such relations in abstract high-dimensional data-spaces. Focusing on clusters of galaxies, we found new scaling relations between their properties obtained using ML. Our relations can enable more accurate inference of cosmology and baryonic feedback from upcoming surveys of galaxy clusters such as ACT, SO, eROSITA and CMB-S4. We also explored the use of ML tools to model the galaxy-halo connection.

New equations for mass estimation of galaxy clusters/groups found using symbolic regression.

The video below is from one of my online talks

3. Using gas-rich dwarf galaxies to probe alternatives to cold, collisionless dark matter
Gas-rich dwarf galaxies located outside the virial radius of their host are relatively pristine systems. Due to the ultra-low radiative cooling rate of gas in these dwarfs, they are very strong calorimetric probes of energy injection by alternative dark matter (DM) candidates. We used these dwarfs to obtain strong constraints on popular DM models like millicharged DM, axion like particles (ALPs) and primordial black holes (PBHs). For dark photon DM and for some DM decay models, these dwarfs gives stronger constraints than all the previous literature. Observations of gas-rich dwarfs from current and upcoming 21cm and optical surveys (e.g., Rubin observatory, Roman telescope) therefore open a new way of probing DM.

Constraints from the gas-rich dwarf Leo T on decay lifetime of DM to e+e-

Here is the link to the PDF of a recent talk and below is the video

4. Analytic covariance matrices for upcoming spectroscopic galaxy surveys

In order to infer cosmological parameters from galaxy survey data, we typically use summary statistics such as the power spectrum and need an accurate estimate of their covariance matrix for the likelihood. The traditional process of obtaining the covariance involves simulating thousands of mock catalogs. We developed a novel analytic method to compute the covariance matrix which is more than four orders of magnitude faster and has excellent agreement with the state-of-the-art mock simulations upto non-linear scales (k~0.6 h/Mpc). We also validated our analytic method by using it to analyze the full-shape SDSS-BOSS survey data.

Results from galaxy power spectrum covariance obtained using our analytic method (2000 mock simulations of BOSS) are in black (orange). Individual matrix elements are compared on the left and parameter constraints from a full-shape BOSS analysis are on the right. Our analytic method is nearly four orders of magnitude faster than the simulation method and provides accurate results.

The video below is from one of my online talks