I use different theoretical models and statistical techniques on different scales to answer most pressing questions. How reionization began, evolved and ended? What small scale surveys will tell us about reionization sources? And how to extract the maximum amount of information from the upcoming large scale surveys?
What i'm doing
How to use insights from small scale simulations/models to improve large scale simulations/models of reionization and galaxy formation?
Forecasting to surveys
What can we learn from future observations?
How to translate measurements into tight constraints on our models?
How to exctract efficiently all information from upcoming surveys?
How I spend my time
Small Scale Astrophysics25%
Large Scale Cosmology25%
Machine Learning & Bayesian statistics40%
DEI & Outreach10%
Mosima is an M.Sc. student in the Astrophysics group at University of the Western Cape, South Africa. Mosima uses machine learning techniques (e.g. denoising U-Nets) to emulate radiation trasport on large scale reionization simulations. Paper submitted to a NeurIPS 2022 workshop.
Ankita is a Ph.D. student in the Department of Physics, Presidency University, India. As a remote pre-doctoral student at the CCA in 2021, Ankita has been developing a flexible semi-analytical model of reionization, coupled to MCMC, that can bridge the gap between the high redshift cosmic dawn constraints (EDGES) and low redshift reionization constraints (neutral fraction, ionizing emissivity, and optical depth).
Ankita is currently generating JWST-mock galaxies images from THESAN simulations and will be using different machine learning techniques (CNNs, and normalizing flows) to recover all possible galactic properties (e.g. Mstar, SFR, Z ..etc) directly from the images.
Yu-Heng is a PhD student in the School of Physics and Astronomy at University of Minnesota. As a remote pre-doctoral student at the CCA in 2021, Yu-Heng has been developing a non-Gaussian generative model of large scale reionization maps that is based solely on summary statistics (e.g. power spectrum and wavelet phase harmonics). Paper submitted to NeurIPS 2022 workshop.
Roy is a PhD student in computer science at the Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel. As a summer scool student at the CCA in 2022, Roy has been developing the HIGlow: High Fidelity Invertible Generative Model for HI Maps. Paper submitted to NeurIPS 2022 workshop.
Bryson is a PhD student in the Astronomy department at New Meixco State University. Bryson has been developing a deep learning model to learn the spectral properties from QSOs absorption lines to provide an efficient VP fitting tool. Paper in preparation.
Nomathemba was an M.Sc. NASSP student in 2021 at University of KwaZulu-Natal, South Africa. She has been working on constraining the global history of reionization using a combination of machine learning and MCMC.
Aaron is undergraduate student at Lehigh University. As a Simons-NSBP scholars summer student at the CCA in 2021, Aaron has been using the CAMELS simulations to constrain cosmological and astrophysical parameters using a combination of machine learning emulators linked to MCMC sampler.
Jahmel is undergraduate at CUNY. As AstroCom NYC summer student. Jahmel has worked on constraining the contribution of galaxies and AGNs using machine learning emulator.
As a Kavli student fellow during the ML school in UC Santa Cruz in 2019, Claire was developing a machine learning model to reconstuct galaxy images.
As a Kavli student fellow during the ML school in UC Santa Cruz in 2019, Claire was testing several machine learning methods to constrain cluster masses from their lensed maps.