Rutgers University Astrophysics Seminar

Star formation histories (SFHs) encode the rich and complex evolution of galaxies over cosmic time. In recent years, we have made significant progress in understanding SFHs, thanks to a combination of new observational data, theoretical insights, and advances in machine learning. In this talk, I will focus on: (i) how we infer SFH information from observations, for both individual objects and ensembles of galaxies, (ii) how we can use cosmological simulations to understand the connections between star formation and the underlying physical processes by studying variability (or 'burstiness') on different timescales, and (iii) the role of novel methods at the intersection of astrostatistics and machine learning to tackle challenges posed by large datasets from current and upcoming observatories. I will conclude with a zoomed out view of the landscape of galaxy evolution research enabled by JWST, and the many things to look forward to with Rubin, Roman, and Euclid in the years ahead
Recommended Reading:
Iyer & Speagle et al. 2024 https://ui.adsabs.harvard.edu/abs/2024ApJ...961...53I/abstract
Iyer et al. 2020 https://ui.adsabs.harvard.edu/abs/2020MNRAS.498..430I/abstract
Iyer et al. 2019 https://ui.adsabs.harvard.edu/abs/2019ApJ...879..116I/abstract