ERIN HAYES

My name is Erin Hayes and I am a Gates-Cambridge Scholar studying for my PhD at the University of Cambridge’s Institute of Astronomy (IoA). At the IoA, I work with Kaisey Mandel in the field of supernova cosmology and strong lensing. Specifically, I developed GausSN, a Bayesian Gaussian Process approach for estimating the delay in time in the appearance of the multiple images of strongly lensed supernovae. The GausSN code is publicly available here. I also use BayeSN, a heirarchical Bayesian SED model, to better understand the properties of Type Ia Supernovae (SNe Ia) in order to more precisely constrain distances from SNe Ia. More broadly, I am interested in transient astronomy, dark matter and dark energy, strong lensing, large scale surveys, and astrostatistics.

Prior to arriving in Cambridge, I earned my BA and MS in Physics at the University of Pennsylvania. While there, I worked with Masao Sako to search for microlensing events in data from the Dark Energy Survey (DES) and to develop a machine learning method for classifying transients and variable objects in data from the Photometric LSST Astronomical Time-domain Classification Challenge (PLAsTiCC) in anticipation of the upcoming surge in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST) and the Nancy Grace Roman Space Telescope.

I also love sharing astronomy with the public. Currently, I am developing a series of jupyter notebooks for introducing students to coding in the field of astronomy, which are available on Github. Outside of research, I spend most of my time dancing, whether that be with the Cambridge University Dance Competition Team or with the Darwin College Cuban Salsa Group. You may also find me playing football, reading, hiking, or traveling whenever I get the opportunity.