Somayeh Khakpash

I am a LSSTC Catalyst Fellow at Rutgers University. I work on applying AI methods to cautsic maps of quasar lensing events to extract useful represntations of these maps and connect them to the underlying lensing parameters. My research is designed to help model microlesning variability of lensed quasars in the era of the Rubin Observatory's Legacy Survey of Space and Time (LSST).
I received my PhD in Physics from Lehigh Univeristy under supervision of Professor Joshua Pepper in August 2020. After that, I was a postdoctoral researcher at university of Delaware working with Professor Federica Bianco for two years. I develop algorithms to extract features from the microlensing light curves and use the features to detect, classify and characterize the events in a fast and efficient way. As part of my previous postdoctoral research, I focused on analyzing light curves of supernovae, creating data-driven templates for subclasses of stripped-envelope supernovae to help classify stellar explosions photometrically and identify unusual photometric behavior.
I am the coordinator of the Rubin LSST TVS microlensing subgroup whose key activities include providing critical scientific input to Rubin Observatory to determine a final survey strategy for LSST that would benefit the microlensing science and ensure the scientific community develops tools to study microlensing in the LSST era. I was also a LSSTC Data Science Fellow and have mastered data engineering, Bayesian statistics, machine learning, image processing, and advance data visualisation. Furthermore, I am actively working as a member of the Roman Galactic Exoplanet Survey Project Infrastructure Team to prepare for the Glactic survey of the Roman Space telescope.

My research addresses fundamental astrophysical questions about the hidden populations of planets, black holes, and the dynamic universe, focusing on two upcoming major astronomical surveys: the Roman Space Telescope and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). These projects will completely change our understanding of the cosmos by capturing vast amounts of data over time, allowing us to discover and study transient astronomical events like galactic and extra-galactic microlensing, supernovae (SNe), and other dynamic phenomena. Microlensing is a unique technique that helps uncover hidden objects in space, such as free-floating planets and black holes, by observing how their gravity bends and magnifies the light from distant stars. This method is essential for discovering planetary systems that would otherwise be impossible to detect. With the Roman and Rubin surveys, which will monitor millions of stars over long periods, my work aims to develop automated tools that will allow us to efficiently detect and analyze these rare events. I began my research career focusing on planetary microlensing, where I developed automated, efficient methods to detect and characterize microlensing events in massive datasets. This work enabled faster and more accurate analysis of planetary systems, advancing our understanding of planets beyond our solar system. I have since expanded my research to include supernovae, where I have developed techniques to understand and interpret these stellar explosions using data from many different surveys over time. As a graduate of the LSST Discovery Alliance Data Science Fellowship Program, I mastered approaches aimed at storing, analyzing, and visualizing large volumes of data, including time series and images. I became an expert in Machine Learning, statistics, and Data Science. Combined with my extensive experience working on light curves (time series of brightness of astronomical objects over time) of various astronomical events, applying statistical and machine-learning methods, I have created novel models for the detection, characterization, and interpretation of data from large photometric surveys. Furthermore, as an LSST Discovery Alliance Catalyst Postdoctoral Fellow, I have gained extensive experience with deep learning models applied on image datasets. Here are some of my main publications: