The field of astrostatistics is at the intersection of astronomy, astro-particle physics, astrophysics, computer science, cosmology, data science, machine learning, solar physics, and statistics. Astrostatistics has manifold opportunities for statisticians and data scientists. Challenges exist from the very small scale including the nature of dark matter particles or the detection and characterization of exoplanets, to the very large scale such as inference on physical properties of the formation and evolution of our Universe. I have had the opportunity to contribute to several areas of astronomy and cosmology, including the detection and characterization of exoplanets, inference and structure detection of the large-scale structure (LSS), modeling the distant Universe using Lyman-alpha forest data, and inference on the stellar initial mass function.
My current research is focused on statistical issues in exoplanet astronomy (ExoStatistics), along with topological data analysis methodology for the large-scale structure of the Universe.
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Topological Data Analysis
Complicated spatial structures are common in many areas of science, but are difficult to quantitatively analyze without losing important information. Examples of spatially complex, but geometrically similar, structures are fibrin clots in biology and the large-scale structure of the Universe. My research includes developing methods using topological summaries of data called persistence diagrams that retrieve and quantify crucial information that is missed in ad-hoc methods by specifically targeting shape-related features.
For a non-technical introduction to TDA in cosmology, see Green, Mintz, Xu, and Cisewski-Kehe (2019) Topology of our cosmology with persistent homology. Chance, 32(3), pp.6-13. [paper]
An exoplanet is a planet that is not our Sun. Astronomers have developed a variety of methods for detecting exoplanets, with one of the most successful being the Radial Velocity (RV) method. The newest instruments (e.g., EXPRES, NEID) used to detect exoplanets with the RV method are highly precise and stable allowing for the possibility of uncovering a population of low-mass exoplanets, such as Earth analogs. To fully employ these excellent data and detect the tiny signals left by this sought-after population of exoplanets, sophisticated statistical methods are needed. Serious challenges to the detection and characterization of these low-mass exoplanets include addressing issues related to stellar activity that can hide or mimic exoplanet signals in the spectra. My research includes developing methods to aid in the detection of low-mass exoplanets, such as Earth analogs, using extreme precision RV spectra in the presence of stellar activity.
For a non-technical introduction overview of this area, see Cisewski (2017) In search of Earth analogues: Detecting exoplanets amid stellar noise. Significance, 14(2), pp.22-25. [paper]