Gravitational wave physicist
After measuring the properties of 57 Binary Black holes, we found that three of them had signs of eccentric inspirals. Furthermore, we found a >99% probability that one of the 57 systems is measurably eccentric. This hints that binary black holes may form from chaotic interactions in dense stellar environments.
Using Geospatial data from NOAA, we localized fires in California. This was done using a real-time ML pipeline and updates authorities every 5 minutes. It has now become a successful startup extending to lighting detection and drone monitoring.
We developed an algorithm which covers polygons embedded on spheres using circles. This is applied to covering gravitational wave skymaps with electromagnetic telescopes.
In a project led by M. Dax, we developed a machine learning algorithm to accurately infer the properties of a binary neutron star within one second and even before the merger.
I'm a developer for "DINGO", a machine-learning code which quickly and accurately infers the properties of compact binaries which emit gravitational waves. This code is now being used by the LIGO-Virgo-KAGRA collaboration.
Hello! I'm Nihar Gupte, a PhD student in the field of gravitational physics. My work focuses on using Bayesian inference to understand the properties of binary black holes and neutron stars. My current research interests revolve around understanding the population properties of eccentric binary black holes and developing machine learning algorithms for gravitational wave parameter estimation. I am currently based in Potsdam, Germany at the Max Planck Institute for Gravitational Physics. I have also been a member of the LIGO Scientific Collaboration since 2021. In my free time I love to draw, rock climb, play volleyball and play the flute.
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