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Shaon Ghosh
Assistant Professor, Physics and Astronomy, College of Science and Mathematics
- Office:
- Richardson Hall 269C
- Email:
- ghoshs@montclair.edu
- Phone:
- 973-655-7797
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Profile
Dr. Shaon Ghosh is an astrophysicist working on detection and analysis of gravitational wave data. Gravitational waves are ripples in the fabric of space-time produced by dynamics of massive objects like black holes and neutron stars. Dr. Ghosh is a member of the LIGO Scientific Collaboration [1], a global collaboration of 1400 scientists, engineers, and software developers. In 2015 LIGO was able to detect gravitational wave for the first time from a pair of coalescing black holes [2]. This was the first demonstration of the capability of the LIGO detectors to detect gravitational waves from astrophysical objects. This discovery led to the Nobel Prize in physics in 2017 to the pioneers of the experiment, Rainer Weiss (M.I.T.), Kip Thorne (Caltech) and Barry Barish (Caltech) [3]. Since then the LIGO detectors and its European counterpart, the Virgo detectors have detected almost 100 events. Two of the detected events were from collisions of two neutron stars and two more were from coalescence of a neutron star and a black hole. One of these events, GW170817, was a collision between two neutron stars which led to the first confirmed detection of a Kilonova [4].
Dr. Ghosh’s expertise lies in the field of multi-messenger astrophysics using gravitational wave. Dr. Ghosh leads LIGO’s efforts in realtime analysis of detected gravitational wave events, known as low-latency alert generation. He has developed a machine learning based technique to rapidly infer the probability of an event detected by the LIGO and the Virgo detectors of having a neutron star [5]. the presence of a neutron star is an important information for astronomers since that may result in an electromagnetic counterpart. The neutron star during its interaction with the second object (either another neutron star, or a black hole) may get distorted and disrupted by tidal forces. This tidal disruption is one of the main contributors of the electromagnetic emission. The machine learning based inferencing tool also provides the probability of the presence of tidally disrupted matter. These two data products are among the primary data products that the LIGO Virgo collaboration provides to the public.
Dr. Ghosh is also engaged in developing tools for understanding the structure of neutron stars. Neutron stars are fascinating objects, with matter inside the neutron star predicted to be denser than the matter inside the atomic nucleus. Producing matter of such density is far beyond the capability of any laboratory. Neutron stars therefore serve as astrophysical laboratories. When two neutron stars orbit around each other their tidal forces on each other produce deformation of their structure that results in modification the emitted gravitational waves. Dr. Ghosh works on extracting the deformability information from their imprint on the gravitational waves. By measuring the tidal deformability, Dr. Ghosh makes inferences on various models of the equation of state of the matter inside the neutron star [6].
[1] https://www.ligo.org
[2] https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102
[3] https://www.nobelprize.org/prizes/physics/2017/press-release/
[4] https://en.wikipedia.org/wiki/Kilonova
[5] https://iopscience.iop.org/article/10.3847/1538-4357/ab8dbe
[6] https://journals.aps.org/prd/abstract/10.1103/PhysRevD.104.083003
Specialization
Astrophysics, Gravitational wave astronomy, Data analysis, Machine learning in astronomy.
Resume/CV
Office Hours
Fall
- Monday
- 12:00 pm - 2:00 pm
- Friday
- 12:45 pm - 1:45 pm
Links
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Documents
Research Projects
RUI: WoU-MMA: Multi-Messenger Astronomy and Astrophysics with Gravitational-Wave Data
The goal of the project is to update LIGO-Virgo-Kagra collaboration's low-latency infrastructure of the source-classification and source-properties inference for the fourth observing run. This requires making the infrastructure compatible with the O4 low-latency system, which involves preparing and training of a machine learning based classifier using massive simulation studies. The PI also plans to update the classifier to use more realistic models of neutron star equation of state. The second goal of the project is to improve the rapid model selection tool GWXtreme that the PI created during the third observing run so that it can be used with a diverse array of gravitational wave waveforms. This will also allow the code to be used for neutron star - black hole binary coalescences.