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Soma Mukherjee

Researcher at University of Texas at Austin

Publications -  280
Citations -  72580

Soma Mukherjee is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: LIGO & Gravitational wave. The author has an hindex of 95, co-authored 266 publications receiving 59549 citations. Previous affiliations of Soma Mukherjee include The University of Texas Rio Grande Valley & Saha Institute of Nuclear Physics.

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Searches for continuous gravitational waves from young supernova remnants in the early third observing run of Advanced LIGO and Virgo

Richard J. Abbott, +1587 more
TL;DR: In this article, the authors present results of three wideband directed searches for continuous gravitational waves from 15 young supernova remnants in the first half of the third Advanced LIGO and Virgo observing run.
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A Statistical Study on In vivo Sorption and Desorption of Water in Ichthyosis Vulgaris

TL;DR: The study discusses the impaired barrier function of the ichthyotic skin and an exponential model of the form ft=θoeθt, where ft denotes fractional conductance at time t, describes the process of desorption with high accuracy.
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ENHANCED 0(g.s.)(+) -> 2(1)(+) E2 TRANSITION STRENGTH IN Sn-112,Sn-114

TL;DR: In this paper, two Coulomb excitation experiments were performed to determine the reduced transition probabilities relative to Sn-116 in order to minimize the systematic errors, and the obtained B(E2 up arrow) values of 0.242(8) e(2)b(2).

Search for Gravitational Waves Associated with Fast Radio Bursts Detected by CHIME/FRB During the LIGO--Virgo Observing Run O3a

The Ligo Scientific Collaboration, +1635 more
TL;DR: In this paper , a targeted search for generic gravitational-wave transients associated with fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project (CHIME/FRB) during the first part of the third observing run of Advanced LIGO and Advanced Virgo was conducted.
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Interferometric data modelling: Issues in realistic data generation

TL;DR: This study describes algorithms developed for modelling interferometric noise in a realistic manner, i.e. incorporating non-stationarity that can be seen in the data from the present generation of interferometers.