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Published in JCAP, 2023
We investigate the prospects of eLISA in addressing the Hubble tension using a three-pronged approach.
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Published in ApJ, 2023
We investigate the prospects of Gaussian processes in reconstructing the Hubble parameter and the Hubble constant, using simulated data, in light of future gravitational wave missions.
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Published in arXiv, 2024
We do a careful investigation of the prospects of dark energy (DE) interacting with cold dark matter (CDM) in alleviating the S8 clustering tension.
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Published in MNRAS, 2024
We study how future Type-Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models.
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Published in ApJS, 2024
We investigate the prospect of reconstructing the cosmic distance ladder of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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