Kinjal Dhar Gupta

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Our study centers on the classification of two subtypes of Cepheid variable stars. Such classification is relatively easy to obtain for nearby galaxies, but as we incorporate new galaxies, the cost of labeling stars calls for some form of model adaptation. Adapting a predictive model to differentiate Cepheids across galaxies is difficult because of the(More)
We address a particular scenario within the area of domain adaptation, where a predictive model obtained from a source domain can be applied directly to a target domain. Both source and target domains share the same input or feature space, but we do not impose any restrictions on the marginal and class posterior distributions (both distributions can(More)
Astroinformatics is an interdisciplinary field of science that applies modern computational tools to the solution of astronomical problems. One relevant subarea is the use of machine learning for analysis of large astronomical repositories and surveys. In this paper we describe a case study based on the classification of variable Cepheid stars using domain(More)
While the current supernova (SN) photometric classification system is based on high resolution spectroscopic observations, the next generation of large scale surveys will be based on photometric light curves of supernovae gathered at an unprecedented rate. Developing an efficient method for SN photometric classification is critical to cope with the rapid(More)
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