35th Annual Report (2022-2023) - ENGLISH

167 ResearchWorkdone: Some of research works carried out during this period are stated below: (a) In the field of Astrostatistics, clustering and classification of different astronomical objects play a very important role. In cluster analysis, the objective is to group the items such that items in the same cluster aremore closely related than those assigned to different clusters. The total number of clusters in the data setmay be known in some cases and maybe unknown in others. There are different methods available for clustering, which can be further categorized under supervised and unsupervised learning techniques. In the case of supervised learning, there are somemodel assumptions but in the case of unsupervised learning, there are no such assumptions. Under both the above-mentioned categories, for clustering and classification, various methods have been developed depending on the nature of the data sets. However, generally, it is difficult to compare the performances of the different techniques. Here we have tried to compare the applicability of some of the clustering techniques on a galaxy data set. To justify the robustness of the variety of unsupervised methods used in our work, a few post-classification techniques are used as supervised learning. Finally, the comparability of clusters, obtained by different techniques, is studied with respect to an ad-hoc technique and they are further justified in terms of astrophysical properties of the galaxies. Our main focus is on unsupervised machine learning algorithms, which are used to perform dimensionality reduction, cluster analysis, visualization and to get an idea regarding the best-unsupervised technique that is appropriate for a galaxy data set. It is found that K- means performs best for the galaxy data set under consideration. (b) We revisit the problem of clustering 1318 new variable stars found in the Milky way. Our recent work distinguishes these stars based on their light curves which are univariate series of brightness from the stars observed at discrete time points. This work proposes a new approach to look at these discrete series as continuous curves over time by transforming them into functional data. Then, functional principal component analysis is performed using these functional light curves. Clustering based on the significant functional principal components reveals two distinct groups of eclipsing binaries with consistency and superiority compared to our previous results. This method is established as a new powerful light curve-based classifier, where implementation of a simple clustering algorithm is effective enough to uncover the true clusters based merely on the first few relevant functional principal components. Simultaneously we discard the noise from the data study involving the higher order functional principal components. Thus the suggestedmethod is very useful for clustering big light curve data sets which is also verified by our simulation study. (c) The star formation histories and chemical evolution of a dwarf spiral galaxy NGC 2403 and a massive spiral galaxy NGC 628 are studied in this work through a simple chemical evolutionary model under the influence of several supernovae-driven galactic outflows. The galactic disk of each galaxy is considered as a collection of some concentric rings each of which evolves independently without exchanging matters among themselves. The disk is formed through continuous accretion of pristine gas from halo. A classical Kennicutt–Schmidt star formation law is taken into account with an exponential gas accretion rate. In order to analyze the impact of outflow, we have taken into account two separate types of supernovae-driven gas outflow, namely supernovae momentum-driven outflow and supernovae energy-driven outflow, both of which depend on the circular velocity of the disk. By comparing our model's anticipated result with observational data, the most viable models are chosen. For the dwarf galaxy NGC 2403, the supernovae energy-driven outflow model yields a better result which indicates that the supernovae energy-driven outflow mechanismplays amajor role in driving the outflows in low mass galaxies. However, for NGC 628, both the outflow models adequately account for the observed features, suggesting that both momentum-driven and energy- driven outflows contribute equally to the outflows of the massive galaxy NGC 628. Furthermore, we contrasted the evolution of radial and global properties of these galaxies. (d) We have tried to explore the origin of the formation of star clusters in our Galaxy and in the Small Magellanic Cloud (SMC) through simulated H-R diagrams and compare those with observed star clusters. The simulation study produces synthetic H-R diagrams through the Markov Chain Monte Carlo (MCMC) technique using the star formation history (SFH), luminosity function (LF), abundance of heavy metal (Z), and a big library of isochrones as basic inputs and compares them with observed H-R diagrams of various star clusters. The distancebased comparison between those two diagrams is carried out through two-dimensional matching of points in the color−magnitude diagram (CMD) after the optimal choice of bin size and appropriate distance function. It is found that in a poor medium of heavy elements (Z = 0.0004), the Gaia LF along with a mixture of multiple Gaussian distributions of the SFH may be the origin of formation of globular clusters (GCs). On the contrary, an enriched medium (Z = 0.019) is generally favoredwith the Gaia LF along with a double power law or Beta-type (i.e., unimodal) SFH, for the formation of globular clusters. For SMC clusters, the choice of an exponential LF and exponential SFH is the proper combination for a poor medium, whereas the Gaia LF with a Beta-type