Asis Kumar Chattopadhyay

Bio: Asis Kumar Chattopadhyay is an academic researcher from University of Calcutta. The author has contributed to research in topics: Cluster analysis & Galaxy. The author has an hindex of 15, co-authored 67 publications receiving 648 citations. Previous affiliations of Asis Kumar Chattopadhyay include Indian Statistical Institute.

Statistical Evidence for Three Classes of Gamma-Ray Bursts

Abstract: Two different multivariate clustering techniques, the K-means partitioning method and the Dirichlet process of mixture modeling, have been applied to the BATSE gamma-ray burst (GRB) catalog, to obtain the optimum number of coherent groups. In the standard paradigm, GRBs are classified into only two groups, the long and short bursts. However, for both of the clustering techniques, the optimal number of classes was found to be three, a result that is consistent with previous statistical analysis. In this classification, the long bursts are further divided into two groups that are primarily differentiated by their total fluence and duration and hence are called low- and high-fluence GRBs. Analysis of GRBs with known redshifts and spectral parameters suggests that low-fluence GRBs have nearly constant isotropic energy output of 1052 ergs, while for the high-fluence ones the energy output ranges from 1052 to 1054 ergs. It is speculated that the three kinds of GRBs reflect three different origins: mergers of neutron star systems, mergers between white dwarfs and neutron stars, and collapse of massive stars.

Structures in the fundamental plane of early‐type galaxies

Abstract: The fundamental plane of early-type galaxies is a rather tight three-parameter correlation discovered more than twenty years ago. It has resisted a both global and precise physical interpretation despite a consequent number of works, observational, theoretical or using numerical simulations. It appears that its precise properties depend on the population of galaxies in study. Instead of selecting a priori these populations, we propose to objectively construct homologous populations from multivariate analyses. We have undertaken multivariate cluster and cladistic analyses of a sample of 56 low-redshift galaxy clusters containing 699 early-type galaxies, using four parameters: effective radius, velocity dispersion, surface brightness averaged over effective radius, and Mg2 index. All our analyses are consistent with seven groups that define separate regions on the global fundamental plane, not across its thickness. In fact, each group shows its own fundamental plane, which is more loosely defined for less diversified groups. We conclude that the global fundamental plane is not a bent surface, but made of a collection of several groups characterizing several fundamental planes with different thicknesses and orientations in the parameter space. Our diversification scenario probably indicates that the level of diversity is linked to the number and the nature of transforming events and that the fundamental plane is the result of several transforming events. We also show that our classification, not the fundamental planes, is universal within our redshift range (0.007 -- 0.053). We find that the three groups with the thinnest fundamental planes presumably formed through dissipative (wet) mergers. In one of them, this(ese) merger(s) must have been quite ancient because of the relatively low metallicity of its galaxies, Two of these groups have subsequently undergone dry mergers to increase their masses. In the k-space, the third one clearly occupies the region where bulges (of lenticular or spiral galaxies) lie and might also have formed through minor mergers and accretions. The two least diversified groups probably did not form by major mergers and must have been strongly affected by interactions, some of the gas in the objects of one of these groups having possibly been swept out. The interpretation, based on specific assembly histories of galaxies of our seven groups, shows that they are truly homologous. They were obtained directly from several observables, thus independently of any a priori classification. The diversification scenario relating these groups does not depend on models or numerical simulations, but is objectively provided by the cladistic analysis. Consequently, our classification is more easily compared to models and numerical simulations, and our work can be readily repeated with additional observables.

Study of NGC 5128 Globular Clusters Under Multivariate Statistical Paradigm

Abstract: An objective classification of the globular clusters (GCs) of NGC 5128 has been carried out by using a model-based approach of cluster analysis. The set of observable parameters includes structural parameters, spectroscopically determined Lick indices and radial velocities from the literature. The optimum set of parameters for this type of analysis is selected through a modified technique of principal component analysis, which differs from the classical one in the sense that it takes into consideration the effects of outliers present in the data. Then a mixture model based approach has been used to classify the GCs into groups. The efficiency of the techniques used is tested through the comparison of the misclassification probabilities with those obtained using the K-means clustering technique. On the basis of the above classification scheme three coherent groups of GCs have been found. We propose that the clusters of one group originated in the original cluster formation event that coincided with the formation of the elliptical galaxy, and that the clusters of the two other groups are of external origin, from tidally stripped dwarf galaxies on random orbits around NGC 5128 for one group, and from an accreted spiral galaxy for the other.

A six-parameter space to describe galaxy diversification

Abstract: Galaxy diversification proceeds by transforming events like accretion, interaction or mergers. These explain the formation and evolution of galaxies that can now be described with many observables. Multivariate analyses are the obvious tools to tackle the datasets and understand the differences between different kinds of objects. However, depending on the method used, redundancies, incompatibilities or subjective choices of the parameters can void the usefulness of such analyses. The behaviour of the available parameters should be analysed before an objective reduction of dimensionality and subsequent clustering analyses can be undertaken, especially in an evolutionary context. We study a sample of 424 early-type galaxies described by 25 parameters, ten of which are Lick indices, to identify the most structuring parameters and determine an evolutionary classification of these objects. Four independent statistical methods are used to investigate the discriminant properties of the observables and the partitioning of the 424 galaxies: Principal Component Analysis, K-means cluster analysis, Minimum Contradiction Analysis and Cladistics. (abridged)

A six-parameter space to describe galaxy diversification

Abstract: Galaxy diversification proceeds by transforming events like accretion, interaction or mergers. These explain the formation and evolution of galaxies that can now be described with many observables. Multivariate analyses are the obvious tools to tackle the datasets and understand the differences between different kinds of objects. However, depending on the method used, redundancies, incompatibilities or subjective choices of the parameters can void the usefulness of such analyses. The behaviour of the available parameters should be analysed before an objective reduction of dimensionality and subsequent clustering analyses can be undertaken, especially in an evolutionary context. We study a sample of 424 early-type galaxies described by 25 parameters, ten of which are Lick indices, to identify the most structuring parameters and determine an evolutionary classification of these objects. Four independent statistical methods are used to investigate the discriminant properties of the observables and the partitioning of the 424 galaxies: Principal Component Analysis, K-means cluster analysis, Minimum Contradiction Analysis and Cladistics. (abridged)

Dwarf galaxies, cold dark matter, and biased galaxy formation

Abstract: A reexamination is conducted of the formation of dwarf, diffuse, metal-poor galaxies due to supernova-driven winds, in view of data on the systematic properties of dwarfs in the Local Group and Virgo Cluster. The critical condition for global gas loss as a result of the first burst of star formation is that the virial velocity lie below an approximately 100 km/sec critical value. This leads, as observed, to two distinct classes of galaxies, encompassing the diffuse dwarfs, which primarily originate from typical density perturbations, and the normal, brighter galaxies, including compact dwarfs, which can originate only from the highest density peaks. This furnishes a statistical biasing mechanism for the preferential formation of bright galaxies in denser regions, enhancing high surface brightness galaxies' clustering relative to the diffusive dwarfs.

The afterglows of swift-era gamma-ray bursts. ii. type i grb versus type ii grb optical afterglows*

Abstract: We use a large sample of GRB afterglow and prompt-emission data (adding further GRB afterglow observations in this work) to compare the optical afterglows (or the lack thereof) of Type I GRBs with those of Type II GRBs. In comparison to the afterglows of Type II GRBs, we find that those of Type I GRBs have a lower average luminosity and show an intrinsic spread of luminosities at least as wide. From late and deep upper limits on the optical transients, we establish limits on the maximum optical luminosity of any associated supernova, confirming older works and adding new results. We use deep upper limits on Type I GRB optical afterglows to constrain the parameter space of possible mini-SN emission associated with a compact-object merger. Using the prompt emission data, we search for correlations between the parameters of the prompt emission and the late optical afterglow luminosities. We find tentative correlations between the bolometric isotropic energy release and the optical afterglow luminosity at a fixed time after trigger (positive), and between the host offset and the luminosity (negative), but no significant correlation between the isotropic energy release and the duration of the GRBs. We also discuss three anomalous GRBs, GRB 060505, GRB 060614, and GRB 060121, in the light of their optical afterglow luminosities. (Abridged)

Data Mining and Machine Learning in Astronomy

Abstract: We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

Data Mining and Machine Learning in Astronomy

Abstract: We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, ...

The third fermi gbm gamma-ray burst catalog: the first six years

Abstract: Since its launch in 2008, the Fermi Gamma-ray Burst Monitor (GBM) has triggered and located on average approximately two.-ray bursts (GRBs) every three days. Here, we present the third of a series ...