Related papers: Classification of Pulsars using Extreme Deconvolut…

Machine learning, algorithms to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using…

We carry out a classification of the glitch amplitudes of radio pulsars using Extreme Deconvolution technique based on the Gaussian Mixture Model, where the observed uncertainties in the glitch amplitudes $\Delta \nu/\nu$ are taken into…

Recently, Lee et al. used Gaussian mixture models (GMM) to study the radio pulsar population. In the distribution of normal pulsars in the P-dotP plane, they found four clusters. We develop this approach further and apply it to different…

We describe an empirical Bayesian approach to determine the most likely size of an astronomical population of sources of which only a small subset are observed above some limiting flux density threshold. The method is most naturally applied…

We use both the conventional and more recently developed methods of cluster analysis to study the data of extra-solar planets. Using the data set with planetary mass M, orbital period P, and orbital eccentricity e, we investigate the…

The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by…

The Extreme Deconvolution method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting…

As performance of dedicated facilities continually improved, massive pulsar candidates are being received, which makes selecting valuable pulsar signals from candidates challenging. In this paper, we designed a deep convolutional neural…

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope (FAST) Survey (CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts…

Open clusters are groups of stars that form at the same time, making them an ideal laboratory to test theories of star formation, stellar evolution, and dynamics in the Milky Way disk. However, the utility of an open cluster can be limited…

We place limits on the population of non-recycled pulsars originating in globular clusters through Monte Carlo simulations and frequentist statistical techniques. We set upper limits on the birth rates of non-recycled cluster pulsars and…

More than 100 radio pulsars have been detected in 24 globular clusters. The largest observed samples are in Terzan 5 and 47 Tucanae, which together contain 45 pulsars. Accurate timing solutions, including positions in the cluster, are known…

A major challenge in cluster analysis is that the number of data clusters is mostly unknown and it must be estimated prior to clustering the observed data. In real-world applications, the observed data is often subject to heavy tailed noise…

Up to November 2022, 267 pulsars have been discovered in 36 globular clusters (GCs). In this paper, we present our studies on the distribution of GC pulsar parameters and the detection efficiency. The power law relation between the average…

Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true underlying structure in the observed data is corrupted by…

We explore the enigmatic population of long-period, apparently non-recycled pulsars in globular clusters, building on recent work by Boyles et al (2011). This population is difficult to explain if it formed through typical core collapse…

We study the evolution of galaxies in clusters by the analysis of a sample of about 3000 galaxies, members of 59 clusters from the ESO Nearby Abell Cluster Survey (ENACS). We distinguish four cluster galaxy populations, based on their…

We propose a Fourier-based approach for optimization of several clustering algorithms. Mathematically, clusters data can be described by a density function represented by the Dirac mixture distribution. The density function can be smoothed…

Cluster analysis of biological samples using gene expression measurements is a common task which aids the discovery of heterogeneous biological sub-populations having distinct mRNA profiles. Several model-based clustering algorithms have…

In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the…