Related papers: Gaussian mixture model and population synthesis of…
We present preliminary results of a pulsar population synthesis of normal pulsars from the Galactic disk using a Markov Chain Monte Carlo method to better understand the parameter space of the assumed model. We use the Kuiper test, similar…
Gaussian mixture models (GMM) are the most widely used statistical model for the $k$-means clustering problem and form a popular framework for clustering in machine learning and data analysis. In this paper, we propose a natural semi-random…
We present results of our pulsar population synthesis of normal pulsars from the Galactic disk using our previously developed computer code. On the same footing, we use slot gap and outer gap models for gamma-ray emission from normal…
We present results of our pulsar population synthesis of ordinary isolated and millisecond pulsars in the Galactic plane. Over the past several years, a program has been developed to simulate pulsar birth, evolution and emission using Monte…
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 consider the problem of clustering data points in high dimensions, i.e. when the number of data points may be much smaller than the number of dimensions. Specifically, we consider a Gaussian mixture model (GMM) with non-spherical…
We present results of a population synthesis of millisecond pulsars from the Galactic disk. Excluding globular clusters, we model the spatial distribution of millisecond pulsars by assuming their birth in the Galactic disk with a random…
Gaussian Mixture Models (GMM) do not adapt well to curved and strongly nonlinear data. However, we can use Gaussians in the curvilinear coordinate systems to solve this problem. Moreover, such a solution allows for the adaptation of…
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 carry out a classification of the observed pulsar dataset into distinct clusters, based on the $P-\dot{P}$ diagram, using Extreme Deconvolution based Gaussian Mixture Model. We then use the Bayesian Information Criterion to select the…
The mass and spin distributions of compact binary gravitational-wave sources are currently uncertain due to complicated astrophysics involved in their formation. Multiple sub-populations of compact binaries representing different…
This paper investigates Gaussian copula mixture models (GCMM), which are an extension of Gaussian mixture models (GMM) that incorporate copula concepts. The paper presents the mathematical definition of GCMM and explores the properties of…
We present the results of a population synthesis of radio and gamma-ray millisecond pulsars (MSPs) from the Galactic Disk (GD). Using 92 radio millisecond pulsars detected in 13 radio surveys and 54 Fermi MSPs detected as point sources in…
Population synthesis studies constitute a powerful method to reconstruct the birth distribution of periods and magnetic fields of the pulsar population. When this method is applied to populations in different wavelengths, it can break the…
We review current understanding of the underlying, as opposed to the observed, pulsar population. The observed sample is heavily biased by selection effects, so that surveys see less than 10% of all potentially observable pulsars. We…
This thesis work represents the first efforts to combine population synthesis studies of the Galactic isolated neutron stars with deep-learning techniques with the aim of better understanding neutron-star birth properties and evolution. In…
Based on well-grounded Galactic neutron star populations formed from radio pulsar population syntheses of canonical pulsars (CPs) and millisecond pulsars (MSPs), we use the latest Fermi-LAT catalog (4FGL-DR4) to investigate the implications…
In Pulsar Timing Array (PTA) data analysis, noise is typically assumed to be Gaussian, and the marginalized likelihood has a well-established analytical form derived within the framework of Gaussian processes. However, this Gaussianity…
Standard Gaussian graphical models (GGMs) implicitly assume that the conditional independence among variables is common to all observations in the sample. However, in practice, observations are usually collected form heterogeneous…
We present a new framework to detect various types of variable objects within massive astronomical time-series data. Assuming that the dominant population of objects is non-variable, we find outliers from this population by using a…