Related papers: Statistical Algorithms for Identification of Astro…
The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for…
The next generation of telescopes will acquire terabytes of image data on a nightly basis. Collectively, these large images will contain billions of interesting objects, which astronomers call sources. The astronomers' task is to construct…
X-ray spectroscopy is a powerful technique for the analysis of the energy distribution of X-rays from astrophysical sources. It allows for the study of the properties, composition, and physical processes taking place at the site of…
We present an algorithm capable of detecting diffuse, dim sources of any size in an astronomical image. These sources often defeat traditional methods for source finding, which expand regions around points of high intensity. Extended…
This review outlines concepts of mathematical statistics, elements of probability theory, hypothesis tests and point estimation for use in the analysis of modern astronomical data. Least squares, maximum likelihood, and Bayesian approaches…
The XMM identification programme (XID) is accumulating Optical/IR images and spectra to identify thousands of XMM sources. The XID Database is the main repository of this data and permits easy selection and correlation of XMM X-ray and UV…
The search for faint emission or absorption lines in astronomical spectra has received considerable attention in recent years, especially in the X-ray wavelength range. These features usually appear as a deficit or excess of counts in a…
We present a new method for determining the sensitivity of X-ray imaging observations, which correctly accounts for the observational biases that affect the probability of detecting a source of a given X-ray flux, without the need to…
A variety of statistical methods for understanding variability in the time domain for low count rate X-ray and gamma-ray sources are explored. Variability can be detected using nonparametric (Anderson-Darling and overdispersion tests) and…
The rapid increase in serendipitous X-ray source detections requires the development of novel approaches to efficiently explore the nature of X-ray sources. If even a fraction of these sources could be reliably classified, it would enable…
We test the effectiveness of photometric redshifts based upon galaxy spectral template fitting for X-ray luminous objects, using a sample of 65 sources detected by Chandra in the field of the Caltech Faint Galaxy Redshift Survey (CFGRS). We…
Extensive astronomical surveys, like those conducted with the {\em Chandra} X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classify…
Today's sensistive, high resolution Chandra X-ray observations allow the study of many populations of X-ray sources. The traditional astronomical tools of photometric diagrams and luminosity functions are now applied to these populations,…
The light we receive from distant astrophysical objects carries information about their origins and the physical mechanisms that power them. The study of these signals, however, is complicated by the fact that observations are often a…
The first release of the Chandra Source Catalog (CSC) contains ~95,000 X-ray sources in a total area of ~0.75% of the entire sky, using data from ~3,900 separate ACIS observations of a multitude of different types of X-ray sources. In order…
When it comes to identifying or to characterizing gamma-ray sources, X-ray observations are of paramount importance. Correlated X-and-gamma-ray flux variations are a powerful identification tool, if the gamma-ray source is unidentified, or…
We describe an X-ray source detection method entirely based on the maximum likelihood analysis, in application to observations with the ART-XC telescope onboard the Spectrum Roentgen Gamma observatory. The method optimally combines the data…
The field of gamma ray astronomy relies heavily on the statistical analysis of data. Because of the paucity of data, and the often large errors associated with detecting gamma rays, analysis and interpretation of the data require…
To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of…
We present a general probabilistic formalism for cross-identifying astronomical point sources in multiple observations. Our Bayesian approach, symmetric in all observations, is the foundation of a unified framework for object matching,…