Deep Sky Images Research Articles

Intelligent constellation detection using pattern recognition

The study of constellations and the mechanism for its identification has always been an captivating area in astronomy, from ancient methods like star charts, to modern techniques of computer vision. Along with continuous discoveries of new celestial objects on a regular basis, the study of constellations are getting equal attention in astronomical data analysis. In this paper, we present an intelligent constellation detection algorithm that identifies the constellation using star patterns in the given image and produces meta-data for it to the end user. That is, for a given a deep sky image, the system effectively detects the stars, produces the orientation and labels the stars and constellations patterns, maintains the database of the images and the detected objects. The system provides a comprehensive user interface for data input and to display the results obtained. The system demonstrated 90% success rate in correctly identifying sources and mapping the constellations. The system also serves as a valuable platform for astronomers studying deep sky images enabling the identification of anomalies in the images such as unidentified objects not cataloged or previously unidentified in constellation images.

Heron and Alpha-Centroidal Mean Filters with Exponential factors for Deep Space Images

In this article, we discuss empirical methods that can be add-on to enhance spatial image filters. Discussing on the implementation of such methods for Deep-Sky images from space telescopes. Spatial image filtering houses mean filters that use kernel method to filter a digital image in the spatial domain. To improve the performance of the existing filters and aid the new upcoming filter, a technique is developed that can be added to the filter to improve their performance. This study gives a detailed analysis of an empirical weight factor and exponential factoring methods for the mean filters that are proposed and implemented. The exponential factoring is applied to the recent mean filters Heron, Centroidal and Inverse-Contraharmonic. Performance analysis of both methods and comparison to existing mean filtering performance are represented. The images are classified into white or black based background and analyzed for performance parameters, comparing the results from the filters and filters with the exponential factoring. The empirical methods were proven to improve the efficiency in denoising of images of the existing mean filters and the new Heron and Centroidal mean filters, hence can be used as add-ons to existing spatial mean filters. One of the empirical techniques, exponential factoring is promising in denoising high density noised images. This novel method is implemented to filter noise from Deep Space images taken by space telescopes. Ultra-high-resolution images are filtered and implemented on Single Board Computers for remote handling, with testing done on MATLAB.

Stars’ Centroid Determination Using PSF-Fitting Method

AbstractThis paper presents an algorithm for restoring telescope images corrupted by turbulence effects and readout noise of a telescope system in order to determine centroid positions of stars, especially the position of a reference star. A computation method employing an accurate centroid estimation algorithm reconstructing apoint spread function(PSF) from the recorded astronomical images has been used. Minimisation of turbulence effects and telescope control system noise in long exposure images acquired and recorded by the ground telescope is proposed. As a solution of the distortion error a minimisation signal is dedicated to GoTo calibration procedures built in control mechanisms of the electromechanical telescope system. The proposed method has been verified in the Matlab environment for real deep sky images recorded by the ground telescope system.

Image simulation with shapelets

We present a method to simulate deep sky images, including realistic galaxy morphologies and telescope characteristics. To achieve a wide diversity of simulated galaxy morphologies, we first use the shapelets formalism to parametrize the shapes of all objects in the Hubble Deep Fields. We measure this distribution of real galaxy morphologies in shapelet parameter space, then resample it to generate a new population of objects. These simulated galaxies can contain spiral arms, bars, discs, arbitrary radial profiles and even dust lanes or knots. To create a final image, we also model observational effects, including noise, pixellization, astrometric distortions and a point-spread function. We demonstrate that they are realistic by showing that simulated and real data have consistent distributions of morphology diagnostics: including galaxy size, ellipticity, concentration and asymmetry statistics. Sample images are made available on the world-wide web. These simulations are useful to develop and calibrate precision image analysis techniques for photometry, astrometry and shape measurement. They can also be used to assess the sensitivity of future telescopes and surveys for applications such as supernova searches, microlensing, proper motions and weak gravitational lensing.

A CCD CAMERA SYSTEM AT CAPILLA PEAK OBSERVATORY, NEW MEXICO

ABSTRACT We describe retrofitting a 61-cm Boller and Chivens reflector with a commercial CCD camera system for deep-sky imaging. Such an equipment upgrade can be done to similar small telescopes for similar costs. This increased sensitivity of the CCD compared to previous detectors greatly enhances the ability of small telescopes to perform many types of observations. The productivity of the telescope is greatly enhanced because of the new regimes of astronomical science opened by the instrumentation upgrade.