ASTROIMAGEJ: IMAGE PROCESSING AND PHOTOMETRIC EXTRACTION FOR ULTRA-PRECISE ASTRONOMICAL LIGHT CURVES

Neural Network Performances in Astronomical Image Processing

In this paper we use neural networks to verify the similarity of real astronomical images to predefined reference profiles. We use an innovative technique to encode images that associates each of them with its most convenient moments, evaluated along the {x, y} axes; in this way we obtain a parsimonious but effective method with respect to the usual pixel by pixel description.

Astronomical Image Processing with Array Detectors

We address the question of astronomical image processing from data obtained with array detectors. We define and analyze the cases of evenly, regularly, and irregularly sampled maps for idealized (i.e., infinite) and realistic (i.e., finite) detectors. We concentrate on the effect of interpolation on the maps, and the choice of the kernel used to accomplish this task. We show how the normalizati...

Supervised detection of anomalous light-curves in massive astronomical catalogs

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the ai...

Building a scalable global data processing pipeline for large astronomical photometric datasets

Astronomical photometry is the science of measuring the flux of a celestial object. Since its introduction in the 1970s the CCD has been the principle method of measuring flux to calculate the apparent magnitude of an object. Each CCD image taken must go through a process of cleaning and calibration prior to its use. As the number of research telescopes increases the overall computing resources...

Feature Extraction and Image Processing