Related papers: Unsupervised Machine Learning for the Classificati…

The need for labeled data is among the most common and well-known practical obstacles to deploying deep learning algorithms to solve real-world problems. The current generation of learning algorithms requires a large volume of data labeled…

In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the…

The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation and…

We consider the problem of classification of points sampled from an unknown probability measure on a Euclidean space. We study the question of querying the class label at a very small number of judiciously chosen points so as to be able to…

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails…

We present followup optical g', r', and i', imaging and spectroscopy of serendipitous X-ray sources detected in 6 archival Chandra, images included in the Chandra, Multiwavelength Project (ChaMP). Of the 486 X-ray sources detected between…

Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…

In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…

The estimation of the number of point-sources in the sky is one the oldest problems in astronomy, yet an easy and efficient method for estimating the uncertainty on these counts is still an open problem. Probabilistic cataloging solves the…

We present the source catalog of a new Chandra ACIS-I observation of NGC 300 obtained as part of the Chandra Local Volume Survey. Our 63 ks exposure covers ~88% of the D25 isophote (R~6.3 kpc) and yields a catalog of 95 X-ray point sources…

The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the…

We present results of a Chandra survey of the ultra-luminous X-ray sources (ULX) in 13 normal galaxies, in which we combine source detection with X-ray flux measurement. 22 ULX were detected, i.e. with L_x > 1 x 10^{39} erg s^{-1} (L_10),…

We search for unresolved X-ray emission from lensed sources in the FOV of 11 CLASH clusters with Chandra data. We consider the solid angle in the lens plane corresponding to a magnification $\mu>1.5$, that amounts to a total of ~100…

We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…

We propose a machine-learning-based technique to determine the number density of radio sources as a function of their flux density, for use in next-generation radio surveys. The method uses a convolutional neural network trained on…

We develop a new diagnostic method to classify galaxies into AGN hosts, star-forming galaxies, and absorption-dominated galaxies by combining the [O III]/Hbeta ratio with rest-frame U-B color. This can be used to robustly select AGNs in…

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make…

A well characterised detection pipeline is an important ingredient for X-ray cluster surveys. We present the final development of the XXL Survey pipeline. The pipeline optimally uses X-ray information by combining many overlapping…

Cosmological probes based on galaxy clusters rely on cluster number counts and large-scale structure information. X-ray cluster surveys are well suited for this purpose, since they are far less affected than optical surveys by projection…

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and…