arXiv subjects

C. Donalek

Publications and source records attributed to C. Donalek.

26 records · Page 2Linked to original sources

arXiv2011

The Catalina Real-Time Transient Survey (CRTS)

Catalina Real-Time Transient Survey (CRTS) is a synoptic sky survey uses data streams from 3 wide-field telescopes in Arizona and Australia, covering the total area of ~30,000 deg2, down to the limiting magnitudes ~ 20 - 21 mag per exposure, with time baselines from 10 min to 6 years (and growing); there are now typically ~ 200 - 300 exposures per pointing, and coadded images reach deeper than 23 mag. The basic goal of CRTS is a systematic exploration and characterization of the faint, variable sky. The survey has detected ~ 3,000 high-amplitude transients to date, including ~ 1,000 supernovae, hundreds of CVs (the majority of them previously uncatalogued), and hundreds of blazars / OVV AGN, highly variable and flare stars, etc. CRTS has a complete open data philosophy: all transients are published immediately electronically, with no proprietary period at all, and all of the data (images, light curves) will be publicly available in the near future, thus benefiting the entire astronomical community. CRTS is a scientific and technological testbed and precursor for the grander synoptic sky surveys to come.

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arXiv2008

New Approaches to Object Classification in Synoptic Sky Surveys

Digital synoptic sky surveys pose several new object classification challenges. In surveys where real-time detection and classification of transient events is a science driver, there is a need for an effective elimination of instrument-related artifacts which can masquerade as transient sources in the detection pipeline, e.g., unremoved large cosmic rays, saturation trails, reflections, crosstalk artifacts, etc. We have implemented such an Artifact Filter, using a supervised neural network, for the real-time processing pipeline in the Palomar-Quest (PQ) survey. After the training phase, for each object it takes as input a set of measured morphological parameters and returns the probability of it being a real object. Despite the relatively low number of training cases for many kinds of artifacts, the overall artifact classification rate is around 90%, with no genuine transients misclassified during our real-time scans. Another question is how to assign an optimal star-galaxy classification in a multi-pass survey, where seeing and other conditions change between different epochs, potentially producing inconsistent classifications for the same object. We have implemented a star/galaxy multipass classifier that makes use of external and a priori knowledge to find the optimal classification from the individually derived ones. Both these techniques can be applied to other, similar surveys and data sets.

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arXiv2008

Towards Real-time Classification of Astronomical Transients

Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up.

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arXiv2008

Astrophysics in S.Co.P.E

S.Co.P.E. is one of the four projects funded by the Italian Government in order to provide Southern Italy with a distributed computing infrastructure for fundamental science. Beside being aimed at building the infrastructure, S.Co.P.E. is also actively pursuing research in several areas among which astrophysics and observational cosmology. We shortly summarize the most significant results obtained in the first two years of the project and related to the development of middleware and Data Mining tools for the Virtual Observatory.

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arXiv2008

Automated Probabilistic Classification of Transients and Variables

There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic) follow-up facilities with varied capabilities in terms of instruments, depth, cadence, wavelengths, etc., most of which are geared toward some specific astrophysical phenomenon. As the number of detected transient events grows, an automated, probabilistic classification of the detected variables and transients becomes increasingly important, so that an optimal use can be made of follow-up facilities, without unnecessary duplication of effort. We describe a methodology now under development for a prototype event classification system; it involves Bayesian and Machine Learning classifiers, automated incorporation of feedback from follow-up observations, and discriminated or directed follow-up requests. This type of methodology may be essential for the massive synoptic sky surveys in the future.

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arXiv2008

The Palomar-Quest Digital Synoptic Sky Survey

We describe briefly the Palomar-Quest (PQ) digital synoptic sky survey, including its parameters, data processing, status, and plans. Exploration of the time domain is now the central scientific and technological focus of the survey. To this end, we have developed a real-time pipeline for detection of transient sources. We describe some of the early results, and lessons learned which may be useful for other, similar projects, and time-domain astronomy in general. Finally, we discuss some issues and challenges posed by the real-time analysis and scientific exploitation of massive data streams from modern synoptic sky surveys.

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arXiv2006

Some Pattern Recognition Challenges in Data-Intensive Astronomy

We review some of the recent developments and challenges posed by the data analysis in modern digital sky surveys, which are representative of the information-rich astronomy in the context of Virtual Observatory. Illustrative examples include the problems of an automated star-galaxy classification in complex and heterogeneous panoramic imaging data sets, and an automated, iterative, dynamical classification of transient events detected in synoptic sky surveys. These problems offer good opportunities for productive collaborations between astronomers and applied computer scientists and statisticians, and are representative of the kind of challenges now present in all data-intensive fields. We discuss briefly some emergent types of scalable scientific data analysis systems with a broad applicability.

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arXiv2005

Comparison between methods for the determination of the primary cosmic ray mass composition from the longitudinal profile of atmospheric cascades

The determination of the primary cosmic ray mass composition from the longitudinal development of atmospheric cascades is still a debated issue. In this work we discuss several data analysis methods and show that if the entire information contained in the longitudinal profile is exploited, reliable results may be obtained. Among the proposed methods FCC ('Fit of the Cascade Curve'), MTA ('Multiparametric Topological Analysis') and NNA ('Neural Net Analysis') with conjugate gradient optimization algorithm give the best accuracy.

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