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Register a new userAstroCat/CVcat: A catalogue on Cataclysmic Variables based on a new framework for online interactive astronomical databases
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We report on the progress of the development of CVcat, an interactive catalogue on Cataclysmic Variables, which is the first application based on AstroCat, a general framework for the installation and maintenance of web-based interactive astronomical databases. Registered users can contribute directly to the catalogue content by adding new objects, object properties, literature references, and annotations. The scientific quality control of the catalogue is carried out by a distributed editorial team. Searches in CVcat can be performed by object name, classification, certain properties or property ranges, and coordinates. Search results can be retrieved in several output formats, including XML. Old database states can be restored in order to ensure the citability of the catalogue. Furthermore, CVcat is designed to serve as a repository for reduced data from publications. Future prospects include the integration of AstroCat-based catalogues in the international network of Virtual Observatories.
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CVcat is a database that contains published data on cataclysmic variables and related objects. Unlike in the existing online sources, the users are allowed to add data to the catalogue. The concept of an ``open catalogue approach is reviewed together with the experience from one year of public usage of CVcat. New concepts to be included in the upcoming AstroCat framework and the next CVcat implementation are presented. CVcat can be found at http://www.cvcat.org.
X-ray catalogues provide a wealth of information on many source types, ranging from compact objects to galaxies, clusters of galaxies, stars, and even planets. Thanks to the huge volume of X-ray sources provided in the 3XMM catalogue, along with many source specific products, many new examples from rare classes of sources can be identified. Through visualising spectra and lightcurves from about 80 observations included in the incremental part of the 3XMM catalogue, 3XMM-DR5, as part of the quality control of the catalogue, we identified two new X-ray sources, 3XMM J183333.1+225136 and 3XMM J184916.1+652943, that were highly variable. This work aims to investigate their nature. Through simple model fitting of the X-ray spectra and analysis of the X-ray lightcurves of 3XMM J183333.1+225136 and 3XMM J184916.1+652943, along with complementary photometry from the XMM-Newton Optical Monitor, Pan-Starrs and the Stella/WiFSIP and Large Binocular Telescope (LBT) spectra, we suggest that the two sources might be magnetic cataclysmic variables (CVs) of the polar type and we determine some of their properties. Both CVs have very hard spectra, showing no soft excess. They are both situated in the local neighbourhood, located within $sim$1 kpc. 3XMM J183333.1+225136 has an orbital period of 2.15 hours. It shows features in the lightcurve that may be a total eclipse of the white dwarf. 3XMM J184916.1+652943 has an orbital period of 1.6 hours. Given that only a small sky area was searched to identify these CVs, future sensitive all sky surveys such as the eROSITA project should be very successful at uncovering large numbers of such sources.
Interactive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system. This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those. However, such an exploration, especially when the set of available options itself can change frequently, can lead to sub-optimal user experiences. We present Explore-Exploit: a framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning. We demonstrate how to integrate this framework with run-time services to leverage online and interactive machine learning out-of-the-box. We also present results demonstrating the efficiencies that can be achieved using the Explore-Exploit framework.
Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in interactive recommender systems (IRSs). IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient. Moreover, data sparsity is another challenging problem that most IRSs are confronted with. While the textual information like reviews and descriptions is less sensitive to sparsity, existing RL-based recommendation methods either neglect or are not suitable for incorporating textual information. To address these two problems, in this paper, we propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for IRSs. Specifically, we leverage textual information to map items and users into a feature space, which greatly alleviates the sparsity problem. Moreover, we design an effective method to construct an action candidate set. By the policy vector dynamically learned from TDDPG-Rec that expresses the users preference, we can select actions from the candidate set effectively. Through experiments on three public datasets, we demonstrate that TDDPG-Rec achieves state-of-the-art performance over several baselines in a time-efficient manner.
Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Surveys (SSS) invariably suffer from a small number of spurious records coming from artefactual effects of the telescope, satellites and junk objects in orbit around earth and physical defects on the photographic plate or CCD. Though relatively small in number these spurious records present a significant problem in many situations where they can become a large proportion of the records potentially of interest to a given astronomer. In this paper we focus on the four most common causes of unwanted records in the SSS: satellite or aeroplane tracks, scratches fibres and other linear phenomena introduced to the plate, circular halos around bright stars due to internal reflections within the telescope and diffraction spikes near to bright stars. Accurate and robust techniques are needed for locating and flagging such spurious objects. We have developed renewal strings, a probabilistic technique combining the Hough transform, renewal processes and hidden Markov models which have proven highly effective in this context. The methods are applied to the SSS data to develop a dataset of spurious object detections, along with confidence measures, which can allow this unwanted data to be removed from consideration. These methods are general and can be adapted to any future astronomical survey data.
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