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1579 Publications visible to you, out of a total of 1579

Abstract (Expand)

Context. Deep optical surveys open the avenue for finding large numbers of BL Lac objects that are hard to identify because they lack the unique properties classifying them as such. While radio or X-rayay surveys typically reveal dozens of sources, recent compilations based on optical criteria alone have increased the number of BL Lac candidates considerably. However, these compilations are subject to biases and may contain a substantial number of contaminating sources. Aims. In this paper we extend our analysis of 182 optically selected BL Lac object candidates from the SDSS with respect to an earlier study. The main goal is to determine the number of bona fide BL Lac objects in this sample. Methods. We examine their variability characteristics, determine their broad-band radio-UV spectral energy distributions (SEDs), and search for the presence of a host galaxy. In addition we present new optical spectra for 27 targets with improved signal-to-noise ratio with respect to the SDSS spectra. Results. At least 59% of our targets have shown variability between SDSS DR2 and our observations by more than 0.1–0.27 mag depending on the telescope used. A host galaxy was detected in 36% of our targets. The host galaxy type and luminosities are consistent with earlier studies of BL Lac host galaxies. Simple fits to broad-band SEDs for 104 targets of our sample derived synchrotron peak frequencies between 13.5 ≤ log 10(νpeak) ≤ 16 with a peak at log 10 ~ 14.5. Our new optical spectra do not reveal any new redshift for any of our objects. Thus the sample contains a large number of bona fide BL Lac objects and seems to contain a substantial fraction of intermediate-frequency peaked BL Lacs.

Authors: S. D. Kügler, K. Nilsson, J. Heidt, J. Esser, T. Schultz

Date Published: 1st Sep 2014

Publication Type: Journal

Abstract

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Authors: Jonathan C. Fuller, Michael Martinez, Rebecca C. Wade

Date Published: 20th Aug 2014

Publication Type: Journal

Abstract

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Authors: Ghulam Mustafa, Xiaofeng Yu, Rebecca C. Wade

Date Published: 11th Jul 2014

Publication Type: InBook

Abstract (Expand)

SABIO-RK (http://sabio.h-its.org/) is a web-accessible, manually curated database that has been established as a resource for biochemical reactions and their kinetic properties with a focus on supporting the computational modeling to create models of biochemical reaction networks. SABIO-RK data are mainly extracted from literature but also directly submitted from lab experiments. In most cases the information in the literature is distributed across the whole publication, insufficiently structured and often described without standard terminology. Therefore the manual extraction of knowledge from the literature requires biological experts to understand the paper and interpret the data. The database offers the literature data in a structured format including annotations to controlled vocabularies, ontologies and external databases which supports modellers, as well as experimentalists, in the very time consuming process of collecting information from different publications. Here we describe the data extraction and curation efforts needed for SABIO-RK and give recommendations for publishing kinetic data in a complete and structured manner.

Authors: Ulrike Wittig, Renate Kania, Meik Bittkowski, Elina Wetsch, Lei Shi, Lenneke Jong, Martin Golebiewski, Maja Rey, Andreas Weidemann, Isabel Rojas, Wolfgang Müller

Date Published: 1st May 2014

Publication Type: Journal

Abstract (Expand)

Regression tasks are common in astronomy, for instance, the estimation of the redshift or the metallicity of galaxies. Generating regression models, however, is often hindered by the heterogeneity of the available input catalogs, which leads to missing data and/or features of differing explanatory power. In this work, we show how simple but effective feature selection schemes from data mining can be used to significantly improve the performance of regression models for photometric redshift and metallicity estimation (even without any particular knowledge of the input parameters' physical properties). Our framework tests huge amounts of possible feature combinations. Since corresponding (single-core) implementations are computationally very demanding, we make use of the massive computational resources provided by nowadays graphics processing units to significantly reduce the overall runtime. This renders an exhaustive search possible, as we demonstrate in our experimental evaluation. We conclude the work by discussing further applications of our approach in the context of large-scale astronomical learning settings.

Authors: K. L. Polsterer, F. Gieseke, Christian Igel, Tomotsugu Goto

Date Published: 1st May 2014

Publication Type: InProceedings

Abstract (Expand)

Nearest neighbor models are among the most basic tools in machine learning, and recent work has demonstrated their effectiveness in the field of astronomy. The performance of these models crucially depends on the underlying metric, and in particular on the selection of a meaningful subset of informative features. The feature selection is task-dependent and usually very time-consuming. In this work, we propose an efficient parallel implementation of incremental feature selection for nearest neighbor models utilizing nowadays graphics processing units. Our framework provides significant computational speed-ups over its sequential single-core competitor of up to two orders of magnitude. We demonstrate the applicability of the overall scheme on one of the most challenging tasks in astronomy: redshift estimation for distant galaxies.

Authors: F. Gieseke, Kai L. Polsterer, Cosmin Eugen Oancea, Christian Igel

Date Published: 17th Mar 2014

Publication Type: InProceedings

Abstract

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Authors: Dagmar Waltemath, Frank T. Bergmann, Claudine Chaouiya, Tobias Czauderna, Padraig Gleeson, Carole Goble, Martin Golebiewski, Michael Hucka, Nick Juty, Olga Krebs, Nicolas Le Novère, Huaiyu Mi, Ion I. Moraru, Chris J. Myers, David Nickerson, Brett G. Olivier, Nicolas Rodriguez, Falk Schreiber, Lucian Smith, Fengkai Zhang, Eric Bonnet

Date Published: 15th Mar 2014

Publication Type: Journal

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