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83 Publications visible to you, out of a total of 83
Featureless classification of light curves

Abstract

Not specified

Authors: S. D. Kügler, N. Gianniotis, K. L. Polsterer

Date Published: 25th Jun 2015

Publication Type: Journal

Autoencoding Time Series for Visualisation

Abstract

Not specified

Authors: N. Gianniotis, S. D. Kügler, P. Tino, K. L. Polsterer, R. Misra

Date Published: 1st May 2015

Publication Type: InProceedings

Determining spectroscopic redshifts by using k nearest neighbor regression. I. Description of method and analysis

Abstract

Not specified

Authors: S. D. Kügler, K. Polsterer, M. Hoecker

Date Published: 1st Apr 2015

Publication Type: Journal

Properties of optically selected BL Lacertae candidates from the SDSS

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-ray …ay 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

Improving the performance of photometric regression models via massive parallel feature selection

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

Speedy Greedy Feature Selection: Better Redshift Estimation via Massive Parallelism

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

Learning morphological maps of galaxies with unsupervised regression

Abstract

Not specified

Authors: Oliver Kramer, Fabian Gieseke, Kai Lars Polsterer

Date Published: 1st Jun 2013

Publication Type: Journal

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