Zobrazeno 1 - 10
of 293
pro vyhledávání: '"BRUNNER, ROBERT J."'
The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accuracy for the star-galaxy classification task, based on manually labeled
Externí odkaz:
http://arxiv.org/abs/1910.14056
We present an extension to the model-free anomaly detection algorithm, Isolation Forest. This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points. We motivate the problem using heat
Externí odkaz:
http://arxiv.org/abs/1811.02141
Autor:
Kim, Edward J., Brunner, Robert J.
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine to automa
Externí odkaz:
http://arxiv.org/abs/1608.04369
Autor:
Brunner, Robert J., Kim, Edward J.
We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the Python programming language with an emp
Externí odkaz:
http://arxiv.org/abs/1604.07397
Autor:
Lee, Jung Lin, Brunner, Robert J.
The Third Reference Catalogue of Bright Galaxies (RC3) is a reasonably complete listing of 23,011 nearby, large, bright galaxies. By using the final imaging data release from the Sloan Digital Sky Survey, we generate scientifically-calibrated FITS mo
Externí odkaz:
http://arxiv.org/abs/1512.01204
We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study galaxy form
Externí odkaz:
http://arxiv.org/abs/1510.07659
Publikováno v:
MNRAS Vol. 455 642-658 (2016)
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and (2) quant
Externí odkaz:
http://arxiv.org/abs/1510.06402
Publikováno v:
MNRAS 2015 453 (1): 507-521
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a more robu
Externí odkaz:
http://arxiv.org/abs/1505.02200
We present the results from a time domain study of absorption lines detected in quasar spectra with repeat observations from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Beginning with over 4500 unique time separation baselines of various
Externí odkaz:
http://arxiv.org/abs/1307.7832
Publikováno v:
Mon.Not.Roy.Astron.Soc.407:420,2010
We measure the angular auto-correlation functions (w) of SDSS galaxies selected to have photometric redshifts 0.1 < z < 0.4 and absolute r-band magnitudes Mr < -21.2. We split these galaxies into five overlapping redshift shells of width 0.1 and meas
Externí odkaz:
http://arxiv.org/abs/1002.1476