10.4k citations · 37.5k across the 130 of their papers we have counts for
8 papers · 1 filter
Star-galaxy classification in the Dark Energy Survey Y1 dataset
I. Sevilla-Noarbe, B. Hoyle, M. J. Marchã +75
We perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. This is done by performin…
Vizic: A Jupyter-based Interactive Visualization Tool for Astronomical Catalogs
W. Yu, M. Carrasco Kind, R. J. Brunner
The ever-growing datasets in observational astronomy have challenged scientists in many aspects, including an efficient and interactive data exploration and visualization. Many too…
Star-galaxy Classification Using Deep Convolutional Neural Networks
Edward J. Kim, Robert J. Brunner
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning…
Creating updated, scientifically-calibrated mosaic images for the RC3 catalogue
Jung Lin Lee, Robert J. Brunner
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 S…
A Hybrid Ensemble Learning Approach to Star-Galaxy Classification
Edward J. Kim, Robert J. Brunner, Matias Carrasco Kind
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 c…
Photometric redshift analysis in the Dark Energy Survey Science Verification data
C. Sánchez, M. Carrasco Kind, H. Lin +74
We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification (SV) period of observation…
SOMz: photometric redshift PDFs with self organizing maps and random atlas
M. Carrasco Kind, R. J. Brunner
In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density…
LSST Science Book, Version 2.0
LSST Science Collaboration, Paul A. Abell, Julius Allison +245
A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade.…