387 citations · 811 across the 4 of their papers we have counts for
4 papers
Spectroscopic Needs for Imaging Dark Energy Experiments: Photometric Redshift Training and Calibration
J. Newman, A. Abate, F. Abdalla +58
Large sets of objects with spectroscopic redshift measurements will be needed for imaging dark energy experiments to achieve their full potential, serving two goals:_training_, i.e…
Data Mining and Machine Learning in Astronomy
Nicholas M. Ball, Robert J. Brunner
We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this fie…
Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers +3
We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in…
The 2dF-SDSS LRG and QSO Survey: QSO clustering and the L-z degeneracy
J. da Angela, T. Shanks, S. M. Croom +12
We combine the QSO samples from the 2dF QSO Redshift Survey (2QZ) and the 2dF-SDSS LRG and QSO Survey (2SLAQ) in order to investigate the clustering of z~1.4 QSOs and measure the c…