1.2k citations · 15.5k across the 163 of their papers we have counts for
9 papers · 1 filter
The XMM Cluster Survey: The Halo Occupation Number of BOSS galaxies in X-ray clusters
Nicola Mehrtens, A. Kathy Romer, Robert C. Nichol +26
We present a direct measurement of the mean halo occupation distribution (HOD) of galaxies taken from the eleventh data release (DR11) of the Sloan Digital Sky Survey-III Baryon Os…
The XMM Cluster Survey: evolution of the velocity dispersion -- temperature relation over half a Hubble time
Susan Wilson, Matt Hilton, Philip J. Rooney +26
We measure the evolution of the velocity dispersion--temperature (--) relation up to using a sample of 38 galaxy clusters drawn from the \textit{XMM}…
Tuning target selection algorithms to improve galaxy redshift estimates
Ben Hoyle, Kerstin Paech, Markus Michael Rau +2
We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient t…
redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data
E. Rozo, E. S. Rykoff, A. Abate +84
We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in…
Measuring photometric redshifts using galaxy images and Deep Neural Networks
Ben Hoyle
We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and adva…
Galaxies in X-ray Selected Clusters and Groups in Dark Energy Survey Data I: Stellar Mass Growth of Bright Central Galaxies Since z~1.2
Y. Zhang, C. Miller, T. Mckay +80
Using the science verification data of the Dark Energy Survey (DES) for a new sample of 106 X-Ray selected clusters and groups, we study the stellar mass growth of Bright Central G…
Accurate photometric redshift probability density estimation - method comparison and application
Markus Michael Rau, Stella Seitz, Fabrice Brimioulle +4
We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density fu…
Anomaly detection for machine learning redshifts applied to SDSS galaxies
Ben Hoyle, Markus Michael Rau, Kerstin Paech +3
We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence…
Data augmentation for machine learning redshifts applied to SDSS galaxies
Ben Hoyle, Markus Michael Rau, Christopher Bonnett +2
We present analyses of data augmentation for machine learning redshift estimation. Data augmentation makes a training sample more closely resemble a test sample, if the two base sa…