Galaxy clustering with photometric surveys using PDF redshift information

Photometric surveys produce large-area maps of the galaxy distribution, but with less accurate redshift information than is obtained from spectroscopic methods. Modern photometric redshift (photo-z) algorithms use galaxy magnitudes, or colors, that are obtained through multi-band imaging to produce a probability density function (PDF) for each galaxy in the map. We used simulated data to study the effect of using different photo-z estimators to assign galaxies to redshift bins in order to compare their effects on angular clustering and galaxy bias measurements. We found that if we use the entire PDF, rather than a single-point (mean or mode) estimate, the deviations are less biased, especially when using narrow redshift bins. When the redshift bin widths are $\Delta z=0.1$, the use of the entire PDF reduces the typical measurement bias from 5%, when using single point estimates, to 3%.Comment: Matches the MNRAS published version. 19 pages, 19 Figure

Local v.s. AWS provisioning: Experience fusing a month’s data on AWS and local provisioning

The Terra ACCESS project provides enhanced access via fused data from all instruments on the NASA TERRA Earth science satellite. The fused data set is 2.4 PB in size and covers the period 2000 - 2015. This document is a technical report from early 2019, comparing the benefits and costs of performing the data fusion on Amazon Web Services and the Illinois campus cluster.NASA Award NNX16AM07AOpe

Photometric redshifts for the next generation of deep radio continuum surveys - I: template fitting

We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multi-wavelength datasets, over the NOAO Deep Wide Field Survey Boötes and COSMOS fields, we assess photometric redshift (photo-z) performance for a sample of 4; 500 radio continuum sources with spectroscopic redshifts relative to those of 63; 000 non radio-detected sources in the same fields. We investigate the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properties. We find that no single template library is able to provide the best performance across all subsets of the radio detected population, with variation in the optimum template set both between subsets and between fields. Through a hierarchical Bayesian combination of the photo-z estimates from all three template sets, we are able to produce a consensus photo-z estimate which equals or improves upon the performance of any individual template set