Location: TAAC 48
Scientific Advisor: Joshua A. Frieman
Affiliations: Kavli Institute for Cosmological Physics
Ph.D. Thesis Defense
Defense date: June 11, 2015
Ph.D. Committee members: Scott Dodelson, Stephan Meyer, Craig Hogan
"To constrain cosmology, and in particular to probe dark energy, from deep optical imaging surveys such as the Dark Energy Survey (DES), requires precise estimates of the redshifts of the distant galaxies they observe. Traditionally, these redshift estimates are made using galaxy colors, but this technique has known limitations and biases. Jennifer's thesis work involved the testing and implementation of a novel technique for estimating redshifts of galaxies, using the fact that they cluster in space with galaxies for which the redshifts may be known from spectroscopic measurements. Using simulations, Jen found that this "clustering redshift" technique accurately reconstructs the galaxy redshift distribution for a survey such as DES. She then applied this technique to determine the redshift distribution for several million galaxies in the first year of DES data, an important result that should prove extremely valuable for the cosmological analysis of these data."
- Joshua A. Frieman, PhD advisor
Thesis Abstract: Accurate determination of photometric redshifts and their errors is critical for large scale structure and weak lensing studies for constraining cosmology from deep, wide imaging surveys. Current photometric redshift methods suffer from bias and scatter due to incomplete training sets. Exploiting the clustering between a sample of galaxies for which we have spectroscopic redshifts and a sample of galaxies for which the redshifts are unknown can allow us to reconstruct the true redshift distribution of the unknown sample. Here we use this method in both simulations and early data from the Dark Energy Survey (DES) to determine the true redshift distributions of galaxies in photometric redshift bins. We find that cross-correlating with the spectroscopic samples currently used for training provides reliable estimates of the true redshift distribution in a photometric redshift bin. We discuss the use of the cross-correlation method in validating template- or learning-based approaches to redshift estimation and its future use in Stage IV surveys.
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