Mapping The Universe: Improving Photometric Redshift Accuracy And Computational Efficiency
Creators: name: Speagle, Joshua
Publisher: name: Zenodo
Year: 2015
Subjects: Astronomy
Redshift
Photometry

description: Correctly modeling and fitting the spectral energy distributions (SED) of galaxies is a crucial component of extracting accurate and reliable photometric redshifts (photo-z’s) from large-scale extragalactic photometric surveys. However, most codes today are unable to derive photo-z’s to the required precision needed for future surveys such as Euclid in feasible amounts of time. To better understand the general features an improved photo-z search should possess, we characterize the shape of the photo-z likelihood surface for several objects with a pre-generated grid of ∼2 million elements, finding that most surfaces are significantly “bumpy” with gradients dominated by large degeneracies outside the region(s) of peak probability. Based on these results, we design an algorithm to efficiently explore pre-computed grids that combines a swarm intelligence version of a Markov Chain Monte Carlo (MCMC)-driven approach with simulated annealing. Using a mock catalog of ∼380,000 COSMOS galaxies, we show that our new algorithm is at least 40–50 times more efficient compared to standard grid-based counterparts while retaining equivalent accuracy. Following this, we develop a new perturbative approach to generating SEDs from a set of “baseline” photometry that can rapidly generate photometry from continuous changes in redshift, reddening, and emission line strengths to sub-percent level accuracy. By combining this approach with an appropriate set of priors, we establish a framework for using what we term “fuzzy” templates, which allows for the use of expanded template libraries in photo-z searches that can simultaneously account for intrinsic, empirical variation in both reddening and individual emission line strengths for a given galaxy template. Finally, we briefly explore the use of importance nested sampling (INS) to better capture multimodal redshift probability distributions. Our findings lay the groundwork for significant improvements in future photo-z searches.
description: Astro 99.
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