Date Published:

October 1, 2016

Abstract:

Using a 4D grid of ˜2 million model parameters (Δz = 0.005)adapted from Cosmological Origins Survey photometric redshift (photo-z)searches, we investigate the general properties of template-basedphoto-z likelihood surfaces. We find these surfaces are filled withnumerous local minima and large degeneracies that generally confoundsimplistic gradient-descent optimization schemes. We combine ensembleMarkov Chain Monte Carlo sampling with simulated annealing to robustlyand efficiently explore these surfaces in approximately constant time.Using a mock catalogue of 384 662 objects, we show our approach samples˜40 times more efficiently compared to a `brute-force' counterpartwhile maintaining similar levels of accuracy. Our results representfirst steps towards designing template-fitting photo-z approacheslimited mainly by memory constraints rather than computation time.

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