Improving the identification of high-z Herschel sources with position priors and optical/NIR and FIR/mm photometric redshifts

We present preliminary results about the detection of high redshift (U)LIRGs in the Bullet cluster field by the PACS and SPIRE instruments within the Herschel Lensing Survey (HLS) Program. We describe in detail a photometric procedure designed to recover robust fluxes and deblend faint Herschel sources near the confusion noise. The method is based on the use of the positions of Spitzer/MIPS 24 μm sources as priors. Our catalogs are able to reliably (5σ) recover galaxies with fluxes above 6 and 10 mJy in the PACS 100 and 160 μm channels, respectively, and 12 to 18 mJy in the SPIRE bands. We also obtain spectral energy distributions covering the optical through the far-infrared/millimeter spectral ranges of all the Herschel detected sources, and analyze them to obtain independent estimations of the photometric redshift based on either stellar population or dust emission models. We exemplify the potential of the combined use of Spitzer position priors plus independent optical and IR photometric redshifts to robustly assign optical/NIR counterparts to the sources detected by Herschel and other (sub-)mm instruments.

The role of parametric assumptions in adaptive Bayesian estimation.

Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter values larger than their counterparts in the psychometric function, (c) stimulus placement at the prior mean, and (d) estimates defined as the posterior mean. Unbiasedness arises in only 10 trials, and 20 trials ensure constant standard errors. The standard error of the estimates equals 0.617 times the inverse of the square root of the number of trials. Other variants yielded bias and larger standard errors.