Photometric redshifts are necessary for enabling large-scale multicolour galaxy surveys to interpret their data and constrain cosmological parameters. While the increased depth of future surveys such as the Large Synoptic Survey Telescope (LSST) will produce higher precision constraints, it will also increase the fraction of sources that are blended. In this paper, we present a Bayesian photometric redshift method for blended sources with an arbitrary number of intrinsic components. This method generalises existing template-based Bayesian photometric redshift (BPZ) methods, and produces joint posterior distributions for the component redshifts that allow uncertainties to be propagated in a principled way. Using Bayesian model comparison, we infer the probability that a source is blended and the number of components that it contains. We extend our formalism to the case where sources are blended in some bands and resolved in others. Applying this to the combination of LSST-and Euclid-like surveys, we find that the addition of resolved photometry results in a significant improvement in the reduction of outliers over the fully-blended case. We make available blendz, a Python implementation of our method.

Future cosmological galaxy surveys such as the Large Synoptic Survey Telescope (LSST) will photometrically observe very large numbers of galaxies. Without spectroscopy, the redshifts required for the analysis of these data will need to be inferred using photometric redshift techniques that are scalable to large sample sizes. The high number density of sources will also mean that around half are blended. We present a Bayesian photometric redshift method for blended sources that uses Gaussian mixture models to learn the joint flux-redshift distribution from a set of unblended training galaxies, and Bayesian model comparison to infer the number of galaxies comprising a blended source. The use of Gaussian mixture models renders both of these applications computationally efficient and therefore suitable for upcoming galaxy surveys.1 The photometric redshift software BPZ (Benítez 2000) is packaged with a set of 8 templates by default.

<div>Abstract<p>Metastasis-associated protein 1 (MTA1), a component of the nuclear remodeling complex and the founding homologue of the MTA family, has been implicated in metastasis, but definitive causative evidence in an animal model system is currently lacking. Here, we show that MTA1 overexpression in transgenic mice is accompanied by a high incidence of spontaneous B cell lymphomas including diffuse large B cell lymphomas (DLBCL). Lymphocytes and lymphoma cells from MTA1-TG mice are hyperproliferative. Lymphomas were transplantable and of clonal origin and were characterized by down-regulation of p27Kip1 as well as up-regulation of Bcl2 and cyclin D1. The significance of these murine studies was established by evidence showing a widespread up-regulation of MTA1 in DLBCL from humans. These findings reveal a previously unrecognized role for the MTA1 pathway in the development of spontaneous B cell lymphomas, and offer a potential therapeutic target in B cell lymphomas. These observations suggest that MTA1-TG mice represent a new model of spontaneous DLBCL associated with high tumor incidence and could be used for therapeutic intervention studies. [Cancer Res 2007;67(15):7062–7]</p></div>

Supplementary Tables 1-4, Figures 1-4, Legends and Methods from Metastasis-Associated Protein 1 Transgenic Mice: A New Model of Spontaneous B-Cell Lymphomas

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