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Added by
Hendrik Hildebrandt
Publication date
2010
fields
Physics
and research's language is
English
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Here we introduce PHAT, the PHoto-z Accuracy Testing programme, an international initiative to test and compare different methods of photo-z estimation. Two different test environments are set up, one (PHAT0) based on simulations to test the basic functionality of the different photo-z codes, and another one (PHAT1) based on data from the GOODS survey. The accuracy of the different methods is expressed and ranked by the global photo-z bias, scatter, and outlier rates. Most methods agree well on PHAT0 but produce photo-z scatters that can differ by up to a factor of two even in this idealised case. A larger spread in accuracy is found for PHAT1. Few methods benefit from the addition of mid-IR photometry. Remaining biases and systematic effects can be explained by shortcomings in the different template sets and the use of priors on the one hand and an insufficient training set on the other hand. Scatters of 4-8% in Delta_z/(1+z) were obtained, consistent with other studies. However, somewhat larger outlier rates (>7.5% with Delta_z/(1+z)>0.15; >4.5% after cleaning) are found for all codes. There is a general trend that empirical codes produce smaller biases than template-based codes. The systematic, quantitative comparison of different photo-z codes presented here is a snapshot of the current state-of-the-art of photo-z estimation and sets a standard for the assessment of photo-z accuracy in the future. The rather large outlier rates reported here for PHAT1 on real data should be investigated further since they are most probably also present (and possibly hidden) in many other studies. The test data sets are publicly available and can be used to compare new methods to established ones and help in guiding future photo-z method development. (abridged)
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GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a variable convolution kernel, pixelization and noise. This is the second in a series of challenges set to the astronomy, computer science and statistics communities, providing a structured environment in which methods can be improved and tested in preparation for planned astronomical surveys. GREAT10 extends upon previous work by introducing variable fields into the challenge. The Galaxy Challenge involves the precise measurement of galaxy shape distortions, quantified locally by two parameters called shear, in the presence of a known convolution kernel. Crucially, the convolution kernel and the simulated gravitational lensing shape distortion both now vary as a function of position within the images, as is the case for real data. In addition, we introduce the Star Challenge that concerns the reconstruction of a variable convolution kernel, similar to that in a typical astronomical observation. This document details the GREAT10 Challenge for potential participants. Continually updated information is also available from http://www.greatchallenges.info.
The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzing astronomical images that will be used to measure weak gravitational lensing. This measurement requires extremely precise estimation of very small galaxy shape distortions, in the presence of far larger intrinsic galaxy shapes and distortions due to the blurring kernel caused by the atmosphere, telescope optics, and instrumental effects. The GREAT3 challenge is posed to the astronomy, machine learning, and statistics communities, and includes tests of three specific effects that are of immediate relevance to upcoming weak lensing surveys, two of which have never been tested in a community challenge before. These effects include realistically complex galaxy models based on high-resolution imaging from space; spatially varying, physically-motivated blurring kernel; and combination of multiple different exposures. To facilitate entry by people new to the field, and for use as a diagnostic tool, the simulation software for the challenge is publicly available, though the exact parameters used for the challenge are blinded. Sample scripts to analyze the challenge data using existing methods will also be provided. See http://great3challenge.info and http://great3.projects.phys.ucl.ac.uk/leaderboard/ for more information.
The success of future large scale weak lensing surveys will critically depend on the accurate estimation of photometric redshifts of very large samples of galaxies. This in turn depends on both the quality of the photometric data and the photo-z estimators. In a previous study, (Bordoloi et al. 2010) we focussed primarily on the impact of photometric quality on photo-z estimates and on the development of novel techniques to construct the N(z) of tomographic bins at the high level of precision required for precision cosmology, as well as the correction of issues such as imprecise corrections for Galactic reddening. We used the same set of templates to generate the simulated photometry as were then used in the photo-z code, thereby removing any effects of template error. In this work we now include the effects of template error by generating simulated photometric data set from actual COSMOS photometry. We use the trick of simulating redder photometry of galaxies at higher redshifts by using a bluer set of passbands on low z galaxies with known redshifts. We find that template error is a rather small factor in photo-z performance, at the photometric precision and filter complement expected for all-sky surveys. With only a small sub-set of training galaxies with spectroscopic redshifts, it is in principle possible to construct tomographic redshift bins whose mean redshift is known, from photo-z alone, to the required accuracy of 0.002(1+z).
We present a galaxy group-finding algorithm, the Photo-z Probability Peaks (P3) algorithm, optimized for locating small galaxy groups using photometric redshift data by searching for peaks in the signal-to-noise of the local overdensity of galaxies in a three-dimensional grid. This method is an improvement over similar two-dimensional matched-filter methods in reducing background contamination through the use of redshift information, allowing it to accurately detect groups at lower richness. We present the results of tests of our algorithm on galaxy catalogues from the Millennium Simulation. Using a minimum S/N of 3 for detected groups, a group aperture size of 0.25 Mpc/h, and assuming photometric redshift accuracy of sigma_z = 0.05 it attains a purity of 84% and detects ~295 groups/deg.^2 with an average group richness of 8.6 members. Assuming photometric redshift accuracy of sigma_z = 0.02, it attains a purity of 97% and detects ~143 groups/deg.^2 with an average group richness of 12.5 members. We also test our algorithm on data available for the COSMOS field and the presently-available fields from the CFHTLS-Wide survey, presenting preliminary results of this analysis.
The Panchromatic Hubble Andromeda Treasury (PHAT) is an on-going Hubble Space Telescope (HST) multi-cycle program that will image one-third of the M31 disk at high resolution, with wavelength coverage from the ultraviolet through the near-infrared. This dataset will allow for the construction of the most complete catalog of stellar clusters obtained for a spiral galaxy. Here, we provide an overview of the PHAT survey, a progress report on the status of observations and analysis, and preliminary results from the PHAT cluster program. Although only ~20% of the survey is complete, the superior resolution of HST has allowed us to identify hundreds of new intermediate and low mass clusters. As a result, the size of the cluster sample within the Year 1 survey footprint has grown by a factor of three relative to previous catalogs.
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