T-PHOT: A new code for PSF-matched, prior-based, multiwavelength extragalactic deconfusion photometry

Abstract

Context. The advent of deep multiwavelength extragalactic surveys has led to the necessity for advanced and fast methods for photometric analysis. In fact, codes which allow analyses of the same regions of the sky observed at different wavelengths and resolutions are becoming essential to thoroughly exploit current and future data. In this context, a key issue is the confusion (i.e. blending) of sources in low-resolution images.Aims. We present -, a publicly available software package developed within the  project. - is aimed at extracting accurate photometry from low-resolution images, where the blending of sources can be a serious problem for the accurate and unbiased measurement of fluxes and colours.Methods. - can be considered as the next generation to , providing significant improvements over and above it and other similar codes (e.g. ). - gathers data from a high-resolution image of a region of the sky, and uses this information (source positions and morphologies) to obtain priors for the photometric analysis of the lower resolution image of the same field. - can handle different types of datasets as input priors, namely i) a list of objects that will be used to obtain cutouts from the real high-resolution image; ii) a set of analytical models (as .fits stamps); iii) a list of unresolved, point-like sources, useful for example for far-infrared (FIR) wavelength domains.Results. By means of simulations and analysis of real datasets, we show that - yields accurate estimations of fluxes within the intrinsic uncertainties of the method, when systematic errors are taken into account (which can be done thanks to a flagging code given in the output). - is many times faster than similar codes like  and  (up to hundreds, depending on the problem and the method adopted), whilst at the same time being more robust and more versatile. This makes it an excellent choice for the analysis of large datasets. When used with the same parameter sets as for  it yields almost identical results (although in a much shorter time); in addition we show how the use of different settings and methods significantly enhances the performance.Conclusions. - proves to be a state-of-the-art tool for multiwavelength optical to far-infrared image photometry. Given its versatility and robustness, - can be considered the preferred choice for combined photometric analysis of current and forthcoming extragalactic imaging surveys.

The ASTRODEEP Frontier Fields catalogues

“…We obtained K and IRAC photometry via a template-fitting technique with T-PHOT (Merlin et al 2015(Merlin et al , 2016b) using galaxy shapes in the detection band H160 as priors. Please check for these throughout and correct accordingly.…”

“…As discussed at length in Merlin et al (2015), the segmentation of the objects obtained by SExtractor map may be too small to capture the whole galaxy shape, potentially leading to biases in the flux estimate with T-PHOT. In order to minimize this effect, we dilated the SExtractor output map with the same procedure described in Galametz et al (2013) before feeding this map to T-PHOT, thereby enlarging the size of the segmented area of each source by a given factor, depending on the original area.…”

SPITZER ULTRA FAINT SURVEY PROGRAM (SURFS UP). II. IRAC-DETECTED LYMAN-BREAK GALAXIES AT 6 ≲ z ≲ 10 BEHIND STRONG-LENSING CLUSTERS

“…We will use the template-fitting software T-PHOT (Merlin et al 2015)-the successor to TFIT (Laidler et al 2007)-to measure the colors between HST and Spitzer IRAC images (see Section 2.2). To prepare the HST images for T-PHOT, we use the public 0 03 pixel −1 CLASH images and block-sum the images to make 0 06 pixel −1 images.…”

“…As described in Merlin et al (2015), T-PHOT is designed to measure the fluxes in the low-resolution image (in our case the IRAC images) for all the sources detected in the high-resolution image (in our case the F160W images). T-PHOT does so by constructing a template for each source; it convolves the cutout of each source in the F160W image by a PSF-transformation kernel that matches the F160W resolution to the IRAC resolution.…”

“…For each source, T-PHOT reports a "covariance index," defined as the ratio between the maximum covariance of the source with its neighbors (max(σ ij )) over its flux variance (σ ii ), which serves as an indicator of how strongly correlated the source's flux is with its closest (or brightest) neighbor. Generally, a high covariance index (1) is associated with more severe blending and large flux errors, at least from simulations (Laidler et al 2007;Merlin et al 2015). Therefore, sources with high covariance indices should be treated with caution.…”