TFIT: A Photometry Package Using Prior Information for Mixed‐Resolution Data Sets

Laidler, V. G.; Papovich, Casey; Grogin, Norman A.; Idzi, R.; Dickinson, Mark; Ferguson, Henry C.; Hilbert, B.; Clubb, K. I.; Ravindranath, Swara

doi:10.1086/523898

Cited by 179 publications

(179 citation statements)

References 24 publications

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“…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.…”

Section: Hst Data and Photometrymentioning

confidence: 99%

“…We use T-PHOT to measure consistent colors between HST and Spitzer IRAC images with a template-fitting approach (see also Laidler et al 2007 for the template-fitting concept employed by T-PHOT.) The template-fitting approach has been demonstrated to work well for blank-field surveys such as CANDELS (Guo et al 2013), but it does require images with zero mean background.…”

Section: Irac Data and Photometrymentioning

confidence: 99%

“…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.…”

Section: Irac Data and Photometrymentioning

confidence: 99%

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

Huang

Bradač

Lemaux

et al. 2016

ApJ

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We study the stellar population properties of the IRAC-detected 6  z  10 galaxy candidates from the Spitzer UltRa Faint SUrvey Program. Using the Lyman Break selection technique, we find a total of 17 galaxy candidates at 6  z  10 from Hubble Space Telescope images (including the full-depth images from the Hubble Frontier Fields program for MACS 1149 and MACS 0717) that have detections at signal-to-noise ratios  3 in at least one of the IRAC 3.6 and 4.5 μm channels. According to the best mass models available for the surveyed galaxy clusters, these IRAC-detected galaxy candidates are magnified by factors of ∼1.2-5.5. Due to the magnification of the foreground galaxy clusters, the rest-frame UV absolute magnitudes M 1600 are between −21.2 and −18.9 mag, while their intrinsic stellar masses are between 2×10 8 M e and 2.9×10 9 M e . We identify two Lyα emitters in our sample from the Keck DEIMOS spectra, one at z Lyα =6.76 (in RXJ 1347) and one at z Lyα =6.32 (in MACS 0454). We find that 4 out of 17 z  6 galaxy candidates are favored by z  1 solutions when IRAC fluxes are included in photometric redshift fitting. We also show that IRAC [3.6]-[4.5] color, when combined with photometric redshift, can be used to identify galaxies which likely have strong nebular emission lines or obscured active galactic nucleus contributions within certain redshift windows.

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Section: Hst Data and Photometrymentioning

confidence: 99%

Section: Irac Data and Photometrymentioning

confidence: 99%

Section: Irac Data and Photometrymentioning

confidence: 99%

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

Huang

Bradač

Lemaux

et al. 2016

ApJ

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“…Users will likely have to, and probably should, measure their own photometry to ensure the best results. This is made simpler by automated tasks such as ColorPro (Coe et al 2006) which measure PSF matched aperture photometry for a combination of space and ground based data, while more complicated routines such as TFIT (Laidler et al 2007) fit high resolution galaxy images using the local PSF for each image.…”

Section: Data Setmentioning

confidence: 99%

PHAT: PHoto-zAccuracy Testing

Hildebrandt

Arnouts

Capak

et al. 2010

A&A

249

306

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Context. Photometric redshifts (photo-z's) have become an essential tool in extragalactic astronomy. Many current and upcoming observing programmes require great accuracy of photo-z's to reach their scientific goals. Aims. Here we introduce PHAT, the PHoto-z Accuracy Testing programme, an international initiative to test and compare different methods of photo-z estimation. Methods. 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 including 18-band photometry and ∼2000 spectroscopic redshifts.Results. The accuracy of the different methods is expressed and ranked by the global photo-z bias, scatter, and outlier rates. While most methods agree very well on PHAT0 there are differences in the handling of the Lyman-α forest for higher redshifts. Furthermore, different methods 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. The accuracy of the other methods is unaffected or suffers when IRAC data are included. Remaining biases and systematic effects can be explained by shortcomings in the different template sets (especially in the mid-IR) and the use of priors on the one hand and an insufficient training set on the other hand. Some strategies to overcome these problems are identified by comparing the methods in detail. Scatters of 4-8% in Δz/(1 + z) were obtained, consistent with other studies. However, somewhat larger outlier rates (>7.5% with Δz/(1 + z) > 0.15; >4.5% after cleaning) are found for all codes that can only partly be explained by AGN or issues in the photometry or the spec-z catalogue. Some outliers were probably missed in comparisons of photo-z's to other, less complete spectroscopic surveys in the past. There is a general trend that empirical codes produce smaller biases than template-based codes. Conclusions. The systematic, quantitative comparison of different photo-z codes presented here is a snapshot of the current state-ofthe-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, upcoming methods to established ones and help in guiding future photo-z method development.

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“…Alternatively, some methods make use of high resolution images to flag blended objects and then measure them at different wavelengths using images of lower spatial resolution (e.g. Laidler et al 2007). Popular softwares like Sextractor (Bertin & Arnouts 1996) use image segmentation to separate blends, which is a technique that was further improved by Zheng et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

Joseph¹,

Courbin²,

Starck³

2016

A&A

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We introduce a new algorithm for colour separation and deblending of multi-band astronomical images called MuSCADeT which is based on Morpho-spectral Component Analysis of multi-band images. The MuSCADeT algorithm takes advantage of the sparsity of astronomical objects in morphological dictionaries such as wavelets and their differences in spectral energy distribution (SED) across multi-band observations. This allows us to devise a model independent and automated approach to separate objects with different colours. We show with simulations that we are able to separate highly blended objects and that our algorithm is robust against SED variations of objects across the field of view. To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies. Although the main driver for our work is the deblending of strong gravitational lenses, our method is fit to be used for any purpose related to deblending of objects in astronomical images. An example of such an application is the separation of the red and blue stellar populations of a spiral galaxy in the galaxy cluster Abell 2744. We provide a python package along with all simulations and routines used in this paper to contribute to reproducible research efforts.

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TFIT: A Photometry Package Using Prior Information for Mixed‐Resolution Data Sets

Cited by 179 publications

References 24 publications

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

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

PHAT: PHoto-zAccuracy Testing

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

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