MegaZ-LRG: a photometric redshift catalogue of one million SDSS luminous red galaxies

Collister, Adrian; Lahav, O.; Blake, Chris; Cannon, R. D.; Croom, S. M.; Drinkwater, Michael J.; Edge, A. C.; Eisenstein, Daniel J.; Loveday, Jon; Nichol, R. C.; Pimbblet, Kevin A.; Propris, Roberto De; Roseboom, I. G.; Ross, Nic; Schneider, Donald P.; Shanks, T.; Wake, David A.

doi:10.1111/j.1365-2966.2006.11305.x

Cited by 93 publications

(121 citation statements)

References 23 publications

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“…For further details on this see Cannon et al (2006). However, for the full MegaZ-LRG sample described in Collister et al (2007), the flux limit is i deV ≤ 20, which means that roughly 1/3 of the sample is fainter than the 2SLAQ flux limit. Details about the photometric redshift estimation are provided in Sect.…”

Section: Megaz-lrgmentioning

confidence: 99%

“…The total number of galaxies is less than that of the full MegaZ-LRG catalogue by Collister et al (2007) because a fraction of the area comes from imaging data that was not yet processed by the shape measurement pipeline used to estimate galaxy shapes. As will be discussed in Sect.…”

Section: Megaz-lrgmentioning

confidence: 99%

“…For a subset of the galaxies, spectroscopic redshift information is required for calibration and cross-checking, which is provided by the 2SLAQ survey (Cannon et al 2006). Consequently, the selection criteria of MegaZ-LRG have been designed to match those of 2SLAQ, using a series of magnitude and colour cuts (for details see Cannon et al 2006;Collister et al 2007). These criteria have an efficiency of 95 % in detecting LRGs in the redshift range 0.4 ≤ z ≤ 0.7, the failures being almost entirely due to Mtype stars.…”

Section: Megaz-lrgmentioning

confidence: 99%

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Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Joachimi¹,

Mandelbaum²,

Abdalla³

et al. 2011

A&A

280

512

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Correlations between the intrinsic shapes of galaxies and the large-scale galaxy density field provide an important tool to investigate galaxy intrinsic alignments, which constitute the major potential astrophysical systematic in cosmological weak lensing (cosmic shear) surveys, but also yield insight into the formation and evolution of galaxies. We measure galaxy position-shape correlations in the MegaZ-LRG sample for more than 800 000 luminous red galaxies for comoving transverse separations of 0.3 < r p < 60 h −1 Mpc, making the first such measurement with a photometric redshift sample. In combination with a re-analysis of several spectroscopic SDSS samples, we constrain an intrinsic alignment model for early-type galaxies over long baselines in redshift (z 0.7) and luminosity (4 mag) with high statistical precision. We develop and test the formalism to incorporate photometric redshift scatter in the modelling of these observations. For r p > 6 h −1 Mpc, the fits to galaxy position-shape correlation functions are consistent with the scaling with r p and redshift of a revised, nonlinear version of the linear alignment model for all samples. An extra redshift dependence ∝ (1 + z) η other is constrained to η other = −0.3 ± 0.8 (1σ). To obtain consistent amplitudes for all data, an additional dependence on galaxy luminosity ∝L β with β = 1.1 +0.3 −0.2 is required. The normalisation of the intrinsic alignment power spectrum is found to be (0.077 ± 0.008) ρ −1 cr for galaxies at redshift 0.3 and r band magnitude of −22 (k-and evolution-corrected to z = 0). Assuming zero intrinsic alignments for blue galaxies, we assess the bias on cosmological parameters for a tomographic CFHTLSlike lensing survey given our new constraints on the intrinsic alignment model parameter space. Both the resulting mean bias and its uncertainty are smaller than the 1σ statistical errors when using the constraints from all samples combined. The addition of MegaZ-LRG data is critical to achieving constraints this strong, reducing the uncertainty in intrinsic alignment bias on cosmological parameters by factors of three to seven.

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Section: Megaz-lrgmentioning

confidence: 99%

Section: Megaz-lrgmentioning

confidence: 99%

Section: Megaz-lrgmentioning

confidence: 99%

See 1 more Smart Citation

Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Joachimi¹,

Mandelbaum²,

Abdalla³

et al. 2011

A&A

280

512

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“…Neural networks have been used e.g. for estimation of photo-z's for the SDSS (Collister et al 2007;Oyaizu et al 2008;Abdalla et al 2008b), as well as forecasts of photometric redshifts for future surveys like the Dark Energy Survey (Banerji et al 2008) and Euclid (Abdalla et al 2008a).…”

Section: Annz (An-e)mentioning

confidence: 99%

PHAT: PHoto-zAccuracy Testing

Hildebrandt

Arnouts

Capak

et al. 2010

A&A

Self Cite

245

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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“…The best known classification models are decision trees (Quinlan 1993), naive Bayes (Duda & Hart 1973), neural networks (Rumelhart et al 1986), support vector machines (Cortes & Vapnik 1995), and Random Forest (Breiman 2001). As summarized by Ball & Brunner (2010), machine learning in astronomy has found a use in star-galaxy separation (e.g., Collister et al 2007), classification of galaxy morphology (e.g., Huertas-Company et al 2008), quasar/AGN classification (e.g., Pichara & Protopapas 2013;Pichara et al 2012), galaxy photometric redshifts (e.g., Gerdes et al 2010), and photometric redshift estimation of quasars (e.g., Wolf 2009).…”

Section: Introductionmentioning

confidence: 99%

Photometric classification of quasars from RCS-2 using Random Forest

Carrasco

Barrientos

Pichara

et al. 2015

A&A

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The classification and identification of quasars is fundamental to many astronomical research areas. Given the large volume of photometric survey data available in the near future, automated methods for doing so are required. In this article, we present a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sample of quasars and stars. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopicallyconfirmed stars and quasars. The algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colors. Exploiting NUV GALEX measurements for a subset of the objects, we refine the classifier by adding new information. An additional subset of the data with WISE W1 and W2 bands is also studied. Upon analyzing 542 897 RCS-2 point sources, the algorithm identified 21 501 quasar candidates with a training-set-derived precision (the fraction of true positives within the group assigned quasar status) of 89.5% and recall (the fraction of true positives relative to all sources that actually are quasars) of 88.4%. These performance metrics improve for the GALEX subset: 6529 quasar candidates are identified from 16 898 sources, with a precision and recall of 97.0% and 97.5%, respectively. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to 99.3% and 99.1%, respectively, for 21 834 quasar candidates from 242 902 sources. We compiled our final catalog (38 257) by merging these samples and removing duplicates. An observational follow up of 17 bright (r < 19) candidates with long-slit spectroscopy at DuPont telescope (LCO) yields 14 confirmed quasars. The results signal encouraging progress in the classification of point sources with Random Forest algorithms to search for quasars within current and future large-area photometric surveys.

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MegaZ-LRG: a photometric redshift catalogue of one million SDSS luminous red galaxies

Cited by 93 publications

References 23 publications

Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

PHAT: PHoto-zAccuracy Testing

Photometric classification of quasars from RCS-2 using Random Forest

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