GAz: a genetic algorithm for photometric redshift estimation

Hogan, Robert; Fairbairn, Malcolm; Seeburn, Navin

doi:10.1093/mnras/stv430

Cited by 36 publications

(30 citation statements)

References 33 publications

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“…In our case, such a sample is provided by the deep and complete GAMA dataset. We also experimented with another photometric redshift code, GAz (Hogan et al 2015) 16 , which gave results similar to ANNz, albeit slightly poorer 17 .…”

Section: Photometric Redshiftsmentioning

confidence: 99%

WISE × SuperCOSMOS PHOTOMETRIC REDSHIFT CATALOG: 20 MILLION GALAXIES OVER 3π STERADIANS

Bilicki

Peacock

Jarrett

et al. 2016

ApJS

114

153

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We cross-match the two currently largest all-sky photometric catalogs, mid-infrared WISE and Super-COSMOS scans of UKST/POSS-II photographic plates, to obtain a new galaxy sample that covers 3π steradians. In order to characterize and purify the extragalactic dataset, we use external GAMA and SDSS spectroscopic information to define quasar and star loci in multicolor space, aiding the removal of contamination from our extended-source catalog. After appropriate data cleaning we obtain a deep wide-angle galaxy sample that is approximately 95% pure and 90% complete at high Galactic latitudes. The catalog contains close to 20 million galaxies over almost 70% of the sky, outside the Zone of Avoidance and other confused regions, with a mean surface density of over 650 sources per square degree. Using multiwavelength information from two optical and two mid-IR photometric bands, we derive photometric redshifts for all the galaxies in the catalog, using the ANNz framework trained on the final GAMA-II spectroscopic data. Our sample has a median redshift of z med = 0.2 but with a broad dN/dz reaching up to z > 0.4. The photometric redshifts have a mean bias of |δz| ∼ 10 −3 , normalized scatter of σ z = 0.033 and less than 3% outliers beyond 3σ z . Comparison with external datasets shows no significant variation of photo-z quality with sky position. Together with the overall statistics, we also provide a more detailed analysis of photometric redshift accuracy as a function of magnitudes and colors. The final catalog is appropriate for 'all-sky' 3D cosmology to unprecedented depths, in particular through cross-correlations with other large-area surveys. It should also be useful for source pre-selection and identification in forthcoming surveys such as TAIPAN or WALLABY.

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Section: Photometric Redshiftsmentioning

confidence: 99%

WISE × SuperCOSMOS PHOTOMETRIC REDSHIFT CATALOG: 20 MILLION GALAXIES OVER 3π STERADIANS

Bilicki

Peacock

Jarrett

et al. 2016

ApJS

114

153

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“…In the ML domain, techniques such as artificial neural networks (ANNs, Tagliaferri et al 2003;Firth et al 2003), boosted decision or regression trees (BDTs, Gerdes et al 2010), Gaussian processes (Way et al 2009), or genetic algorithms (Hogan et al 2015), to list just a few, are calibrated (trained) on spec-z samples, which have the relevant set of passbands measured, to derive the mapping from photometry to spec-zs, and the best-fit solution is then propagated to the target data with photometry only. These methods are usually agnostic to any physics, and thus need wellcontrolled and representative training sets to work properly.…”

Section: Introductionmentioning

confidence: 99%

Photometric redshifts for the Kilo-Degree Survey

Bilicki

Hoekstra

Brown

et al. 2018

A&A

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We present a machine-learning photometric redshift (ML photo-z) analysis of the Kilo-Degree Survey Data Release 3 (KiDS DR3), using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the Bayesian Photometric Redshift (BPZ) code, at least up to zphot ≲ 0.9 and r ≲ 23.5. At the bright end of r ≲ 20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-z derivation, as well as when VIKING and WISE infrared (IR) bands are added. While the fiducial four-band ugri setup gives a photo-z bias 〈δz/(1 + z)〉 = −2 × 10−4 and scatter σδz/(1+z) < 0.022 at mean 〈z〉 = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 μm, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives 〈δz/(1 + z)〉 < 4 × 10−5 and σδz/(1+z) < 0.019. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimised for low-redshift studies such as galaxy-galaxy lensing, is limited to r ≲ 20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.

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“…ANNZ; Firth, Lahav & Somerville 2003;Collister & Lahav 2004), nearest-neighbour (NN) (Ball et al 2008), genetic algorithms (e.g. Hogan, Fairbairn & Seeburn 2015), self-organized maps (Geach 2012) and random forest (Kind & Brunner 2013), to name but a few, rely on a significant fraction of sources in a photometric catalogue having spectroscopic redshifts. These 'true' redshifts are used to train the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A sparse Gaussian process framework for photometric redshift estimation

Almosallam

Lindsay

Jarvis

et al. 2015

Mon. Not. R. Astron. Soc.

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Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Synthetic dataset simulating the Euclid survey and real data from SDSS DR12 are used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learning algorithms. Standard implementations of most regression algorithms use the minimization of the sum of squared errors as the objective function. For redshift inference, this induces a bias in the posterior mean of the output distribution, which can be problematic. In this paper we directly minimize the target metric ∆z = (z s − z p )/(1 + z s ) and address the bias problem via a distribution-based weighting scheme, incorporated as part of the optimization objective. The results are compared with other machine learning algorithms in the field such as Artificial Neural Networks (ANN), Gaussian Processes (GPs) and sparse GPs. The proposed framework reaches a mean absolute ∆z = 0.0026(1 + z s ), over the redshift range of 0 z s 2 on the simulated data, and ∆z = 0.0178(1 + z s ) over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNZ used in the literature. We also investigate how the relative size of the training sample affects the photometric redshift accuracy. We find that a training sample of >30 per cent of total sample size, provides little additional constraint on the photometric redshifts, and note that our GP formalism strongly outperforms ANNZ in the sparse data regime for the simulated data set.

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GAz: a genetic algorithm for photometric redshift estimation

Cited by 36 publications

References 33 publications

WISE × SuperCOSMOS PHOTOMETRIC REDSHIFT CATALOG: 20 MILLION GALAXIES OVER 3π STERADIANS

WISE × SuperCOSMOS PHOTOMETRIC REDSHIFT CATALOG: 20 MILLION GALAXIES OVER 3π STERADIANS

Photometric redshifts for the Kilo-Degree Survey

A sparse Gaussian process framework for photometric redshift estimation

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