Rejection Criteria Based on Outliers in the KiDS Photometric Redshifts and PDF Distributions Derived by Machine Learning

Amaro, Valeria; Cavuoti, S.; Brescia, M.; Riccio, G.; Tortora, C.; D'Addona, Maurizio; Veneri, Michele Delli; Napolitano, N. R.; Radovich, M.; Longo, Giuseppe

doi:10.1007/978-3-030-65867-0_11

Cited by 4 publications

(4 citation statements)

References 51 publications

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“…This code has been used in some current sky surveys, e.g., KiDS (Cavuoti et al 2015) and Sloan Digital Sky Survey (SDSS; Brescia et al 2014). For our comparison, we adopt the MLPQNA photo-z catalog from Amaro et al (2021), where they have used the same data presented in §2.2 to train and test their networks.…”

Section: External Photo-z Catalog By Mlpqnamentioning

confidence: 99%

“…GaZNet-1 MLPQNA adopted for outliers in photo-z estimates (see details in e.g., Cavuoti et al 2012, Amaro et al 2021) and gives a measure of the fallibility of the method. In addition, the mean bias in this work is labeled as µ δz .…”

Section: Statistical Parametersmentioning

confidence: 99%

“…If the training sample covers a representative redshift range and the ML model is well trained, photo-z can be obtained with extremely high precision. Different tools for photo-z based on ML have been successfully tested on multi-band photometry data, for example estimating photoz with ANNs (ANNz, Collister & Lahav 2004;ANNz2 Sadeh et al 2016) or the Multi-Layer Perceptron trained with Quasi-Newton Algorithm (MLPQNA, Cavuoti et al 2012, Amaro et al 2021.…”

Section: Introductionmentioning

confidence: 99%

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Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

Li¹,

Napolitano²,

Feng³

et al. 2022

Preprint

Self Cite

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Aims. In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. Methods. As a first application of this tool, we estimate photo-z of a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on ∼ 140 000 galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG_AUTO< 21) and low redshift (z < 0.8) systems, however, we could use ∼ 6500 galaxies in the range 0.8 < z < 3 to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the 9-band magnitudes and colours, from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey. Results. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD=0.014 for lower redshift and NMAD=0.041 for higher redshift galaxies) and low fraction of outliers (0.4% for lower and 1.27% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a ∼ 10 − 35% improvement in precision at different redshifts and a ∼ 45% reduction in the fraction of outliers. We finally discuss that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to 0.3%.

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Section: External Photo-z Catalog By Mlpqnamentioning

confidence: 99%

Section: Statistical Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

Li¹,

Napolitano²,

Feng³

et al. 2022

Preprint

Self Cite

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show abstract

“…This definition is usually adopted for outliers in photometric redshifts determination (see detail in Amaro et al 2021). Finally, NMAD is defined as…”

Section: Testing the Performancesmentioning

confidence: 99%

GAlaxy Light profile convolutional neural NETworks (GaLNets). I. fast and accurate structural parameters for billion galaxy samples

Li,

Napolitano,

Roy

et al. 2021

Preprint

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Next generation large sky surveys will observe up to billions of galaxies for which basic structural parameters are needed to study their evolution. This is a challenging task that, for ground-based observations, is complicated by the seeing limited point-spread-function (PSF). To perform fast and accurate analysis of galaxy surface brightness, we have developed a family of supervised Convolutional Neural Network (CNN) tools to derive Sersic profile parameters of galaxies. In this work, we present the first two Galaxy Light profile convolutional neural Networks (GaLNets) of this family. A first one, trained using galaxy images only (GaLNet-1), and a second one, trained with both galaxy images and the local PSF (GaLNet-2). We have compared the results from the two CNNs with structural parameters (namely the total magnitude, the effective radius, Sersic index etc.) derived on set of galaxies from the Kilo-Degree Survey (KiDS) by 2DPHOT, as a representative of "standard" PSFconvolved Sersic fitting tools. The comparison shows that, provided a suitable prior distribution is adopted, GaLNet-2 can reach an accuracy as high as 2DPHOT, while GaLNet-1 performs slightly worse because it misses the information on the local PSF. In terms of computational speed, both GaLNets are more than three orders of magnitude faster than standard methods. This first application of CNN to ground-based galaxy surface photometry shows that CNNs are promising tools to perform parametric analyses of very large samples of galaxy light profiles, as expected from surveys like Vera Rubin/LSST, Euclid and the Chinese Space Station Telescope.

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Galaxy morphoto-Z with neural Networks (GaZNets)

Napolitano

Feng

et al. 2022

A&A

View full text Add to dashboard Cite

Aims. In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. Methods. As a first application of this tool, we estimate photo-z for a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on ∼ 140 000 galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG_AUTO< 21) and low-redshift (z < 0.8) systems; however, we could use ∼ 6500 galaxies in the range 0.8 < z < 3 to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the nine-band magnitudes and colors from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey. Results. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD=0.014 for lower redshift and NMAD=0.041 for higher redshift galaxies) and a low fraction of outliers (0.4% for lower and 1.27% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a ∼ 10% − 35% improvement in precision at different redshifts and a ∼ 45% reduction in the fraction of outliers. We finally discuss the finding that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to 0.3%.

show abstract

Rejection Criteria Based on Outliers in the KiDS Photometric Redshifts and PDF Distributions Derived by Machine Learning

Cited by 4 publications

References 51 publications

Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

GAlaxy Light profile convolutional neural NETworks (GaLNets). I. fast and accurate structural parameters for billion galaxy samples

Galaxy morphoto-Z with neural Networks (GaZNets)

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