Mixture models for photometric redshifts

Agnello, Adriano; Gall, C.

doi:10.1051/0004-6361/202039675

Cited by 12 publications

(7 citation statements)

References 47 publications

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“…Machine learning algorithms to estimate the redshifts will likely continue to become better; Ref. [26] has summarized the accuracy for several different algorithms, and while the CMNN estimator fares quite well in comparison to most of the other machine learning estimators, the authors of [13] state that it is not optimized for the absolute best fit, but rather aims to assess differences in survey strategy.…”

Section: Discussionmentioning

confidence: 99%

Cosmology requirements on supernova photometric redshift systematics for the Rubin LSST and Roman Space Telescope

Mitra

Linder

2021

Phys. Rev. D

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Some million type Ia supernovae (SN) will be discovered and monitored during upcoming wide area time domain surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). For cosmological use, accurate redshifts are needed among other characteristics; however the vast majority of the SN will not have spectroscopic redshifts, even for their host galaxies, only photometric redshifts. We assess the redshift systematic control necessary for robust cosmology. Based on the photometric vs true redshift relation generated by machine learning applied to a simulation of 500,000 galaxies as observed with LSST quality, we quantify requirements on systematics in the mean relation and in the outlier fraction and deviance so as not to bias dark energy cosmological inference. Certain redshift ranges are particularly sensitive, motivating spectroscopic followup of SN at z ≲ 0.2 and around z ≈ 0.5-0.6. Including Nancy Grace Roman Space Telescope near infrared bands in the simulation, we reanalyze the constraints, finding improvements at high redshift but little at the low redshifts where systematics lead to strong cosmology bias. We identify a complete spectroscopic survey of SN host galaxies for z ≲ 0.2 as a highly favored element for robust SN cosmology.

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Section: Discussionmentioning

confidence: 99%

Cosmology requirements on supernova photometric redshift systematics for the Rubin LSST and Roman Space Telescope

Mitra

Linder

2021

Phys. Rev. D

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“…• supervised feed-forward neural networks (Collister and Lahav, 2004;Vanzella et al, 2004;Brescia et al, 2013Brescia et al, , 2014Brescia et al, , 2015Brescia et al, , 2019Cavuoti et al, 2014;Almosallam et al, 2016;Sadeh et al, 2016); • self-adaptive methods for the detection and removal of anomalies from photometric and spectroscopic data (Hoyle et al, 2015;Baron and Poznanski, 2017;Reis et al, 2019); • Support Vector Machines (Zheng and Zhang, 2012;Zhang and Zhao, 2014;Han et al, 2016;Jones and Singal, 2017); • tree-based (Carrasco Kind and Brunner, 2013;Jouvel et al, 2017;Meshcheryakov et al, 2018); • k-Nearest Neighbors (kNN) (Graham et al, 2018;Curran, 2020); • Gaussian processes (Bonfield et al, 2010;Almosallam et al, 2016); • Mixture Density Networks (Ansari et al, 2020); • unsupervised models for clustering and for estimating the coverage of the parameter space (Way and Klose, 2012;Masters et al, 2015;Stensbo-Smidt et al, 2017) or for calibration purposes (Hildebrandt et al, 2010;Masters et al, 2015;Wright et al, 2020); • deep Neural Networks, especially relevant for the photo-z prediction from images Chong and Yang, 2019;Pasquet et al, 2019); • hybrid methods for the selection of photometric redshifts considered particularly accurate and useful for cosmological purposes (Bonnett et al, 2016;Leistedt and Hogg, 2017;…”

Section: General Aspects Of the Photo-z Estimation With MLmentioning

confidence: 99%

Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

Brescia

Cavuoti

Razim

et al. 2021

Front. Astron. Space Sci.

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The importance of the current role of data-driven science is constantly increasing within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characterized by complex and high-volume information requiring efficient and, as much as possible, automated exploration tools. Furthermore, to accomplish main and legacy science objectives of future or incoming large and deep survey projects, such as James Webb Space Telescope (JWST), James Webb Space Telescope (LSST), and Euclid, a crucial role is played by an accurate estimation of photometric redshifts, whose knowledge would permit the detection and analysis of extended and peculiar sources by disentangling low-z from high-z sources and would contribute to solve the modern cosmological discrepancies. The recent photometric redshift data challenges, organized within several survey projects, like LSST and Euclid, pushed the exploitation of the observed multi-wavelength and multi-dimensional data or ad hoc simulated data to improve and optimize the photometric redshifts prediction and statistical characterization based on both Spectral Energy Distribution (SED) template fitting and machine learning methodologies. They also provided a new impetus in the investigation of hybrid and deep learning techniques, aimed at conjugating the positive peculiarities of different methodologies, thus optimizing the estimation accuracy and maximizing the photometric range coverage, which are particularly important in the high-z regime, where the spectroscopic ground truth is poorly available. In such a context, we summarize what was learned and proposed in more than a decade of research.

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“…Carrasco Kind & Brunner 2013Hogan et al 2015;Sadeh et al 2016;Gomes et al 2018;Graham et al 2018) and those based on spectral templates (e.g. Brammer et al 2008;Molino et al 2014;Beck et al 2017;Benítez 2000;Ansari et al 2021). Performance-wise, no clear winner has yet emerged, mostly due to various assumptions underlying each estimation approach (Schmidt et al 2020).…”

Section: Introductionmentioning

confidence: 99%

TOPz: Photometric redshifts for J-PAS

Laur

Tempel

Tamm

et al. 2022

A&A

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Context. The importance of photometric galaxy redshift estimation is rapidly increasing with the development of specialised powerful observational facilities. Aims. We develop a new photometric redshift estimation workflow TOPz to provide reliable and efficient redshift estimations for the upcoming large-scale survey J-PAS which will observe 8500 deg 2 of the northern sky through 54 narrow-band filters. Methods. TOPz relies on template-based photo-z estimation with some added J-PAS specific features and possibilities. We present TOPz performance on data from the miniJPAS survey, a precursor to the J-PAS survey with an identical filter system. First, we generated spectral templates based on the miniJPAS sources using the synthetic galaxy spectrum generation software CIGALE. Then we applied corrections to the input photometry by minimising systematic offsets from the template flux in each filter. To assess the accuracy of the redshift estimation, we used spectroscopic redshifts from the DEEP2, DEEP3, and SDSS surveys, available for 1989 miniJPAS galaxies with r < 22 mag AB . We also tested how the choice and number of input templates, photo-z priors, and photometric corrections affect the TOPz redshift accuracy. Results. The general performance of the combination of miniJPAS data and the TOPz workflow fulfills the expectations for J-PAS redshift accuracy. Similarly to previous estimates, we find that 38.6% of galaxies with r < 22 mag reach the J-PAS redshift accuracy goal of dz/(1 + z) < 0.003. Limiting the number of spectra in the template set improves the redshift accuracy up to 5%, especially for fainter, noise-dominated sources. Further improvements will be possible once the actual J-PAS data become available.

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Mixture models for photometric redshifts

Cited by 12 publications

References 47 publications

Cosmology requirements on supernova photometric redshift systematics for the Rubin LSST and Roman Space Telescope

Cosmology requirements on supernova photometric redshift systematics for the Rubin LSST and Roman Space Telescope

Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

TOPz: Photometric redshifts for J-PAS

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