Estimating Photometric Redshifts Using Genetic Algorithms

Miles, N.J.; Freitas, Alex A.; Serjeant, S.

doi:10.1007/978-1-84628-666-7_6

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…There exist several techniques which are commonly used to estimate redshifts from photometry and can be divided in to: (i) template fitting techniques (e.g., Benítez, 2000;Bolzonella et al, 2000;Ilbert et al, 2006), and (ii) ML (or empirical) techniques (e.g. Connolly et al, 1995;Collister and Lahav, 2004;Wadadekar, 2005;Miles et al, 2007;O'Mill et al, 2011;Reis et al, 2012;. In template fitting techniques, a set of synthetic spectra are determined from synthesised stellar population models for a given set of metallicities, star formation histories and initial mass functions, among other properties.…”

Section: Introductionmentioning

confidence: 99%

The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts

Elliott

Souza

Krone-Martins

et al. 2015

Astronomy and Computing

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Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ∼ 1% for simulated and ∼ 2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely thousands of galaxies. Our software is made publicly available as a user-friendly package developed in Python, R and via an interactive web application. This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope.

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Section: Introductionmentioning

confidence: 99%

The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts

Elliott

Souza

Krone-Martins

et al. 2015

Astronomy and Computing

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show abstract

“…Connolly et al 1995;Collister & Lahav 2004;Wadadekar 2005;Miles et al 2007;O'Mill et al 2011;Reis et al 2012;Carrasco Kind & Brunner 2013) and template-fitting-based methods (e.g., Benítez 2000;Bolzonella et al 2000;Ilbert et al 2006). The former uses magnitudes and/or colours of a spectroscopically measured sample for training the method, which is then applied to the photometric sample.…”

Section: Introductionmentioning

confidence: 99%

The first analytical expression to estimate photometric redshifts suggested by a machine

Krone-Martins

Ishida

2014

Monthly Notices of the Royal Astronomical Society: Letters

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We report the first analytical expression purely constructed by a machine to determine photometric redshifts (z phot ) of galaxies. A simple and reliable functional form is derived using 41, 214 galaxies from the Sloan Digital Sky Survey Data Release 10 (SDSS-DR10) spectroscopic sample. The method automatically dropped the u and z bands, relying only on g, r and i for the final solution. Applying this expression to other 1, 417, 181 SDSS-DR10 galaxies, with measured spectroscopic redshifts (z spec ), we achieved a mean (z phot − z spec )/(1 + z spec ) 0.0086 and a scatter σ (z phot −zspec)/(1+zspec) 0.045 when averaged up to z 1.0. The method was also applied to the PHAT0 dataset, confirming the competitiveness of our results when faced with other methods from the literature. This is the first use of symbolic regression in cosmology, representing a leap forward in astronomy-data-mining connection.

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WISE-PS1-STRM: neural network source classification and photometric redshifts for WISE×PS1

Beck

Dodds

Szapudi

2022

Monthly Notices of the Royal Astronomical Society

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We cross-match between the WISE All-Sky and PS1 3π DR2 source catalogues. The resulting catalogue has 354 590 570 objects, significantly fewer than the parent PS1 catalogue, but its combination of optical and infrared colours facilitate both better source classification and photometric redshift estimation. We perform a neural network-based classification of the objects into galaxies, quasars, and stars, then run neural network-based photometric redshift estimation for the galaxies. The star sample purity and quasar sample completeness measures improve substantially, and the resulting photo-z’s are significantly more accurate in terms of statistical scatter and bias than those calculated from PS1 properties alone. The catalogue will be a basis for future large-scale structure studies, and will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.

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Estimating Photometric Redshifts Using Genetic Algorithms

Cited by 5 publications

References 17 publications

The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts

The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts

The first analytical expression to estimate photometric redshifts suggested by a machine

WISE-PS1-STRM: neural network source classification and photometric redshifts for WISE×PS1

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