Abstract
We present a robust approach to estimating the redshift of galaxies using Pan-STARRS1 photometric data. Our approach is an application of the algorithm proposed for the SDSS Data Release 12. It uses a training set of 2 313 724 galaxies for which the spectroscopic redshift is obtained from SDSS, and magnitudes and colours are obtained from the Pan-STARRS1 Data Release 2 survey. The photometric redshift of a galaxy is then estimated by means of a local linear regression in a 5D magnitude and colour space. Our approach achieves an average bias of Δ̅z̅n̅o̅r̅m̅ = −1.92 × 10−4, a standard deviation of σ(Δznorm) = 0.0299, and an outlier rate of Po = 4.30% when cross-validating the training set. Even though the relation between each of the Pan-STARRS1 colours and the spectroscopic redshifts is noisier than for SDSS colours, the results obtained by our approach are very close to those yielded by SDSS data. The proposed approach has the additional advantage of allowing the estimation of photometric redshifts on a larger portion of the sky (∼3/4 vs ∼1/3). The training set and the code implementing this approach are publicly available at the project website.
Highlights
In the last two decades, there has been a rise in the development of large photometric surveys, like the Sloan Digital Sky Survey (SDSS, York et al 2000), the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS, Chambers et al 2016), and the Dark Energy Survey (DES, Dark Energy Survey Collaboration 2016)
We explain how we deal with the potential problem of missing information (Sect. 2.4). This approach has been designed to work on both Data Release 1 (DR1) and Data Release 2 (DR2), in this paper we present the results corresponding to DR2
Our approach is an application of the method proposed by Beck et al (2016) for the SDSS DR12, based on a local linear regression in a 5D magnitude and colour space
Summary
In the last two decades, there has been a rise in the development of large photometric surveys, like the Sloan Digital Sky Survey (SDSS, York et al 2000), the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS, Chambers et al 2016), and the Dark Energy Survey (DES, Dark Energy Survey Collaboration 2016). Some examples of these techniques are ANNz (Collister & Lahav 2004), ANNz2 (Sadeh et al 2016), TPZ (Carrasco Kind & Brunner 2013), GPz (Almosallam et al 2016), METAPhoR (Cavuoti et al 2017), or the nearest-neighbor color-matching photometric redshift estimator of Graham et al (2018) Another example of a machine learning approach to the computation of photometric redshifts is presented in Beck et al (2016); a large sample of galaxies (about 2 million) with both photometric and spectroscopic information is used as a training set to estimate the redshift of all the galaxies in SDSS Data Release 12 (DR12, Alam et al 2015) using a local linear regression.
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