Mixture models for photometric redshifts
| Autor: | Christa Gall, Zoe Ansari, Adriano Agnello |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
DATA RELEASE Extinction (astronomy) statistical [methods] FOS: Physical sciences Astrophysics Astrophysics::Cosmology and Extragalactic Astrophysics 01 natural sciences CLASSIFICATION surveys 0103 physical sciences Mixture distribution Instrumentation and Methods for Astrophysics (astro-ph.IM) 010303 astronomy & astrophysics Astrophysics::Galaxy Astrophysics Photometric redshift Physics 010308 nuclear & particles physics Astronomy and Astrophysics Quasar miscellaneous [astronomical databases] Mixture model CATALOG Redshift GALAXY Space and Planetary Science Outlier Probability distribution SURVEY DESIGN Astrophysics - Instrumentation and Methods for Astrophysics catalogs Astrophysics - Cosmology and Nongalactic Astrophysics |
| Zdroj: | Ansari, Z, Agnello, A & Gall, C 2021, ' Mixture models for photometric redshifts ', Astronomy & Astrophysics, vol. 650, A90 . https://doi.org/10.1051/0004-6361/202039675 |
| DOI: | 10.1051/0004-6361/202039675 |
| Popis: | Determining photometric redshifts to high accuracy is paramount to measure distances in wide-field cosmological experiments. With only photometric information at hand, photo-zs are prone to systematic uncertainties in the intervening extinction and the unknown underlying spectral-energy distribution of different astrophysical sources. Here, we aim to resolve these model degeneracies and obtain a clear separation between intrinsic physical properties of astrophysical sources and extrinsic systematics. We aim at estimates of the full photo-z probability distributions, and their uncertainties. We perform a probabilistic photo-z determination using Mixture Density Networks (MDN). The training data-set is composed of optical ($griz$) point-spread-function and model magnitudes and extinction measurements from the SDSS-DR15, and WISE midinfrared ($3.4 \mu$m and $4.6 \mu$m) model magnitudes. We use Infinite Gaussian Mixture models to classify the objects in our data-set as stars, galaxies or quasars, and to determine the number of MDN components to achieve optimal performance. The fraction of objects that are correctly split into the main classes is 94%. Our method improves the bias of photometric redshift estimation (i.e. the mean $\Delta z$ = (zp - zs)/(1 + zs)) by one order of magnitude compared to the SDSS photo-z, and decreases the fraction of $3 \sigma$ outliers (i.e. 3rms$(\Delta z) < \Delta z$). The relative, root-mean-square systematic uncertainty in our resulting photo-zs is down to 1.7% for low-redshift galaxies (zs $ Comment: 14 pages, 9 figures, 7 tables, submitted to A&A 14/10/2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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