Deep learning based photometric redshifts for the Kilo-Degree Survey Bright Galaxy Sample
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866918182740033536 |
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| author | William, Anjitha John Jalan, Priyanka Bilicki, Maciej Hellwing, Wojciech A. |
| author_facet | William, Anjitha John Jalan, Priyanka Bilicki, Maciej Hellwing, Wojciech A. |
| contents | In cosmological analyses, precise redshift determination remains pivotal for understanding cosmic evolution. However, with only a fraction of galaxies having spectroscopic redshifts (spec-$z$s), the challenge lies in estimating redshifts for a larger number. To address this, photometry-based redshift (photo-$z$) estimation, employing machine learning algorithms, is a viable solution. Identifying the limitations of previous methods, this study focuses on implementing deep learning (DL) techniques within the Kilo-Degree Survey (KiDS) Bright Galaxy Sample for more accurate photo-$z$ estimations. Comparing our new DL-based model against prior `shallow' neural networks, we showcase improvements in redshift accuracy. Our model gives mean photo-$z$ bias $\langle Δz\rangle= 10^{-3}$ and scatter $\mathrm{SMAD}(Δz)=0.016$, where $Δz = (z_\mathrm{phot}-z_\mathrm{spec})/(1+z_\mathrm{spec})$. This research highlights the promising role of DL in revolutionizing photo-$z$ estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08043 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep learning based photometric redshifts for the Kilo-Degree Survey Bright Galaxy Sample William, Anjitha John Jalan, Priyanka Bilicki, Maciej Hellwing, Wojciech A. Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics In cosmological analyses, precise redshift determination remains pivotal for understanding cosmic evolution. However, with only a fraction of galaxies having spectroscopic redshifts (spec-$z$s), the challenge lies in estimating redshifts for a larger number. To address this, photometry-based redshift (photo-$z$) estimation, employing machine learning algorithms, is a viable solution. Identifying the limitations of previous methods, this study focuses on implementing deep learning (DL) techniques within the Kilo-Degree Survey (KiDS) Bright Galaxy Sample for more accurate photo-$z$ estimations. Comparing our new DL-based model against prior `shallow' neural networks, we showcase improvements in redshift accuracy. Our model gives mean photo-$z$ bias $\langle Δz\rangle= 10^{-3}$ and scatter $\mathrm{SMAD}(Δz)=0.016$, where $Δz = (z_\mathrm{phot}-z_\mathrm{spec})/(1+z_\mathrm{spec})$. This research highlights the promising role of DL in revolutionizing photo-$z$ estimation. |
| title | Deep learning based photometric redshifts for the Kilo-Degree Survey Bright Galaxy Sample |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2312.08043 |