HAYATE: Photometric redshift estimation by hybridising machine learning with template fitting
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911768909971456 |
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| author | Tanigawa, Shingo Glazebrook, Karl Jacobs, Colin Labbe, Ivo Qin, Alex K. |
| author_facet | Tanigawa, Shingo Glazebrook, Karl Jacobs, Colin Labbe, Ivo Qin, Alex K. |
| contents | Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template fitting methods but may not generalise well on new data that deviates from that in the training set. In this work, we present a Hybrid Algorithm for WI(Y)de-range photo-z estimation with Artificial neural networks and TEmplate fitting (HAYATE), a novel photo-z method that combines template fitting and data-driven approaches and whose training loss is optimised in terms of both redshift point estimates and probability distributions. We produce artificial training data from low-redshift galaxy SEDs at z<1.3, artificially redshifted up to z=5. We test the model on data from the ZFOURGE surveys, demonstrating that HAYATE can function as a reliable emulator of EAZY for the broad redshift range beyond the region of sufficient spectroscopic completeness. The network achieves precise photo-z estimations with smaller errors ($σ_{NMAD}$) than EAZY in the initial low-z region (z<1.3), while being comparable even in the high-z extrapolated regime (1.3<z<5). Meanwhile, it provides more robust photo-z estimations than EAZY with the lower outlier rate ($η_{0.2}\lesssim 1\%$) but runs $\sim100$ times faster than the original template fitting method. We also demonstrate HAYATE offers more reliable redshift PDFs, showing a flatter distribution of Probability Integral Transform scores than EAZY. The performance is further improved using transfer learning with spec-z samples. We expect that future large surveys will benefit from our novel methodology applicable to observations over a wide redshift range. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00323 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HAYATE: Photometric redshift estimation by hybridising machine learning with template fitting Tanigawa, Shingo Glazebrook, Karl Jacobs, Colin Labbe, Ivo Qin, Alex K. Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template fitting methods but may not generalise well on new data that deviates from that in the training set. In this work, we present a Hybrid Algorithm for WI(Y)de-range photo-z estimation with Artificial neural networks and TEmplate fitting (HAYATE), a novel photo-z method that combines template fitting and data-driven approaches and whose training loss is optimised in terms of both redshift point estimates and probability distributions. We produce artificial training data from low-redshift galaxy SEDs at z<1.3, artificially redshifted up to z=5. We test the model on data from the ZFOURGE surveys, demonstrating that HAYATE can function as a reliable emulator of EAZY for the broad redshift range beyond the region of sufficient spectroscopic completeness. The network achieves precise photo-z estimations with smaller errors ($σ_{NMAD}$) than EAZY in the initial low-z region (z<1.3), while being comparable even in the high-z extrapolated regime (1.3<z<5). Meanwhile, it provides more robust photo-z estimations than EAZY with the lower outlier rate ($η_{0.2}\lesssim 1\%$) but runs $\sim100$ times faster than the original template fitting method. We also demonstrate HAYATE offers more reliable redshift PDFs, showing a flatter distribution of Probability Integral Transform scores than EAZY. The performance is further improved using transfer learning with spec-z samples. We expect that future large surveys will benefit from our novel methodology applicable to observations over a wide redshift range. |
| title | HAYATE: Photometric redshift estimation by hybridising machine learning with template fitting |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2402.00323 |