Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910372266508288 |
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| author | Jones, Evan Do, Tuan Boscoe, Bernie Singal, Jack Wan, Yujie Nguyen, Zooey |
| author_facet | Jones, Evan Do, Tuan Boscoe, Bernie Singal, Jack Wan, Yujie Nguyen, Zooey |
| contents | We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for the science goals of upcoming large-scale surveys such as LSST, however common machine learning methods typically provide only point estimates and lack uncertainties on predictions. We turn to Bayesian neural networks (BNNs) as a promising way to provide accurate predictions of redshift values with uncertainty estimates. We have compiled a galaxy data set from the Hyper Suprime-Cam Survey with grizy photometry, which is designed to be a smaller scale version of large surveys like LSST. We use this data set to investigate the performance of a neural network (NN) and a probabilistic BNN for photo-z estimation and evaluate their performance with respect to LSST photo-z science requirements. We also examine the utility of photo-z uncertainties as a means to reduce catastrophic outlier estimates. The BNN outputs the estimate in the form of a Gaussian probability distribution. We use the mean and standard deviation as the redshift estimate and uncertainty. We find that the BNN can produce accurate uncertainties. Using a coverage test, we find excellent agreement with expectation -- 67.2$\%$ of galaxies between $0 < 2.5$ have 1-$σ$ uncertainties that cover the spectroscopic value. We also include a comparison to alternative machine learning models using the same data. We find the BNN meets two out of three of the LSST photo-z science requirements in the range $0 < z < 2.5$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13179 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks Jones, Evan Do, Tuan Boscoe, Bernie Singal, Jack Wan, Yujie Nguyen, Zooey Cosmology and Nongalactic Astrophysics 1 We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for the science goals of upcoming large-scale surveys such as LSST, however common machine learning methods typically provide only point estimates and lack uncertainties on predictions. We turn to Bayesian neural networks (BNNs) as a promising way to provide accurate predictions of redshift values with uncertainty estimates. We have compiled a galaxy data set from the Hyper Suprime-Cam Survey with grizy photometry, which is designed to be a smaller scale version of large surveys like LSST. We use this data set to investigate the performance of a neural network (NN) and a probabilistic BNN for photo-z estimation and evaluate their performance with respect to LSST photo-z science requirements. We also examine the utility of photo-z uncertainties as a means to reduce catastrophic outlier estimates. The BNN outputs the estimate in the form of a Gaussian probability distribution. We use the mean and standard deviation as the redshift estimate and uncertainty. We find that the BNN can produce accurate uncertainties. Using a coverage test, we find excellent agreement with expectation -- 67.2$\%$ of galaxies between $0 < 2.5$ have 1-$σ$ uncertainties that cover the spectroscopic value. We also include a comparison to alternative machine learning models using the same data. We find the BNN meets two out of three of the LSST photo-z science requirements in the range $0 < z < 2.5$. |
| title | Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks |
| topic | Cosmology and Nongalactic Astrophysics 1 |
| url | https://arxiv.org/abs/2306.13179 |