Predicting the neutral hydrogen content of galaxies from optical data using machine learning Article Swipe
Mika Rafieferantsoa
,
Sambatra Andrianomena
,
Romeel Davè
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/sty1777
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/sty1777
We develop a machine learning-based framework to predict the Hi content of galaxies using \nmore straightforwardly observable quantities such as optical photometry and environmental \nparameters. We train the algorithm on z = 0 - 2 outputs from the Mufasa cosmological \nhydrodynamic simulation, which includes star formation, feedback, and a heuristic model to \nquench massive galaxies that yields a reasonable match to a range of survey data including Hi. \nWe employ a variety of machine learning methods (regressors), and quantify their performance \nusing the root mean square error (rmse) and the Pearson correlation coefficient (r). Considering \nSDSS photometry, 3rd nearest neighbor environment and line of sight peculiar velocities \nas features, we obtain r > 0:8 accuracy of the Hi-richness prediction, corresponding to \nrmse< 0:3. Adding near-IR photometry to the features yields some improvement to the \nprediction. Compared to all the regressors, random forest shows the best performance, with \nr > 0:9 at z = 0, followed by a Deep Neural Network with r > 0:85. All regressors exhibit \na declining performance with increasing redshift, which limits the utility of this approach \nto z ~<1, and they tend to somewhat over-predict the Hi content of low-Hi galaxies which \nmight be due to Eddington bias in the training sample.We test our approach on the RESOLVE \nsurvey data. Training on a subset of RESOLVE, we find that our machine learning method can \nreasonably well predict the Hi-richness of the remaining RESOLVE data, with rmse~ 0:28. \nWhenwe train on mock data fromMufasa and test onRESOLVE, this increases to rmse~ 0:45. \nOur method will be useful for making galaxy-by-galaxy survey predictions and incompleteness \ncorrections for upcoming Hi 21cm surveys such as the LADUMA and MIGHTEE surveys on \nMeerKAT, over regions where photometry is already available.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/sty1777
- https://academic.oup.com/mnras/article-pdf/479/4/4509/25180437/sty1777.pdf
- OA Status
- bronze
- Cited By
- 21
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2792591694
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2792591694Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/mnras/sty1777Digital Object Identifier
- Title
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Predicting the neutral hydrogen content of galaxies from optical data using machine learningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-07-04Full publication date if available
- Authors
-
Mika Rafieferantsoa, Sambatra Andrianomena, Romeel DavèList of authors in order
- Landing page
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https://doi.org/10.1093/mnras/sty1777Publisher landing page
- PDF URL
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https://academic.oup.com/mnras/article-pdf/479/4/4509/25180437/sty1777.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/mnras/article-pdf/479/4/4509/25180437/sty1777.pdfDirect OA link when available
- Concepts
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Physics, Galaxy, Random forest, Photometry (optics), Mean squared error, Astrophysics, Photometric redshift, Redshift, Artificial neural network, Content (measure theory), Machine learning, Artificial intelligence, Statistics, Stars, Computer science, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2, 2022: 2, 2021: 2, 2020: 9Per-year citation counts (last 5 years)
- References (count)
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69Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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