Predicting the global far-infrared SED of galaxies via machine learning techniques
- Author
- Wouter Dobbels, Maarten Baes (UGent) , Sébastien Viaene (UGent) , S. Bianchi, J. I. Davies, V. Casasola, C. J. R. Clark, J. Fritz, M. Galametz, F. Galliano, A. Mosenkov, Angelos Nersesian (UGent) and Ana Trčka (UGent)
- Organization
- Abstract
- Context: Dust plays an important role in shaping a galaxy’s spectral energy distribution (SED). It absorbs ultraviolet (UV) to near- infrared (NIR) radiation and re-emits this energy in the far-infrared (FIR). The FIR is essential to understand dust in galaxies. However, deep FIR observations require a space mission, none of which are still active today. Aims: We aim to infer the FIR emission across six Herschel bands, along with dust luminosity, mass, and effective temperature, based on the available UV to mid-infrared (MIR) observations. We also want to estimate the uncertainties of these predictions, compare our method to energy balance SED fitting, and determine possible limitations of the model. Methods: We propose a machine learning framework to predict the FIR fluxes from 14 UV–MIR broadband fluxes. We used a low redshift sample by combining DustPedia and H-ATLAS, and extracted Bayesian flux posteriors through SED fitting. We trained shallow neural networks to predict the far-infrared fluxes, uncertainties, and dust properties. We evaluated them on a test set using a root mean square error (RMSE) in log-space. Results: Our results (RMSE = 0.19 dex) significantly outperform UV–MIR energy balance SED fitting (RMSE = 0.38 dex), and are inherently unbiased. We can identify when the predictions are off, for example when the input has large uncertainties on WISE 22 μm, or when the input does not resemble the training set. Conclusions: The galaxies for which we have UV–FIR observations can be used as a blueprint for galaxies that lack FIR data. This results in a ‘virtual FIR telescope’, which can be applied to large optical-MIR galaxy samples. This helps bridge the gap until the next FIR mission.
- Keywords
- galaxies, photometry, galaxies, ISM, infrared, galaxies, PECTRAL ENERGY-DISTRIBUTIONS, INTERSTELLAR DUST, SKY SURVEY, EVOLUTION, ULTRAVIOLET, EMISSION, EXPLORER, MISSION, MODEL, MAPS
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8645267
- MLA
- Dobbels, Wouter, et al. “Predicting the Global Far-Infrared SED of Galaxies via Machine Learning Techniques.” ASTRONOMY & ASTROPHYSICS, vol. 634, 2020, doi:10.1051/0004-6361/201936695.
- APA
- Dobbels, W., Baes, M., Viaene, S., Bianchi, S., Davies, J. I., Casasola, V., … Trčka, A. (2020). Predicting the global far-infrared SED of galaxies via machine learning techniques. ASTRONOMY & ASTROPHYSICS, 634. https://doi.org/10.1051/0004-6361/201936695
- Chicago author-date
- Dobbels, Wouter, Maarten Baes, Sébastien Viaene, S. Bianchi, J. I. Davies, V. Casasola, C. J. R. Clark, et al. 2020. “Predicting the Global Far-Infrared SED of Galaxies via Machine Learning Techniques.” ASTRONOMY & ASTROPHYSICS 634. https://doi.org/10.1051/0004-6361/201936695.
- Chicago author-date (all authors)
- Dobbels, Wouter, Maarten Baes, Sébastien Viaene, S. Bianchi, J. I. Davies, V. Casasola, C. J. R. Clark, J. Fritz, M. Galametz, F. Galliano, A. Mosenkov, Angelos Nersesian, and Ana Trčka. 2020. “Predicting the Global Far-Infrared SED of Galaxies via Machine Learning Techniques.” ASTRONOMY & ASTROPHYSICS 634. doi:10.1051/0004-6361/201936695.
- Vancouver
- 1.Dobbels W, Baes M, Viaene S, Bianchi S, Davies JI, Casasola V, et al. Predicting the global far-infrared SED of galaxies via machine learning techniques. ASTRONOMY & ASTROPHYSICS. 2020;634.
- IEEE
- [1]W. Dobbels et al., “Predicting the global far-infrared SED of galaxies via machine learning techniques,” ASTRONOMY & ASTROPHYSICS, vol. 634, 2020.
@article{8645267, abstract = {{Context: Dust plays an important role in shaping a galaxy’s spectral energy distribution (SED). It absorbs ultraviolet (UV) to near- infrared (NIR) radiation and re-emits this energy in the far-infrared (FIR). The FIR is essential to understand dust in galaxies. However, deep FIR observations require a space mission, none of which are still active today. Aims: We aim to infer the FIR emission across six Herschel bands, along with dust luminosity, mass, and effective temperature, based on the available UV to mid-infrared (MIR) observations. We also want to estimate the uncertainties of these predictions, compare our method to energy balance SED fitting, and determine possible limitations of the model. Methods: We propose a machine learning framework to predict the FIR fluxes from 14 UV–MIR broadband fluxes. We used a low redshift sample by combining DustPedia and H-ATLAS, and extracted Bayesian flux posteriors through SED fitting. We trained shallow neural networks to predict the far-infrared fluxes, uncertainties, and dust properties. We evaluated them on a test set using a root mean square error (RMSE) in log-space. Results: Our results (RMSE = 0.19 dex) significantly outperform UV–MIR energy balance SED fitting (RMSE = 0.38 dex), and are inherently unbiased. We can identify when the predictions are off, for example when the input has large uncertainties on WISE 22 μm, or when the input does not resemble the training set. Conclusions: The galaxies for which we have UV–FIR observations can be used as a blueprint for galaxies that lack FIR data. This results in a ‘virtual FIR telescope’, which can be applied to large optical-MIR galaxy samples. This helps bridge the gap until the next FIR mission.}}, articleno = {{A57}}, author = {{Dobbels, Wouter and Baes, Maarten and Viaene, Sébastien and Bianchi, S. and Davies, J. I. and Casasola, V. and Clark, C. J. R. and Fritz, J. and Galametz, M. and Galliano, F. and Mosenkov, A. and Nersesian, Angelos and Trčka, Ana}}, issn = {{0004-6361}}, journal = {{ASTRONOMY & ASTROPHYSICS}}, keywords = {{galaxies,photometry,galaxies,ISM,infrared,galaxies,PECTRAL ENERGY-DISTRIBUTIONS,INTERSTELLAR DUST,SKY SURVEY,EVOLUTION,ULTRAVIOLET,EMISSION,EXPLORER,MISSION,MODEL,MAPS}}, language = {{eng}}, pages = {{23}}, title = {{Predicting the global far-infrared SED of galaxies via machine learning techniques}}, url = {{http://doi.org/10.1051/0004-6361/201936695}}, volume = {{634}}, year = {{2020}}, }
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