Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a generative artificial intelligence (AI) method capable of predicting optical galaxy spectra from photometric broadband ima...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
|
| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/ad8bbe |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850138382303232000 |
|---|---|
| author | Lars Doorenbos Eva Sextl Kevin Heng Stefano Cavuoti Massimo Brescia Olena Torbaniuk Giuseppe Longo Raphael Sznitman Pablo Márquez-Neila |
| author_facet | Lars Doorenbos Eva Sextl Kevin Heng Stefano Cavuoti Massimo Brescia Olena Torbaniuk Giuseppe Longo Raphael Sznitman Pablo Márquez-Neila |
| author_sort | Lars Doorenbos |
| collection | DOAJ |
| description | Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a generative artificial intelligence (AI) method capable of predicting optical galaxy spectra from photometric broadband images alone. This method draws from the latest advances in diffusion models in combination with contrastive networks. We pass multiband galaxy images into the architecture to obtain optical spectra. From these, robust values for galaxy properties can be derived with any methods in the spectroscopic toolbox, such as standard population synthesis techniques and Lick indices. When trained and tested on 64 × 64 pixel images from the Sloan Digital Sky Survey, the global bimodality of star-forming and quiescent galaxies in photometric space is recovered, as well as a mass–metallicity relation of star-forming galaxies. The comparison between the observed and the artificially created spectra shows good agreement in overall metallicity, age, Dn4000, stellar velocity dispersion, and E ( B − V ) values. Photometric redshift estimates of our generative algorithm can compete with other current, specialized deep learning techniques. Moreover, this work is the first attempt in the literature to infer velocity dispersion from photometric images. Additionally, we can predict the presence of an active galactic nucleus up to an accuracy of 82%. With our method, scientifically interesting galaxy properties, normally requiring spectroscopic inputs, can be obtained in future data sets from large-scale photometric surveys alone. The spectra prediction via AI can further assist in creating realistic mock catalogs. |
| format | Article |
| id | doaj-art-14ffb6c4ec7f47e69b95d42182755f24 |
| institution | OA Journals |
| issn | 1538-4357 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-14ffb6c4ec7f47e69b95d42182755f242025-08-20T02:30:35ZengIOP PublishingThe Astrophysical Journal1538-43572024-01-01977113110.3847/1538-4357/ad8bbeGalaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion ModelsLars Doorenbos0https://orcid.org/0000-0002-0231-9950Eva Sextl1https://orcid.org/0009-0001-5618-4326Kevin Heng2https://orcid.org/0000-0003-1907-5910Stefano Cavuoti3https://orcid.org/0000-0002-3787-4196Massimo Brescia4https://orcid.org/0000-0001-9506-5680Olena Torbaniuk5https://orcid.org/0000-0003-4465-2564Giuseppe Longo6https://orcid.org/0000-0002-9182-8414Raphael Sznitman7https://orcid.org/0000-0001-6791-4753Pablo Márquez-Neila8https://orcid.org/0000-0001-5722-7618AIMI, ARTORG Center, University of Bern , Murtenstr. 50, CH-3008 Bern, Switzerland ; lars.doorenbos@unibe.chUniversitäts-Sternwarte , Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, 81679 München, Germany ; sextl@usm.lmu.deUniversitäts-Sternwarte , Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, 81679 München, Germany ; sextl@usm.lmu.deINAF—Astronomical Observatory of Capodimonte , Salita Moiariello 16, I-80131 Napoli, Italy; INFN—Sezione di Napoli , via Cinthia 9, 80126 Napoli, ItalyINAF—Astronomical Observatory of Capodimonte , Salita Moiariello 16, I-80131 Napoli, Italy; INFN—Sezione di Napoli , via Cinthia 9, 80126 Napoli, Italy; Department of Physics, University Federico II , Strada Vicinale Cupa Cintia, 21, 80126 Napoli, ItalyDepartment of Physics and Astronomy “Augusto Righi,” University of Bologna , via Piero Gobetti 93/2, 40129 Bologna, ItalyDepartment of Physics, University Federico II , Strada Vicinale Cupa Cintia, 21, 80126 Napoli, ItalyAIMI, ARTORG Center, University of Bern , Murtenstr. 50, CH-3008 Bern, Switzerland ; lars.doorenbos@unibe.chAIMI, ARTORG Center, University of Bern , Murtenstr. 50, CH-3008 Bern, Switzerland ; lars.doorenbos@unibe.chModern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a generative artificial intelligence (AI) method capable of predicting optical galaxy spectra from photometric broadband images alone. This method draws from the latest advances in diffusion models in combination with contrastive networks. We pass multiband galaxy images into the architecture to obtain optical spectra. From these, robust values for galaxy properties can be derived with any methods in the spectroscopic toolbox, such as standard population synthesis techniques and Lick indices. When trained and tested on 64 × 64 pixel images from the Sloan Digital Sky Survey, the global bimodality of star-forming and quiescent galaxies in photometric space is recovered, as well as a mass–metallicity relation of star-forming galaxies. The comparison between the observed and the artificially created spectra shows good agreement in overall metallicity, age, Dn4000, stellar velocity dispersion, and E ( B − V ) values. Photometric redshift estimates of our generative algorithm can compete with other current, specialized deep learning techniques. Moreover, this work is the first attempt in the literature to infer velocity dispersion from photometric images. Additionally, we can predict the presence of an active galactic nucleus up to an accuracy of 82%. With our method, scientifically interesting galaxy properties, normally requiring spectroscopic inputs, can be obtained in future data sets from large-scale photometric surveys alone. The spectra prediction via AI can further assist in creating realistic mock catalogs.https://doi.org/10.3847/1538-4357/ad8bbeGalaxy propertiesGalaxy photometryGalaxy spectroscopySky surveysNeural networksConvolutional neural networks |
| spellingShingle | Lars Doorenbos Eva Sextl Kevin Heng Stefano Cavuoti Massimo Brescia Olena Torbaniuk Giuseppe Longo Raphael Sznitman Pablo Márquez-Neila Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models The Astrophysical Journal Galaxy properties Galaxy photometry Galaxy spectroscopy Sky surveys Neural networks Convolutional neural networks |
| title | Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models |
| title_full | Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models |
| title_fullStr | Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models |
| title_full_unstemmed | Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models |
| title_short | Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models |
| title_sort | galaxy spectroscopy without spectra galaxy properties from photometric images with conditional diffusion models |
| topic | Galaxy properties Galaxy photometry Galaxy spectroscopy Sky surveys Neural networks Convolutional neural networks |
| url | https://doi.org/10.3847/1538-4357/ad8bbe |
| work_keys_str_mv | AT larsdoorenbos galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT evasextl galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT kevinheng galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT stefanocavuoti galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT massimobrescia galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT olenatorbaniuk galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT giuseppelongo galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT raphaelsznitman galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels AT pablomarquezneila galaxyspectroscopywithoutspectragalaxypropertiesfromphotometricimageswithconditionaldiffusionmodels |