Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models

Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila·June 26, 2024

Summary

This study presents a generative AI method that predicts optical galaxy spectra from photometric broadband images using diffusion models and contrastive networks. The model, trained on SDSS 64x64-pixel data, accurately captures global galaxy properties like star-forming and quiescent populations, as well as a mass-metallicity relation for star-forming galaxies. It estimates properties such as metallicity, age, Dn4000, stellar velocity dispersion, and E(B-V), and even predicts photometric redshifts and AGN presence. The AI bypasses the need for spectroscopic templates, enhancing mock catalog creation and enabling the extraction of valuable galaxy properties from large-scale surveys without direct spectroscopic data. The research highlights the potential of AI in analyzing astronomical images and its implications for future surveys like Euclid and LSST.

Key findings

14

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

Could you please provide more specific information or context about the paper you are referring to? This will help me better understand the problem it aims to solve and whether it is a new problem or not.


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title or some details about the paper you are referring to so I can assist you in identifying the scientific hypothesis it seeks to validate.


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel approach that utilizes generative AI methods to predict optical galaxy spectra solely from photometric broad-band images . This method integrates the latest advancements in diffusion models along with contrastive networks to achieve this task. By inputting multi-band galaxy images into the architecture, the model can generate optical spectra, enabling the derivation of robust galaxy properties using various spectroscopic techniques like standard population synthesis methods and Lick indices . When tested on 64 × 64-pixel images from the Sloan Digital Sky Survey, the model successfully captures the global bimodality of star-forming and quiescent galaxies in photometric space, as well as the relationship between mass and metallicity . The proposed method in the paper offers several key characteristics and advantages compared to previous methods:

  1. Generative AI Approach: The paper introduces a generative AI approach that leverages diffusion models and contrastive networks to predict optical galaxy spectra from photometric broad-band images. This approach allows for the generation of spectra directly from images, eliminating the need for traditional spectroscopic observations.

  2. Integration of Latest Advancements: By integrating the latest advancements in diffusion models and contrastive networks, the proposed method achieves higher accuracy and fidelity in predicting galaxy spectra. This integration enhances the model's ability to capture complex spectral features and relationships.

  3. Robust Galaxy Property Derivation: The model's ability to generate optical spectra from images enables the derivation of robust galaxy properties using standard population synthesis methods and Lick indices. This facilitates the analysis of galaxy properties without the need for time-consuming and expensive spectroscopic observations.

  4. Global Bimodality and Relationships: When tested on galaxy images from the Sloan Digital Sky Survey, the model successfully captures the global bimodality of star-forming and quiescent galaxies in photometric space. Additionally, it accurately represents the relationship between galaxy mass and metallicity, showcasing the model's capability to capture important astrophysical relationships.

  5. Efficiency and Cost-Effectiveness: By eliminating the need for spectroscopic observations to derive galaxy properties, the proposed method offers a more efficient and cost-effective alternative for studying galaxies. Researchers can analyze large datasets of galaxy images and derive valuable insights without the constraints of traditional spectroscopic observations.

Overall, the characteristics and advantages of the proposed method in the paper demonstrate its potential to revolutionize the study of galaxies by offering a more efficient, accurate, and cost-effective approach to predicting optical spectra and deriving galaxy properties from images.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

To provide you with information on related research and noteworthy researchers in a specific field, I would need more details about the topic you are referring to. Could you please specify the field or topic you are interested in so that I can assist you better?


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate the generation of complete galaxy spectra from photometric broadband images alone. The experiments involved varying factors such as image resolution and the number of bands used in the generative AI model. Different scenarios were considered, including cases where only the g-band was provided to the AI model. The experiments aimed to predict spectra based on the training data and evaluate the performance based on the mean squared error (MSE) between the generated spectra and the observed ones . The results indicated that reducing image resolution while keeping all five bands led to an increase in MSE, signifying a stronger difference between predicted and observed spectra. Additionally, having more bands available improved the performance of the method. The experiments highlighted the importance of both spatial information and magnitudes for successful spectrum generation, with the impact of additional bands, especially small-band filters, likely to further enhance results .


What is the dataset used for quantitative evaluation? Is the code open source?

To provide you with the most accurate information, I need more details about the specific project or research you are referring to. Could you please provide more context or details about the dataset and code you are inquiring about?


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study focused on generating complete galaxy spectra solely from photometric broadband images using a generative AI model . The findings indicated that spatial information and magnitudes are crucial for successful spectrum generation, with the number of bands available significantly impacting the performance of the method . Additionally, the study highlighted the importance of incorporating additional bands, especially small-band filters, to further enhance the results .

Moreover, the paper discussed the challenges of measuring stellar metallicity from photometric data alone due to the age-metallicity-dust degeneracy in galaxies . The results showed that the predicted spectra coincided well with the observed spectra in terms of overall metallicity, age, Dn4000, stellar velocity dispersion, and E(B-V) values . The study also successfully inferred velocity dispersion from photometric images, showcasing the potential of AI in predicting galaxy properties that traditionally require spectroscopic inputs .

Overall, the experiments and results detailed in the paper offer substantial evidence supporting the scientific hypotheses under investigation, demonstrating the feasibility and accuracy of generating galaxy spectra from photometric images and the potential of AI in predicting essential galaxy properties .


What are the contributions of this paper?

The paper presents a generative AI method that can predict optical galaxy spectra solely from photometric broad-band images. This method utilizes diffusion models and contrastive networks to achieve this capability. By inputting multi-band galaxy images into the architecture, optical spectra can be obtained, enabling the derivation of robust galaxy properties using various spectroscopic analysis techniques .


What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include in-depth research studies, complex problem-solving initiatives, detailed data analysis, comprehensive strategic planning, or thorough product development processes. Essentially, any work that requires a deep dive into the subject matter or requires a high level of expertise to further progress can be continued in depth.

Tables

1

Introduction
Background
Evolution of AI in astronomy
Importance of spectroscopic data in galaxy studies
Objective
To develop a novel AI method for spectrum prediction
Enhance mock catalog creation and data analysis without spectroscopy
Method
Data Collection
SDSS Dataset
Source: Sloan Digital Sky Survey (SDSS) broadband images
Resolution: 64x64 pixels
Data Preprocessing
Image processing techniques
Feature extraction for model input
Model Architecture
Diffusion Models
Generative modeling using diffusion processes
Contrastive Networks
Integration for capturing global galaxy properties
Training and Evaluation
Training methodology
Performance metrics: star-forming/quiescent populations, mass-metallicity relation, etc.
Predictive Capabilities
Estimation of Properties
Metallicity, age, Dn4000, stellar velocity dispersion, E(B-V)
Photometric Redshifts
Predicting redshifts from broadband images
AGN Detection
Identifying active galactic nuclei
Applications
Mock catalog generation for future surveys
Value extraction from large-scale surveys with limited spectroscopy
Implications and Future Directions
AI in Astronomical Image Analysis
Advancements in AI-driven galaxy studies
LSST and Euclid Surveys
Potential impact on data analysis and resource utilization
Conclusion
Summary of key findings
Limitations and future research possibilities
Basic info
papers
astrophysics of galaxies
instrumentation and methods for astrophysics
artificial intelligence
Advanced features
Insights
Which dataset was the model trained on for this study?
How does this AI method impact the analysis of astronomical images and future surveys like Euclid and LSST?
What are some global galaxy properties accurately captured by the model?
What type of AI method is used in this study to predict galaxy spectra?

Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models

Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila·June 26, 2024

Summary

This study presents a generative AI method that predicts optical galaxy spectra from photometric broadband images using diffusion models and contrastive networks. The model, trained on SDSS 64x64-pixel data, accurately captures global galaxy properties like star-forming and quiescent populations, as well as a mass-metallicity relation for star-forming galaxies. It estimates properties such as metallicity, age, Dn4000, stellar velocity dispersion, and E(B-V), and even predicts photometric redshifts and AGN presence. The AI bypasses the need for spectroscopic templates, enhancing mock catalog creation and enabling the extraction of valuable galaxy properties from large-scale surveys without direct spectroscopic data. The research highlights the potential of AI in analyzing astronomical images and its implications for future surveys like Euclid and LSST.
Mind map
Identifying active galactic nuclei
Predicting redshifts from broadband images
Metallicity, age, Dn4000, stellar velocity dispersion, E(B-V)
Integration for capturing global galaxy properties
Generative modeling using diffusion processes
Feature extraction for model input
Image processing techniques
Resolution: 64x64 pixels
Source: Sloan Digital Sky Survey (SDSS) broadband images
Potential impact on data analysis and resource utilization
Advancements in AI-driven galaxy studies
Value extraction from large-scale surveys with limited spectroscopy
Mock catalog generation for future surveys
AGN Detection
Photometric Redshifts
Estimation of Properties
Performance metrics: star-forming/quiescent populations, mass-metallicity relation, etc.
Training methodology
Contrastive Networks
Diffusion Models
Data Preprocessing
SDSS Dataset
Enhance mock catalog creation and data analysis without spectroscopy
To develop a novel AI method for spectrum prediction
Importance of spectroscopic data in galaxy studies
Evolution of AI in astronomy
Limitations and future research possibilities
Summary of key findings
LSST and Euclid Surveys
AI in Astronomical Image Analysis
Applications
Predictive Capabilities
Training and Evaluation
Model Architecture
Data Collection
Objective
Background
Conclusion
Implications and Future Directions
Method
Introduction
Outline
Introduction
Background
Evolution of AI in astronomy
Importance of spectroscopic data in galaxy studies
Objective
To develop a novel AI method for spectrum prediction
Enhance mock catalog creation and data analysis without spectroscopy
Method
Data Collection
SDSS Dataset
Source: Sloan Digital Sky Survey (SDSS) broadband images
Resolution: 64x64 pixels
Data Preprocessing
Image processing techniques
Feature extraction for model input
Model Architecture
Diffusion Models
Generative modeling using diffusion processes
Contrastive Networks
Integration for capturing global galaxy properties
Training and Evaluation
Training methodology
Performance metrics: star-forming/quiescent populations, mass-metallicity relation, etc.
Predictive Capabilities
Estimation of Properties
Metallicity, age, Dn4000, stellar velocity dispersion, E(B-V)
Photometric Redshifts
Predicting redshifts from broadband images
AGN Detection
Identifying active galactic nuclei
Applications
Mock catalog generation for future surveys
Value extraction from large-scale surveys with limited spectroscopy
Implications and Future Directions
AI in Astronomical Image Analysis
Advancements in AI-driven galaxy studies
LSST and Euclid Surveys
Potential impact on data analysis and resource utilization
Conclusion
Summary of key findings
Limitations and future research possibilities
Key findings
14

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

Could you please provide more specific information or context about the paper you are referring to? This will help me better understand the problem it aims to solve and whether it is a new problem or not.


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title or some details about the paper you are referring to so I can assist you in identifying the scientific hypothesis it seeks to validate.


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel approach that utilizes generative AI methods to predict optical galaxy spectra solely from photometric broad-band images . This method integrates the latest advancements in diffusion models along with contrastive networks to achieve this task. By inputting multi-band galaxy images into the architecture, the model can generate optical spectra, enabling the derivation of robust galaxy properties using various spectroscopic techniques like standard population synthesis methods and Lick indices . When tested on 64 × 64-pixel images from the Sloan Digital Sky Survey, the model successfully captures the global bimodality of star-forming and quiescent galaxies in photometric space, as well as the relationship between mass and metallicity . The proposed method in the paper offers several key characteristics and advantages compared to previous methods:

  1. Generative AI Approach: The paper introduces a generative AI approach that leverages diffusion models and contrastive networks to predict optical galaxy spectra from photometric broad-band images. This approach allows for the generation of spectra directly from images, eliminating the need for traditional spectroscopic observations.

  2. Integration of Latest Advancements: By integrating the latest advancements in diffusion models and contrastive networks, the proposed method achieves higher accuracy and fidelity in predicting galaxy spectra. This integration enhances the model's ability to capture complex spectral features and relationships.

  3. Robust Galaxy Property Derivation: The model's ability to generate optical spectra from images enables the derivation of robust galaxy properties using standard population synthesis methods and Lick indices. This facilitates the analysis of galaxy properties without the need for time-consuming and expensive spectroscopic observations.

  4. Global Bimodality and Relationships: When tested on galaxy images from the Sloan Digital Sky Survey, the model successfully captures the global bimodality of star-forming and quiescent galaxies in photometric space. Additionally, it accurately represents the relationship between galaxy mass and metallicity, showcasing the model's capability to capture important astrophysical relationships.

  5. Efficiency and Cost-Effectiveness: By eliminating the need for spectroscopic observations to derive galaxy properties, the proposed method offers a more efficient and cost-effective alternative for studying galaxies. Researchers can analyze large datasets of galaxy images and derive valuable insights without the constraints of traditional spectroscopic observations.

Overall, the characteristics and advantages of the proposed method in the paper demonstrate its potential to revolutionize the study of galaxies by offering a more efficient, accurate, and cost-effective approach to predicting optical spectra and deriving galaxy properties from images.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

To provide you with information on related research and noteworthy researchers in a specific field, I would need more details about the topic you are referring to. Could you please specify the field or topic you are interested in so that I can assist you better?


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate the generation of complete galaxy spectra from photometric broadband images alone. The experiments involved varying factors such as image resolution and the number of bands used in the generative AI model. Different scenarios were considered, including cases where only the g-band was provided to the AI model. The experiments aimed to predict spectra based on the training data and evaluate the performance based on the mean squared error (MSE) between the generated spectra and the observed ones . The results indicated that reducing image resolution while keeping all five bands led to an increase in MSE, signifying a stronger difference between predicted and observed spectra. Additionally, having more bands available improved the performance of the method. The experiments highlighted the importance of both spatial information and magnitudes for successful spectrum generation, with the impact of additional bands, especially small-band filters, likely to further enhance results .


What is the dataset used for quantitative evaluation? Is the code open source?

To provide you with the most accurate information, I need more details about the specific project or research you are referring to. Could you please provide more context or details about the dataset and code you are inquiring about?


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study focused on generating complete galaxy spectra solely from photometric broadband images using a generative AI model . The findings indicated that spatial information and magnitudes are crucial for successful spectrum generation, with the number of bands available significantly impacting the performance of the method . Additionally, the study highlighted the importance of incorporating additional bands, especially small-band filters, to further enhance the results .

Moreover, the paper discussed the challenges of measuring stellar metallicity from photometric data alone due to the age-metallicity-dust degeneracy in galaxies . The results showed that the predicted spectra coincided well with the observed spectra in terms of overall metallicity, age, Dn4000, stellar velocity dispersion, and E(B-V) values . The study also successfully inferred velocity dispersion from photometric images, showcasing the potential of AI in predicting galaxy properties that traditionally require spectroscopic inputs .

Overall, the experiments and results detailed in the paper offer substantial evidence supporting the scientific hypotheses under investigation, demonstrating the feasibility and accuracy of generating galaxy spectra from photometric images and the potential of AI in predicting essential galaxy properties .


What are the contributions of this paper?

The paper presents a generative AI method that can predict optical galaxy spectra solely from photometric broad-band images. This method utilizes diffusion models and contrastive networks to achieve this capability. By inputting multi-band galaxy images into the architecture, optical spectra can be obtained, enabling the derivation of robust galaxy properties using various spectroscopic analysis techniques .


What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include in-depth research studies, complex problem-solving initiatives, detailed data analysis, comprehensive strategic planning, or thorough product development processes. Essentially, any work that requires a deep dive into the subject matter or requires a high level of expertise to further progress can be continued in depth.

Tables
1
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