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Peer-reviewedCC BY 4.0The European Physical Journal C

Data-driven modeling of Galactic diffuse emission with multi-wavelength observations

  • Xi Liu1
  • Xiao-Dong Li1
  • Sujie Lin1,2,3
  • Yihan Liu1
  • Chengyu Shao1
  • Lili Yang1,4
  • Le Zhang1
4 affiliations
  1. 1Sun Yat-sen University
  2. 2Ocean University of China
  3. 3Mathematics and Physics, Ocean University of China
  4. 4University of Johannesburg
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Abstract

We present a data-driven investigation of Galactic diffuse emission. Using multi-frequency Planck maps (30–857 GHz), we construct a non-linear mapping between microwave-to-far-infrared and gamma-ray intensity through supervised machine learning. Our models achieve high predictive accuracy ( R2> R 2 > 0.90 in the 0.1–10 GeV range), demonstrating that multi-band Planck emission encodes sufficient information to reconstruct both spatial morphology and spectral properties of diffuse gamma-ray emission. By analyzing model performance across different frequency bands and spatial regions, we find that the high-frequency Planck bands are the dominant predictors, indicating that the learned mapping is primarily driven by gas- and dust-correlated structures encoded in the adopted interstellar emission model. Above 10 GeV, the increasing relevance of the low-frequency bands is consistent with synchrotron-traced cosmic-ray electron structures that may be associated with leptonic inverse Compton emission. Residual maps reveal coherent large-scale structures, including Loop I and III, highlighting regions where standard interstellar emission models are incomplete or biased. Compared with the GALPROP model, our machine learning approach yields a higher R2=0.95 R 2 = 0.95 and lower mean absolute relative error (14.7%) in the inner Galactic disk and the Galactic Center (GC) region at ∼ ∼ 4.3 GeV. Our results illustrate that machine learning serves as a physically interpretable tool for multi-messenger astrophysics, providing a data-driven baseline for separating non-standard emission components and deriving new constraints on cosmic-ray propagation and interstellar medium structure.

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Cite

BibTeXRIS
BibTeX
@article{liu2026datadriven,
  title = {{Data-driven modeling of Galactic diffuse emission with multi-wavelength observations}},
  author = {Xi Liu and Xiao-Dong Li and Sujie Lin and Yihan Liu and Chengyu Shao and Lili Yang and Le Zhang},
  journal = {The European Physical Journal C},
  year = {2026},
  month = sep,
  doi = {10.1140/epjc/s10052-026-16408-2},
  url = {https://link.springer.com/10.1140/epjc/s10052-026-16408-2},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Data-driven modeling of Galactic diffuse emission with multi-wavelength observations
AU  - Xi Liu
AU  - Xiao-Dong Li
AU  - Sujie Lin
AU  - Yihan Liu
AU  - Chengyu Shao
AU  - Lili Yang
AU  - Le Zhang
PY  - 2026
DA  - 2026/09/30
JO  - The European Physical Journal C
DO  - 10.1140/epjc/s10052-026-16408-2
UR  - https://link.springer.com/10.1140/epjc/s10052-026-16408-2
AB  - We present a data-driven investigation of Galactic diffuse emission. Using multi-frequency Planck maps (30–857 GHz), we construct a non-linear mapping between microwave-to-far-infrared and gamma-ray intensity through supervised machine learning. Our models achieve high predictive accuracy ( R^2> R 2 > 0.90 in the 0.1–10 GeV range), demonstrating that multi-band Planck emission encodes sufficient information to reconstruct both spatial morphology and spectral properties of diffuse gamma-ray emission. By analyzing model performance across different frequency bands and spatial regions, we find that the high-frequency Planck bands are the dominant predictors, indicating that the learned mapping is primarily driven by gas- and dust-correlated structures encoded in the adopted interstellar emission model. Above 10 GeV, the increasing relevance of the low-frequency bands is consistent with synchrotron-traced cosmic-ray electron structures that may be associated with leptonic inverse Compton emission. Residual maps reveal coherent large-scale structures, including Loop I and III, highlighting regions where standard interstellar emission models are incomplete or biased. Compared with the GALPROP model, our machine learning approach yields a higher R^2=0.95 R 2 = 0.95 and lower mean absolute relative error (14.7%) in the inner Galactic disk and the Galactic Center (GC) region at ∼ ∼ 4.3 GeV. Our results illustrate that machine learning serves as a physically interpretable tool for multi-messenger astrophysics, providing a data-driven baseline for separating non-standard emission components and deriving new constraints on cosmic-ray propagation and interstellar medium structure.
SN  - 1434-6052
ER  -