Niebla: an open-source code for modelling the extragalactic background light

TL;DR

Niebla introduces an open-source Python package for forward-folding modelling of the Extragalactic Background Light (EBL), enabling customizable inputs to compute EBL spectra and their opacities for very high-energy gamma rays. The framework combines optical stellar emissivity (via SSPs and cosmic star-formation history) with infrared dust reemission (via spectral templates or grey-body populations) and optional ALP or other contributions, all within a self-consistent cosmological integration. The authors fit three dust-reemission prescriptions to a broad compilation of EBL data, finding Chary and 2BB templates provide the best fits, though current data yield sizable reduced χ^2 values due to cross-survey tensions. A practical use case demonstrates that, for a simple power-law intrinsic spectrum, a future Mkn 501–like flare observed by LHAASO could differentiate dust models, underscoring niebla’s potential to constrain EBL properties with forthcoming VHE observations.

Abstract

Extragalactic very high-energy (VHE; $E>100\,$GeV) gamma rays suffer absorption in interactions with photons of the Extragalactic Background Light (EBL). The EBL is an isotropic diffuse photon field from optical to infrared wavelengths, which is difficult to measure directly due to strong foreground emission. We present niebla, the first open-source code to compute the EBL using a forward-folding approach that accepts fully customizable inputs. This software enables a detailed modelling of the influence of EBL opacities on VHE observations and facilitates the distinction between different dust reemission models. The code models the optical background primarily from stellar emission, by evolving the spectrum of a single stellar population as a function of redshift, considering mean metallicity evolution and star formation rate density. Additional sources to the EBL can be provided by the user. The code already includes optional contributions from, e.g., stripped stars, intra-halo light, or the decay of axion dark matter. The optical emissivity is then absorbed by interstellar dust and reemitted in the infrared regime. We provide multiple prescriptions to model this process, using spectral dust templates or a combination of blackbodies. We provide three EBL models calculated with different dust reemission prescriptions, which have been fitted to various observational data sets. In addition, we showcase the versatility of our model through a simulated observation of the blazar Markarian 501 in a high-flux state with the Large High Altitude Air Shower Observatory array. We find that the simulated VHE spectrum is highly sensitive to the EBL opacity coming from the infrared. Our model will therefore allow the community to distinguish between different dust reemission models and constrain EBL parameters with future observations.

Figures (10)

• Figure 1: Set of synthetic spectra for dust reemission from ULIRGs chary2001. We use $f_\mathrm{TIR} = 3\times10^9$ to convert between galactic and SSP luminosities, and $\lambda_\mathrm{TIR, cut}=5.5$µm, represented by a shaded grey region below this value. We show the original spectra as solid lines, and our extrapolations as dashed lines.
• Figure 2: Synthetic spectra of dust emission in the infrared for different metallicities generated with the BOSA software 2021BOSA. The spectra are already normalized to $L_\mathrm{TIR} = 1\,\mathrm{L}_\odot$.
• Figure 3: Overview of the EBL measurements used in this analysis. Data have been taken from hill_spectrum_2018 and Biteau_The_MM_EGAL_spectrum. Upper limits of the EBL are represented as grey open symbols, lower limits as coloured filled symbols. More detailed description of the measurements is given in Section \ref{['section:MeasurementsEBL']}. The best-fit results of our EBL models with three different prescriptions for the dust reemission are also displayed, see Section \ref{['subsection:ResultsDiffDustModels']}.
• Figure 4: Emissivity data for specific wavelengths as a function of redshift. Observational data is taken from the compilation by finke_modeling_2022. Solid lines are our best-fit curves for each of our models. The $1\,\sigma$ uncertainty band is too thin to be seen. Model A from finke_modeling_2022 is shown as dashed black lines for comparison.
• Figure 5: The SFRD as a function of redshift, with the $1\,\sigma$ uncertainty shown as light bands for each dust reemission model. Note that the bands overlap. Observational data compiled by the_fermi-lat_collaboration_gamma-ray_2018 are shown with different symbols. The parametrization of madau_cosmic_2014 is shown as a dashed line for comparison.