Probing the 3D Structures of Supernovae through IR Signatures of CO and SiO

arXiv:2604.18339 · astro-ph.HE · Submitted 2026-04-20 · Read on arXiv

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Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: Today's paper: "Probing the 3D Structures of Supernovae through IR Signatures of CO and SiO".

Jocelyn: The gist:

Vera: First, who's behind it and why it matters.

Title and authors: Vera: So, to recap this paper, "Probing the three dee Structures of Supernovae through IR Signatures of CO and SiO," they are using infrared signatures of these two molecules to look at the three dimensional structures within supernova ejecta <ref:2604.18339#pg1,Probing the 3D Structures of Supernovae through IR Signatures of CO and>.

Jocelyn: They're not just looking at one molecule; they're looking at how CO and SiO form together, and how those features map onto different physical structures in the star that explodes >

Subrahmanyan: The authors are T. Mera, P. Hoeflich, C. R. Burns, C. Ashall, K. Medler, E. Fereidouni, W. B. Hoogendam, M Shahbandeh and S Shiber and C M Pfeffer and E Baron and J Lu and N Morrell E Hsiao and M Phillips >

Vera: The authors are connecting these molecular observations to the physics of instability scales in the ejecta that we can't easily see directly through light >

Jocelyn: They are building a tool called MOFAT that uses JWST spectra to drive simulations, which means they are using real sky data to test their model parameters >

Subrahmanyan: The implication here is that we might finally be able to connect the observational signatures of these molecules directly to the underlying physics of structure formation in the explosion, which is what we need for cosmology >

The paper's summary: Vera: They summarize their MOFAT approach by saying they use JWST spectra to drive full radiation transport simulations to find a set of parameters that best reproduces the observed molecular profiles >

Jocelyn: They are using observations to converge on those parameters, assuming a large scale spherical geometry while allowing for multidimensional clump-like structures in their stationary solution >

Subrahmanyan: The key elements of the implementation include defining an inner radius r min and an outer radius r max, and then characterizing the clumps by their shape or flattening parameter epsilon, their relative size to the radial dimension, denoted as rR, and a density enhancement factor f c >

Vera: They also define the probability for a ray to hit a clump within some distance r using that formula we talked about earlier, P(r) = n(r) r S c(epsilon, r c) >

Jocelyn: This setup allows them to integrate along the p j direction to find the probability of hitting a clump at a velocity v j, and then include the effects of those clumps based on their chord length s >

Subrahmanyan: They state that this makes one dimensional and two dimensional simulations practical but they pose an explodability problem, typically requiring an artificial explosion method or additional effects due to neutrino transport >

The paper's improvements: Vera: One improvement they suggest is that MOFAT can be used to describe the structures of the molecular region as it is seen, which lets AI systems map the Si/O/C layers of the SN envelope >

Jocelyn: That capability enables tomography, providing a direct link to the progenitor by identifying boundaries between CO and SiO regions >

Subrahmanyan: They suggest that AI systems can be trained to utilize high optical depth features for structural probing because MOFAT uses optically thick fundamental bands to test clump parameters until the total absolute flux and the flux ratios between vibrational modes optimally reproduce observations >

Vera: This lets AI infer instabilities from evolutionary or explosion physics by characterizing clump parameters like shape and size >

Jocelyn: The system can use parameter correlation analysis because it identifies moderately strong linear relationships for the parameter pairs (n, v one), (n, T one), (v one T one), and (v one f c; region one) > <ref:2604.18339#pg1>

Subrahmanyan: That lets AI understand how physical parameters are coupled in the solution space and identify degeneracies that require low-latency extended time-frame observations >

Conclusion: Vera: So to wrap up, the paper on "Probing the three dee Structures of Supernovae through IR Signatures of CO and SiO" shows us how these molecular features can be used to map out the Si/O/C layers > <ref:2604.18339#pg1,Probing the 3D Structures of Supernovae through IR Signatures of CO and>

Jocelyn: It gives us a way to use observations for tomography, which connects those structures back to the progenitor star itself >

Subrahmanyan: The main implication is that we can use this method to infer instabilities from explosion physics by characterizing the clump parameters like shape and size that we see in the data >

Vera: It also points us toward needing more detailed time coverage observations, because they flag that separating these two length scales requires low-latency extended time-frame observations >

Jocelyn: So, all in all, this work using MOFAT is a powerful way to use JWST data to probe the three dee structures of supernovae through the CO and SiO signatures > <ref:2604.18339#pg1,the 3D structures of supernovae through>

Subrahmanyan: From the perspective of cosmology, this helps connect structure formation scales with neutrino physics and requires those extended time-frame observations to really map out chemical stratification in these layers >

T. Mera, P. Hoeflich, C. R. Burns, C. Ashall, K. Medler, E. Fereidouni, W. B., * M., * E., * J., * N., * E., * M.

Department of Physics, Florida State University · Observatories of the Carnegie Institution for Science · Institute for Astronomy, University of Hawai’i at Manoa · Space Telescope Science Institute · Planetary Science Institute · Hamburger Sternwarte · Department of Physics & Astronomy, Michigan State University · Las Campanas Observatory, Carnegie Observatories

astro-ph.HE

Submitted: 2026-04-20

Updated: 2026-10-13

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 72/100

The gist: The gist: A new public-domain MOlecular Fitting Analysis Tool (MOFAT) was developed to probe molecule-forming regions in supernovae (SNe) through analysis of molecular features in the near- and

Key concepts

MOFAT: A new data-driven tool that uses JWST spectra to run time-independent simulations. It looks for physical properties of supernova regions by fitting molecular features, allowing researchers to reconstruct abundances and identify small-scale structures.
Clump Parameters: These describe the structure of the molecular gas, including its shape (flattening parameter $\epsilon$), size relative to the radius ($rR$), and density enhancement factor ($f_c$). These parameters help model how clumps interact with radiation and affect spectral features.
Optical Depth Effects: When light passes through dense gas, its intensity changes based on how much material is present. Traditional models struggle with this because they often use simple one-zone approaches. MOFAT overcomes this by including multidimensional structures to accurately reproduce observed flux ratios between different molecular bands.
CO and SiO Formation Link: The study found that the formation of CO molecules in the inner, CO-rich region acts as a trigger for the formation of SiO. This suggests a specific chemical pathway where one molecule's presence facilitates the creation of another, revealing complex chemical interactions within the supernova ejecta.

Terminology

Abundance — 1× in this paper · explained in 1 episode
Trace — 1× in this paper · explained in 2 episodes

Summary

The gist: A new public-domain MOlecular Fitting Analysis Tool (MOFAT) was developed to probe molecule-forming regions in supernovae (SNe) through analysis of molecular features in the near- and mid-infrared because it employs a novel data-driven approach to explore the physical properties of these regions using time-independent radiative transfer simulations that include multidimensional, clumplike structures.

Motivation

The study addresses the difficulty in studying the distribution of dust nucleation sites through CO and SiO because both observational and theoretical evidence suggests that the ejecta of CC SNe are structured and mixed on various instability scales. Current time-dependent forward modeling employs spherical models, which successfully explain the timing of molecule formation but fail to reproduce the absolute flux ratio between CO overtone and fundamental bands by factors of 2–5. The paper proposes an inverse data-driven approach where observations drive full radiation transport simulations that include 3D morphology effects to reproduce spectra at a given time.

MOFAT Methodology

MOFAT is designed to describe the molecular region of CC SNe using observations by utilizing JWST spectra to drive full radiation transport simulations to converge on a set of parameters that best reproduces the observed molecular profiles. The tool assumes a spherically symmetric large-scale geometry and uses a stationary solution of the radiation transport equations that can include multidimensional clump-like structures. Key aspects of the implementation include:

The molecular region is defined by an inner radius, rmin, and an outer radius, rmax.

Clump parameters are characterized by the shape or flattening parameter (ϵ), their relative size to the radial dimension (rR), and their density enhancement factor (fc).

The probability for a ray to interact with a clump in this region within some distance, ∆r, is defined by P(r) = n(r)∆rSc(ϵ, rc).

Results and Findings for SN 2024ggi

The application of MOFAT to SN 2024ggi at +285 and +385 days post-explosion yielded several key findings regarding CO and SiO formation.

CO Formation: CO formation triggers SiO formation in the inner layers of the CO-rich region previously studied.

SiO Evolution: The inner edge of the SiO-emitting region recedes from velocities of v1 ≈ 1,500 to 1,000 km s−1, indicating continued SiO formation.

SiO Mass Change: The SiO mass decreases from ∼ (2 − 6) × 10−3 M⊙ by roughly an order of magnitude, suggesting ongoing evaporation.

Cooling and Dust: SiO features indicate clumping, but most of the flux originates from optically thin regions, and SiO contributes negligibly to cooling, with no evidence for dust formation.

Parameter Sensitivity and Interpretation

The sensitivity analysis demonstrated how free parameters constrain the solution; for instance, total mass and NLTE strength are driven by the overtone. The correlation analysis revealed that comparable solutions lie along a complex hypersurface within the parameter space, with rR, fc, and ϵ exhibiting bimodal distributions indicating different formation scales. This suggests that prolate or oblate structures may reflect different formation scales associated with neutrino-driven or Rayleigh–Taylor instabilities. The paper concludes that while clump-like structures are mostly necessary to explain molecular features, confidence in their presence requires optically thick molecular bands.

Future Directions

The authors caution that MOFAT assumes large-scale spherical symmetry by assuming uniformly distributed clumps with the same relative sizes and shapes. To fully constrain clump types, full 3D time-dependent radiationhydrodynamic simulations may be needed, which would require a large grid of models and many well-sampled SNe. Extended time coverage observations of an SN would allow MOFAT to use tomography to recreate the envelope structure including chemical stratification, instability scales, and temperature structures. This mapping of the chemical layers can give insight into additional mixing lengths, potentially separate from the length scales of the clumps. The separation of these two length scales further emphasizes the need for low-latency extended time-frame observations that cover early CO formation all the way to dust formation.

Acknowledgments

T.M. acknowledges the support of friends, family, and fellow collaborators throughout the entirety of his PhD during which this work was conducted. T.M., P.H., and E.F. acknowledge the support by the National Science Foundation NSF awards AST-1715133 and AST- AST-230639 for enabling the development of the methods and computational codes. T.M., K.M., E.B., C.A., M.S., and P.H. acknowledge support from NASA grants JWST-GO-02114, JWST-GO-02122, JWST-GO-04522, JWST-GO-04217, JWST-GO-04436, JWST-GO-03726, JWST-GO-05057, JWST-GO-05290, JWST-GO-06023, JWST-GO-06677, JWST-GO-06213, and JWST-GO-0923. W.B.H. acknowledges support from the National Science Foundation Graduate Research Fellowship Program under Grant No. 2236415.

Data Availability

The observations of SN 2024ggi can be accessed via doi:10.17909/3v0v-0a76. MOFAT will be available at tycobrahe.github.io upon the completion of the thesis and will contain all models shown in this manuscript.

References

Abell´an, F. J., Indebetouw, R., Marcaide, J. M., et al. 2017, ApJL, 842, L24. Arnett, W. D., Bahcall, J. N., Kirshner, R. P., & Woosley, S. E. 1989, ARA&A, 27, 629. Arnett, W. D., Meakin, C., Viallet, M., et al. 2015, ApJ, 809, 30. Banerjee, D. P. K., Joshi, V., Evans, A., et al. 2018, MNRAS, 481, 806. Baron, E., Ashall, C., DerKacy, J. M., et al. 2025, arXiv e-prints, arXiv:2507.18753. Basko, M. 1994, ApJ, 425, 264. Bethe, H. A., & Wilson, J. R. 1985, ApJ, 295, 14. Blondin, J., Gogilashvili, M., Murphy, J., & O’Connor, E. P. 2025a, MNRAS, 537, 1182.

Improvements for AI systems

The MOFAT tool can be used to describe the structures of the molecular region as it is seen, which allows AI systems to map the Si/O/C layers of the SN envelope. This capability enables tomography, providing a direct link to the progenitor by identifying boundaries between CO and SiO regions.
AI systems can be trained to utilize high optical depth features for structural probing, as MOFAT uses optically thick fundamental bands to test clump parameters until the total absolute flux and the flux ratios between vibrational modes optimally reproduce observations. This allows AI to infer instabilities from evolutionary or explosion physics by characterizing clumping parameters like shape and size.
The system can utilize parameter correlation analysis, as it identifies moderately strong linear relationships for the parameter pairs (n, v1), (n, T1), (v1, T1), and (v1, fc; region 1). This allows AI to understand how physical parameters are coupled in the solution space and identify degeneracies that require low-latency extended time-frame observations.
The AI can be trained to distinguish between different instability types by analyzing the parameter distributions in Figure 16, noting that comparable solutions lie along a complex hypersurface within the parameter space and that parameters like rR, fc, and ϵ exhibit bimodal distributions, indicating the presence of two (or more) distinct regions of solutions corresponding to different clump sizes and shapes.
The system can perform automated sensitivity analysis on molecular features by using the normalized flow model trained on 4000 randomly sampled models to generate a dense representation of the parameter space, allowing it to test how variations in parameters affect specific observables like the flux ratios between vibrational modes.

Abstract

We present a new public-domain MOlecular Fitting Analysis Tool (MOFAT) designed to probe molecule-forming regions in supernovae (SNe) through analysis of molecular features in the near- and mid-infrared. MOFAT employs a novel data-driven approach to explore the physical properties of these regions using time-independent radiative transfer simulations that include multidimensional, clump-like structures, constrained by high-precision observations. Such structures are required to reproduce the flux ratio between fundamental and overtone bands, overcoming limitations of traditional one-zone forward-modeling, such as optical-depth effects and initial configurations. Our approach enables spectral fits that can reconstruct overall abundances and temperatures and determine parameterized small-scale structures associated with physical instabilities. We systematically study the relationship between physical parameters and the profiles of CO and SiO, showing that free parameters are constrained, while detection of small-scale structure requires optically thick bands. As a demonstration, MOFAT is applied to SN2024ggi at +285 and +385 days post-explosion. We find that CO formation triggers SiO formation in the inner layers of the CO-rich region previously studied. The inner edge of the SiO-emitting region recedes from velocities of v1 from 1500 to 1000 km/s, indicating continued SiO formation. The SiO mass decreases from about (2-6)E-3 Mo by roughly an order of magnitude, suggesting ongoing evaporation. SiO features indicate clumping, but most of the flux originates from optically thin regions. SiO contributes negligibly to cooling, and we find no evidence for dust formation. Finally, we discuss observational strategies to trace the evolution of molecule formation and its connection to dust formation.