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A Radiation-Hydrodynamic Light Curve Grid and Interpolation Framework for Stripped-Envelope Supernovae

This paper presents a comprehensive grid of 8,148 radiation-hydrodynamic stripped-envelope supernova light curves and an interpolation framework for parameter inference, demonstrating that while the widely used Arnett model fits light-curve morphology well, it fails to reliably recover true physical parameters and introduces spurious correlations, thereby underscoring the necessity of physically motivated hydrodynamic models for accurate population studies.

Original authors: Qiliang Fang, Takashi J. Moriya

Published 2026-08-13

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CC BY 4.0

Original authors: Qiliang Fang, Takashi J. Moriya

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the universe as a giant, chaotic construction site where massive stars are the skyscrapers. Sometimes, these skyscrapers run out of fuel and collapse, triggering a spectacular explosion called a supernova. When a star is stripped of its outer hydrogen layers before it dies, leaving behind a bare helium core, it creates a specific type of explosion known as a "stripped-envelope supernova." Astronomers are obsessed with these events because they are like cosmic time capsules; by studying the light they emit, we can figure out how big the star was, how hard it exploded, and what kind of heavy elements it forged. However, reading the story written in that light is tricky. It's like trying to guess the ingredients of a cake just by looking at a photo of the finished dessert; you can see the frosting and the shape, but figuring out exactly how much flour or sugar went in requires a very smart recipe.

For decades, scientists have used a popular, simplified "recipe" called the Arnett model to decode these explosions. It's fast and easy to use, like a quick calculator app. But just like a simple calculator might struggle with complex physics, this model makes some big assumptions that might not be true in the messy reality of a dying star. The big question has been: Is this quick recipe giving us the right answers, or is it just making things up that look right?

In this paper, two researchers, Qiliang Fang and Takashi J. Moriya, decided to build a massive, super-detailed library of 8,148 different "virtual" supernova explosions to test this. Instead of using the quick calculator, they ran complex, physics-heavy simulations that track how gas, radiation, and heat actually move inside an exploding star. They created a grid that covers a wide range of star sizes, explosion energies, and amounts of radioactive nickel (the "fuel" that makes the light glow). They then built a clever "interpolation" tool—a kind of smart bridge—that allows them to fill in the gaps between their 8,148 simulations, creating a continuous map of what every possible explosion should look like.

When they used their new, high-tech map to analyze two real, well-observed supernovae (SN 2007Y and SN 2009jf), they found it could successfully reproduce the observed light curves, proving their method works. However, the real punchline came when they used their library as a "control lab" to test the old Arnett model. They fed the Arnett model fake data generated by their own perfect simulations and asked it to guess the true values. The results were surprising: the Arnett model was terrible at guessing the actual mass of the ejected material. Even worse, it invented a fake connection between the explosion's energy and the amount of nickel produced. In their simulations, these two things were completely independent, but the Arnett model's simplified math forced them to look like they were linked.

The authors suggest that while the Arnett model is a handy tool for getting a rough idea of what's happening, it can't be trusted to give precise numbers about the star's true nature. It's like using a blurry photo to measure the exact weight of a person; you might get the general shape, but the specific numbers will be off. Their work shows that relying on these simplified models for large studies of supernova populations could lead scientists to draw the wrong conclusions about how these stars live and die. Instead, they argue we need to use these heavy-duty, physics-based simulations to get the real story right.

Problem
Stripped-envelope supernovae (SESNe) offer critical insights into massive-star evolution and core-collapse physics. While time-domain surveys have increased the number of well-observed SESNe, extracting reliable physical parameters (such as ejecta mass, explosion energy, and nickel mass) from light curves remains challenging. The standard approach relies on analytical Arnett-type models, which assume constant opacity, centrally concentrated heating, and homologous expansion. However, the extent to which these simplifying assumptions introduce systematic biases or spurious correlations in inferred parameters has not been rigorously quantified. Conversely, while radiation-hydrodynamic models offer a more physically self-consistent description, their application to observational data is hindered by computational expense and the discrete nature of simulation grids.

Methodology
The authors constructed a comprehensive grid of 8,148 radiation-hydrodynamic light-curve models for SESNe.

Progenitors and Explosion: Massive star evolution was computed using MESA (non-rotating, solar metallicity) for zero-age-main-sequence masses ( $M_{ZAMS}$ ) from 11.5 to 20.0 $M_\odot$ . Hydrogen envelopes were artificially removed to create bare helium stars, which were evolved to core oxygen depletion. Explosions were triggered phenomenologically by depositing thermal energy in the inner 0.2 $M_\odot$ , varying the asymptotic explosion energy ( $E_K$ ) and ejecta mass ( $M_{ej}$ ).
Radiative Transfer: The hydrodynamic outputs were processed using the STELLA code to calculate bolometric light curves. The grid spans a range of $M_{ej}$ (motivated by late-phase spectral constraints), specific kinetic energy ( $\eta = E_K/M_{ej}$ ), radioactive nickel mass ( $M_{Ni}$ ), and a mixing parameter ( $f_{mix}$ ) representing the fraction of ejecta uniformly mixed.
Interpolation Framework: To enable continuous parameter inference, the authors developed a four-dimensional linear interpolation scheme. This framework estimates light curves at arbitrary points in the parameter space by combining the 16 surrounding grid points, weighted by their relative distances.
Validation and Application: The interpolation accuracy was tested via a stringent cross-validation (injection-recovery) method where target models were reconstructed using only non-identical grid points. The framework was then applied to two observed SNe Ib (2007Y and 2009jf) using Markov Chain Monte Carlo (MCMC) analysis.
Benchmarking Arnett Models: The numerical grid served as a controlled laboratory to test Arnett-type models. Synthetic light curves from the grid were fitted with the Arnett model (using fixed opacity $\kappa = 0.06$ cm $^2$ g $^{-1}$ ) to compare inferred parameters against the known "true" inputs.

Key Contributions

Large-Scale Simulation Grid: The release of a grid of 8,148 SESN light curves covering a representative range of ejecta masses, explosion energies, nickel masses, and mixing degrees.
Interpolation-Based Inference: A novel framework that bridges the gap between discrete numerical simulations and observational data, allowing for continuous exploration of the physical parameter space with high computational efficiency.
Systematic Bias Quantification: A rigorous assessment of the Arnett model using the numerical grid as ground truth, revealing specific limitations in parameter recovery.

Results

Light Curve Dependencies: The study confirms that $L_{peak}$ , $t_{peak}$ , and light-curve shapes are complex functions of $M_{ej}$ , $\eta$ , $M_{Ni}$ , and $f_{mix}$ . Notably, increasing $M_{Ni}$ increases all timescales, a behavior not predicted by standard Arnett models. Strong mixing ( $f_{mix}$ ) reduces $L_{peak}$ and creates shoulder-like features in the post-peak evolution.
Interpolation Accuracy: The interpolation scheme is highly accurate, with median residuals below 0.01 dex and a conservative uncertainty estimate of 0.02 dex.
Application to SNe 2007Y and 2009jf:
- For SN 2007Y, the model successfully reproduced the light curve and color evolution, inferring an ejecta mass of $\sim 1.86 M_\odot$ consistent with weak nebular [O I] emission.
- For SN 2009jf, the inference revealed a strong degeneracy between ejecta mass ( $M_{ej}$ ) and mixing ( $f_{mix}$ ). Two distinct solutions (low mass/high mixing and high mass/low mixing) produced nearly identical bolometric light curves, and color evolution provided limited additional constraints.
Arnett Model Limitations:
- Ejecta Mass: The Arnett model fails to recover the true ejecta mass; inferred values show no significant correlation with true values ( $\rho = 0.19$ ) and cluster in a narrow range ( $\sim 1.7–2.5 M_\odot$ ) regardless of the input.
- Spurious Correlations: While $E_K$ and $M_{Ni}$ are independent in the simulation grid, the Arnett fitting procedure induces a spurious positive correlation ( $M_{Ni} \propto E_K^{4/3}$ ). This arises because the Arnett model assumes constant opacity, whereas in reality, higher $M_{Ni}$ increases ionization and opacity, broadening the light curve. The Arnett fit compensates by inferring larger $M_{ej}$ and $E_K$ .

Significance
The paper demonstrates that while Arnett-type models provide efficient phenomenological descriptions of SESN light curves, the physical parameters they infer (particularly ejecta mass) are often unreliable and do not reflect the true underlying physics. The apparent correlations observed in population studies (e.g., between explosion energy and nickel mass) may be artifacts of the inference method rather than intrinsic physical properties. Consequently, the authors argue for the necessity of physically motivated radiation-hydrodynamic models in population studies to avoid misleading conclusions about progenitor evolution and explosion mechanisms. The developed interpolation framework offers a practical, accurate tool for applying these complex models to large observational datasets.

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