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XSNAP: An X-ray Supernova Analysis Pipeline with Application to the Type II Supernova 2024ggi

Ferdinand, W. V. Jacobson-Galán, M. M. Kasliwal, Erez A. Zimmerman

TL;DR

This work addresses how X-ray observations can reveal the mass-loss history of SN progenitors by analyzing multi-epoch X-ray data of SN 2024ggi. It introduces XSNAP, an open-source, end-to-end pipeline that unifies spectral extraction and modeling across CXO, XMM-Newton, and Swift-XRT, and applies it with an absorbed thermal Bremsstrahlung model to derive a steady mass-loss rate of $\dot{M} = (6.2 \pm 0.2) \times 10^{-5} \, M_{igodot} \, \mathrm{yr^{-1}}$ (for $v_{ m wind} = 20$ km s$^{-1}$) over the last $\sim117$ years before explosion. The analysis reveals a dense, confined CSM with $\mathrm{NH}_{\rm int} > 10^{22}$ cm$^{-2}$ in the first ~$16$ days and an X-ray luminosity decay $L_X \propto t^{-0.99}$, consistent with a wind-like density profile $\rho_{\rm CSM}(r) \propto r^{-2}$. XSNAP provides reproducible, end-to-end processing from raw counts to CSM interpretation and is applicable to future SN X-ray studies as well as other X-ray sources. The results advance our understanding of late-stage mass loss in SN II progenitors and demonstrate the practical impact of a standardized, open-source analysis workflow.

Abstract

X-ray observations of Type II supernovae (SNe II) probe the physics of supernova (SN) shocks and the mass-loss histories of their progenitor stars. We present multi-epoch, X-ray observations of SN II 2024ggi ($D \approx 7.2 \ \rm Mpc$) from ${\it Swift}$-XRT, ${\it Chandra}$ and ${\it XMM}$, which cover $\sim 1 - 344$ days since first light. We analyze these observations using a new open-source Python package called $\texttt{XSNAP}$, which standardizes a unified command-line interface for instrument-specific reduction and spectral extraction. $\texttt{XSNAP}$ introduces application programming interfaces for per-epoch spectral modeling through $\texttt{PyXspec}$ and $\texttt{emcee}$ Markov chain Monte Carlo fitting. We employ ${\tt XSNAP}$ to model the multi-epoch X-ray spectra of SN 2024ggi with an absorbed thermal bremsstrahlung model and calculate a steady progenitor mass-loss rate of $(6.2\pm0.2)\times10^{-5}\,M_{\odot}\,\mathrm{yr^{-1}}$ $(v_{\rm wind} = 20 \ \rm km \ s^{-1})$, for which the detected X-ray emission traces the final 117 years before explosion. The software is publicly available on GitHub, with a released package on the Python Package Index (PyPI).

XSNAP: An X-ray Supernova Analysis Pipeline with Application to the Type II Supernova 2024ggi

TL;DR

This work addresses how X-ray observations can reveal the mass-loss history of SN progenitors by analyzing multi-epoch X-ray data of SN 2024ggi. It introduces XSNAP, an open-source, end-to-end pipeline that unifies spectral extraction and modeling across CXO, XMM-Newton, and Swift-XRT, and applies it with an absorbed thermal Bremsstrahlung model to derive a steady mass-loss rate of (for km s) over the last years before explosion. The analysis reveals a dense, confined CSM with cm in the first ~ days and an X-ray luminosity decay , consistent with a wind-like density profile . XSNAP provides reproducible, end-to-end processing from raw counts to CSM interpretation and is applicable to future SN X-ray studies as well as other X-ray sources. The results advance our understanding of late-stage mass loss in SN II progenitors and demonstrate the practical impact of a standardized, open-source analysis workflow.

Abstract

X-ray observations of Type II supernovae (SNe II) probe the physics of supernova (SN) shocks and the mass-loss histories of their progenitor stars. We present multi-epoch, X-ray observations of SN II 2024ggi () from -XRT, and , which cover days since first light. We analyze these observations using a new open-source Python package called , which standardizes a unified command-line interface for instrument-specific reduction and spectral extraction. introduces application programming interfaces for per-epoch spectral modeling through and Markov chain Monte Carlo fitting. We employ to model the multi-epoch X-ray spectra of SN 2024ggi with an absorbed thermal bremsstrahlung model and calculate a steady progenitor mass-loss rate of , for which the detected X-ray emission traces the final 117 years before explosion. The software is publicly available on GitHub, with a released package on the Python Package Index (PyPI).

Paper Structure

This paper contains 13 sections, 3 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Observations
  3. Chandra X-ray Observatory
  4. XMM-Newton
  5. Swift X-ray Telescope
  6. Analysis
  7. X-ray Spectral Modeling
  8. CSM Density Profile Analysis
  9. The Pipeline
  10. Discussion
  11. Conclusion
  12. Contour Plot of Best Fit Parameter
  13. X-Ray Observation Logs

Figures (4)

  • Figure 1: Top: Plot of fitted spectrum from CXO at $\delta t=10.90 \ \rm days$ (left) and $\delta t=16.20 \ \rm days$ (right) since first light. Blue (left) and green (right) points are observed X-ray spectra and black dashed lines are the best-fit absorbed thermal Bremsstrahlung model. Bottom: Plot of fitted spectrum from XMM data $\delta t=55.03 \ \rm days$ (left) and $\delta t=85.40 \ \rm days$ (right) since first light. Observed spectra are represented in blue (EPIC-pn), orange (EPIC-MOS1), and magenta (EPIC-MOS2) and black dashed lines are the best-fit absorbed thermal Bremsstrahlung model.
  • Figure 2: Left: Plot of fitted unabsorbed $0.3 - 10 \ \rm keV$ X-ray luminosity light curve from XMM-Newton (red circles), CXO (cyan squares), and Swift-XRT (orange triangles) observations. The red dashed line is the fitted line with $L \propto t^{-0.99}$. Right: Plot of unabsorbed $0.3 - 10 \ \rm keV$ X-ray luminosity light curve of SN 2024ggi compared to a sample of Type IIP SNe. References: Schlegel_1999Schlegel_2001, Pooley_2002, 2002IAUC.8024....2P2004IAUC.8390....1P, 10.1111/j.1365-2966.2007.12258.x, Immler_2007, 2012ATel.3995....1I, Chakraborti_2013Chakraborti_2016, Szalai_2019, 2024ApJ...970...96I, AJ2025
  • Figure 3: Left: Plot of fitted density profile. The blue circles are the data derived from analysis and the red dashed line is the fitted line with $\rho \propto r^{-2}$. Right: Plot of fitted density profile to a previous study by Jacobson-Galan2024. The r1w4 (green dashed line), r1w6 (red dashed line), and m1em2 (orange dashed line) are best-matched models from CMFGEN model grid Jacobson-Galan2024Jacobson-Galan2024b. The magenta star is the transition point where the CSM goes from optically thick to thin to electron scattering Jacobson-Galan2024. The blue circles are the data derived from analysis and the blue dashed line is the fitted line with $\rho \propto r^{-2}$. The purple pentagons are SN 2023ixf densities derived from X-ray observations AJ2025. The grey dashed lines are reference density profile lines if the mass-loss rates are $10^{-4} \ \rm M_{\odot} \ yr^{-1}$, $10^{-5} \ \rm M_{\odot} \ yr^{-1}$, and $10^{-6} \ \rm M_{\odot} \ yr^{-1}$ respectively for $v_{\rm wind} = 20 \ \rm km \ s^{-1}$.
  • Figure 4: Top left: Contour plot of the best fit parameter values for the column density and the Bremsstrahlung normalization from XRT data at $\delta t = 3.33 \ \rm days$. Top right: Contour plot of the best fit parameter values for the column density and the Bremsstrahlung normalization from XRT data at $\delta t = 8.36 \ \rm days$. Bottom left: Contour plot of the best fit parameter values for the column density and the Bremsstrahlung normalization from CXO data at $\delta t = 10.90 \ \rm days$. Bottom right: Contour plot of the best fit parameter values for the column density and the Bremsstrahlung normalization from CXO data at $\delta t = 16.20 \ \rm days$. Both parameters are well constrained in all the epochs, i.e. at $\delta t = 3.33 \ \rm days$, $\delta t = 8.36 \ \rm days$, $\delta t = 10.90 \ \rm days$, $\delta t = 16.20 \ \rm days$. The blue line represents $1\sigma$ range, the green line represents $2\sigma$ range, and the red line represents $3\sigma$ range.