The X-Ray Luminous Type Ibn SN 2022ablq: Estimates of Preexplosion Mass Loss and Constraints on Precursor Emission

Creators: Pellegrino, C.¹; Modjaz, M.¹; Takei, Y.^{2, 3, 4}; Tsuna, D.^{3, 5}; Newsome, M.^{6, 7}; Pritchard, T.⁸; Baer-Way, R.¹; Bostroem, K. A.⁹; Chandra, P.¹⁰; Charalampopoulos, P.¹¹; Dong, Y.¹²; Farah, J.^{6, 7}; Howell, D. A.^{6, 7}; McCully, C.⁶; Mohamed, S.¹; Padilla Gonzalez, E.^{6, 7}; Terreran, G.^{6, 7}

1. University of Virginia
2. Kyoto University
3. University of Tokyo
4. RIKEN
5. California Institute of Technology
6. Las Cumbres Observatory Global Telescope Network
7. University of California, Santa Barbara
8. Goddard Space Flight Center
9. University of Arizona
10. National Radio Astronomy Observatory
11. University of Turku
12. University of California, Davis

Abstract

Type Ibn supernovae (SNe Ibn) are rare stellar explosions powered primarily by interaction between the SN ejecta and H-poor, He-rich material lost by their progenitor stars. Multiwavelength observations, particularly in the X-rays, of SNe Ibn constrain their poorly understood progenitor channels and mass-loss mechanisms. Here we present Swift X-ray, ultraviolet, and ground-based optical observations of the Type Ibn SN 2022ablq, only the second SN Ibn with X-ray detections to date. While similar to the prototypical Type Ibn SN 2006jc in the optical, SN 2022ablq is roughly an order of magnitude more luminous in the X-rays, reaching unabsorbed luminosities L_X ∼ 4 × 10⁴⁰ erg s⁻¹ between 0.2–10 keV. From these X-ray observations we infer time-varying mass-loss rates between 0.05 and 0.5 M_⊙ yr⁻¹ peaking 0.5–2 yr before explosion. This complex mass-loss history and circumstellar environment disfavor steady-state winds as the primary progenitor mass-loss mechanism. We also search for precursor emission from alternative mass-loss mechanisms, such as eruptive outbursts, in forced photometry during the 2 yr before explosion. We find no statistically significant detections brighter than M ≈ −14—too shallow to rule out precursor events similar to those observed for other SNe Ibn. Finally, numerical models of the explosion of an ∼15 M_⊙ helium star that undergoes an eruptive outburst ≈1.8 yr before explosion are consistent with the observed bolometric light curve. We conclude that our observations disfavor a Wolf–Rayet star progenitor losing He-rich material via stellar winds and instead favor lower-mass progenitor models, including Roche-lobe overflow in helium stars with compact binary companions or stars that undergo eruptive outbursts during late-stage nucleosynthesis stages.

Copyright and License

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Acknowledgement

We thank the anonymous referee for helpful comments and feedback. C.P., M.M., and R.B.W. acknowledge support in part from ADAP program grant No. 80NSSC24K0180 and from NSF grant AST-2206657. This work makes use of observations from the Las Cumbres Observatory global telescope network. The LCO group is supported by NSF grants AST-1911151 and AST-1911225. We acknowledge the use of public data from the Swift data archive. D.T. is supported by the Sherman Fairchild Postdoctoral Fellowship at the California Institute of Technology. Research by Y.D. is supported by NSF grant AST-2008108. K.A.B. is supported by an LSST-DA Catalyst Fellowship; this publication was thus made possible through the support of grant 62192 from the John Templeton Foundation to LSST-DA. P.C. acknowledges support via Research Council of Finland (grant 340613).

Software References

astroML (J. Vanderplas & A. Connolly 2012), astropy (Astropy Collaboration et al. 2013, 2018, 2022), CHIPS (Y. Takei et al. 2022, 2024), HEAsoft (Nasa High Energy Astrophysics Science Archive Research Center (Heasarc) 2014), HOTPANTS (A. Becker 2015), jupyter (F. Perez & B. E. Granger 2007; T. Kluyver et al. 2016), matplotlib (J. D. Hunter 2007), numpy (C. R. Harris et al. 2020), pandas (W. McKinney 2010; pandas development team 2024), python (G. Van Rossum & F. L. Drake 2009), and scipy (P. Virtanen et al. 2020; R. Gommers et al. 2024). Software citation information aggregated using The Software Citation Station (T. Wagg & F. Broekgaarden 2024).

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