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ABSTRACT We present multiwavelength observations of supernova (SN) 2017hcc with the Chandra X-ray telescope and the X-ray telescope onboard Swift (Swift-XRT) in X-ray bands, with the Spitzer and the TripleSpec spectrometer in near-infrared (IR) and mid-IR bands and with the Karl G. Jansky Very Large Array (VLA) for radio bands. The X-ray observations cover a period of 29 to 1310 d, with the first X-ray detection on day 727 with the Chandra. The SN was subsequently detected in the VLA radio bands from day 1000 onwards. While the radio data are sparse, synchrotron-self absorption is clearly ruled out as the radio absorption mechanism. The near- and the mid-IR observations showed that late time IR emission dominates the spectral energy distribution. The early properties of SN 2017hcc are consistent with shock breakout into a dense mass-loss region, with $$\dot{M} \sim 0.1$$ M⊙ yr−1 for a decade. At few 100 d, the mass-loss rate declined to ∼0.02 M⊙ yr−1, as determined from the dominant IR luminosity. In addition, radio data also allowed us to calculate a mass-loss rate at around day 1000, which is two orders of magnitude smaller than the mass-loss rate estimates around the bolometric peak. These values indicate that the SN progenitor underwent an enhanced mass-loss event a decade before the explosion. The high ratio of IR to X-ray luminosity is not expected in simple models and is possible evidence for an asymmetric circumstellar region.
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A Poisson Process AutoDecoder for X-Ray Sources
Abstract X-ray observing facilities, such as the Chandra X-ray Observatory and the eROSITA, have detected over a million astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for common tasks such as source classification, physical property derivation, and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present the Poisson Process AutoDecoder (PPAD), which is a neural field decoder that maps fixed-length latent features to continuous Poisson rate functions across energy band and time via unsupervised learning. PPAD reconstructs the rate function and yields a representation at the same time. We demonstrate the efficacy of PPAD via reconstruction, regression, classification, and anomaly detection experiments using the Chandra Source Catalog.
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- PAR ID:
- 10648142
- Publisher / Repository:
- AAS
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 988
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 143
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3847/1538-4357/add72e
