SHORT VERSUS LONG AND COLLAPSARS VERSUS NON-COLLAPSARS: A QUANTITATIVE CLASSIFICATION OF GAMMA-RAY BURSTS

By O. Bromberg, E. Nakar, T. Piran, R. Sari. 2012

146 citations recorded by the source corpus.

Gamma-ray bursts (GRBs) are traditionally divided into long and short according to their durations (≶2 s). It was generally believed that this reflects a different physical origin: collapsars (long) and non-collapsars (short). We have recently shown that the duration distribution of collapsars is flat, namely, independent of the duration, at short durations. Using this model for the distribution of Collapsars we determine the duration distribution of non-Collapsars and estimate the probability that a burst with a given duration (and hardness) is a Collapsar or not. We find that this probability depends strongly on the spectral window of the observing detector. While the commonly used limit of 2 s is conservative and suitable for BATSE bursts, 40% of Swift's bursts shorter than 2 s are Collapsars and the division ≶0.8 s is more suitable for Swift. We find that the duration overlap of the two populations is very large. On the one hand there is a non-negligible fraction of non-Collapsars longer than 10 s, while on the other hand even bursts shorter than 0.5 s in the Swift sample have a non-negligible probability to be Collapsars. Our results enable the construction of non-Collapsar samples while controlling the Collapsar contamination. They also highlight that no firm conclusions can be drawn based on a single burst and they have numerous implications concerning previous studies of non-Collapsar properties that were based on the current significantly contaminated Swift samples of localized short GRBs. Specifically (1) all known short bursts with z > 1 are most likely Collapsars; (2) the only short burst with a clear jet break is most likely a Collapsar, indicating our lack of knowledge concerning non-Collapsar beaming; and (3) the existence of non-Collapsars with durations up to 10 s imposes new challenges to non-Collapsar models.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/5bd576a8acea05fe4d850f67457df38e5c2a08d8

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In Astronomy and Astrophysics

  1. Bing Zhang
  2. E. Berger
  3. Pawan Kumar
  4. B. Metzger
  5. Xuefeng Wu
  6. T. Piran
  7. Z. Dai
  8. W. Fong

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