Search for: All records

Abstract There is no consensus yet on whether the precursor and the main burst of gamma-ray bursts (GRBs) have the same origin, and their jet composition is still unclear. In order to further investigate this issue, we systematically search 21 Fermi GRBs with both a precursor and main burst for spectral analysis. We first perform Bayesian time-resolved spectral analysis and find that almost all the precursors and the main bursts (94.4%) exhibit thermal components and that the vast majority of them have a low-energy spectral index (α; 72.2%) that exceeds the limit of synchrotron radiation. We then analyze the evolution and correlation of the spectral parameters and find that approximately half of theα(50%) of the precursors and the main bursts evolve in a similar pattern, while peak energy (E_p; 55.6%) behaves similarly, and their evolution is mainly characterized by flux tracking; for theα−F(the flux) relation, more than half of the precursors and the main bursts (61.1%) exhibit roughly similar patterns; theE_p−Frelation in both the precursor and main burst (100%) exhibits a positive correlation of at least moderate strength. Next, we constrain the outflow properties of the precursors and the main bursts and find that most of them exhibit typical properties of photosphere radiation. Finally, we compare the time-integrated spectra of the precursors and the main bursts and find that nearly all of them are located in similar regions of the Amati relation and follow the Yonetoku relation. Therefore, we conclude that main bursts are continuations of precursors and may share a common physical origin. 

Abstract The jet composition in gamma-ray bursts (GRBs) is still an unsolved issue. We try to provide some clues to the issue by analyzing the spectral properties of GRB 160509A and GRB 130427A with a main burst and a postburst. We first perform Bayesian time-resolved spectral analysis and compare the spectral components and spectral properties of the main bursts and postbursts of the two bursts and find that both bursts have the thermal components, and the thermal components are mainly found in the main bursts, while the postbursts are mainly dominated by the nonthermal components. We also find that the low-energy spectral indices of some time bins in the main bursts of these two GRBs exceed the so-called synchronous dead line, and in the postburst, only GRB 160509A has four time bins exceeding the dead line, while none of GRB 130427A exceed the dead line. We then constrain the outflow properties of both bursts and find that the main bursts is consistent with the typical properties of photosphere radiation. Therefore, our results support the transition of the GRB jet component from the fireball to the Poynting-flux-dominated jet. Finally, after analyzing the correlation and parameter evolution of the spectral parameters of the two bursts, we find that the correlations of the spectral parameters have different behaviors in the main bursts and postbursts. The parameter evolution trends of the main bursts and postbursts also show consistent and inconsistent behavior; therefore, we currently cannot determine whether the main bursts and postbursts come from the same origin. 

Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation and opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.