The Swift γ-ray burst MIDEX mission

Krimm, H. A.

doi:10.1016/j.newar.2003.12.045

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only more sensitive X-ray observations than those performed with the BeppoSAX WFCs can enlarge the GRB sample, improve the statistical quality of the time resolved spectra, and thus test the results obtained with BeppoSAX on the X-ray transient absorption lines. These observations are expected to be only partly covered by the upcoming satellite Swift (e.g., Krimm 2004 given that these lines are observed only during the leading edge of the bursts, while the Swift Burst Alert Telescope has an energy passband from 15 to 150 keV. However Swift can solve the issue of the hard X-ray lines observed with Ginga.…”

Section: Discussionmentioning

confidence: 99%

The Prompt X‐Ray Emission of GRB 011211: Possible Evidence of a Transient Absorption Feature

Frontera

Amati

Zand

et al. 2004

ApJ

View full text Add to dashboard Cite

We report on observation results of the prompt X-and γ-ray emission from GRB011211. This event was detected with the Gamma-Ray Burst Monitor and one of the Wide Field Cameras aboard the BeppoSAX satellite. The optical counterpart of the GRB was soon identified and its redshift determined (z = 2.140), while with the XMM-Newton satellite, the X-ray afterglow emission was detected. Evidence of soft X-ray emission lines was reported by Reeves et al. (2002), but not confirmed by other authors. In investigating the spectral evolution of the prompt emission we find the possible evidence of a transient absorption feature at 6.9 +0.6 −0.5 keV during the rise of the primary event. The significance of the feature is derived with non parametric tests and numerical simulations, finding a chance probability which ranges from 3 × 10 −3 down to 4 × 10 −4 . The feature shows a Gaussian profile and an equivalent width of 1.2 +0.5 −0.6 keV. We discuss our results and their possible interpretation.

show abstract

Section: Discussionmentioning

confidence: 99%

The Prompt X‐Ray Emission of GRB 011211: Possible Evidence of a Transient Absorption Feature

Frontera

Amati

Zand

et al. 2004

ApJ

View full text Add to dashboard Cite

show abstract

The effect of hydrogen plasma treatment on the Au–CdZnTe interface

Min

Wang

et al. 2013

Surface and Coatings Technology

View full text Add to dashboard Cite

Identification of Extended Emission Gamma-Ray Burst Candidates Using Machine Learning

Garcia-Cifuentes

Becerra

Colle

et al. 2023

ApJ

View full text Add to dashboard Cite

Gamma-ray bursts (GRBs) have been classified traditionally based on their duration. The increasing number of extended emission (EE) GRBs, lasting typically more than 2s but with properties similar to those of short GRBs, challenges the traditional classification criteria. In this work, we use the t-distributed stochastic neighbor embedding (t-SNE), a machine-learning technique, to classify GRBs. We present the results for GRBs observed until 2022 July by the Swift/BAT (Burst Alert Telescope) instrument in all its energy bands. We show the effects of varying the learning rate and perplexity parameters as well as the benefit of preprocessing the data by a nonparametric noise-reduction technique. Consistently with previous works, we show that the t-SNE method separates GRBs into two subgroups. We also show that EE GRBs reported by various authors under different criteria tend to cluster in a few regions of our t-SNE maps and identify seven new EE GRB candidates by using the gamma-ray data provided by the automatic pipeline of Swift/BAT and the proximity with previously identified EE GRBs.

show abstract

The Swift γ-ray burst MIDEX mission

Cited by 3 publications

References 13 publications

The Prompt X‐Ray Emission of GRB 011211: Possible Evidence of a Transient Absorption Feature

The Prompt X‐Ray Emission of GRB 011211: Possible Evidence of a Transient Absorption Feature

The effect of hydrogen plasma treatment on the Au–CdZnTe interface

Identification of Extended Emission Gamma-Ray Burst Candidates Using Machine Learning

Contact Info

Product

Resources

About