Mass modelling techniques for Gamma Ray Burst missions
Mass modelling techniques for Gamma Ray Burst missions
The current status of gamma ray astronomy is briefly reviewed with the focus on Gamma Ray Bursts (GRBs), detection methods and the various sources of background that a space telescope will encounter. Every gamma ray instrument encounters a high level of background noise in relation to the signal and understanding the structure and modulation of this background is vital to extracting the best science from an instrument. The ever evolving Mass Modelling technique is reviewed and demonstrated in the context of The INTEGRAL Mass Model (TIMM). Discussion relating to isotropy and homogeneity is also included.
Swift is the next mission expected to make a significant impact on the field of GRBs and it is to be launched in September 2003 by NASA. The Swift Mass Model (SwiMM) is presented. The applications of SwiMM include the optimisation of the graded-Z passive shielding design, the predicted likelihood of false triggers when encountering trapped charged particle fluxes and the effects of GRB self-contamination through flux reprocessing. The effects of GRB flux self-contamination are also explored in the context of BATSE burst data for GRB 920525 and GRB 910503 and it is shown that the current detector response for the Spectroscopy Detectors (SD) is inadequate for off-axis bursts. This further emphasises the importance of correctly accounting for this re-processed flux when observing GRBs with Swift.
Swift’s Burst Alert Telescope (BAT) is required to have a wide field-of-view to maximise the number of GRB triggers. This has the added benefit of being able to perform an unprecedented all-sky survey in this resulting energy band. The application of SwiMM to the all-sky survey is discussed with each aspect of the modelling technique verified with independent empirical data.
University of Southampton
Willis, Dave
6507516e-18b8-4be2-b46a-eb7b0e4674ea
2003
Willis, Dave
6507516e-18b8-4be2-b46a-eb7b0e4674ea
Willis, Dave
(2003)
Mass modelling techniques for Gamma Ray Burst missions.
University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
The current status of gamma ray astronomy is briefly reviewed with the focus on Gamma Ray Bursts (GRBs), detection methods and the various sources of background that a space telescope will encounter. Every gamma ray instrument encounters a high level of background noise in relation to the signal and understanding the structure and modulation of this background is vital to extracting the best science from an instrument. The ever evolving Mass Modelling technique is reviewed and demonstrated in the context of The INTEGRAL Mass Model (TIMM). Discussion relating to isotropy and homogeneity is also included.
Swift is the next mission expected to make a significant impact on the field of GRBs and it is to be launched in September 2003 by NASA. The Swift Mass Model (SwiMM) is presented. The applications of SwiMM include the optimisation of the graded-Z passive shielding design, the predicted likelihood of false triggers when encountering trapped charged particle fluxes and the effects of GRB self-contamination through flux reprocessing. The effects of GRB flux self-contamination are also explored in the context of BATSE burst data for GRB 920525 and GRB 910503 and it is shown that the current detector response for the Spectroscopy Detectors (SD) is inadequate for off-axis bursts. This further emphasises the importance of correctly accounting for this re-processed flux when observing GRBs with Swift.
Swift’s Burst Alert Telescope (BAT) is required to have a wide field-of-view to maximise the number of GRB triggers. This has the added benefit of being able to perform an unprecedented all-sky survey in this resulting energy band. The application of SwiMM to the all-sky survey is discussed with each aspect of the modelling technique verified with independent empirical data.
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Published date: 2003
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Local EPrints ID: 465037
URI: http://eprints.soton.ac.uk/id/eprint/465037
PURE UUID: 320bb904-9b09-4923-8006-30f6715b69ea
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Date deposited: 05 Jul 2022 00:18
Last modified: 16 Mar 2024 19:54
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Author:
Dave Willis
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