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SNeT: computer-assisted SuperNovae Tracking

Bao, Di (2014) SNeT: computer-assisted SuperNovae Tracking. Master's Thesis, University of Pittsburgh. (Unpublished)

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In astronomy, supernovae are stellar explosions whose observation can help shed light on the star formation process and provide reference points for cosmological distances. Supernovae are detected at different phases of their lifecycle and their observation is further complicated by time and resource constraints. Although there exists automated supernovae detection pipelines, follow-up observations by individual researchers are handled manually, both in terms of keeping a list of interesting supernovae worth observing and also planning out the exact schedule for observations, given telescope access and temporal constraints.

This thesis designs and develops the SNeT (computer-assisted SuperNovae Tracking) system, as a tool to help astronomers collect supernovae data, manage their lists of interest and observation plans, and most importantly, generate good observation plans automatically, that can later be further adapted. Specifically, SNeT takes a list of supernovae, their associated temporal constraints, and user preferences, and it generates a plan that satisfies the constraints and preferences, maximizes data acquisition, while minimizing time and resource usage. In addition, the user can interact with the system and give feedback on the generated plans in order to customize SNeT’s planning behavior via its self-tuning. The SNeT prototype system is currently evaluated by supernovae researchers from the Department of Physics and Astronomy of the University of Pittsburgh.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Bao, Didib20@pitt.eduDIB20
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChrysanthis, Panospanos@cs.pitt.eduPANOS
Committee CoChairLabrinidis, Alexandroslabrinid@cs.pitt.eduLABRINID
Committee MemberWood-Vasey, Michaelwmwv@pitt.eduWMWV
Date: 24 January 2014
Date Type: Publication
Defense Date: 25 November 2013
Approval Date: 24 January 2014
Submission Date: 6 December 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 97
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: scheduling, learning, capacity shaping, greedy algorithm, heuristic functions, Astroshelf
Date Deposited: 24 Jan 2014 21:39
Last Modified: 15 Nov 2016 14:16


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