Accidental Outcomes Guide Punishment in a “Trembling Hand” Game

How do people respond to others' accidental behaviors? Reward and punishment for an accident might depend on the actor's intentions, or instead on the unintended outcomes she brings about. Yet, existing paradigms in experimental economics do not include the possibility of accidental monetary allocations. We explore the balance of outcomes and intentions in a two-player economic game where monetary allocations are made with a “trembling hand”: that is, intentions and outcomes are sometimes mismatched. Player 1 allocates $10 between herself and Player 2 by rolling one of three dice. One die has a high probability of a selfish outcome, another has a high probability of a fair outcome, and the third has a high probability of a generous outcome. Based on Player 1's choice of die, Player 2 can infer her intentions. However, any of the three die can yield any of the three possible outcomes. Player 2 is given the opportunity to respond to Player 1's allocation by adding to or subtracting from Player 1's payoff. We find that Player 2's responses are influenced substantially by the accidental outcome of Player 1's roll of the die. Comparison to control conditions suggests that in contexts where the allocation is at least partially under the control of Player 1, Player 2 will punish Player 1 accountable for unintentional negative outcomes. In addition, Player 2's responses are influenced by Player 1's intention. However, Player 2 tends to modulate his responses substantially more for selfish intentions than for generous intentions. This novel economic game provides new insight into the psychological mechanisms underlying social preferences for fairness and retribution

Development of fast track finding algorithms for densely packed straw tube trackers and its application to Ξ\Xi (1820) hyperon reconstruction for the PANDA experiment

Diese Arbeit befasst sich mit der Entwicklung von Spurfindungsalgorithmen für das PANDA-Experiment bei FAIR, das $\overline {p}$$\it p$-Annihilationen verwendet, um Phänomene der QCD bei mittleren Energien zu untersuchen. Die Hauptthemen der Arbeit konzentrieren sich auf die Entwicklung eines primären Spurfinders für Teilchen, die direkt vom $\overline {p}$$\it p$ Wechselwirkungspunkt (IP) kommen und seiner Online-Fähigkeit, sowie die Entwicklung eines sekundären Spurfinders für Spuren, die nicht vom primären IP kommen. Der primäre Spurfinder erreicht eine Effizienz von etwa 90%, was mit dem derzeit verwendeten Standard-Tracker vergleichbar ist, und halbiert zudem die Anzahl falsch gefundener Spuren. Die Portierung des Algorithmus auf eine GPU verbessert die Geschwindigkeit um einen Faktor von fünf. Der sekundäre Spurfinder verbessert die Effizienz von Sekundärspuren um 20%-Punkte und führt zu einer Verbesserung der Gesamteventrekonstruktionsrate der Reaktion $\it p$$\overline {p}$ $\rightarrow$ $\Xi$$^+$$\Xi$ (1820) $^-$ um einen Faktor von vier, von 0.3% auf 1.2%.This thesis deals with the development of track finding algorithms for the PANDA experiment at FAIR, that will use $\overline {p}$$\it p$ annihilations to study medium energy QCD phenomena. The main topics of the thesis focus on the development of a primary track finder for particles coming directly from the $\overline {p}$$\it p$ interaction point (IP) and its online capability, as well as the development of a secondary track finder for tracks which are displaced from the primary IP. The primary track finder not only achieves an efficiency for primary tracks of about 90%, which is comparable to the currently existing standard tracker, but also reduces the number of wrongly found tracks by a factor of two. Porting the algorithm onto a GPU improves its speed by a factor of five. The secondary track finder improves the finding rate of secondary tracks by 20%-points, resulting in an improvement of the full event reconstruction rate of a typical hyperon reaction, e.g. $\it p$$\overline {p}$ $\rightarrow$ $\Xi$$^+$$\Xi$ (1820) $^-$, by a factor of four, from 0.3% to 1.2%

Track finding for the PANDA detector based on Hough transformations

The PANDA experiment at FAIR (Facility for Antiproton and Ion Research) in Darmstadt is currently under construction. In order to reduce the amount of data collected during operation, it is essential to find all true tracks and to be able to distinguish them from false tracks. Part of the preparation for the experiment is therefore the development of a fast online track finder.This work presents an online track finding algorithm based on Hough transformations, which is comparable in quality and performance to the currently best offline track finder in PANDA. In contrast to most track finders the algorithm can handle the challenge of extended hits delivered by PANDA’s central Straw Tube Tracker and thus benefit from its precise spatial resolution. Furthermore, optimization methods are presented that improved the ghost ratio as well as the speed of the algorithm by 70 %. Due to further development potential in terms of displaced vertex finding and speed optimization on GPUs, this algorithm promises to exceed the quality and speed of other track finders developed for PANDA

25th International Conference on Computing in High Energy & Nuclear Physics

The PANDA experiment at FAIR (Facility for Antiproton and Ion Research) in Darmstadt is currently under construction. In order to reduce the amount of data collected during operation, it is essential to find all true tracks and to be able to distinguish them from false tracks. Part of the preparation for the experiment is therefore the development of a fast online track finder. This work presents an online track finding algorithm based on Hough transfor- mations, which is comparable in quality and performance to the currently best offline track finder in PANDA. In contrast to most track finders the algorithm can handle the challenge of extended hits delivered by PANDA’s central Straw Tube Tracker and thus benefit from its precise spatial resolution. Furthermore, optimization methods are presented that improved the ghost ratio as well as the speed of the algorithm by 70 %. Due to further development potential in terms of displaced vertex finding and speed optimization on GPUs, this algorithm promises to exceed the quality and speed of other track finders developed for PANDA

Development of fast track finding algorithms for densely packed straw tube trackers and its application to Ξ\Xi (1820) hyperon reconstruction for the PANDA experiment

Diese Arbeit befasst sich mit der Entwicklung von Spurfindungsalgorithmen für das PANDA-Experiment bei FAIR, das $\overline {p}$$\it p$-Annihilationen verwendet, um Phänomene der QCD bei mittleren Energien zu untersuchen. Die Hauptthemen der Arbeit konzentrieren sich auf die Entwicklung eines primären Spurfinders für Teilchen, die direkt vom $\overline {p}$$\it p$ Wechselwirkungspunkt (IP) kommen und seiner Online-Fähigkeit, sowie die Entwicklung eines sekundären Spurfinders für Spuren, die nicht vom primären IP kommen. Der primäre Spurfinder erreicht eine Effizienz von etwa 90%, was mit dem derzeit verwendeten Standard-Tracker vergleichbar ist, und halbiert zudem die Anzahl falsch gefundener Spuren. Die Portierung des Algorithmus auf eine GPU verbessert die Geschwindigkeit um einen Faktor von fünf. Der sekundäre Spurfinder verbessert die Effizienz von Sekundärspuren um 20%-Punkte und führt zu einer Verbesserung der Gesamteventrekonstruktionsrate der Reaktion $\it p$$\overline {p}$ $\rightarrow$ $\Xi$$^+$$\Xi$ (1820) $^-$ um einen Faktor von vier, von 0.3% auf 1.2%.This thesis deals with the development of track finding algorithms for the PANDA experiment at FAIR, that will use $\overline {p}$$\it p$ annihilations to study medium energy QCD phenomena. The main topics of the thesis focus on the development of a primary track finder for particles coming directly from the $\overline {p}$$\it p$ interaction point (IP) and its online capability, as well as the development of a secondary track finder for tracks which are displaced from the primary IP. The primary track finder not only achieves an efficiency for primary tracks of about 90%, which is comparable to the currently existing standard tracker, but also reduces the number of wrongly found tracks by a factor of two. Porting the algorithm onto a GPU improves its speed by a factor of five. The secondary track finder improves the finding rate of secondary tracks by 20%-points, resulting in an improvement of the full event reconstruction rate of a typical hyperon reaction, e.g. $\it p$$\overline {p}$ $\rightarrow$ $\Xi$$^+$$\Xi$ (1820) $^-$, by a factor of four, from 0.3% to 1.2%

Track Finding for the PANDA Detector Based on Hough Transformations

The PANDA experiment at FAIR (Facility for Antiproton and Ion Research) in Darmstadt is currently under construction. In order to reduce the amount of data collected during operation, it is essential to find as many true tracks as possible and to be able to distinguish them from false tracks. Part of the preparation for the experiment is the development of a fast online track finder. This work presents an online track finding algorithm based on Hough transformations, which is comparable in quality and performance to the currently best offline track finder in PANDA. In contrast to most track finders the algorithm can handle the challenge of extended hits delivered by PANDA’s central Straw Tube Tracker and thus benefit from its precise spatial resolution. Furthermore, optimization methods are presented that improved the ghost ratio as well as the speed of the algorithm by 70 %. Due to further development potential in terms of track finding for secondary particles and speed optimization on GPUs, this algorithm promises to exceed the quality and speed of other track finders developed for PANDA

Secondary Track Finding for PANDA

Track reconstruction is essential for a meaningful physics analysis of data from complex detectors such as PANDA. For hyperon detection this task is even more challenging because hyperons typically several centimeters before they decay. Therefore, a secondary track finder for PANDA's barrel part will be presented. This algorithm, the ApolloniusTripletTrackFinder, is the only algorithm currently available at PANDA designed to find tracks not coming from the interaction point. Therefore, the finding rate for secondary particles is much higher (about 20 %-points) than for the currently existing algorithms. Combining this algorithm with a primary track finder promises to improve the reconstruction rate for hyperon decays. The performance of this algorithm for simple test cases and simulated physics processes will be presented