Dependencies and Separation of Duty Constraints in GTRBAC

A Generalized Temporal Role Based Access Control (GTRBAC) model that captures an exhaustive set of temporal constraint needs for access control has recently been proposed. GTRBAC’s language constructs allow one to specify various temporal constraints on role, user-role assignments and role-permission assignments. In this paper, we identify various time-constrained cardinality, control flow dependency and separation of duty constraints (SoDs). Such constraints allow specification of dynamically changing access control requirements that are typical in today’s large systems. In addition to allowing specification of time, the constraints introduced here also allow expressing access control policies at a finer granularity. The inclusion of control flow dependency constraints allows defining much stricter dependency requirements that are typical in workflow types of applications

Rossiter-McLaughlin Effect Measurements for WASP-16, WASP-25 and WASP-31

We present new measurements of the Rossiter-McLaughlin (RM) effect for three WASP planetary systems, WASP-16, WASP-25 and WASP-31, from a combined analysis of their complete sets of photometric and spectroscopic data. We find a low amplitude RM effect for WASP-16 (Teff = 5700 \pm 150K), suggesting that the star is a slow rotator and thus of an advanced age, and obtain a projected alignment angle of lambda = -4.2 degrees +11.0 -13.9. For WASP-25 (Teff = 5750\pm100K) we detect a projected spin-orbit angle of lambda = 14.6 degrees \pm6.7. WASP-31 (Teff = 6300\pm100K) is found to be well-aligned, with a projected spin-orbit angle of lambda = 2.8degrees \pm3.1. A circular orbit is consistent with the data for all three systems, in agreement with their respective discovery papers. We consider the results for these systems in the context of the ensemble of RM measurements made to date. We find that whilst WASP-16 fits the hypothesis of Winn et al. (2010) that 'cool' stars (Teff < 6250K) are preferentially aligned, WASP-31 has little impact on the proposed trend. We bring the total distribution of the true spin-orbit alignment angle, psi, up to date, noting that recent results have improved the agreement with the theory of Fabrycky & Tremaine (2007) at mid-range angles. We also suggest a new test for judging misalignment using the Bayesian Information Criterion, according to which WASP-25 b's orbit should be considered to be aligned.Comment: 20 pages, 14 tables, 10 figures. Accepted to MNRA

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Determination of muon momentum in the MicroBooNE LArTPC using an improved model of multiple Coulomb scattering

We discuss a technique for measuring a charged particle's momentum by means of multiple Coulomb scattering (MCS) in the MicroBooNE liquid argon time projection chamber (LArTPC). This method does not require the full particle ionization track to be contained inside of the detector volume as other track momentum reconstruction methods do (range-based momentum reconstruction and calorimetric momentum reconstruction). We motivate use of this technique, describe a tuning of the underlying phenomenological formula, quantify its performance on fully contained beam-neutrino-induced muon tracks both in simulation and in data, and quantify its performance on exiting muon tracks in simulation. Using simulation, we have shown that the standard Highland formula should be re-tuned specifically for scattering in liquid argon, which significantly improves the bias and resolution of the momentum measurement. With the tuned formula, we find agreement between data and simulation for contained tracks, with a small bias in the momentum reconstruction and with resolutions that vary as a function of track length, improving from about 10% for the shortest (one meter long) tracks to 5% for longer (several meter) tracks. For simulated exiting muons with at least one meter of track contained, we find a similarly small bias, and a resolution which is less than 15% for muons with momentum below 2 GeV/c. Above 2 GeV/c, results are given as a first estimate of the MCS momentum measurement capabilities of MicroBooNE for high momentum exiting tracks

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC

The low-noise operation of readout electronics in a liquid argon time projection chamber (LArTPC) is critical to properly extract the distribution of ionization charge deposited on the wire planes of the TPC, especially for the induction planes. This paper describes the characteristics and mitigation of the observed noise in the MicroBooNE detector. The MicroBooNE's single-phase LArTPC comprises two induction planes and one collection sense wire plane with a total of 8256 wires. Current induced on each TPC wire is amplified and shaped by custom low-power, low-noise ASICs immersed in the liquid argon. The digitization of the signal waveform occurs outside the cryostat. Using data from the first year of MicroBooNE operations, several excess noise sources in the TPC were identified and mitigated. The residual equivalent noise charge (ENC) after noise filtering varies with wire length and is found to be below 400 electrons for the longest wires (4.7 m). The response is consistent with the cold electronics design expectations and is found to be stable with time and uniform over the functioning channels. This noise level is significantly lower than previous experiments utilizing warm front-end electronics.Comment: 36 pages, 20 figure