Direct Photon Identification with Artificial Neural Network in the Photon Spectrometer PHOS

A neural network method is developed to discriminate direct photons from the neutral pion background in the PHOS spectrometer of the ALICE experiment at the LHC collider. The neural net has been trained to distinguish different classes of events by analyzing the energy-profile tensor of a cluster in its eigen vector coordinate system. Monte-Carlo simulations show that this method diminishes by an order of magnitude the probability of $\pi^0$-meson misidentification as a photon with respect to the direct photon identification efficiency in the energy range up to 120 GeV.Comment: 12 pages, TeX (or Latex, etc), https://edms.cern.ch/document/406291/

Strategic Time-Based Metering that Assures Separation for Integrated Operations in a Terminal Airspace

This paper reports an algorithm for strategic time-based metering of air traffic arriving and departing from a large ( tens of nautical miles) area (called here, the commitment region) around an airport or metroplex. The algorithm assures separation continuously in time and avoids a dictation of intent to an aircraft crew. This is accomplished by allowing an aircraft (specifically, its Flight Management System) to specify, and commit to, an intended route and ground speed profile along that route within the commitment region, and by supplying the time at which to enter the region to the aircraft crew. The airspace that comprises the commitment region need not be confined to the terminal airspace and can include some of the enroute space: the size and shape of the commitment region are parameters in the algorithm. An exact formula for including speed profile uncertainty in the algorithm is provided. The algorithm is applied to a number of data sets recorded during actual air traffic operations in the Southern California TRACON in July of 2014 and the Atlanta TRACON in November and December of 2013. The results of the numerical simulations indicate that the algorithm succeeds at keeping the aircraft separated, but introduces, in its current implementation, more separation than that observed in actual operations. This excess separation can be reduced by modeling more accurately the Visual Flight Rules separation practices, a direction for future research

Optimal Routing and Control of Multiple Agents Moving in a Transportation Network and Subject to an Arrival Schedule and Separation Constraints

We address the problem of navigating a set of moving agents, e.g. automated guided vehicles, through a transportation network so as to bring each agent to its destination at a specified time. Each pair of agents is required to be separated by a minimal distance, generally agent-dependent, at all times. The speed range, initial position, required destination, and required time of arrival at destination for each agent are assumed provided. The movement of each agent is governed by a controlled differential equation (state equation). The problem consists in choosing for each agent a path and a control strategy so as to meet the constraints and reach the destination at the required time. This problem arises in various fields of transportation, including Air Traffic Management and train coordination, and in robotics. The main contribution of the paper is a model that allows to recast this problem as a decoupled collection of problems in classical optimal control and is easily generalized to the case when inertia cannot be neglected. Some qualitative insight into solution behavior is obtained using the Pontryagin Maximum Principle. Sample numerical solutions are computed using a numerical optimal control solver