Terminal area automatic navigation, guidance, and control research using the Microwave Landing System (MLS). Part 4: Transition path reconstruction along a straight line path containing a glideslope change waypoint

The necessary algorithms to reconstruct the glideslope change waypoint along a straight line in the event the aircraft encounters a valid MLS update and transition in the terminal approach area are presented. Results of a simulation of the Langley B737 aircraft utilizing these algorithms are presented. The method is shown to reconstruct the necessary flight path during MLS transition resulting in zero cross track error, zero track angle error, and zero altitude error, thus requiring minimal aircraft response

Tracking Performance of the Scintillating Fiber Detector in the K2K Experiment

The K2K long-baseline neutrino oscillation experiment uses a Scintillating Fiber Detector (SciFi) to reconstruct charged particles produced in neutrino interactions in the near detector. We describe the track reconstruction algorithm and the performance of the SciFi after three years of operation.Comment: 24pages,18 figures, and 1 table. Preprint submitted to NI

Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12

In this article we describe the development of machine learning models to assist the CLAS12 tracking algorithm by identifying tracks through inferring missing segments in the drift chambers. Auto encoders are used to reconstruct missing segments from track trajectory. Implemented neural network was able to reliably reconstruct missing segment positions with accuracy of $\approx 0.35$ wires, and lead to recovery of missing tracks with accuracy of $>99.8\%$

A neural network z-vertex trigger for Belle II

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D ($r - \varphi$) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.Comment: Proceedings of the 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Preprint, reviewed version (only minor corrections

Track Reconstruction Performance in CMS

The expected performance of track reconstruction with LHC events using the CMS silicon tracker is presented. Track finding and fitting is accomplished with Kalman Filter techniques that achieve efficiencies above 99% on single muons with pT>1 GeV/c. Difficulties arise in the context of standard LHC events with a high density of charged particles, where the rate of fake combinatorial tracks is very large for low pT tracks, and nuclear interactions in the tracker material reduce the tracking efficiency for charged hadrons. Recent improvements with the CMS track reconstruction now allow to efficiently reconstruct charged tracks with pT down to few hundred MeV/c and as few as three crossed layers, with a very small fake fraction, by making use of an optimal rejection of fake tracks in conjunction with an iterative tracking procedure.Comment: 4 pages, 3 figures, proceedings of the 11th Topical Seminar on Innovative Particle and Radiation Detectors (IPRD08

Classification of Pixel Tracks to Improve Track Reconstruction from Proton-Proton Collisions

In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN (Conseil européen pour la recherche nucléaire) uses a massive underground particle collider, called the Large Hadron Collider or LHC, to produce particle collisions at extremely high speeds. There are several layers of detectors in the collider that track the pathways of particles as they collide. The data produced from collisions contains an extraneous amount of background noise, i.e., decays from known particle collisions produce fake signal. Particularly, in the first layer of the detector, the pixel tracker, there is an overwhelming amount of background noise that hinders analysts from seeing true track reconstruction. This paper aims to find and optimize methods that are instrumental in figuring out how the true particle track can be decoupled from the background noise produced at the pixel tracker level of the detector. The results of this study include successful implementation of machine learning techniques to classify signal and background from particle collision data. From these results, it was concluded that neural networks are a successful resource for analyzing and processing particle collision data to reconstruct particle pathways

A Multifunctional Processing Board for the Fast Track Trigger of the H1 Experiment

The electron-proton collider HERA is being upgraded to provide higher luminosity from the end of the year 2001. In order to enhance the selectivity on exclusive processes a Fast Track Trigger (FTT) with high momentum resolution is being built for the H1 Collaboration. The FTT will perform a 3-dimensional reconstruction of curved tracks in a magnetic field of 1.1 Tesla down to 100 MeV in transverse momentum. It is able to reconstruct up to 48 tracks within 23 mus in a high track multiplicity environment. The FTT consists of two hardware levels L1, L2 and a third software level. Analog signals of 450 wires are digitized at the first level stage followed by a quick lookup of valid track segment patterns. For the main processing tasks at the second level such as linking, fitting and deciding, a multifunctional processing board has been developed by the ETH Zurich in collaboration with Supercomputing Systems (Zurich). It integrates a high-density FPGA (Altera APEX 20K600E) and four floating point DSPs (Texas Instruments TMS320C6701). This presentation will mainly concentrate on second trigger level hardware aspects and on the implementation of the algorithms used for linking and fitting. Emphasis is especially put on the integrated CAM (content addressable memory) functionality of the FPGA, which is ideally suited for implementing fast search tasks like track segment linking.Comment: 6 pages, 4 figures, submitted to TN