Neural network trigger algorithms for heavy quark event selection in a fixed target high energy physics experiment

Abstract The study of particles containing heavy quarks is currently a major topic in high energy physics. In this paper, neural net trigger algorithms are developed to distinghish heavy quark (signal) events from light quark (background) events in a fixed target experiment. The event tracks which are parametrized by the impact parameter D and the angle Φ of the track with respect to the beam line, vary in number and in position in the Φ - D plane. An invariant second-order moment feature set and an invariant D -sequence representation are derived to characterize the signal and background event track patterns in the Φ - D plane. A three-layer perceptron is trained to classify events as signal/background via the moments and D -sequences. A nearest neighbor classifier is also developed to serve as a benchmark for comparing the performance of the neural net triggers. Results indicate that the selected moment feature set and the D -sequence representation contain essential signal/background discriminatory information. The results also show that the neural network trigger algorithms are superior to the nearest neighbor trigger algorithms. A very high discrimination against background events and a very high efficiency for selecting signal events is obtained with the D -sequence neural net trigger algorithm

Neural Network Trigger Algorithms for Heavy Quark Event Selection in a Fixed Target High Energy Physics Experiment*

ABSTRACT The study of particles containing heavy quarks is currently a major topic in High Energy Physics. In this paper. neural net trigger algorithms are developed to distinguish heavy quark (signal) events from light quark (background) events in a fixed target experiment. The event tracks which are parametrized by the impact parameter D and the angle @ of the track with respect to the beam line, vary in number and in position in the Q-D plane. An invariant second order moment feature set and an invariant D-sequence representation are derived to characterize the signal and background event track patterns in the Q-D plane. A 3-layer perceptron is trained to classify events as signal/background via the moments and D-sequences. A nearest neighbor classifier is also developed to serve as a benchmark for comparing the performance of the neural net triggers. Results indicate that the selected moment feature set and the D-sequence representation contain essential signal/background discriminatory information. The results also show that the neural network trigger algorithms are superior to the nearest neighbor trigger algorithms, A very high discrimination against background events and a very high efficiency for selecting signal events is obtained with the D-sequence neural net trigger algorithm. SUMMARY The study of particles containing heavy quarks is currently a major topic in High Energy Physics and, in this paper, neural net trigger algorithms are developed to distinguish heavy quark (signal) events from light quark (background) events in a fixed target experiment. The event tracks which are parameterized by the impact parameter D and the angle 4, of a track with respect to the beam line vary in number and in position in the 0-D plane and cannot, therefore, be used as inputs to a neural network directly. This problem is overcome by deriving an invariant second order moment feature set and a Dsequence representation to characterize the signal and background tracks in the Q-D plane. The moments feature set characterizes the dispersion of the tracks and the orientation of the tracks in the (P-D plane. The D-sequence which is obtained through a simple set of transformations captures the track variations along the D-axis. A 3-layer perceptron is trained to classify events as signal/background via the normalized moments feature set and the Dsequences. The key to a successful study of heavy quark physics is a very high discrimination against background events and a high efficiency for selecting signal events. A training strategy is developed to keep the background misclassifications at a minimum. A nearest neighbor classifier is also developed to serve as a benchmark for comparing the performance of the neural net trigger algorithms. The very high efficiency obtained for rejecting background events and for selecting signal events clearly indicate that the selected moment feature set and the D-sequence representation contain essential signal/background discriminatory information. The results obtained also show that the neural net triggers are superior to the nearest neighbor triggers and the D-sequence neural net trigger is superior to the moments neural net trigger. It is important to note that the results obtained are very impressive as tests on randomly selected events indicate that, in many cases, it is impossible to visually distinguish between signal and background events from the track patterns in the O-D plane

Investigation of a VLSI neural network chip as part of a secondary vertex trigger

Abstract An analog VLSI neural network chip (ETANN) has been trained to detect secondary vertices in simulated data for a fixed target heavy flavour production experiment. The detector response and associative memory track finding were modelled by a simulation, but the vertex detection was performed in hardware by the neural network chip and requires only a few microseconds per event. The chip correctly tags 30% of the heavy flavour events while rejecting 99% of the background, and is thus well adapted for secondary vertex triggering applications. A general purpose VME module for interfacing the ETANN to experiments, equipped with ADC/DAC circuits and a 68070 CPU, is also presented

Report of the Higgs Working Group of the Tevatron Run 2 SUSY/Higgs Workshop

This report presents the theoretical analysis relevant for Higgs physics at the upgraded Tevatron collider and documents the Higgs Working Group simulations to estimate the discovery reach in Run 2 for the Standard Model and MSSM Higgs bosons. Based on a simple detector simulation, we have determined the integrated luminosity necessary to discover the SM Higgs in the mass range 100-190 GeV. The first phase of the Run 2 Higgs search, with a total integrated luminosity of 2 fb-1 per detector, will provide a 95% CL exclusion sensitivity comparable to that expected at the end of the LEP2 run. With 10 fb-1 per detector, this exclusion will extend up to Higgs masses of 180 GeV, and a tantalizing 3 sigma effect will be visible if the Higgs mass lies below 125 GeV. With 25 fb-1 of integrated luminosity per detector, evidence for SM Higgs production at the 3 sigma level is possible for Higgs masses up to 180 GeV. However, the discovery reach is much less impressive for achieving a 5 sigma Higgs boson signal. Even with 30 fb-1 per detector, only Higgs bosons with masses up to about 130 GeV can be detected with 5 sigma significance. These results can also be re-interpreted in the MSSM framework and yield the required luminosities to discover at least one Higgs boson of the MSSM Higgs sector. With 5-10 fb-1 of data per detector, it will be possible to exclude at 95% CL nearly the entire MSSM Higgs parameter space, whereas 20-30 fb-1 is required to obtain a 5 sigma Higgs discovery over a significant portion of the parameter space. Moreover, in one interesting region of the MSSM parameter space (at large tan(beta)), the associated production of a Higgs boson and a b b-bar pair is significantly enhanced and provides potential for discovering a non-SM-like Higgs boson in Run 2.Comment: 185 pages, 124 figures, 55 table

Status and Prospects of Top-Quark Physics

The top quark is the heaviest elementary particle observed to date. Its large mass of about 173 GeV/c^2 makes the top quark act differently than other elementary fermions, as it decays before it hadronises, passing its spin information on to its decay products. In addition, the top quark plays an important role in higher-order loop corrections to standard model processes, which makes the top quark mass a crucial parameter for precision tests of the electroweak theory. The top quark is also a powerful probe for new phenomena beyond the standard model. During the time of discovery at the Tevatron in 1995 only a few properties of the top quark could be measured. In recent years, since the start of Tevatron Run II, the field of top-quark physics has changed and entered a precision era. This report summarises the latest measurements and studies of top-quark properties and gives prospects for future measurements at the Large Hadron Collider (LHC).Comment: 76 pages, 35 figures, submitted to Progress in Particle and Nuclear Physic

Search for the Standard Model Higgs Boson Produced in Association with a WW Boson in the Isolated-Track Charged-Lepton Channel Using the Collider Detector at Fermilab

This dissertation presents an experimental search for the Standard Model Higgs boson produced in association to the W boson in proton antiproton collisions at a center of mass energy of 1.96 TeV and recorded with the Collider Detector at Fermilab. We improve the sensitivity of the WH search by 17% through increased signal yield by 33% by introducing a novel method to reconstruct charged lepton candidates based on an isolated track, as well as a novel method to combine triggers in order to maximize the signal yield and yet not use an OR between triggers. The observed (median expected) 95% confidence level SM Higgs upper limits on cross section times branching ratio vary between 2.39 x SM (2.73 x SM) for a Higgs mass of 100 GeV/c^2 to 31.1 x SM (31.2 x SM) for a Higgs mass of 150 GeV/c^2, while the value for a 115 GeV/c^2 Higgs boson is that of 5.08 x SM (3.79 x SM).Comment: thesis, 208 page

Inference Aware Neural Optimization for Top Pair Cross-Section Measurements with CMS Open Data

In recent years novel inference techniques have been developed based on the construction of summary statistics with neural networks by minimizing inference-motivated losses via automatic differentiation. The inference-aware summary statistics aim to be optimal with respect to the statistical inference goal of high energy physics analysis by accounting for the effects of nuisance parameters during the model training. One such technique is INFERNO (P. de Castro and T. Dorigo, Comp.\ Phys.\ Comm.\ 244 (2019) 170) which was shown on toy problems to outperform classical summary statistics for the problem of confidence interval estimation in the presence of nuisance parameters. In this thesis the algorithm is extended to common high energy physics problems based on a differentiable interpolation technique. In order to test and benchmark the algorithm in a real-world application, a complete, systematics-dominated analysis of the CMS experiment, "Measurement of the top-quark pair production cross section in the tau+jets channel in pp collisions at sqrt(s) = 7 TeV" (CMS Collaboration, The European Physical Journal C, 2013) is reproduced with CMS Open Data. The application of the INFERNO-powered neural network architecture to this analysis demonstrates the potential to reduce the impact of systematic uncertainties in real LHC analysis