tih the United States C-apartment of Energy

Tutorial on neural network applications in high energy physics: A 1992 perspective

We present a cooperative multi-modular neural network architecture: a Multi-Layer Perceptron (MLP), followed by a Radial Basis Function network (RBF). We show that, in the LEP experiment of electron-positron collision run at CERN, this architecture was able to outperform both a simple multi-layer perceptron, a multi-modular MLP+LVQ (LVQ: Learning Vector Quantization) and MLP+RBF trained sequentially and a conventional technique (Discriminant Analysis).

MLP-RBF: A Cooperative Multi-Modular Neural Network Application in High-Energy Physics

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

The Use of Neural Networks in High-Energy Physics

The Higgs bosons and the top quark decay into rich and diverse final states, containing both light and heavy quarks, gluons, photons as well as W and Z bosons. The precise identification and reconstruction of these final states at the FCC-ee relies on the capability of the detector to provide excellent flavour tagging, jet energy and angular resolution, and global kinematic event reconstruction. Excellent flavour tagging performance requires low material vertex and tracking detectors, and advanced machine learning (ML) techniques as successfully employed in LHC experiments. In addition, the Z pole run will provide abundant samples of heavy flavour partons that can be used for calibration of the tagging algorithms. For the reconstruction of jets, leptons and missing energy, particle-flow algorithms are crucial to explore the full potential of the highly granular tracking and calorimeter systems, and give access to excellent energy-momentum resolution and precise identification of heavy bosons in their hadronic decays. This enables, among many other key elements, the reconstruction of Higgsstrahlung processes with leptonically and hadronically decaying Z bosons, and an almost background-free identification of top quark pair events. Exploiting the full available kinematic constraints together with exclusive jet clustering algorithms will allow for the optimisation of global event reconstruction with kinematic fitting techniques.

arXiv : Higgs and top physics reconstruction challenges and opportunities at FCC-ee

Abstract Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level observables that are necessary ingredients to jet flavour identification. An algorithm for reconstructing the track parameters and covariance matrix of charged particles for an arbitrary tracking sub-detector geometries has been developed. Additional modules allowing for particle identification using time-of-flight and ionizing energy loss information have been implemented. A jet flavour identification algorithm based on a graph neural network architecture and exploiting all available particle level information has been developed. The impact of different detector design assumptions on the flavour tagging performance is assessed using the FCC-ee IDEA detector prototype. 

/pdf/jet-flavour-tagging-for-future-colliders-with-fast-i96s0av9.pdf

Jet flavour tagging for future colliders with fast simulation

Tagging b quark events in ALEPH with neural networks

Tagging B quark events in ALEPH with neural networks : comparison of different methods

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Tagging b quark events in ALEPH with neural networks

Tagging B quark events in ALEPH with neural networks : comparison of different methods