Machine learning has played an important role in the analysis of high-energy physics data
for decades. The emergence of deep learning in 2012 allowed for machine learning tools …

We investigate a new structure for machine learning classifiers built with neural networks
and applied to problems in high-energy physics by expanding the inputs to include not only …

This biennial Review summarizes much of particle physics. Using data from previous
editions, plus 2158 new measurements from 551 papers, we list, evaluate, and average …

We explore the use of graph networks to deal with irregular-geometry detectors in the
context of particle reconstruction. Thanks to their representation-learning capabilities, graph …

Neural Networks presents concepts of neural-network models and techniques of parallel
distributed processing in a three-step approach:-A brief overview of the neural structure of …

Using detailed simulations of calorimeter showers as training data, we investigate the use of
deep learning algorithms for the simulation and reconstruction of single isolated particles …

Decays of b hadrons into final states containing a D0 meson and a muon are used to
measure the bb¯ production cross-section in proton–proton collisions at a centre-of-mass …

Double-layered hydroxides with hydrotalcite structure were synthesized with Mg/Al atomic
ratios of 2.5 and 3 and with different contents of exchangeable Cl− and CO2− 3anions. The …

We present a technique for translating a black-box machine-learned classifier operating on
a high-dimensional input space into a small set of human-interpretable observables that can …

Measurements of the production of the weakly decaying charmed hadrons: D 0, D+, D s+
and Λ c+ in both Z^ 0 → c ̄ c and Z^ 0 → b ̄ b events are reported. By summing the partial …