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 …

Our knowledge of the fundamental particles of nature and their interactions is summarized
by the standard model of particle physics. Advancing our understanding in this field has …

The mass of the W boson, a mediator of the weak force between elementary particles, is
tightly constrained by the symmetries of the standard model of particle physics. The Higgs …

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 book is a guide to the practical application of statistics in data analysis as typically
encountered in the physical sciences. It is primarily addressed at students and professionals …

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 …

Elementary particles can be identified through various techniques, depending on the
purpose of the measurement and which relevant quantities, such as time, energy, and …

Large damages and losses resulting from floods are widely reported across the globe. Thus,
the identification of the flood-prone zones on a flood susceptibility map is very essential. To …

Novelty detection is the machine learning task to recognize data, which belong to an
unknown pattern. Complementary to supervised learning, it allows us to analyze data model …

A novel technique to identify and split clusters created by multiple charged particles in the
ATLAS pixel detector using a set of artificial neural networks is presented. Such merged …