  The thesis arises in the context of deep learning applications to particle
physics. The dissertation follows two main parallel streams: the development of
hardware-accelerated tools for event simulation in high-energy collider
physics, and the optimization of deep learning models for reconstruction
algorithms at neutrino detectors. Event generation is a central concept in
high-energy physics phenomenology studies. The state-of-the-art software
dedicated to Monte Carlo simulation is often written for general-purpose
computing architectures (CPUs), which allow great flexibility but are not
compatible with specialized accelerating devices, GPUs. We present two original
tools, PDFFlow and MadFlow, that manage to combine these two aspects in Python.
PDFFlow, is a Parton Distribution Functions interpolator, while MadFlow aims at
building a complete tool suite to accelerate the whole event generation
framework. The reconstruction pipeline at neutrino detectors is comprised of
many different algorithms that work in synergy to extract a high-level
representation of detector data. All the most important experiments in neutrino
physics are developing software to automatically process and extract this
information. This work describes the implementation of deep learning techniques
to improve neutrino reconstruction efficiency at the ProtoDUNE-SP detector. Two
original contributions are presented concerning raw data denoising and a
hit-clustering procedure named "slicing". Both denoising and slicing involve
the implementation and the training of novel neural network architectures,
based on state-of-the-art models in machine learning, such as feed-forward,
convolutional and graph neural networks. They represent a proof of concept that
these models are indeed capable of providing an important impact on signal
reconstruction at neutrino detectors.
