Algorithmic differentiation (AD) allows to compute derivative of
computer-implemented function. Among other applications, such
derivatives are useful across domains for gradient-based design
optimization and parameter fitting. In the context of high-energy
physics, AD may allow to systematically improve detector designs based on end-to-end simulations of detectors. We have recently...
Machine learning methods are being introduced to all stages of data reconstruction and analysis in various high energy physics experiments. We present the development and application of convolutional neural networks with modified autoencoder architecture. These networks are aimed at reconstructing the pulse arrival time and amplitude in individual scintillating crystals in the PADME experiment...
Machine learning algorithms have proven to be powerful tools for identifying and classifying different types of particles. This is especially useful in experiments like the ATLAS experiment at CERN. The large and complex amount of data generated from proton-proton collisions at the Large Hadron Collider (LHC) require advanced techniques to accurately identify various particle signatures for...
I will present and discuss several proposed metrics, based on integral probability measures, for the evaluation of generative models (and, more generally, for the comparison of different generators). Some of the metrics are particularly efficient to be computed in parallel and show good performances. I will first compare the metrics on toy multivariate/multimodal distributions, and then focus...
We investigate the transduction-less readout of light signals from hadronic showers in a homogeneous calorimeter by nanowires that can be arranged in a network, communicating through the time-encoding of light pulses, and offering fast, energy-efficient local computation and generation of informative high-level primitives for the precise measurement of shower energy and the identification of...
The escalating demand for data processing in particle physics research has spurred the exploration of novel technologies to enhance efficiency and speed of calculations. This study presents the development of a porting of MADGRAPH, a widely used tool in particle collision simulations, to FPGA using High-Level Synthesis (HLS).
Experimental evaluation is ongoing, but preliminary assessments...
In the field of the Web of Things (WoT), there has been significant progress in connecting diverse real-world objects, integrating them into the virtual realm, and ensuring their seamless interoperability. Achieving this objective necessitates a focus on developing intelligent web services capable of autonomously executing tasks, adapting to evolving object contexts, and user preferences. This...
High granularity has become a desirable feature in hadron calorimeters after the parallel realizations that 1) the hadronic decay of boosted heavy particles could be successfully identified within fat jets, and 2) that particle flow techniques relying on detailed structure of the hadronic showers are an invaluable technique for event reconstruction. In this work we study if arbitrarily high...
Particle detectors at accelerators generate large amount of data, requiring analysis to derive insights. Collisions lead to signal pile up, where multiple particles produce signals in the same detector sensors, complicating individual signal identification. This contribution describes the implementation of a deep learning algorithm on a Versal ACAP device for improved processing via...
In this contribution, we explore advanced algorithms designed for real-time particle searches, utilizing the enhanced parallelization capabilities of modern GPU-based trigger schemes. These algorithms focus on detecting reconstructed particle tracks with high precision. By projecting physics candidates onto 2D histograms of flight distance and mass hypotheses at a remarkable 30 MHz rate, the...
The research involves extensive calculations and simulations to predict the cross-sections and kinematic distributions of the ttHγ final state, using advanced computational tools such as MadGraph and PYTHIA. The thesis also includes an analysis of detector-level simulations using DELPHES to assess the feasibility of observing this rare process at the Large Hadron Collider (LHC). A detailed...
