Super-Kamiokande is a 50-kton Water Cherenkov detector, operating since 1996 in the Kamioka mine, Japan, whose broad scientific program spans from neutrino physics to baryon number violating processes, such as proton decay. In this preliminary study I show the development of a Deep Learning model, based on Convolutional Neural Networks (CNN) and Residual Neural Networks (ResNet), for event...
Arrays of imaging atmospheric Cherenkov telescopes (IACTs) are exceptional instruments for probing the very-high-energy gamma-ray sky. These telescopes focus Cherenkov light, emitted from air showers initiated by very-high-energy gamma rays and cosmic rays, onto the camera plane. A high-speed camera then digitizes the longitudinal development of the air shower, capturing its spatial, temporal,...
The Forward Wall (FW) detector in the HADES experiment at GSI/FAIR relies on accurate photomultiplier tube (PMT) gain tuning to ensure precise energy measurements and correct energy measurement range. Traditional calibration methods depend on iterative manual adjustments using cosmic muons, making them time-consuming and susceptible to systematic variations caused by PMT aging and...
Cherenkov rings play a crucial role in identifying charged particles in high-energy physics (HEP) experiments. The size of the light cone depends directly on the mass and momentum of the particle that produced it. Most Cherenkov ring pattern reconstruction algorithms currently used in HEP experiments rely on a likelihood fit to the photo-detector response, which often consumes a significant...
We present a novel Bayesian anomaly detection framework, applied to supernova analysis, that exploits a custom-built, differentiable, and highly parallelisable JAX implementation of the commonly used SNcosmo framework. In our framework, each supernova’s flux is modelled via the SALT3 formalism, with the core computation—integrating the model flux over observational bandpasses—being fully...
Modern high-energy physics experiments generate massive, high-dimensional datasets that demand advanced strategies for anomaly detection. This talk presents EagleEye, a novel density-based method designed to compare two multivariate distributions on a point-by-point basis using a distribution-free approach rooted in Bernoulli and binomial statistics. EagleEye’s deterministic framework,...
Multimessenger astrophysics relies on multiple observational data channels, necessitating efficient methods for analyzing events of astrophysical origin. With the continuous increase in both volume and complexity of data from modern observatories, advanced Machine Learning techniques have become very useful for identifying and classifying signals effectively.
My project aim at developing a...
As searches at the LHC probe increasingly rare signals against an overwhelming background of Standard Model events, progressively tighter selection criteria are applied to enhance signal-rich regions. Simulated background samples serve as the basis for hypothesis testing, enabling comparisons between observed data and expected Standard Model backgrounds. However, this approach becomes...
Most of the antimatter in cosmic rays is produced by collisions of high energy particles with the interstellar medium while they propagate through it. The detection of an antimatter component over the collisional background can be used to investigate new sources, as the presence of dark matter annihilations in the halo. A possible smoking gun for dark matter is given by the detection of...
Reconstructing the initial density field of the universe is crucial for improving cosmological parameter constraints. In this work, we employ a U-Net architecture to reconstruct the initial density field from simulated 21-cm and CO line intensity maps from the Epoch of Reionization (EoR). These tracers provide complementary information, with 21-cm maps capturing low-density neutral regions and...
(Sub-)millimeter single-dish telescopes offer two key advantages compared to interferometers: they can efficiently map larger portions of the sky, and they can recover larger spatial scales. Nonetheless, fluctuations in the atmosphere, the dominant noise source in ground-based observations, limit the accurate retrieval of signals from astronomical sources. We introduce maria...
Model-independent anomaly detection for Beyond the Standard Model (BSM) searches in high-energy physics faces significant challenges due to the lack of tractable methods to build rich background priors as well as inherent uncertainties in simulated background processes. Traditional unsupervised ML approaches to anomaly detection, commonly train models on background samples produced by a single...
The Euclid Space Telescope aims to map the geometry of the dark universe with unprecedented precision, requiring exceptional data fidelity from its Visible Instrument (VIS). However, radiation damage introduces charge transfer inefficiencies (CTI), distorting observations over time. Trap pumping is a novel technique for localizing and characterizing radiation-induced defects in the detector...
We discuss data compression methods and evidence of learned structure in using Normalising Flows to perform the conditional mapping of nuclear equation of state data given observed parameters from gravitational wave signals of binary neutron star mergers. We use a convolutional autoencoder to compress unified high density equations of state - including data from the neutron star crust - to a...
Searches for new physics at the LHC traditionally use advanced simulations to model Standard Model (SM) processes in high-energy collisions. These are then compared with predictions from new-physics theories like dark matter and supersymmetry. However, despite extensive research, no definitive signs of physics beyond the Standard Model (BSM) have been found since the Higgs boson's...
This contribution discusses an anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets. Using 139 fb−1 of proton-proton collision data at sqrt(s) = 13 TeV, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider, we aim to identify new physics without relying on a specific signal model. The analysis employs two...
The Standard Model of particle physics has been successful in describing fundamental particles and their interactions, yet it fails to explain concepts like dark matter or the hierarchy problem, motivating the search for physics beyond the Standard Model. Despite an extensive search program at the LHC, no hints for new physics have been found so far. Anomaly detection has been introduced as a...
Unsupervised machine learning algorithms are powerful tools for identifying potential new physics at the LHC, enabling the separation of standard model (SM) background events from anomalous signal events without relying on predefined signal hypotheses. Autoencoders (AEs) are frequently employed in such tasks, but their effectiveness is often hindered by the reconstruction of outliers. In this...
The results of the ARDE project will be presented, aiming to develop innovative algorithms based on neural network architectures to discriminate between signals induced by electrons and γ-rays in semiconductor detectors, specifically in Si(Li) and HPGe. The algorithm performances for internal conversion electron spectroscopy measurements in an energy range from ∼300 keV to ∼1-2 MeV will be...
Graph neural networks (GNNs) have become state-of-the-art tools across diverse scientific disciplines due to their ability to model complex relationships in datasets that lack simple spatial or sequential structures. In this talk, we present recent advancements in the deep full event interpretation (DFEI) framework [García Pardiñas, J., et al. Comput. Softw. Big Sci. 7 (2023) 1, 12]. The DFEI...
The ATLAS detector at CERN’s Large Hadron Collider (LHC) is a complex system composed of multiple subdetectors, each designed to capture complementary aspects of particle interactions. Thus, accurate understanding of the physical phenomena under study requires effectively combining information from these components.
This work focuses on the key challenge of associating data from the inner...
Identifying products of ultrarelativistic collisions delivered by the LHC and RHIC colliders is one of the crucial objectives of experiments such as ALICE and STAR, which are specifically designed for this task. They allow for a precise Particle Identification (PID) over a broad momentum range.
Traditionally, PID methods rely on hand-crafted selections, which compare the recorded signal of...
