This presentation focuses on unsupervised representation learning. We first introduce the concept of representation learning, contrasting it with supervised learning. We then discuss several approaches to unsupervised representation learning, including those based on autoencoders, discriminators, contrastive and generative methods. Next, we shift our focus to generative models, discussing...
Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simulations, and the high-dimensionality of optimisation problems pose significant challenges in generating the required data for training state-of-the-art machine learning models. In this work, we introduce Cheetah, a...
Using a spiking neural network and a modeling of the silicon tracker for the CMS upgraded detector, we demonstrate the unsupervised learning application of identification of charged particle tracks in presence of background, and characterize the detection efficiency, fake rate, and differentiation of output signals for particles of different momenta and charge.
Muon collisions are considered a promising mean for exploring the energy frontier, leading to a detailed study of the possible feasibility issues. Beam intensities of the order of $10^{12}$ muons per bunch are needed to achieve the necessary luminosity, generating a high flux of secondary and tertiary particles that reach both the machine elements and the detector region. To limit the impact...
The rapid development of ML and AI applications requires training a large number of models. One of the ways to organize training of them is the automated machine learning (AutoML) approach, where there is no human control over the training result. A crucial prerequisite for AutoML is the stability of the training model incorporated within it. This study presents an approach to identifying the...
Contemporary post-quantum cryptographic protocols rely on worst-case intractability assumptions and consist of multiple intricate steps. In contrast, in this talk we shall explore a model system that directly addresses fundamental computational challenges and that can be mapped on a random neural networks.
We investigate the collision resistance property of a specific class of neural...
Applying algorithmic differentiation to particle simulations like Geant4 would allow us to evaluate derivatives of simulation outputs with respect to inputs, e.g. of the mean energy depositions in calorimeter layers with respect to geometry parameters. Such derivatives could become instrumental for a number of application like detector optimization or parameter fitting in HEP. However, besides...
Current state-of-the-art in charged particle tracking follows a two-step paradigm where a graph neural network optimizes an intermediate prediction-loss during training and is later combined with a discrete, non-differentiable, optimization step during inference, constructing disconnected track candidates. In this talk, we introduce and assess a novel end-to-end differentiable tracking...
Detection of neutrinos at ultra-high energies (UHE, E >$10^{17}$eV) would open a new window to the most violent phenomena in our universe. However, owing to the expected small flux of UHE neutrinos, the detection rate will be small, with just a handful of events per year, even for large future facilities like the IceCube-Gen2 neutrino observatory at the South Pole.
In this contribution, we...
Cosmic muon interactions leading to the in-situ production of long-lived radioisotopes may introduce a significant background in the context of rare event searches conducted deep underground. Specifically, the delayed decay of $^{77(m)}$Ge emerges as the primary contributor from in-situ cosmogenic sources for the neutrinoless double-beta decay search with $^{76}$Ge. The future LEGEND-1000...
GENETIS aims to use AI to find optimal designs of instruments for greater science outcomes. Initially, we are using genetic algorithms to evolve optimal antenna designs for the detection of astrophysical neutrinos and is building a prototype of what is the first antenna evolved for a science outcome. The Nebulous spin-off project is building antenna designs from building blocks “LEGO”-style...
The fidelity of detector simulation is crucial for precision experiments, such as DUNE which uses liquid argon time projection chambers (LArTPCs). We can improve the detector simulation by performing dedicated calibration measurements. Using conventional calibration approaches, typically we are only able to tackle individual detector processes per measurement. However, the detector effects are...
The detection of high-energy astrophysical neutrinos by IceCube has opened a new window on our Universe. While IceCube has measured the flux of these neutrinos at energies up to several PeV, much remains to be discovered regarding their origin and nature. TAMBO is a next-generation neutrino observatory specifically designed to detect tau neutrinos in the 1-100 PeV energy range, enabling tests...
Generative Adversarial Neural Networks (GANN) are used to simulate the multiple scattering of muons crossing matter. In previous works, a GANN was designed and trained, successfully predicting the angular and spatial deviation distributions including their correlations. In this work we show that GANNs can be so good at this task that correct POCA images can be reconstructed from their randomly...
Muon Cargo is a project funded by the Spanish Port Authority aiming at installing a Muography portal for container inspection in the port of Santander. This talk offers a panoramic of the status of the project focusing on the development of two AI algorithms: a YOLOv8 based system to perform semantic segmentation on POCA-based images, and a Variational Autoencoder to identify unsual,...
The provision of exact and consistent derivative information is important for numerous applications arising from optimization purposes as for example optimal control problems. However, even the pure simulation of complex systems may require the computation of derivative information. Implicit integration methods are prominent examples for this case.
The talk will present the technique of...
Setup design plays a pivotal role in experiment development, particularly in high-energy physics, where vast temporal and spatial scales dictate the course of research for decades. Our research, embedded in the MODE Collaboration, aims to generalize Machine Learning tools for creating a differentiable pipeline capable of suggesting optimal configurations for the Muon Collider Electromagnetic...
Understanding the nature of dark matter is one of the greatest challenges faced by Particle Physics in the XXI century. To date, the only hint about a positive identification of the dark matter comes from the DAMA/LIBRA experiment in the Gran Sasso National Laboratory (Italy). For more than 20 years, it has observed an annual modulation in the low-energy detection rate of its NaI(Tl) crystals,...
In this presentation, I will discuss the forward modeling of the DSA 2000 radio interferometer, an array set to exceed the capabilities of any existing or planned radio interferometer. Our approach leverages forward modeling to design and validate the system, ensuring it meets scientific requirements, budget constraints, and computational feasibility. I will introduce our JAX-based forward...
Information Field Theory (IFT) offers a powerful framework for the analysis of experimental data. The fundamental objective of IFT is the reconstruction of continuous fields from noisy and sparse data. By combining Bayesian probabilities with computational techniques from quantum field theory and statistical mechanics, IFT allows for efficient inference in high-dimensional problems.
In this...
The planned IceCube-Gen2 radio neutrino detector at the South Pole will enhance the detection of cosmic ultra-high-energy neutrinos. It is crucial to make use of the time available until its construction to optimize the detector design. A fully differentiable pipeline, from signal generation to detector response, would allow for the application of gradient descent techniques to explore the...
We propose an optimization system for a Parallel-Plate Avalanche Counter with Optical Readout designed for heavy-ion tracking and imaging. Exploiting differentiable programming, we model the reconstruction of the position for different detector configurations and build an optimization cycle that minimizes an objective function. We analyze the performance improvement using this method,...
Metallic-magnetic calorimeters (MMCs) - like the maXs-detector series developed within the SPARC collaboration - have become a promising new tool for high precision X-ray spectroscopy. Because of their unique working principles, MMCs combine several advantages over conventional energy- and wavelength-dispersive photon detectors. They can reach spectral resolving powers of up to $E / \Delta E...
One of the primary challenges for future nuclear fusion power plants is understanding how neutron irradiation affects reactor materials. To tackle this issue, the IFMIF-DONES project aims to build a facility capable of generating a neutron source in order to irradiate different material samples. This will be achieved by colliding a deuteron beam with a lithium jet. In this work, within the...
In the HypHI project, which started in 2006 at GSI-FAIR, we aim to study proton- and neutron-rich hypernuclei produced in the ion-induced collisions. The successful observation of light hypernuclei in the 6Li – 12C collisions during our first experimental campaign in 2009 – 2010 has paved a new way to study these bound states of protons, neutrons, and hyperons [1]. For future experiments, both...
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...
Muon scattering tomography allows for the imaging of the density of unknown volumes through the measurement of the incoming and outgoing tracks scattering angle. One significant source of imprecision of the technique comes from the dependence of muon momentum on the multiple scattering process that muons undergo in the material. This can be alleviated by including dedicated momentum...
Machine learning holds significant potential for improving Muon Scattering Tomography (MST) material identification. However, the complexity of acquiring sufficient MST data for machine learning applications remains a significant challenge. To circumvent this, there is a growing interest in creating MST synthetic datasets using Geant4, a software that can accurately simulate muon-material...
In the civil engineering industry, there is an increasing demand for innovative non-destructive evaluation methods, especially for critical infrastructure such as bridges, as current techniques fall short. Muography, a non-invasive technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmic-ray muons within the scanned volume. Due to their...
Accurately simulating the response of monolithic active pixel sensors requires detailed technology computer-aided design simulations of the electric field inside the chip. This is used to model the electron propagation from their point of origin to potential collection. Specialized simulation software, such as Allpix², has been developed for this purpose. However, the electric field is often...
This presentation will describe a NASA project called the Universal Simulation and Modelling Language (USML) that is used as the computational engine for a mission called the Active Learning Physics Simulator (ALPS).
Background:
When performing physical simulations, there is a tradeoff between accuracy and computation time. For example, atomic-scale simulations are highly accurate but...
Deep learning algorithms have excelled in various domains. Despite this success, few deep-learning models have seen full end-to-end deployment in gravitational-wave searches, both in real-time and on archival data. In particular, there is a lack of standardized software tools for quick implementation and development of novel AI ideas. We address this gap by developing the ML4GW and HERMES...
With the growing datasets of HEP experiments, statistical analysis becomes more computationally demanding, requiring improvements in existing statistical analysis software. One way forward is to use Automatic Differentiation (AD) in likelihood fitting, which is often done with RooFit (a toolkit that is part of ROOT.) As of recently, RooFit can generate the gradient code for a given likelihood...
Advanced optimizations for source transformation based automatic differentiation
Clad is a LLVM/Clang plugin designed to provide automatic differentiation (AD) for C++ mathematical functions. It generates code for computing derivatives modifying abstract syntax tree using LLVM compiler features. Clad supports forward- and...
Kokkos is a high-performance library allowing scientists to develop performance-portable C++ code capable of running on CPUs, GPUs and exotic hardware. The Kokkos infrastructure enables researchers to write generic code for libraries, frameworks, and scientific simulations such as climate simulation tools like Albany and HOMMEXX that can later be run on a large scale on any supercomputing...
Positron Emission Tomography (PET) is a functional imaging technique in nuclear medicine in which a radioactive tracer is injected into the patient to examine metabolic and physiological processes. Reducing the radiation dose to the patient is desirable and can be achieved by administering lower amounts of radiotracer. However, low-dose examinations result in increased noise level in the...
Objective: One of the mayor challenges in positron emission tomography (PET) is to increase system efficiency without sacrificing spatial resolution. Including the contribution of inter-crystal scatter (ICS) events during image reconstruction is one way of achieving this aim, provided a method for estimating the primary photon path in such events is available. The IRIS group (IFIC, Valencia)...
Accurate timing characterization of radiation events is crucial in nuclear medicine, particularly for Positron Emission Tomography (PET). In PET, achieving a good coincidence resolving time (CRT) between detector pairs enhances the Time-of-Flight (TOF) information for each detected coincidence, which significantly improves the signal-to-noise ratio of the images. This study introduces a method...
The application of neural networks in medical physics has shown significant promise in improving imaging techniques and treatment verification. The IRIS group of IFIC (Valencia) is an expert in developing Compton cameras for medical applications. The group employs neural networks to enhance the performance of such devices in different aspects. This work summarizes three key research studies...
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...
Multi-dimensional parameter spaces are commonly encountered in astroparticle physics theories that attempt to capture novel phenomena. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to this community. Effectively sampling these spaces is crucial to bridge the gap between experiment and theory. Several innovations have...
We present Numba-Enzyme, a gradient-providing Just-in-time (JIT) compiler for simulations in Python providing rewrite-free access to gradients for Numba, a popular LLVM-based Python compiler for simulations. In recent years a number of simulation areas have started to expand beyond efficient simulations, and began to utilize gradients for gradient-based optimization, differentiable simulation...
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...
