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Dr Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))23/09/2024, 09:30
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Jose Salt (Instituto de Fisica Corpuscular (IFIC) - Universidad de Valencia), Roberto Ruiz De Austri23/09/2024, 09:31
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Tommaso Dorigo (INFN Padova and LTU)23/09/2024, 09:35
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Dr Danilo Rezende (Google Deep Mind)23/09/2024, 10:00
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...
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Jan Kaiser23/09/2024, 11:30Particle Physics
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...
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Dr Emanuele Coradin (University of Padova)23/09/2024, 11:50Particle Physics
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.
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Luca Castelli23/09/2024, 12:10Particle Physics
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...
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Alexey Boldyrev (NRU Higher School of Economics (Moscow, Russia))23/09/2024, 12:30Particle Physics
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...
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Prof. Riccardo Zecchina (Università Bocconi)23/09/2024, 14:30
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...
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Max Aehle (Fachhochschule Worms (DE)), Max Aehle23/09/2024, 15:30Computer Science
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...
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Tobias Kortus (University of Kaiserslautern-Landau (RPTU))23/09/2024, 15:50Computer Science
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...
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Christian Glaser (Uppsala University)23/09/2024, 16:35Astroparticle Physics
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.
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In this contribution, we... -
Dr Ann-Kathrin Schuetz (Lawrence Berkeley National Laboratory)23/09/2024, 16:55Astroparticle Physics
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...
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Amy Connolly (The Ohio State University)23/09/2024, 17:15Astroparticle Physics
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...
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Yifan Chen (SLAC National Accelerator Laboratory (US))23/09/2024, 17:35
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...
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Dr Alfonso Andres Garcia Soto (IFIC)23/09/2024, 17:55Astroparticle Physics
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...
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Zahraa Zaher24/09/2024, 09:00Muography
TomOpt is a software package designed to optimize the geometric configuration and specifications of detectors intended for tomography using cosmic-ray muon scattering. Differentiable programming is utilized by the software to model muon interactions with detectors and scanned volumes, infer volume properties, and perform loss minimization in an optimization cycle. We introduce the...
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Pablo Martinez Ruiz Del Arbol (Universidad de Cantabria and CSIC (ES))24/09/2024, 09:20Muography
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...
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Carlos Diez (Muon Tomography Systems S.L.)24/09/2024, 09:40Muography
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,...
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Prof. Andrea Walther (Humboldt Universität Berlin)24/09/2024, 10:00
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...
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Florian Wolfgang Stummer (University of London (GB))24/09/2024, 11:30Particle Physics
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Ritwika Chakraborty (PSI)24/09/2024, 11:50Particle Physics
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Federico Nardi (Universita e INFN, Padova (IT) - LPC Clermont)24/09/2024, 12:10Particle Physics
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...
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Jan Kieseler (KIT - Karlsruhe Institute of Technology (DE))24/09/2024, 12:30Particle Physics
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Iván Coarasa Casas (CAPA, University of Zaragoza, Spain)24/09/2024, 14:30Astroparticle Physics
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,...
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Joshua Albert (Caltech)24/09/2024, 14:50Astroparticle Physics
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...
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Frederik Krieger (RWTH Aachen University)24/09/2024, 15:10Astroparticle Physics
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.
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In this... -
Philipp Pilar (Uppsala University)24/09/2024, 15:30Astroparticle Physics
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...
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Mario Krenn (MPI)24/09/2024, 16:30Nuclear Applications
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María Pereira24/09/2024, 16:50Nuclear Applications
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,...
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Mr Marc Oliver Herdrich (Helmholtz-Institute Jena, Germany)24/09/2024, 17:10Nuclear Applications
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...
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Galo Gallardo Romero (Artificial Intelligence Engineer at HI Iberia)24/09/2024, 17:30Nuclear Applications
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...
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Samuel Escrig López (Instituto de Estructura de la Materia (IEM - CSIC))24/09/2024, 17:50Nuclear Applications
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...
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Xuan Tung Nguyen (INFN and RPTU)24/09/2024, 18:25Poster session
Algorithmic differentiation (AD) allows to compute derivative of
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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... -
Muhammad Awais24/09/2024, 18:25
Neuromorphic Computing draws inspiration from the brain. Resistive Random-Access Memory (ReRAM) is gaining attention for its potential use in neuromorphic computing, mimicking neural networks for efficient data processing. Recent advancements introduce innovative materials such as metal oxides (e.g., HfO₂, Ta₂O₅), perovskites, and 2D materials like graphene, which enhance the scalability,...
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Kalina Dimitrova (University of Sofia - St. Kliment Ohridski (BG))24/09/2024, 18:25Poster session
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...
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Mr Alexander Kyuroson (Lulea Technical University)24/09/2024, 18:25
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Mr Isaac Morales Santana (Universidad de Granada)24/09/2024, 18:25
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Marta Lanzac Berrocal (Univ. of Valencia and CSIC (ES))24/09/2024, 18:25Poster session
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...
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Mr Samuel Álvarez Lueje (Universidad de Oviedo)24/09/2024, 18:25
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Samuele Grossi (Università degli studi di Genova & INFN sezione di Genova)24/09/2024, 18:25Poster session
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...
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Enrico Lupi (INFN Padova and University of Padova)24/09/2024, 18:25Poster session
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...
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Hector Gutierrez Arance (Univ. of Valencia and CSIC (ES))24/09/2024, 18:25Poster session
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).
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Experimental evaluation is ongoing, but preliminary assessments... -
Nawel SEKKAL (University Abou Bekr Belkaid Tlemcen)24/09/2024, 18:25Poster session
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...
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Andrea De Vita (Universita e INFN, Padova (IT))24/09/2024, 18:25Poster session
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...
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Francisco Hervas Alvarez (Univ. of Valencia and CSIC (ES))24/09/2024, 18:25Poster session
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...
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Mr Florian Bury (University of Bristol)25/09/2024, 09:00Muography
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...
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Angel Bueno Rodriguez (German Aerospace Center (DLR))25/09/2024, 09:20Muography
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...
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Mr William O'Donnell (University of Glasgow)25/09/2024, 09:40Muography
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...
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Alexander Schilling (University of Kaiserslautern-Landau (RPTU))25/09/2024, 10:00Computer Science
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...
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Stephen Casey (NASA Kennedy Space Center)25/09/2024, 10:20Computer Science
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:
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When performing physical simulations, there is a tradeoff between accuracy and computation time. For example, atomic-scale simulations are highly accurate but... -
Eric Anton Moreno (Massachusetts Institute of Technology (US))25/09/2024, 10:40Astroparticle Physics
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...
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Vassil Vasilev (Princeton University (US))25/09/2024, 11:30Computer Science
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...
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Maksym Andriichuk25/09/2024, 11:50Computer Science
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...
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Atell-Yehor Krasnopolski25/09/2024, 12:10Computer Science
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...
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Christian Glaser (Uppsala University), Emma Torro Pastor (Univ. of Valencia and CSIC (ES)), Lisa Kusch (TU Eindhoven)25/09/2024, 12:30
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Mr Ezzat Elmoujarkach (Universität zu Lübeck - Institute of Medical Engineering)25/09/2024, 14:20Medical Physics
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...
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Karol Brzezinski (IFIC (CSIC-UV))25/09/2024, 14:40Medical Physics
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)...
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José Andrés Avellaneda González Not Supplied25/09/2024, 15:00Medical Physics
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...
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Javier Pérez Curbelo (Instituto de Física Corpuscular (IFIC) (CSCI-UV))25/09/2024, 15:20Medical Physics
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...
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Brij Kishor Jashal (Rutherford Appleton Laboratory)Poster session
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...
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Dorian Amaral (Rice University)Astroparticle Physics
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...
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Ludger Paehler (Technical University of Munich)Computer Science
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...
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Mr Kamran Ahmad (National Centre for Physics (PK))Poster session
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...
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