Moderadores
COMCHA
- Arantza Oyanguren (IFIC- Valencia)
- Luca Fiorini (IFIC / U. Valencia - CSIC)
COMCHA
- Luca Fiorini (IFIC / U. Valencia - CSIC)
- Arantza Oyanguren (IFIC- Valencia)
COMCHA
- Luca Fiorini (IFIC / U. Valencia - CSIC)
- Arantza Oyanguren (IFIC- Valencia)
NA64 is a fixed target experiment at CERN Super Proton Synchrotron accelerator using complementary high-energy beams: e$^{-/+}$, muon, and hadron beams. The experiment performed the first search for dark sectors using a high-energy muon beam and the novel missing-momentum technique, relying on a scintillator-based trigger. The signal signature consists of an outgoing muon that has lost a...
Integrating FPGA modules requires consistent structural connections, interface descriptions, and verification files. These descriptions can diverge when modules or synthesis-generated ports change. We present FORGE (Framework for Orchestrated RTL Generation and Evaluation), a Python framework that resolves module interface contracts and a declarative topology into a shared intermediate...
Measurements of proocessing power, power consumption and efficiency are an imperative for the the high luminosity LHC collider and future colliders.
Several studies have been conducted to understand how to optimize the
energy usage in terms of the computing architectures and the efficiency of the running algorithms.
Several architectures (CPUs, GPUs, and FPGAs) are evaluated to assess their...
Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions. In this work, we present a Machine Learning framework designed to emulate complex, often non-Gaussian likelihood landscapes using gradient-boosted regression trees (XGBoost). We discuss the...
In preparation for the High-Luminosity LHC, the CMS experiment is upgrading its Level-1 Trigger system to handle increased luminosity and pile-up. The new trigger system opens up a plethora of possibilities to detect non-conventional signatures such as those arising from long-lived particles (LLPs). In particular such LLPs may decay far away from the interaction point and decay to hadrons on...
The High-Energy Physics and Worldwide LHC Computing Grid communities face significant challenges in comprehending their global data flows through the world's research and education networks. The Scientific Network Tags (Scitags) initiative strives to tackle this issue through several strategies and has created a set of tools, standards and proof-of-concept demonstrators that have shown the...
KM3NeT is an underwater neutrino telescope consisting of two detectors, ARCA and ORCA, situated off the coast of Italy and France respectively, which detect neutrinos through Cherenkov radiation. The detectors, each an array of vertical detection lines, are being deployed while already taking data.
Neutrino oscillations, implying neutrino masses, are clear evidence of physics beyond the...
Parton Distribution Functions (PDFs) are extracted from experimental data through high-dimensional global fits that combine perturbative-QCD calculations, flexible parametrizations, correlated systematic uncertainties, and statistical inference. As experimental precision improves, technical choices like dataset selection and theory settings become crucial.
However, one must not forget...
We present an update on our prototype Retrieval-Augmented Generation (RAG) and agentic LLM tool designed to accelerate and support high-energy physics analyses. As a case study, we applied the system to the published 2016 Λb → Λγ Run 2 LHCb analysis. Reproducing legacy workflows is often slow and error-prone due to fragmented code, dispersed documentation, personnel turnover, and software...
Designing optimal experiments in nuclear and particle physics is often a challenging and computationally expensive task. In this context, machine learning and differentiable programming are rapidly emerging as powerful tools for the end-to-end optimization of detector parameters.
In this work, we present the machine learning optimization of a neutron tomography system based on a stack of...
