This workshop brings together the international community working on solenoidal spectrometers to discuss recent advances and future directions. We aim to showcase high-quality contributions that reflect the current state of the art, both in terms of physics cases and instrumentation developments.

Through a combination of invited talks, selected oral presentations, and posters, the program will highlight the most relevant and impactful work in the field. We look forward to your participation in fostering fruitful discussions and new collaborations.

The workshop will cover a broad range of topics, including:

• Physics with solenoidal spectrometers – applications in experimental studies with emphasis on low-energy nuclear physics.
• Technology and operation of solenoidal spectrometers – active targets (e.g., TPCs), silicon detector systems, and other detection technologies.
• Data acquisition systems – architecture, design, and integration for complex experiments.
• Data analysis techniques and software tools – methods for event reconstruction, calibration, and interpretation.
• Theoretical aspects in low-energy nuclear physics – state-of-the-art models and frameworks.

More info at: https://indico.global/event/15272/

 Stellar clusters are critical constituents within galaxies. Widely studied in the Local Universe (albeit not understood), stunning observations by the James Webb Space Telescope are revealing the presence of massive star clusters in a variety of galactic environments across cosmic time. These observations raise questions about the formation, interaction and eventual fate of star clusters within galaxies: Does the process of star formation change over cosmic history? How will the energy released by massive stars in the massive star clusters affect the evolution of the galaxy? Can those clusters survive to the present day or will they disrupt into the stellar body of the galaxy? And are bright star clusters good tracers of their environment?

In the first part of my talk I will review current numerical approaches to model the intertwined evolution of star clusters and galaxies, and I will discuss their perks and caveats. I will also present SCALES, a novel method to model clusters built via gas accretion and hierarchical merging with sub-clusters implemented within the hydrodynamical code GIZMO, and I will discuss what physics are required to reproduce the formation of star cluster populations.

For the second part of my talk, I will present current and upcoming efforts of expanding our knowledge of star cluster populations in galaxy clusters to earlier cosmic times using JWST/NIRCam imaging, and how can these datasets be used to map the mass distribution in these environments.

Director: Javier Mas Solé

The International Meeting on Fundamental Physics is the Spanish Symposium on high energy particle physics with the longest tradition. Since its creation in 1973, it has been organized on an annual basis by each of the main Spanish groups in turn. Its scientific programs have included dedicated reviews with the latest results in the fields of particle and astroparticle physics and cosmology.

The 2025 edition is organised by the Instituto Galego de Altas Enerxías (IGFAE) and the Universidade de Santiago de Compostela (USC).

Link to the event webpage: https://indico.global/event/14697/

Symposium

The first day of the meeting will be dedicated to the European Strategy of Particle Physics (ESPP) update. We will hold a special symposium gathering the conclusions from the recent Venice meeting and discussing the next steps, with a focus on the Spanish HEP community.

 We are going to be discussing two central results for the AdS/CFT. One is from the field theory side, namely the reduction of the 10d, N=1 SYM to 4d, N=4 SYM and the so-called Breitenlohner-Freedman bound which has to do with the stability of a given supergravity background. 

The IGFAE Summer Fellows that have been working at IGFAE during September 2025 will make a presentation of their work. The event will take place at Colegio Mayor Universitario San Agustín.

Andrés Núñez Bernárdez | Katherine Ocampo Franco - Machine Learning Tools for Precision Tests of the Standard Model at LHCb

Leptons are a fundamental class in the Standard Model (SM) of particle physics. According to this model, there is no apparent difference between leptons apart from their masses - this is known as Lepton Flavour Universality (LFU). However, recent measurements of LFU observables show significant disagreement with respect to SM predictions, which could suggest new physics beyond SM. To reduce their uncertainty and solve the current situation, these LFU observables need to be measured with great precision. In particular, the experimental data has to be carefully processed to separate “signal” and “background” events. This selection includes Machine Learning (ML) tools that improve the efficiency of this procedure. This project will introduce the student to the use of ML in high-energy physics. In particular, the student will: Analyse real data from LHCb, one of the main experiments of the Large Hadron Collider at CERN (the world’s largest and most powerful particle accelerator). Learn how ML is used for measuring LFU observables with better accuracy, by implementing tools such as neural networks, decision trees and more. Train and optimise their own ML models with different Python environments, which they could apply to other physical phenomena and beyond.

Andrés has been focused on ML study for B->D* 3Pi decays. This decay is used as the normalization channel for the semileptonic ratio R(D*), one of the LFU observables that is studied at LHCb. In this particular case, the D* meson decays into a pion and a D meson, which then decays into a kaon and three pions.

Katherine has worked on ML study for B->D 3Pi decays: a related LFU observable is the semileptonic ratio of branching fractions R(D). One possible choice for the normalization channel is B->D 3Pi, where D decays into two pions and a kaon. 

Supervisor: Julio Nóvoa Fernández.

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Álvaro Iturbe Jabaloyes - Energy Transfer and Structure Formation in Hamiltonian equations

This project investigates the fundamental mechanisms of energy transfer and the emergence of coherent structures in Hamiltonian equations, which describe a wide range of physical systems including aspects of general relativity, cold atom systems, and fluid dynamics. The central aim is to understand how nonlinearity and dispersion interact to redistribute energy across scales, leading to the formation of structures such as solitons, condensates, and even black holes. The project contributes to a deeper understanding of nonlinear Hamiltonian dynamics and the universal behaviors that emerge from energy-conserving laws. These questions have significant implications for both theoretical and applied physics, including the prediction of turbulent behavior and the development of control strategies in nonlinear media physics is preferred.

Supervisor: Anxo Fariña Biasi

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Enrique Rodríguez Ramos - Classifiers for LLPs searches at LHCb

Several Beyond the Standard Model (BSM) theories predict the existence of Long-Lived Particles (LLPs), which may largely escape detection by current particle physics experiments. However, these particles could occasionally decay into known elementary particles—such as muons—making their detection possible under certain conditions.

The LHCb detector provides excellent precision in reconstructing both production and decay vertices. Leveraging this capability with Machine Learning (ML) techniques can significantly enhance the sensitivity to LLP signatures. In this project, we aim to develop and evaluate such ML tools by comparing the performance of various algorithms within the XGBoost library.

Supervisor: Pablo Eduardo Menéndez-Valdés

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Luis Arcas Morcillo - Machine Learning techniques to look for rare physical signals

The Standard Model of Particle Physics is the most precise description of the subatomic world that exists. Despite that, we continue to subject it to strenuous experimental tests, which include the searches for physical processes that are very unlikely to occur. An observation with a higher probability than predicted would be a clear sign of new physics. Machine Learning provides us an excellent toolkit to analyze these very rare processes. In this project, we will take data collected by the LHCb experiment, one of the four main detectors at the Large Hadron Collider at CERN, and use Machine Learning techniques to look for rare physical signals.

Supervisor: Miguel Fernández Gómez

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Juan Esteban Suárez Avendaño

This project is an introduction to to loop corrections in Quantum Field Theory (QFT). 

Focusing on the simplest QED process: electron-positron annihilation into a muon pair, the student has analyzed both the Leading-Order (LO) Feynman diagram and its Next-to-Leading-Order (NLO) correction with photon radiation. 

The aim was to develop familiarity with tree- and loop-level calculations, understand the origin of singularities, and apply techniques to handle divergences.

Expected outcomes:

• Understanding of Feynman diagram rules and their application to QED processes 
• Tree-level and one-loop calculations for e⁺e⁻ → μ⁺μ⁻
• Cross-section computation and application of dimensional regularization
• Optional: determination of the tau lepton mass from cross-section ratios
• Optional: estimation of Nc from the ratio of hadronic to leptonic cross-sections

Supervisor: Lin Chen

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Brais Otero Lema - Generative Models for Fast Simulation of a Neutron Tomography Detector

Neutron tomography is an innovative technique for the detailed analysis of dense materials such as metals and alloys. Thanks to the high penetration capability of neutrons, it is possible to detect internal manufacturing defects and, in certain cases, determine the chemical composition of a sample without destroying it. To achieve high precision in neutron tomography, optimizing the components of the tomograph is essential. One of the main challenges lies in the simulation and optimization of the primary detector, a process that requires significant computational resources. To address this, the use of generative machine learning models offers a groundbreaking approach: these models can greatly accelerate simulations when trained to mimic the detector’s response under various experimental conditions, drastically reducing computational cost. The aim of this project is to explore the potential of generative models in the simulation and optimization of the neutron tomography system.

Objectives:

• Gain an understanding of how the neutron tomography system and its main detector work.
• Become familiar with fundamental machine learning concepts and implement a generative neural network to replicate the detector’s response under different configurations.
• Develop programming skills by working with Python and commonly used machine learning libraries such as scikit-learn, TensorFlow and PyTorch, with the potential to explore GPU-accelerated training techniques.

Supervisor: María Pereira Martínez

Directors: Carlos A. Salgado e Bin Wu

Tribunal: Elena G. Ferreiro, François Gelis e Liliana Apolinário

Neutrinos represent one of the most intricate areas in particle physics and provide evidence for physics beyond the Standard Model. They encode significant implications for our understanding of the Universe and potentially conceal pathways to new physics. New-generation detectors and innovative reconstruction techniques bring us into the precision era of neutrino oscillations. Yet, a comprehensive analysis strategy is still needed to tackle the most challenging and fundamental measurements: the neutrino mass ordering, essential for determining neutrino masses, and the lepton charge-parity asymmetry (CP phase), expected to be key in explaining the baryon asymmetry of the Universe.

Various detectors exploiting different technologies are collecting data to achieve these physics objectives. Combined analyses of multiple datasets maximize the sensitivity achievable in these experiments. However, state-of-the-art methods fail to harness the information and synergies of the data. This is the motivation, to address these hurdles from the ground up with an innovative and comprehensive analysis program, exploring advanced statistical methods, and enhancing the performance and capabilities of neutrino analyses.

Additionally, this project will develop in parallel with the deployment of the Hyper-Kamiokande detector (HK). HK is the third generation of neutrino observatories located near Kamioka, Japan. Instrumented with over 20,000 photomultipliers and containing a quarter of a megaton of ultra-pure water, the detector will be exposed to a high-intensity neutrino beam, with the main goal of measuring the leptonic CP phase.

In this talk, we will navigate between both projects to craft the precise measurement of the remaining neutrino properties.