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Summer Fellowships

Last modified by Ricardo Julio Rodríguez Fernández on 2025/07/10 20:58
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edited by Ricardo Julio Rodríguez Fernández
on 2025/07/10 16:07
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To version 5.1
edited by Ricardo Julio Rodríguez Fernández
on 2025/07/10 20:58
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Summary

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1 -Xabier Cid Vidal and María Pereira Martínez
1 +Xabier Cid Vidal and Miguel Fernández Gómez
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1 -=== Generative Models for Fast Simulation of a Neutron Tomography Detector ===
2 -==== Supervisor: María Pereira Martínez
1 +=== Machine Learning techniques to look for rare physical signals ===
2 +==== Supervisor: Miguel Fernández Gómez
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5 -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.
5 +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.
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7 -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.
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9 -The aim of this project is to explore the potential of generative models in the simulation and optimization of the neutron tomography system. In this context, the summer student will:
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11 -* Gain an understanding of how the neutron tomography system and its main detector work.
12 -* Become familiar with fundamental machine learning concepts and implement a generative neural network to replicate the detector’s response under different configurations.
13 -* 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.
7 +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.