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Summer Fellowships

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1 -Xabier Cid Vidal - Pablo Eduardo Menéndez-Valdés Pérez
1 +Xabier Cid Vidal and Pablo Eduardo Menéndez-Valdés Pérez
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1 -=== Generative Models for Fast Simulation of a Neutron Tomography Detector ===
2 -==== Supervisor: María Pereira Martínez
1 +=== Classifiers for LLPs searches at LHCb ===
2 +==== Supervisor: Pablo Eduardo Menéndez-Valdés Pérez
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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 +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.
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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 +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.