Changes for page Xabier Cid Vidal and Alejandro Novo Cal

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1		-=== Communicating Generative Machine Learning Models for Neutron Tomography Simulation ===
2		-==== Supervisor: María Pereira Martínez
	1	+=== Machine Learning for Axion-Like Particles and Quirks Searches at LHCb ===
	2	+==== Supervisor: Alejandro Novo Cal
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5		-Neutron tomography is an innovative technique for the detailed analysis of dense materials, allowing for the detection of internal defects and chemical composition without destroying the sample. Optimizing the main detector of the tomograph requires simulations that demand high computational resources. To address this, generative machine learning models offer a revolutionary approach to speed up simulations by learning to mimic the detector's response under different experimental conditions. In this project, the student will implement and train a generative neural network using Python libraries (like TensorFlow or PyTorch) to simulate the neutron tomograph and validate the model against traditional simulations.
	5	+The Standard Model requires new theories to explain phenomena such as the universe's baryonic asymmetry or dark matter. Popular proposals include Axion-Like Particles (ALPs) with detectable decay signatures, and exotically signed particles called Quirks that leave characteristic movement patterns in the detectors. The LHCb detector provides the precision needed to study these new particles. In this project, we will develop a Machine Learning algorithm to be applied in a specific search for ALPs and/or Quirks, studying its applicability depending on the mass and lifetime of the ALPs to improve the detection sensitivity of these new physics models.