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Leonardo Lima da Silva

Publications and source records attributed to Leonardo Lima da Silva.

2 recordsLinked to original sources

In Situ Coherence Measurements of Scattered Light in Magnetically Trapped Cold Atomic Clouds: Probe-Driven Atomic Dynamics

The use of temporal correlations in scattered photons to probe the microscopic dynamics of ultracold quantum gases has emerged as a powerful, minimally destructive approach for in situ analysis. Here, we demonstrate that temporal coherence spectroscopy can quantitatively characterize atomic motion in a magnetic trap, despite the perturbative effects of the probing light. By measuring the first-order correlation function g (1) ($τ$ ) of light scattered by a 87 Rb cloud confined in a quadrupole trap, we identify radiation-pressure-induced acceleration and heating as the origin of the apparent discrepancy between coherence spectra and temperatures inferred from time-of-flight measurements. A simple dynamical model incorporating these effects restores agreement between theory and experiment, establishing coherence spectroscopy as a reliable in situ probe of velocity distributions in trapped atomic ensembles. Our results pave the way for time-resolved studies of nonequilibrium dynamics and thermalization processes in confined cold gases, complementing conventional destructive imaging techniques.

physics.atom-ph↗

Jet Quenching Identification via Supervised Learning in Simulated Heavy-Ion Collisions

Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information about the complex dynamics of parton-medium interactions during hard scatterings. In this work, we apply sequential machine learning architectures to the jet declustering history tree, achieving improved classification performance compared with static models that learn only from a single stage of the jet evolution. We find that models trained on different medium implementations exhibit meaningful performance modification under cross-domain validation, indicating that machine learning is sensitive to implementation-specific features that traditional observables may not resolve. These results suggest new opportunities for using machine learning as an analysis tool to overcome some of the limitations of traditional jet-modification studies.

hep-ph↗