arXiv subjects

João A. Gonçalves

Publications and source records attributed to João A. Gonçalves.

2 recordsLinked to original sources

arXiv2026

On the Codesign of Scientific Experiments and Industrial Systems

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

physics.ins-det↗

arXiv2025

All is More: Energy Flow Networks for Jet Quenching

Jet quenching, the modification of jets by the quark-gluon plasma in heavy-ion collisions, provides a sensitive probe of the properties of the medium. A jet-by-jet discrimination study between proton-proton and lead-lead jets using energy flow networks and simple baselines, explicitly retaining medium response and underlying event contamination is presented. As references, linear discriminants and neural networks have been trained on high-level observables such as $N$-subjettiness and energy flow polynomials, including an extended energy flow polynomial set, in order to quantify the achievable performance without constituent-level learning. Energy flow networks are then trained on jet constituents and extended to observable-enhanced energy flow networks that concatenate standardized $N$-subjettiness and/or energy flow polynomials to the energy flow network latent space. In the realistic scenario, including both underlying event contamination and medium response, observable-enhanced energy flow networks set state-of-the-art performance with receiver operating characteristic area under the curve of $\simeq 0.83$, improving markedly over linear and non-linear baselines and previous work with different architectures. Finally, results from moment energy flow networks, an energy flow network variant that attains comparable area under the curve with a substantially more compact and interpretable latent space are shown. These results establish energy-flow-network-based approaches (especially when enhanced with physics-motivated observables) as practical and robust tools for jet-quenching studies.

hep-ph↗