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Vittorio Palladino

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Computer Vision Artificial Intelligence hep-ex Machine Learning physics.acc-ph physics.ins-det

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preprint2026arXiv

DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imaging with Quantum Detectors

Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensity measurements. While deep learning approaches have achieved promising results on static scenes, two critical limitations remain unaddressed: existing architectures fail to exploit temporal coherence across frames, leaving dynamic ghost imaging largely unsolved, and they assume additive Gaussian noise models that do not reflect the true Poissonian statistics of real single-photon hardware. We present DynGhost (Dynamic Ghost Imaging Transformer), a transformer architecture that addresses both limitations through alternating spatial and temporal attention blocks. Our quantum-aware training framework, based on physically accurate detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization, resolves the distribution shift that causes classical models to fail under realistic hardware constraints. Experiments across multiple benchmarks demonstrate that DynGhost outperforms both traditional reconstruction methods and existing deep learning architectures, with particular gains in dynamic and photon-starved settings.

preprint2026arXiv

EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving

As large language models span dense, mixture-of-experts, and state-space architectures and are deployed on heterogeneous accelerators under increasingly diverse multimodal workloads, optimising inference energy has become as critical as optimizing latency and throughput. Existing approaches either treat latency as an energy proxy or rely on data-hungry black-box surrogates. Both fail under varying parallelism strategies: latency and energy optima diverge in over 20% of configurations we tested, and black-box surrogates require hundreds of profiling samples to generalize across model families and hardware. We present EnergyLens, which uses symbolic regression as a structure-discovery tool over profiling data to derive a single twelve-parameter closed-form energy model expressed in terms of system properties such as degree of parallelism, batch size, and sequence length. Unlike black-box surrogates, EnergyLens decouples tensor and pipeline parallelism contributions and separates prefill from decode energy, making its predictions physically interpretable and actionable. Fitted from as few as 50 profiling measurements, EnergyLens achieves 88.2% Top-1 configuration selection accuracy across many evaluation scenarios compared to 60.9% for the closest prior analytical baseline, matches the predictive accuracy of ensemble ML methods with 10x fewer profiling samples, and extrapolates reliably to unseen batch sizes and hardware platforms without structural modification, making it a practical, interpretable tool for energy-optimal LLM deployment.

preprint2012arXiv

European Strategy for Accelerator-Based Neutrino Physics

Massive neutrinos reveal physics beyond the Standard Model, which could have deep consequences for our understanding of the Universe. Their study should therefore receive the highest level of priority in the European Strategy. The discovery and study of leptonic CP violation and precision studies of the transitions between neutrino flavours require high intensity, high precision, long baseline accelerator neutrino experiments. The community of European neutrino physicists involved in oscillation experiments is strong enough to support a major neutrino long baseline project in Europe, and has an ambitious, competitive and coherent vision to propose. Following the 2006 European Strategy for Particle Physics (ESPP) recommendations, two complementary design studies have been carried out: LAGUNA/LBNO, focused on deep underground detector sites, and EUROnu, focused on high intensity neutrino facilities. LAGUNA LBNO recommends, as first step, a conventional neutrino beam CN2PY from a CERN SPS North Area Neutrino Facility (NANF) aimed at the Pyhasalmi mine in Finland. A sterile neutrino search experiment which could also be situated in the CERN north area has been proposed (ICARUS-NESSIE) using a two detector set-up, allowing a definitive answer to the 20 year old question open by the LSND experiment. EUROnu concluded that a 10 GeV Neutrino Factory, aimed at a magnetized neutrino detector situated, also, at a baseline of around 2200 km (+-30%), would constitute the ultimate neutrino facility; it recommends that the next 5 years be devoted to the R&D, preparatory experiments and implementation study, in view of a proposal before the next ESPP update. The coherence and quality of this program calls for the continuation of neutrino beams at CERN after the CNGS, and for a high priority support from CERN and the member states to the experiments and R&D program.

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