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Showing 1–5 of 5 results
Advanced filters: Author: Virginia D’Auria Clear advanced filters

  • Modern neutrino experiments require precise tuning of energy response parameters, a task complicated by the parameters’ nonlinear behavior and strong correlations. The authors present neural density estimators using normalizing flows and transformers integrating them with Bayesian nested sampling to achieve near-zero systematic biases and uncertainties limited only by statistics, offering a flexible framework for particle physics applications

    • Arsenii Gavrikov
    • Andrea Serafini
    • Lucia Votano
    ResearchOpen Access
    Communications Physics
    P: 1-18

  • The rat is a widely-used model for human biology, but we must be aware of metabolic differences. Here, the authors reconstruct the genome-scale metabolic network of the rat, and after reconciling it with an improved human metabolic model, demonstrate the power of the models to integrate toxicogenomics data, providing species-specific biomarker predictions in response to a panel of drugs.

    • Edik M. Blais
    • Kristopher D. Rawls
    • Jason A. Papin
    ResearchOpen Access
    Nature Communications
    Volume: 8, P: 1-15

  • The DarkSide-20k collaboration reports the sensitivity of its detector, currently under construction, to models predicting light dark matter particles. This includes Weakly Interacting Massive Particles and particles interacting with bound electrons of argon atoms.

    • F. Acerbi
    • P. Adhikari
    • M. P. Zykova
    ResearchOpen Access
    Communications Physics
    Volume: 7, P: 1-9