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# Continuous normalizing flow

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  1. arXiv

    Quasar periodicity tests drop the DRW null: Nullasar learns noise from light curves and reaches AUC 0.9648 on AR(2) data

    Quasar variability is usually tested for periodic signals against a damped random walk (DRW) null, and a mis-specified noise model biases the significance estimates. Nullasar uses flow matching to learn the statistical structure of stochastic variability from an ensemble of synthetic light curves without assuming a noise model, then generates null realizations conditioned on a single observed curve. On synthetic AR(2) light curves its periodicity-detection AUC is 0.9648, averaged over three independently trained models, above 0.9180 for fitting AR(2) per curve; in the AR(1) case it matches fitting the correct DRW model.

  2. SciPost Physics

    AllShowers unifies calorimeter shower simulation for electrons, photons, and charged and neutral hadrons in one generative model, surpassing prior single-particle models on hadronic showers

    AllShowers is a continuous normalizing flow generative model with a Transformer architecture that simulates calorimeter showers for electrons, photons, and charged and neutral hadrons in the highly granular ILD detector using a single model, covering a wide range of incident energies and angles without retraining and surpassing previous single-particle-type models in hadronic shower fidelity.

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