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Pablo Barham Alzás

Publications and source records attributed to Pablo Barham Alzás.

2 recordsLinked to original sources

Continuum contribution to charged-current absorption of low-energy $ν_e$ on $^{40}$Ar

Accurate modeling of the absorption of tens-of-MeV $ν_e$ on $^{40}$Ar is needed to enable measurements of astrophysical neutrinos using large liquid argon time projection chamber (LArTPC) detectors, such as those planned for the Deep Underground Neutrino Experiment (DUNE). We revisit the MARLEY neutrino interaction model used in present estimates of DUNE sensitivity to supernova and solar neutrino signals. Multiple theoretical refinements are pursued, especially in the unbound continuum region of nuclear excitation energy. Inclusive charged-current neutrino-argon cross sections are calculated using a hybrid strategy. Nuclear transitions to unbound states are treated using a Hartree-Fock Continuum Random Phase Approximation (HF-CRPA) model, including forbidden contributions. Allowed transitions to low-lying discrete levels are also included using indirect measurements and approximate corrections for the momentum transfer dependence. Exclusive predictions are obtained by coupling these calculations with a statistical nuclear de-excitation model. The impact on observables of interest for DUNE and similar experiments is examined in terms of both total and differential cross sections. Our refined calculations predict a lower allowed portion of the cross section relative to the prior MARLEY model. At neutrino energies appreciably below 100 MeV, the inclusion of forbidden transitions does not fully compensate for the loss of allowed strength. For a representative neutrino burst from a galactic core-collapse supernova, our results suggest that MARLEY 1.2.0 overestimates the event yield in a DUNE-like detector by approximately 20%. However, because this overestimation is more severe at backwards angles, use of the charged-current $ν_e$-$^{40}$Ar reaction for supernova pointing may be more feasible than previously expected.

hep-ph↗

Differentiable nuclear deexcitation simulation for low energy neutrino physics

Neutrino-nucleus interactions play an important role in present and future neutrino experiments. The accurate simulation of these interactions at low energies ($<$100 MeV) is crucial for the detection and study of supernova, solar and atmospheric neutrinos. In particular, the reconstruction of the incoming neutrino properties depends on the ability to measure the products from the deexcitation of the final state nucleus after the initial neutrino-nucleus scattering reaction. A realistic nuclear deexcitation model that can correctly manage the theoretical uncertainties in the process is key to determine the response of a detector to low energy neutrinos and estimate the overall systematic uncertainties that affect it. Automatic differentiation frameworks like PyTorch or JAX, widely used in the field of Machine Learning, provide us the tools to compute exact gradients of the simulation outputs with respect to the model parameters. Such differentiable simulators can be applied in simulator tuning to match observed data and forward modeling to efficiently infer the impact of parameter distributions to the physics output, or for parameter inference acting as a likelihood estimator. This paradigm can pave the way to a fully differentiable analysis of the whole simulation chain or direct integration with preexisting Machine Learning tools. As a proof of concept, in this work we implement a fully differentiable nuclear deexcitation simulation based on a simplified version of the Hauser-Feshbach statistical emission model.

hep-ex↗