GPU-accelerated spectrum reweighting for new-physics searches in solar neutrino--electron scattering

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Original abstract

Precision measurements of neutrino--electron elastic scattering provide low-energy tests of weak interactions and beyond-the-Standard-Model effects. Non-standard interactions (NSIs) and an anomalous neutrino magnetic moment modify the differential cross section through different kinematic terms, but both can alter the normalization and shape of the recoil-electron spectrum. Likelihood tests are computationally costly when each parameter point requires the recoil spectrum to be propagated through a detector response obtained from Monte Carlo (MC) simulation. We present a GPU-accelerated spectrum-reweighting framework that avoids regenerating detector MC samples for each new-physics parameter point. Bin-to-bin weights are applied at the recoil-spectrum level and folded with a fixed two-dimensional response model in recoil and reconstructed energy. This keeps the detector response inside the likelihood calculation while reducing each parameter update to operations on precomputed spectra and response kernels. The implementation uses NVIDIA Thrust transformation--reduction primitives and is compiled from a common source for CUDA and OpenMP back ends. In the benchmarks considered here, one likelihood evaluation takes ${\sim}87$ ms on an NVIDIA RTX 3080Ti and ${\sim}52$ ms on an NVIDIA A30X; the latter gives a $58\times$ speedup over a single CPU thread and ${\sim}2.5\times$ over a fully loaded 64-thread CPU. The consumer-GPU result demonstrates that interactive parameter scans are feasible on a single workstation. The main acceleration, however, comes from avoiding detector-MC regeneration at each parameter point rather than from GPU execution alone. The framework applies to neutrino--electron scattering analyses in which the new-physics dependence can be represented by reweighting an existing recoil spectrum, including flavor NSI and magnetic-moment cases studied here.

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• Official / arXiv page
• PDF from original source

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