Thu·Oct 6, 2022Thursday·October 6, 2022

1. 01*

   Rediscovery of Numerical Lüscher's Formula from the Neural Network

   Yu Lu🇨🇳 · Yi-Jia Wang🇨🇳 · Ying Chen🇨🇳 · Jia-Jun Wu🇨🇳

   We present that by predicting the spectrum in discrete space from the phase shift in continuous space, the neural network can remarkably reproduce the numerical Lüscher's formula to a high precision. The model-independent property of the Lüscher's formula is naturally realized by the generalizability of the neural network. This exhibits the great potential of the neural network to extract model-independent relation between model-dependent quantities, and this data-driven approach could greatly facilitate the discovery of the physical principles underneath the intricate data.

   hep-latcs.LGhep-phhep-thCPC(2024)·4 citations
2. 02*

   Improved local truncation schemes for the higher-order tensor renormalization group method

   Jacques Bloch🇩🇪 · Robert Lohmayer🇩🇪 · Maximilian Meister🇩🇪 · Michael Nunhofer🇩🇪

   The higher-order tensor renormalization group is a tensor-network method providing estimates for the partition function and thermodynamical observables of classical and quantum systems in thermal equilibrium. At every step of the iterative blocking procedure, the coarse-grid tensor is truncated to keep the tensor dimension under control. For a consistent tensor blocking procedure, it is crucial that the forward and backward tensor modes are projected on the same lower dimensional subspaces. In this paper we present two methods, the SuperQ and the iterative SuperQ method, to construct tensor truncations that reduce or even minimize the local approximation errors, while satisfying this constraint.

   hep-latcond-mat.stat-mechNPB(2023)·5 citations
3. 03*

   Form factor and model dependence in neutrino-nucleus cross section predictions

   Daniel Simons🇺🇸 · Noah Steinberg🇺🇸 · Alessandro Lovato🇺🇸 · Yannick Meurice🇺🇸 · Noemi Rocco🇺🇸 · Michael Wagman🇺🇸

   To achieve its design goals, the next generation of neutrino-oscillation accelerator experiments requires percent-level predictions of neutrino-nucleus cross sections supplemented by robust estimates of the theoretical uncertainties involved. The latter arise from both approximations in solving the nuclear many-body problem and in the determination of the single- and few-nucleon quantities taken as input by many-body methods. To quantify both types of uncertainty, we compute flux-averaged double-differential cross sections using the Green's function Monte Carlo and spectral function methods as well as different parameterizations of the nucleon axial form factors based on either deuterium bubble-chamber data or lattice quantum chromodynamics calculations. The cross-section results are compared with available experimental data from the MiniBooNE and T2K collaborations. We also discuss the uncertainties associated with $N\to \Delta$ transition form factors that enter the two-body current operator. We quantify the relations between neutrino-nucleus cross section and nucleon form factor uncertainties. These relations enable us to determine the form factor precision targets required to achieve a given cross-section precision.

   ↳ hep-phhep-latnucl-thJ.Phys.G(2025)·26 citations

* Reconstructed cohort: no mailing for this day survives in the archive. Papers are grouped by their submission times and arXiv's announcement cut-off, assuming announcement without delay; positions follow identifier order. Validated at ~91% exact-day agreement against the archived era.