No abstract

The interpretation of present and future neutrino experiments requires accurate theoretical predictions for neutrino-nucleus scattering rates. Neutrino structure functions can be reliably evaluated in the deep-inelastic scattering regime within the perturbative QCD (pQCD) framework. At low momentum transfers (Q2 ≲ few GeV2), inelastic structure functions are however affected by large uncertainties which distort event rate predictions for neutrino energies Eν up to the TeV scale. Here we present a determination of neutrino inelastic structure functions valid for the complete range of energies relevant for phenomenology, from the GeV region entering oscillation analyses to the multi-EeV region accessible at neutrino telescopes. Our NNSFν approach combines a machine-learning parametrisation of experimental data with pQCD calculations based on state-of-the-art analyses of proton and nuclear parton distributions (PDFs). We compare our determination to other calculations, in particular to the popular Bodek-Yang model. We provide updated predictions for inclusive cross sections for a range of energies and target nuclei, including those relevant for LHC far-forward neutrino experiments such as FASERν, SND@LHC, and the Forward Physics Facility. The NNSFν determination is made available as fast interpolation LHAPDF grids, and it can be accessed both through an independent driver code and directly interfaced to neutrino event generators such as GENIE.

We present a collection of tools automating the efficient computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. These programs, however, accept inputs and produce outputs that are usually very different from each other. The industrialization of theory predictions is achieved by a framework which harmonizes inputs (runcard, parameter settings), standardizes outputs (in the form of grids), produces reusable intermediate objects, and carefully tracks all meta data required to reproduce the computation. Parameter searches and fitting of non-perturbative objects are exemplary use cases that require a full or partial re-computation of theory predictions and will thus benefit of such a toolset. As an example application we present a study of the impact of replacing NNLO QCD K-factors in a PDF fit with the exact NNLO predictions.

We discuss the sensitivity of theoretical predictions of observables used in searches for new physics to parton distributions (PDFs) at large momentum fraction x. Specifically, we consider the neutral-current Drell-Yan production of gauge bosons with invariant masses in the TeV range, for which the forwardbackward asymmetry of charged leptons from the decay of the gauge boson in its rest frame is a traditional probe of new physics. We show that the qualitative behaviour of the asymmetry depends strongly on the assumptions made in determining the underlying PDFs. We discuss and compare the large-x behaviour of various different PDF sets, and find that they differ significantly. Consequently, the shape of the asymmetry observed at lower dilepton invariant masses, where all PDF sets are in reasonable agreement because of the presence of experimental constraints, is not necessarily reproduced at large masses where the PDFs are mostly unconstrained by data. It follows that the shape of the asymmetry at high masses may depend on assumptions made in the PDF parametrization, and thus deviations from the traditionally expected behaviour cannot be taken as a reliable indication of new physics. We demonstrate that forward-backward asymmetry measurements could help in constraining PDFs at large x and discuss the accuracy that would be required to disentangle the effects of new physics from uncertainties in the PDFs in this region.

The determination of Parton Distribution Functions from a finite set of data is a typical example of an inverse problem. Inverse problems are notoriously difficult to solve, in particular when a robust determination of the uncertainty in the result is needed. We present a Bayesian framework to deal with this problem and discuss first results from a closure test.