Related papers: First Estimation of Model Parameters for Neutrino-…
The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) was designed to reconstruct neutrino events from the Fermilab Booster Neutrino Beam (BNB) with the parallel goals of measuring neutron production in interactions with oxygen…
Simulation-based Bayesian inference (SBI) can be used to estimate the parameters of complex mechanistic models given observed model outputs without requiring access to explicit likelihood evaluations. A prime example for the application of…
A substantial fraction of systematic uncertainties in neutrino oscillation experiments stems from the lack of precision in modeling the nucleus when describing the neutrino-nucleus interactions. The Spectral Function (SF) model features a…
The Booster Neutrino Experiment (MiniBooNE) searches for numu-to-nue oscillations using the O(1 GeV) neutrino beam produced by the Booster synchrotron at the Fermi National Accelerator Laboratory (FNAL). The Booster delivers protons with 8…
Precise, accurate neutrino flux predictions for neutrino beam experiments are crucial for physics results. Flux predictions for the Booster Neutrino Beam at Fermilab were first published in 2009 by the MiniBooNE collaboration. It is no…
Neutrino event generators are an essential tool needed for the extraction of neutrino mixing parameters, the mass hierarchy and a CP violating phase from long-baseline experiments. In this article I first describe the theoretical basis and…
Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likelihoods, it often requires a large number of simulations and has…
We study the effects of non-standard interactions on the oscillation pattern of atmospheric neutrinos. We use neutrino oscillograms as our main tool to infer the role of non-standard interactions (NSI) parameters at the probability level in…
Determination of neutrino mass ordering and precision measurement of neutrino oscillation parameters are the foremost goals of the JUNO experiment. Here, we explore the effects of scalar non-standard interactions (sNSI) on the electron…
Neutrino oscillations physics is entered in the precision era. In this context accelerator-based neutrino experiments need a reduction of systematic errors to the level of a few percent. Today one of the most important sources of systematic…
Detecting neutrinos and extracting the information they bring along is an ambitions task that requires a detailed understanding of neutrino-nucleus interactions over a broad energy range. We present calculations for quasi-elastic…
In recent years a number of new theoretical models have been implemented into Monte-Carlo neutrino interaction event generators. Being able to compare multiple model predictions is invaluable to the field, especially as it is unfortunately…
Since neutrinos can escape from dense regions without being deflected, they are promising candidates to study the new physics at the sources that produce them. With the increasing development of more sensitive detectors in the coming years,…
Neural networks are being extensively used for modelling data, especially in the case where no likelihood can be formulated. Although in the case of X-ray spectral fitting, the likelihood is known, we aim to investigate the neural networks…
Aided by advances in neural density estimation, considerable progress has been made in recent years towards a suite of simulation-based inference (SBI) methods capable of performing flexible, black-box, approximate Bayesian inference for…
Bayesian inference represents a principled way to incorporate Earth structure uncertainty in full-waveform moment tensor inversions, but traditional approaches generally require significant approximations that risk biasing the resulting…
Simulation-Based Inference (SBI) deals with statistical inference in problems where the data are generated from a system that is described by a complex stochastic simulator. The challenge for inference in these problems is that the…
The challenges in neutrino-nucleus cross section modeling and its impact on neutrino oscillation experiments are widely recognized. However, a comprehensive and theoretically robust estimation of cross section uncertainties has been…
Simulation-based inference (SBI) enables parameter inference by training neural networks on forward simulations. It is being applied both for intractable likelihoods as well as under time constraints on the posterior sampling. After…
Recent neutrino oscillation experiments used high atomic number nuclear targets to attain sufficient interaction rates. The use of these complex targets introduced systematic uncertainties due to the nuclear effects in the experimental…