arXiv subjects

G. N. Perdue

Publications and source records attributed to G. N. Perdue.

At least 37 records · Page 2Linked to original sources

arXiv2023

Simultaneous measurement of muon neutrino quasielastic-like cross sections on CH, C, water, Fe, and Pb as a function of muon kinematics at MINERvA

This paper presents the first simultaneous measurement of the quasielastic-like neutrino-nucleus cross sections on C, water, Fe, Pb and scintillator (hydrocarbon or CH) as a function of longitudinal and transverse muon momentum. The ratio of cross sections per nucleon between Pb and CH is always above unity and has a characteristic shape as a function of transverse muon momentum that evolves slowly as a function of longitudinal muon momentum. The ratio is constant versus longitudinal momentum within uncertainties above a longitudinal momentum of 4.5GeV/c. The cross section ratios to CH for C, water, and Fe remain roughly constant with increasing longitudinal momentum, and the ratios between water or C to CH do not have any significant deviation from unity. Both the overall cross section level and the shape for Pb and Fe as a function of transverse muon momentum are not reproduced by current neutrino event generators. These measurements provide a direct test of nuclear effects in quasielastic-like interactions, which are major contributors to long-baseline neutrino oscillation data samples.

hep-ex↗

arXiv2022

Measurement of inclusive charged-current $ν_μ$ cross sections as a function of muon kinematics at $ \sim6~GeV$ on hydrocarbon

MINERvA presents a new analysis of inclusive charged-current neutrino interactions on a hydrocarbon target. We report single and double-differential cross sections in muon transverse and longitudinal momentum. These measurements are compared to neutrino interaction generator predictions from GENIE, NuWro, GiBUU, and NEUT. In addition, comparisons against models with different treatments of multi-nucleon correlations, nuclear effects, resonant pion production, and deep inelastic scattering are presented. The data recorded corresponds to $10.61\times10^{20}$ protons on target with a peak neutrino energy of approximately 6 GeV. The higher energy and larger statistics of these data extend the kinematic range for model testing beyond previous MINERvA inclusive charged-current measurements. The results are not well modeled by several generator predictions using a variety of input models.

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arXiv2022

Improved constraint on the MINERvA medium energy neutrino flux using $\barνe^{-} \!\rightarrow \barνe^{-}$ data

Processes with precisely known cross sections, like neutrino electron elastic scattering ($νe^{-} \!\rightarrow νe^{-}$) and inverse muon decay ($ν_μe^{-} \!\rightarrow μ^{-} ν_e$) have been used by MINERvA to constrain the uncertainty on the NuMI neutrino beam flux. This work presents a new measurement of neutrino elastic scattering with electrons using the medium energy \numubar enhanced NuMI beam. A sample of 578 events after background subtraction is used in combination with the previous measurement on the \numu beam and the inverse muon decay measurement to reduce the uncertainty on the \numu flux in the \numu-enhanced beam from 7.6\% to 3.3\% and the \numubar flux in the \numubar-enhanced beam from 7.8\% to 4.7\%.

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arXiv2022

Measurement of inclusive charged-current $ν_{\numu}$ scattering on hydrocarbon at { } 6 GeV with low three-momentum transfer

The \minerva experiment reports double-differential cross-section measurements for $ν_μ$-carbon interactions with three-momentum transfer $|\vec{q}| < 1.2$ GeV obtained with medium energy exposures in the NuMI beam. These measurements are performed as a function of the three-momentum transfer and an energy transfer estimator called the available energy defined as the energy that would be visible in the detector. The double differential cross sections are compared to the GENIE and NuWro predictions along with the modified version of GENIE which incorporates new models for better agreement with earlier measurements from MINERvA. In these measurements, the quasi-elastic, resonance, and multi-nucleon knockout processes appear at different kinematics in this two-dimensional space. The results can be used to improve models for neutrino interactions needed by neutrino oscillation experiments.

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arXiv2022

Simultaneous measurement of proton and lepton kinematics in quasielastic-like $ν_μ$-hydrocarbon interactions from 2 to 20 GeV

Neutrino charged-current quasielastic-like scattering, a reaction category extensively used in neutrino oscillation measurements, probes nuclear effects that govern neutrino-nucleus interactions. This Letter reports the first measurement of the triple-differential cross section for $ν_μ$ quasielastic-like reactions using the hydrocarbon medium of the MINERvA detector exposed to a wide-band beam spanning 2 $\leq$ E$_ν\leq$ 20 GeV. The measurement maps the correlations among transverse and longitudinal muon momenta and summed proton kinetic energies, and compares them to predictions from a state-of-art simulation. Discrepancies are observed that likely reflect shortfalls with modeling of pion and nucleon intranuclear scattering and/or spectator nucleon ejection from struck nuclei. The separate determination of leptonic and hadronic variables can inform experimental approaches to neutrino-energy estimation.

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arXiv2022

Use of Neutrino Scattering Events with Low Hadronic Recoil to Inform Neutrino Flux and Detector Energy Scale

Charged-current neutrino interactions with low hadronic recoil ("low-nu") have a cross-section that is approximately constant versus neutrino energy. These interactions have been used to measure the shape of neutrino fluxes as a function of neutrino energy at accelerator-based neutrino experiments such as CCFR, NuTeV, MINOS and MINERvA. In this paper, we demonstrate that low-nu events can be used to measure parameters of neutrino flux and detector models and that utilization of event distributions over the upstream detector face can discriminate among parameters that affect the neutrino flux model. From fitting a large sample of low-nu events obtained by exposing MINERvA to the NuMI medium-energy beam, we find that the best-fit flux parameters are within their a priori uncertainties, but the energy scale of muons reconstructed in the MINOS detector is shifted by 3.6% (or 1.8 times the a priori uncertainty on that parameter). These fit results are now used in all MINERvA cross-section measurements, and this technique can be applied by other experiments operating at MINERvA energies, such as DUNE.

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arXiv2022

Neutral pion reconstruction using machine learning in the MINERvA experiment at $\langle E_ν\rangle \sim 6$ GeV

This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013-2017, with an average neutrino energy of $6$ GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two gammas from 71\% to 89\% and improves the efficiency of the reconstruction by approximately 40\%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with $\langle E_ν\rangle$ between 1-10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current $ν_e$ events arising from $ν_μ \rightarrow ν_{e}$ appearance.

hep-ex↗

arXiv2022

Constraint of the MINERvA Medium Energy Neutrino Flux using Neutrino-Electron Elastic Scattering

Elastic neutrino scattering on electrons is a precisely-known purely leptonic process that provides a standard candle for measuring neutrino flux in conventional neutrino beams. Using a total sample of 810 neutino-electron scatters after background subtraction, the measurement reduces the normalization uncertainty on the muon neutrino NuMI flux between 2 and 20 GeV from 7.5% to 3.9%. This is the most precise measurement of neutrino-electron scattering to date, will reduce uncertainties on MINERvA's absolute cross section measurements, and demonstrates a technique that can be used in future neutrino beams such as LBNF.

hep-ex↗

arXiv2022

Vertex finding in neutrino-nucleus interaction: A Model Architecture Comparison

We compare different neural network architectures for Machine Learning (ML) algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package "Multi-node Evolutionary Neural Networks for Deep Learning" (MENNDL), developed at Oak Ridge National Laboratory (ORNL). The two architectures resulted in a similar performance which suggests that the systematics associated with the optimized network architecture are small. Furthermore, we find that while the domain expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

hep-ex↗

arXiv2021

Constraining the NuMI neutrino flux using inverse muon decay reactions in MINERvA

Inverse muon decay, $ν_μe^-\toμ^-ν_e$, is a reaction whose cross-section can be predicted with very small uncertainties. It has a neutrino energy threshold of $\approx 11$ GeV and can be used to constrain the high-energy part of the flux in the NuMI neutrino beam. This reaction is the dominant source of events which only contain high-energy muons nearly parallel to the direction of the neutrino beam. We have isolated a sample of hundreds of such events in neutrino and anti-neutrino enhanced beams, and have constrained the predicted high-energy flux.

hep-ex↗

arXiv2021

Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.

astro-ph.GA↗

arXiv2021

Exploring Neutrino-Nucleus Interactions in the GeV Regime using MINERvA

With the advance of particle accelerator and detector technologies, the neutrino physics landscape is rapidly expanding. As neutrino oscillation experiments enter the intensity and precision frontiers, neutrino-nucleus interaction measurements are providing crucial input. MINERvA is an experiment at Fermilab dedicated to the study of neutrino-nucleus interactions in the regime of incident neutrino energies from one to few GeV. The experiment recorded neutrino and antineutrino scattering data with the NuMI beamline from 2009 to 2019 using the Low-Energy and Medium-Energy beams that peak at 3 GeV and 6 GeV, respectively. This article reviews the broad spectrum of interesting nuclear and particle physics that MINERvA investigations have illuminated. The newfound, detailed knowledge of neutrino interactions with nuclear targets thereby obtained is proving essential to continued progress in the neutrino physics sector.

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arXiv2021

An Error Analysis Toolkit for Binned Counting Experiments

We introduce the MINERvA Analysis Toolkit (MAT), a utility for centralizing the handling of systematic uncertainties in HEP analyses. The fundamental utilities of the toolkit are the MnvHnD, a powerful histogram container class, and the systematic Universe classes, which provide a modular implementation of the many universe error analysis approach. These products can be used stand-alone or as part of a complete error analysis prescription. They support the propagation of systematic uncertainty through all stages of analysis, and provide flexibility for an arbitrary level of user customization. This extensible solution to error analysis enables the standardization of systematic uncertainty definitions across an experiment and a transparent user interface to lower the barrier to entry for new analyzers.

hep-ex↗

arXiv2021

DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)$-$ and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ${\sim}20\%$. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

astro-ph.IM↗

arXiv2020

Domain adaptation techniques for improved cross-domain study of galaxy mergers

In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques. Here we demonstrate the use of two techniques - Maximum Mean Discrepancy (MMD) and adversarial training with Domain Adversarial Neural Networks (DANN) - for the classification of distant galaxy mergers from the Illustris-1 simulation, where the two domains presented differ only due to inclusion of observational noise. We show how the addition of either MMD or adversarial training greatly improves the performance of the classifier on the target domain when compared to conventional machine learning algorithms, thereby demonstrating great promise for their use in astronomy.

astro-ph.IM↗

arXiv2020

High-statistics measurement of neutrino quasielastic-like scattering at =~6 GeV on a hydrocarbon target

We measure neutrino charged current quasielastic-like scattering on hydrocarbon at high statistics using the wide-band NuMI beam with neutrino energy peaked at 6 GeV. The double-differential cross section is reported in terms of muon longitudinal and transverse momentum. Cross-section contours versus lepton momentum components are approximately described by a conventional generator-based simulation, however discrepancies are observed for transverse momenta above 0.5 GeV/c for longitudinal momentum ranges 3 to 5 GeV/c and 9 to 20 GeV/c. The single differential cross section versus momentum transfer squared ($dσ/dQ_{QE}^2$) is measured over a four-decade range of $Q^2$ that extends to $10~GeV^2$. The cross section turn-over and fall-off in the $Q^2$ range 0.3 to $10~GeV^2$ is not fully reproduced by generator predictions that rely on dipole form factors. Our measurement probes the axial-vector content of the hadronic current and complements the electromagnetic form factor data obtained using electron-nucleon elastic scattering. These results help oscillation experiments because they probe the importance of various correlations and final-state interaction effects within the nucleus, which have different effects on the visible energy in detectors.

hep-ex↗

arXiv2020

Double-Differential Inclusive Charged-Current $ν_μ$ Cross Sections on Hydrocarbon in MINERvA at $\langle E_ν \rangle \sim$ 3.5 GeV

MINERvA reports inclusive charged-current cross sections for muon neutrinos on hydrocarbon in the NuMI beamline. We measured the double-differential cross section in terms of the longitudinal and transverse muon momenta, as well as the single-differential cross sections in those variables. The data used in this analysis correspond to an exposure of $3.34 \times 10^{20}$ protons on target with a peak neutrino energy of approximately 3.5 GeV. Measurements are compared to the GENIE, NuWro and GiBUU neutrino cross-section predictions, as well as a version of GENIE modified to produce better agreement with prior exclusive MINERvA measurements. None of the models or variants were able to successfully reproduce the data across the entire phase space, which includes areas dominated by each interaction channel.

hep-ex↗

arXiv2020

GEANT4 Parameter Tuning Using Professor

The Geant4 toolkit is used extensively in high energy physics to simulate the passage of particles through matter and to predict effects such as detector efficiencies and smearing. Geant4 uses many underlying models to predict particle interaction kinematics, and uncertainty in these models leads to uncertainty in high energy physics measurements. The Geant4 collaboration recently made free parameters in some models accessible through partnership with Geant4 developers. We present a study of the impact of varying parameters in three Geant4 hadronic physics models on agreement with thin target datasets and describe fits to these datasets using the Professor model tuning framework. We find that varying parameters produces substantially better agreement with some datasets, but that more degrees of freedom are required for full agreement. This work is a first step towards a common framework for propagating uncertainties in Geant4 models to high energy physics measurements, and we outline future work required to complete that goal.

physics.ins-det↗