arXiv subjects

M. A. Ramírez

Publications and source records attributed to M. A. Ramírez.

29 records · Page 2Linked to original sources

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.

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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.

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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.

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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.

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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.

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arXiv2019

Neutron measurements from anti-neutrino hydrocarbon reactions

Charged-current anti-neutrino interactions on hydrocarbon scintillator in the MINERvA detector are used to study activity from their final-state neutrons. To ensure that most of the neutrons are from the primary interaction, rather than hadronic reinteractions in the detector, the sample is limited to momentum transfers below 0.8 GeV/c. From 16,129 interactions, 15,246 neutral particle candidates are observed. The reference simulation predicts 64\% of these candidates are due to neutrons from the anti-neutrino interaction directly, but also overpredicts the number of candidates by 15\% overall, which is beyond the standard uncertainty estimates for models of neutrino interactions and neutron propagation in the detector. Using the measured distributions for energy deposition, time of flight, position, and speed, we explore the sensitivity to the details those two aspects of the models. We also use multiplicity distributions to evaluate the presence of a two-nucleon knockout process. These results provide critical new information toward a complete description of the hadronic final state of neutrino interactions, which is vital to neutrino oscillation experiments.

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arXiv2019

Measurement of $\barν_μ$ charged-current single $π^{-}$ production on hydrocarbon in the few-GeV region using MINERvA

The antineutrino scattering channel $\barν_μ \,\text{CH} \rightarrow μ^{+} \,π^{-} \,X$(nucleon(s)) is analyzed in the incident energy range 1.5 to 10 GeV using the MINERvA detector at Fermilab. Differential cross sections are reported as functions of $μ^{+}$ momentum and production angle, $π^{-}$ kinetic energy and production angle, and antineutrino energy and squared four-momentum transfer. Distribution shapes are generally reproduced by simulations based on the GENIE, NuWro, and GiBUU event generators, however GENIE (GiBUU) overestimates (underestimates) the cross-section normalizations by 8% (10%). Comparisons of data with the GENIE-based reference simulation probe conventional treatments of cross sections and pion intranuclear rescattering. The distribution of non-track vertex energy is used to decompose the signal sample into reaction categories, and cross sections are determined for the exclusive reactions $μ^{+} π^{-} n$ and $ μ^+ π^{-} p$. A similar treatment applied to the published MINERvA sample $\barν_μ \,\text{CH} \rightarrow μ^{+} \,π^{0} \,X$(nucleon(s)) has determined the $μ^{+} π^{0} n$ cross section, and the latter is used with $σ(π^{-} n)$ and $σ(π^{-} p)$ to carry out an isospin decomposition of $\barν_μ$-induced CC($π$). The ratio of magnitudes and relative phase for isospin amplitudes $A_{3}$ and $A_{1}$ thereby obtained are: $R^{\barν} = 0.99 \pm 0.19$ and $ϕ^{\barν} = 93^{\circ} \pm 7^{\circ}$. Our results are in agreement with bubble chamber measurements made four decades ago.

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arXiv2018

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.

physics.data-an↗

arXiv2018

Measurement of Quasielastic-Like Neutrino Scattering at $\left< E_ν\right> \sim 3.5$~ GeV on a Hydrocarbon Target

MINERvA presents a new analysis of neutrino induced quasielastic-like interactions in a hydrocarbon tracking target. We report a double-differential cross section using the muon transverse and longitudinal momentum. In addition, differential cross sections as a function of the square of the four-momentum transferred and the neutrino energy are calculated using a quasielastic hypothesis. Finally, an analysis of energy deposited near the interaction vertex is presented. These results are compared to modified GENIE predictions as well as a NuWro prediction. All results use a dataset produced by $3.34\times10^{20}$ protons on target creating a neutrino beam with a peak energy of approximately 3.5 GeV

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