Measurement of Neutrino Absolute Deep Inelastic Scattering Cross Section in Iron, Lead, Carbon, and Plastic Using MINERvA Detector at $E_{\nu} = 6$ \GeV

Wospakrik, M.

doi:10.2172/1484178

Cited by 3 publications

(4 citation statements)

References 101 publications

(161 reference statements)

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“…We consider a DIS cross section measurement in the MINERνA passive neutrino targets [39,40]. DIS events involve scattering with partons inside a nucleus and are characterized by high momentum transfer squared (Q 2 ) and high invariant mass values of the final state hadronic system (W).…”

Section: Results Of Dcnn Studymentioning

confidence: 99%

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

et al. 2018

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A: We present a simulation-based study using deep convolutional neural networks (DC-NNs) to identify neutrino interaction vertices in the MINERνA 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. MINERνA 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.

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Section: Results Of Dcnn Studymentioning

confidence: 99%

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

et al. 2018

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“…The cross section approximately scales linearly with the reconstructed neutrino energy. Figures taken from [134]. .…”

Section: Leftmentioning

confidence: 99%

“…The best fit of the DIS cross section using neutrino interactions originating from hydrocarbonbased and lead-based regions are shown in figure 3.4. It's interesting to note that the difference between the hydrocarbon and lead, which have considerably different size nuclei, is relatively small [134].…”

Section: General Formmentioning

confidence: 99%

The Accelerator Neutrino-Neutron Interaction Experiment

Pershing

2020

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“…Pion energy refers to the hadronic (non-muon energy). predict the interaction or primary vertex [143]. Whether the fiducial volume of interest of a particular analysis is in the tracker or in the passive targets region, affects the definition of the vertex for analyses not using ML.…”

Section: Vertex Reconstructionmentioning

confidence: 99%

$\nu_{\mu}$-Induced CC Coherent $\pi^{+}$ Production Off Carbon, Hydrocarbon, Iron, and Lead Using the MINER&#957;A Detector

Delgado

Alejandro²

2020

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Doctors Trung Le and Daniel Ruterbories helped me with their expertise on a daily basis, also with unsurpassable quality and patience. A special mention to Doctor Leo Bellantoni, the first one to dedicate a lot of time an effort in myself as a grad student.My parents Irma and José who always showed me the example to follow and the inspiration to move forward. I cannot express how much I thank you for teaching me to pursue what I want.My siblings Pepe, Juan and Mariana, perhaps not aware of your contribution, thank you for always being there.My wife Maricruz and my Daughter AnaMá, my strongest reason, the pushing force, and the most precious beings I could ever met. I owe you so much, you have given me everything.

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Measurement of Neutrino Absolute Deep Inelastic Scattering Cross Section in Iron, Lead, Carbon, and Plastic Using MINERvA Detector at $E_{\nu} = 6$ \GeV

Cited by 3 publications

References 101 publications

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

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

The Accelerator Neutrino-Neutron Interaction Experiment

$\nu_{\mu}$-Induced CC Coherent $\pi^{+}$ Production Off Carbon, Hydrocarbon, Iron, and Lead Using the MINER&#957;A Detector

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Measurement of Neutrino Absolute Deep Inelastic Scattering Cross Section in Iron, Lead, Carbon, and Plastic Using MINERvA Detector at $E_{\nu} = 6$ \GeV

Cited by 3 publications

References 101 publications

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

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

The Accelerator Neutrino-Neutron Interaction Experiment

$\nu_{\mu}$-Induced CC Coherent $\pi^{+}$ Production Off Carbon, Hydrocarbon, Iron, and Lead Using the MINER&amp;#957;A Detector

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$\nu_{\mu}$-Induced CC Coherent $\pi^{+}$ Production Off Carbon, Hydrocarbon, Iron, and Lead Using the MINERνA Detector