First measurement of 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>ν</mml:mi><mml:mi>μ</mml:mi></mml:msub></mml:math>
 charged-current 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mi>π</mml:mi><mml:mn>0</mml:mn></mml:msup></mml:math>
 production on argon with the MicroBooNE detector

Adams, C.; Alrashed, M.; An, R.; Anthony, J.; Asaadi, J.; Ashkenazi, A.; Auger, M.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bhat, A.; Bhattacharya, Kolahal; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Terrazas, I. Caro; Carr, R.; Fernández, R. Castillo; Cavanna, F.; Cerati, G. B.; Chen, H.; Chen, Y.; Church, E.; Cianci, D.; Cohen, E.; Collin, Gabriel; Conrad, J. M.; Convery, M. R.; Cooper-Troendle, L.; Crespo-Anadón, J. I.; Tutto, M. Del; Devitt, D.; Dı́az, A.; Duffy, K.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero, L.; Esquivel, J.; Evans, Justin J.; Fadeeva, A.A.; Fitzpatrick, R. S.; Fleming, B. T.; Franco, D.; Furmanski, A. P.; García-Gámez, D.; Genty, V.; Goeldi, D.; Gollapinni, S.; Goodwin, O.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guénette, R.; Guzowski, P.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, Colton; Horton-Smith, G. A.; Hourlier, A.; Huang, E.-C.; James, C.; Vries, J. Jan de; Ji, X.; Jiang, L.; Johnson, R. A.; Joshi, J.; Jöstlein, H.; Jwa, Y.-J.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Lepetic, I.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Marcocci, S.; Mariani, C.; Marshall, J.; Martín-Albo, J.; Caicedo, D. A. Martínez; Mastbaum, A.; Meddage, V.; Mettler, T.; Mistry, K.; Mogan, A.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murphy, M.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Pandey, V.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S. F.; Pavlović, Ž.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Ren, Lu; Rochester, Lynn; Ross-Lonergan, M.; Rohr, C. Rudolf von; Russell, B.; Scanavini, G.; Schmitz, D.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sharankova, R.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; John, J. St.; Strauss, T.; Sutton, K.; Sword-Fehlberg, S.; Szelc, A. M.; Tagg, N.; Tang, W.; Terao, K.; Thomson, M. A.; Thornton, R. T.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Pontseele, W. Van De; Water, R. G. Van de; Viren, B.; Weber, M.; Wei, H.; Wickremasinghe, D. A.; Wierman, K.; Williams, Z.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yarbrough, G.; Yates, L. E.; Zeller, G. P.; Zennamo, J.; Zhang, C.

doi:10.1103/physrevd.99.091102

Cited by 43 publications

(38 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We expect this approach to have a big potential in analysis and extraction of geometrical properties from image data. In further work we plan to adapt this technique to the location of tracks in 3D tomographic microscopy full resolution data, or data of the Liquid Argon Time Projection Chamber detectors [25], [26], which would be a direct extension of this approach. Also adding more samples with higher track number in the training dataset is expected to improve efficiency and resolution in cases with high track density.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

Vladymyrov

Ariga

2020

J. Inst.

View full text Add to dashboard Cite

Efficient tracking algorithm is a crucial part of particle tracking detectors. While big work was done in designing plethora of various algorithms, they usually require tedious tuning for each use case. (Weakly) supervised Machine Learning-based approaches can leverage the actual raw data for maximal performance. Yet in realistic scenarios sufficient high-quality labeled data is not available. While sometimes training can be performed on simulated data, often appropriate simulation of detector noise is impossible, compromising this approach. Here we propose a novel fully unsupervised approach to track reconstruction. The introduced model for learning to disentangle the factors of variation in a geometrically meaningful way employs geometrical space invariances. We train it through constraints on the equivariance between the image space and the latent representation in a Deep Convolutional Autoencoder. Using experimental results on synthetic data we show requirement of the variety of the space transformations for meaningful disentanglement of factors of variation. We also demonstrate performance of our model on real data from tracking detectors.

show abstract

Section: Discussion and Outlookmentioning

confidence: 99%

Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

Vladymyrov

Ariga

2020

J. Inst.

View full text Add to dashboard Cite

show abstract

“…In future experiments, deep learning methods show promise for reconstruction, in addition to selection. MicroBooNE has recently implemented semantic segmentation, a technique which uses the series of convolutional filters used for categorization to make predictions about what particle produced each energy deposit [226]. They use this to identify all the reconstructed hits likely produced by electromagnetic particles.…”

Section: Machine Learning At Next Generation Experimentsmentioning

confidence: 99%

White Paper on New Opportunities at the Next-Generation Neutrino Experiments (Part 1: BSM Neutrino Physics and Dark Matter)

Argüelles

Aurisano

Batell

et al. 2019

Self Cite

View full text Add to dashboard Cite

The authors of this paper primarily consist of those who participated in the New Opportunities at Next-Generation Neutrino Experiments workshop held on the campus of the University of Texas at Arlington. Given the anticipated and growing importance of BSM topics that can be explored in the next-generation neutrino experiments, the authors of this white paper are strong advocates of these topics and aim to become a de facto working group for the upcoming decadal study of the APS Division of Particles and Fields. This white paper is the first step to accomplish these goals, and provides a benchmark for ourselves to check progress toward making BSM physics a primary, yet complementary, topical area to precision neutrino oscillation parameter measurements. The authors plan to follow up this white paper and workshop with the subsequent series to ensure continued improvement and broadening of the BSM topics accessible to neutrino experiments, and to draw increasing attention to this field throughout the future. New Opportunities at the Next-Generation Neutrino Experiments White Paper New Opportunities at the Next-Generation Neutrino Experiments White Paper

show abstract

“…Although CC π 0 production differs from NC in the theoretical description of the primary neutrino interaction, it shares many nuclear physics effects in common with the NC case. MicroBooNE's recent measurement of ν µ CC π 0 production on argon [13], the heaviest target nucleus for which this process has been studied to date, provides a helpful test of models of these nuclear effects.…”

Section: Charged-current Neutral Pion Production Cross Sectionmentioning

confidence: 99%

Recent Neutrino Cross Section Measurements from MicroBooNE

Gardiner¹

2019

Proceedings of XXIX International Symposium on Lepton Photon Interactions at High Energies — PoS(LeptonPhoton2019)

View full text Add to dashboard Cite

Next-generation precision measurements of neutrino properties will require carefully controlled systematic uncertainties, including theoretical uncertainties in models of neutrino-nucleus interactions. Despite their strengths as a neutrino detector technology, liquid argon time projection chambers (LArTPCs) employ a target material (argon) for which neutrino scattering cross sections remain largely unmeasured. To better constrain neutrino interaction uncertainties for future argon-based neutrino oscillation experiments, the MicroBooNE LArTPC experiment is pursuing a wide-ranging program of cross section measurements. These proceedings present some of the first physics results from that effort, including studies of inclusive charged-current ν µ interactions, neutral pion production, and multi-proton final states.

show abstract

First measurement of νμ charged-current π0 production on argon with the MicroBooNE detector

Cited by 43 publications

References 33 publications

Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

White Paper on New Opportunities at the Next-Generation Neutrino Experiments (Part 1: BSM Neutrino Physics and Dark Matter)

Recent Neutrino Cross Section Measurements from MicroBooNE

Contact Info

Product

Resources

About