Particle Tracking Classification in the CONNIE Experiment
Abstract
The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is a particle physics experiment located at the Angra 2 Nuclear Reactor. Using silicon Skipper-CCDs, CONNIE aims to identify and investigate coherent elastic neutrino-nucleus scattering (CEνNS). However, background particles from various sources, combined with the large volume of unlabeled data, present significant challenges, making statistical analysis difficult. To address these challenges, this work investigates and implements machine learning and image processing methodologies to classify distinct CONNIE events. A labeled dataset of real experimental events will also be created using the Annotation Redundancy with Targeted Quality Assurance method. This approach involves multiple annotators labeling the same data, enabling comparison to identify discrepancies, refine labeling accuracy, and enhance dataset reliability.References
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Aguilar-Arevalo, A. et al. (2019). Exploring low-energy neutrino physics with the Coherent Neutrino Nucleus Interaction Experiment. Physical Review D, 100(9):092005. (CONNIE Collab.).
Aguilar-Arevalo, A. A. et al. (2024). Searches for CEνNS and Physics beyond the Standard Model using Skipper-CCDs at CONNIE. arXiv:2403.15976 [hep-ex]. (CONNIE Collab.).
Arnquist, I. et al. (2023). The DAMIC-M Experiment: Status and First Results. SciPost Phys. Proc., 12:014. (DAMIC-M Collab.).
Bar, O., Bibrzycki, L., Niedźwiecki, M., et al. (2021). Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors. Sensors, 21(22).
Bartovsky, J., Schneider, D., Dokladalova, E., et al. (2011). Morphological classification of particles recorded by the Timepix detector. In 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA), pages 343–348.
Bonifazi, C. (2017). Experimento CONNIE para detecção de neutrinos com CCDs na usina nuclear de Angra II. [link]. (CONNIE Collab.). Instrumentação Científica em Física de Altas Energias no Brasil.
Brun, R. and Rademakers, F. (1997). ROOT: An object oriented data analysis framework. Nucl. Instrum. Meth. A, 389:81–86.
Fatima, S., Hussain, A., Amir, S., Ahmed, S. H., and Aslam, S. (2023). XGBoost and Random Forest Algorithms: An in Depth Analysis. Pakistan Journal of Scientific Research, 3:26–31.
Furnell, W., Shenoy, A., Fox, E., and Hatfield, P. (2019). First results from the LUCID-Timepix spacecraft payload onboard the TechDemoSat-1 satellite in Low Earth Orbit. Adv. Space Res., 63:1523–1540.
Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing. Prentice Hall, Upper Saddle River, NJ, 3rd edition.
Haas, T. (2007). Particle event. Symmetry Magazine. Available at: [link]. Accessed: 2024-10-20.
Hachaj, T., Bibrzycki, L., and Piekarczyk, M. (2021). Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network. Sensors, 21(6).
Homola, P. et al. (2020). Cosmic-Ray Extremely Distributed Observatory. Symmetry, 12(11):1835. (CREDO Collab.).
Kayser, B. and Parke, S. (2009). The neutrinos. Fermi National Accelerator Laboratory. Available at: [link]. Accessed: 2024-10-20.
Nasteva, I. (2021). Low-energy reactor neutrino physics with the CONNIE experiment. J. Phys. Conf. Ser., 2156(1):012115. (CONNIE Collab.).
Pence, W. D., Chiappetti, L., Page, C. G., Shaw, R. A., and Stobie, E. (2010). Definition of the Flexible Image Transport System (FITS), version 3.0. AA, 524:A42.
Piekarczyk, M. et al. (2021). CNN-Based Classifier as an Offline Trigger for the CREDO Experiment. Sensors, 21(14):4804.
Tiffenberg, J., Sofo-Haro, M., Drlica-Wagner, et al. (2017). Single-Electron and Single-Photon Sensitivity with a Silicon Skipper CCD. Physical Review Letters, 119(13). (SENSEI Collab.).
Tseng, T., Stent, A., and Maida, D. (2020). Best Practices for Managing Data Annotation Projects. CoRR, abs/2009.11654.
Ventura, P. Z. M. (2024). Skipper-CCDs para detecção de neutrinos: primeiros resultados com o experimento CONNIE. Master’s thesis, Universidade Federal do Rio de Janeiro.
Winter, M., Bourbeau, J., Bravo, S., Campos, F., Meehan, M., et al. (2019). Particle Identification In Camera Image Sensors Using Computer Vision. Astroparticle Physics, 104:42–53.
Čelko, T. (2021). Support for annotating and classifying particles detected by Timepix3. Bachelor’s thesis, Charles University.
Published
2025-09-29
How to Cite
SANTOS, Sara Mirthis Dantas dos; NASTEVA, Irina; COSTA, Paula Dornhofer Paro.
Particle Tracking Classification in the CONNIE Experiment. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1009-1020.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.14311.
