Reconstruction and Particle Identification with CYGNO Experiment
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34167-0_10
Reference6 articles.
1. Bertone, G., Hooper, D., Silk, J., Particle dark matter: evidence, candidates and constraints. Phys. Rep. 405(5–6), 279–390 (2005). https://doi.org/10.1016/j.physrep.2004.08.031
2. Marafini, M., et al.: ORANGE: a high sensitivity particle tracker based on optically read out GEM. Nucl. Instrum. Meth. A 845, 285–288 (2017). https://doi.org/10.1016/j.nima.2016.04.014
3. Amaro, F. D., Baracchini, E., et al.: The CYGNO experiment, MPDI (2022). https://doi.org/10.3390/instruments6010006
4. Baracchini, E., et al.: Identification of low energy nuclear recoils in a gas time projection chamber with optical readout. Meas. Sci. Technol 32, 025902 (2020)
5. Baracchini, E., et al.: A density-based clustering algorithm for the CYGNO data analysis. J. Instrum. 15. (2020). https://doi.org/10.1088/1748-0221/15/12/T12003
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