MARTA: a high-energy cosmic-ray detector concept for high-accuracy muon measurement

Abreu, P.; Andringa, S.; Assis, P.; Blanco, A.; Martins, Victor Barbosa; Brogueira, P.; Carolino, N.; Cazón, L.; Cerda, Marcos; Cernicchiaro, G.; Colalillo, R.; Conceição, R.; Cunha, Orlando; Almeida, R. M. de; Souza, V. de; Diogo, F.; Dobrigkeit, C.; Espadanal, J.; Espirito-Santo, C.; Ferreira, Miguel; Ferreira, Paulo Ricardo Araújo; Fonte, P.; Giaccari, U.; Gonçalves, P.; Guarino, F.; Lippmann, O. C.; Lopes, L.; Luz, Ricardo Jorge Barreira; Maurizio, D.; Marujo, F.; Mazur, Péter; Mendes, Luis Miguel Domingues; Pereira, A.; Pimenta, M.; Prado, R. R.; Řídký, J.; Sarmento, R.; Scarso, C.; Souza, John Manuel de; Tomé, B.; Trávničék, P.; Vícha, J.; Wolters, H.; Zas, E.

doi:10.1140/epjc/s10052-018-5820-2

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…Another option would be to use dedicated muon detectors, buried or shielded, as it is being done in LHAASO [14] or simply below the water tank, as proposed in the MARTA 1 project [15]. These hybrid experiments have many advantages as the shower components can be scrutinised by independent detectors.…”

Section: Wcd Configurationmentioning

confidence: 99%

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

et al. 2021

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The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient muon tagging (and counting) can be achieved using a water Cherenkov detector with a reduced water volume and 4 PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis based on machine learning (ML). The developed analysis has been tested for different shower and array configurations. The output of the ML analysis, the probability of having a muon in the WCD station, has been used to notably discriminate between gamma and hadron induced showers with $$S/ \sqrt{B} \sim 4$$ S / B ∼ 4 for shower with energies $$E_0 \sim 1\,$$ E 0 ∼ 1 TeV. Finally, for proton-induced showers, an estimator of the number of muons was built by means of the sum of the probabilities of having a muon in the stations. Resolutions about $$20\%$$ 20 % and a negligible bias are obtained for vertical showers with $$N_{\mu } > 10$$ N μ > 10 .

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Section: Wcd Configurationmentioning

confidence: 99%

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

et al. 2021

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“…At the University of Santiago de Compostela (Spain), a medium‐size tRPC detector (1.2 × 1.5 m 2 ) with a space and time resolution of σ x , y ∼3 cm and σ t ∼300 ps, respectively, has been installed circa 2014 as part of the Trasgo Project (Belver et al., 2010; Blanco et al., 2014). It is named TRAGALDABAS (TRAsGo for the AnaLysis of the nuclear matter Decay, the Atmosphere, the earth B‐Field And the Solar activity), and it has been designed and built at LabCAF in collaboration with LIP (e.g., Abreu et al., 2018). The angular resolution with its present vertical layout is 2–3° and the maximum zenith angle of the accepted tracks is close to 50° (Figure 1a).…”

Section: The Cosmic Ray Detectormentioning

confidence: 99%

Atmospheric Temperature Effect in Secondary Cosmic Rays Observed With a 2 m² Ground‐Based tRPC Detector

Riádigos

García-Castro

González-Díaz

et al. 2020

Earth and Space Science

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A high time resolution 2 m 2 tracking detector, based on timing Resistive Plate Chamber (tRPC) cells, has been installed at the Faculty of Physics of the University of Santiago de Compostela (Spain) in order to improve our understanding of the cosmic rays arriving at the Earth's surface. Following a short commissioning of the detector, a study of the atmospheric temperature effect of the secondary cosmic ray component was carried out. To take into account this effect, temperature coefficients, W T (h), were obtained from cosmic ray data using a method based on Principal Component Analysis (PCA). The results obtained show good agreement with the theoretical expectation. The method successfully removes the correlation present between the different atmospheric layers, which would be dominant otherwise. We briefly describe the initial calibration and pressure correction procedures, essential to isolate the temperature effect. Overall, the measured cosmic ray rate displays the expected anticorrelation with the effective atmospheric temperature, through the coefficient α T = −0.279±0.051%/K. Rates follow the seasonal variations, and unusual short-term events are clearly identified too.

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“…At the University of Santiago de Compostela (Spain), a medium-size tRPC detector (1.2 × 1.5 m 2 ) with a space and time resolution of σ x,y ∼ 3 cm and σ t ∼ 300 ps, respectively, has been installed circa 2014 (Blanco et al, 2014). It is named TRAGALDABAS (TRAsGo for the AnaLysis of the nuclear matter Decay, the Atmosphere, the earth B-Field And the Solar activity), and it has been designed and built at LIP-workshops (e.g., (Abreu et al, 2018)). The angular resolution with its present vertical layout is 2-3 • and the maximum zenith angle of the accepted tracks is close to 50 • (Figure 1a).…”

Section: The Muon Telescope (Mt)mentioning

confidence: 99%

Atmospheric Temperature Effect in secondary cosmic rays observed with a two square meter ground-based detector

Riádigos,

García-Castro,

González-Díaz

et al. 2020

Preprint

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A high resolution 2 m 2 tracking detector, based on timing Resistive Plate Chamber (tRPC) cells, has been installed at the Faculty of Physics of the University of Santiago de Compostela (Spain) in order to improve our understanding of the cosmic rays arriving at the Earth's surface. Following a short commisioning of the detector, a study of the atmospheric temperature effect of the secondary cosmic ray component was carried out. A method based on Principal Component Analysis (PCA) has been implemented in order to obtain the distribution of temperature coefficients, W T (h), using as input the measured rate of nearly vertical cosmic ray tracks, showing good agreement with the theoretical expectation. The method succesfully removes the correlation present between the different atmospheric layers, that would be dominant otherwise. We briefly describe the initial calibration and pressure correction procedures, essential to isolate the temperature effect. Overall, the measured cosmic ray rate displays the expected anticorrelation with the effective atmospheric temperature, through the coefficient α T = −0.279 ± 0.051 %/K. Rates follow the seasonal variations, and unusual short-term events are clearly identified too.

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MARTA: a high-energy cosmic-ray detector concept for high-accuracy muon measurement

Cited by 15 publications

References 27 publications

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

Atmospheric Temperature Effect in Secondary Cosmic Rays Observed With a 2 m² Ground‐Based tRPC Detector

Atmospheric Temperature Effect in secondary cosmic rays observed with a two square meter ground-based detector

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MARTA: a high-energy cosmic-ray detector concept for high-accuracy muon measurement

Cited by 15 publications

References 27 publications

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

Atmospheric Temperature Effect in Secondary Cosmic Rays Observed With a 2 m2 Ground‐Based tRPC Detector

Atmospheric Temperature Effect in secondary cosmic rays observed with a two square meter ground-based detector

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Atmospheric Temperature Effect in Secondary Cosmic Rays Observed With a 2 m² Ground‐Based tRPC Detector