The cosmic-ray energy spectrum around the knee measured by the Tibet-III air-shower array

Amenomori, M.; Ayabe, S.; Bi, X. J.; Chen, D.; Cui, S. W.; Danzengluobu,; Ding, L. K.; Ding, X. H.; Feng, C.; Feng, Z.; Feng, Z. Y.; Gao, X. Y.; Geng, Q. X.; Guo, H. W.; He, H. H.; He, M.; Hibino, K.; Hotta, N.; Hu, Haibing; Huang, J.; Huang, Q.; Jia, H. Y.; Kajino, F.; Kasahara, K.; Katayose, Y.; Kato, C.; Kawata, K.; Labaciren,; Le, G. M.; Li, A.F.; Li, J.Y.; Lou, Y.; Lü, H.; Lu, S. L.; Meng, X. R.; Mizutani, K.; Mu, J.; Munakata, K.; Nagai, A.; Nanjo, H.; Nakamura, Masaki; Ohnishi, M.; Ohta, I.; Onuma, H.; Ouchi, T.; Ozawa, S.; Ren, J. R.; Saito, T.; Saito, Takeshi; Sakata, M.; Sako, T. K.; Sasaki, Takashi; Shibata, M.; Shirai, T.; Sugimoto, H.; Takita, M.; Tan, Y. H.; Tateyama, N.; Torii, Shoji; Tsuchiya, H.; Udo, S.; Wang, B.; Wang, H.; Wang, X.; Wang, Y.G.; Wu, H. R.; Xue, L.; Yamamoto, Y.; Yan, C. T.; Yang, X. C.; Yasue, S.; Ye, Z.H.; Yu, G. C.; Yuan, A. F.; Yuda, T.; Zhang, H.M.; Zhang, J.L.; Zhang, N.J.; Zhang, X.Y.; Zhang, Y.; Zhang, Yi; Zhaxisangzhu,; Zhou, X. X.

doi:10.1016/j.nuclphysbps.2007.11.021

Cited by 6 publications

(4 citation statements)

References 9 publications

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Section: Backgrounds To Boosted Dark Mattermentioning

confidence: 98%

“…E −2.7 at higher energies [70]. The scattering process ψ B e − → ψ B e − with an energetic outgoing electron faces a large background from charged-current (CC) electron-neutrino scattering ν e n → e − p when the outgoing proton is not detected, as well as ν e p → e + n since Cherenkovbased experiments cannot easily distinguish electrons from positrons.…”

Section: Jcap10(2014)062mentioning

confidence: 99%

See 1 more Smart Citation

(In)direct detection of boosted dark matter

Agashe

Cui

Necib

et al. 2014

J. Cosmol. Astropart. Phys.

195

283

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We present a new multi-component dark matter model with a novel experimental signature that mimics neutral current interactions at neutrino detectors. In our model, the dark matter is composed of two particles, a heavier dominant component that annihilates to produce a boosted lighter component that we refer to as boosted dark matter. The lighter component is relativistic and scatters off electrons in neutrino experiments to produce Cherenkov light. This model combines the indirect detection of the dominant component with the direct detection of the boosted dark matter. Directionality can be used to distinguish the dark matter signal from the atmospheric neutrino background. We discuss the viable region of parameter space in current and future experiments.

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Section: Backgrounds To Boosted Dark Mattermentioning

confidence: 98%

Section: Jcap10(2014)062mentioning

confidence: 99%

(In)direct detection of boosted dark matter

Agashe

Cui

Necib

et al. 2014

J. Cosmol. Astropart. Phys.

195

283

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“…The MLP method has been applied in several previous air shower observation experiments, and its validity has been demonstrated for specific purposes. For instance, Tibet ASγ applied an MLP to the combined observation data obtained from the Tibet air shower array and the shower core detector for measuring particles in the shower core, leading to spectra of proton and helium as the main components of the elemental ratios of cosmic rays with energies in the petaelectron volt range [17][18][19][20]. This method requires the extraction of feature values from the particle density distribution in advance; however, it also requires clarification of which feature values are practical for separation.…”

Section: Separation Methods For Gamma-showers and Hadronic-showers An...mentioning

confidence: 99%

Neural networks for separation of cosmic gamma rays and hadronic cosmic rays in air shower observation with a large area surface detector array

Okukawa,

Hara,

Hibino

et al. 2024

Mach. Learn.: Sci. Technol.

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The Tibet ASγ experiment has been observing cosmic gamma rays and cosmic rays in the energy range from teraelectron volts to several tens of petaelectron volts with a surface detector array since 1990.The derivation of cosmic gamma-ray flux is made by finding the excess distribution of the arrival direction of air showers above background cosmic rays. In 2014, the underground water Cherenkov muon detector (MD) was added to separate cosmic gamma rays from the background on the basis of the muon-less feature of the air showers of gamma-ray origin; hybrid observations using these two detectors were started at this time.In the present study, we developed methods to separate gamma-ray-induced air showers and hadronic cosmic-ray-induced ones using the measured particle number density distribution to improve the sensitivity of cosmic gamma-ray measurements using the Tibet air shower array data alone before the installation of the MD.We tested two approaches based on neural networks. The first method used feature values representing the lateral spread of the secondary particles, and the second method used the shower image data.To compare the separation performance of each method, we analyzed Monte Carlo air shower events in the vertically incident direction with mono-initial-energy gamma rays and protons.When discriminated by a single feature, the feature with the highest separation performance has an area under the curve (AUC) value of 0.701 for a gamma-ray energy of 10 TeV and 0.808 for 100 TeV. A separation method with a multilayer perceptron (MLP) based on multiple features has AUC values of 0.761 for a gamma-ray energy of 10 TeV and 0.854 for 100 TeV, which represents an improvement of approximately 5 % in the AUC value compared with the single-feature case. We also found that the feature values that effectively contribute to the separation vary depending on the energy.A separation method with a convolutional neural network (CNN) using the shower image data has AUC values of 0.781 for a gamma-ray energy of 10 TeV and 0.901 for 100 TeV, which are approximately 5 % higher than those of the MLP method. We applied the CNN separation method to Monte Carlo gamma-ray and cosmic-ray events from the Crab Nebula in the energy range 10−100 TeV.The AUC values range from 0.753 to 0.879, and the significance of the observed gamma-ray excess is improved by 1.3 to 1.8 times compared with the case without the separation procedure.

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“…The major background to the boosted DM signal comes from atmospheric neutrinos, which are produced through interactions of cosmic rays with protons and nuclei in the earth's atmosphere. Atmospheric neutrino energy spectrum peaks around 1 GeV and follows a power law 𝐸 −2.7 at higher energies [250]. The scattering process 𝜓 𝐵 𝑒 − → 𝜓 𝐵 𝑒 − with an energetic outgoing electron faces a large background from chargedcurrent (CC) electron-neutrino scattering 𝜈 𝑒 𝑛 → 𝑒 − 𝑝 when the outgoing proton is not detected, as well as 𝜈 𝑒 𝑝 → 𝑒 + 𝑛 since Cherenkov-based experiments cannot easily distinguish electrons from positrons.…”

Section: Backgrounds To Boosted Dark Mattermentioning

confidence: 99%

Boosting (In)direct Detection of Dark Matter

Necib

2017

Preprint

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In this thesis, I study the expected direct and indirect detection signals of dark matter. More precisely, I study three aspects of dark matter; I use hydrodynamic simulations to extract properties of weakly interacting dark matter that are relevant for both direct and indirect detection signals, and construct viable dark matter models with interesting experimental signatures. First, I analyze the full scale Illustris simulation, and find that Galactic indirect detection signals are expected to be largely symmetric, while extragalactic signals are not, due to recent mergers and the presence of substructure. Second, through the study of the high resolution Milky Way simulation Eris, I find that metal-poor halo stars can be used as tracers for the dark matter velocity distribution. I use the Sloan Digital Sky Survey to obtain the first empirical velocity distribution of dark matter, which weakens the expected direct detection limits by up to an order of magnitude at masses 10 GeV. Finally, I expand the weakly interacting dark matter paradigm by proposing a new dark matter model called boosted dark matter. This novel scenario contains a relativistic component with interesting hybrid direct and indirect detection signatures at neutrino experiments. I propose two search strategies for boosted dark matter, at Cherenkov-based experiments and future liquid-argon neutrino detectors.

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The cosmic-ray energy spectrum around the knee measured by the Tibet-III air-shower array

Cited by 6 publications

References 9 publications

(In)direct detection of boosted dark matter

(In)direct detection of boosted dark matter

Neural networks for separation of cosmic gamma rays and hadronic cosmic rays in air shower observation with a large area surface detector array

Boosting (In)direct Detection of Dark Matter

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