Observation of the Crab Nebula with LHAASO-KM2A − a performance study

Cited by

1. Xian, Y.-J., Fang, J. Investigating the Origin of Ultra-high-energy γ-ray Sources from Supernova Remnants Interacting with Molecular Clouds[J]. Research in Astronomy and Astrophysics, 2025, 25(9): 095009. doi: 10.1088/1674-4527/adeaf1
2. Hou, Q., Zhou, Q., Chen, T. et al. New generation Cherenkov telescope array: HADAR and its performance introduction[J]. Nuclear Physics B, 2025. doi: 10.1016/j.nuclphysb.2025.116935
3. Yan, K., Wu, S., Liu, R.-Y. Anisotropic Diffusion of e± in Pulsar Halos over Multiple Coherence of Magnetic Fields[J]. Astrophysical Journal, 2025, 987(1): 19. doi: 10.3847/1538-4357/add6a4
4. Cao, Z., Aharonian, F., Axikegu, Zuo, X. et al. Deep view of composite SNR CTA1 with LHAASO in γ-rays up to 300 TeV[J]. Science China Physics Mechanics and Astronomy, 2025, 68(7): 279503. doi: 10.1007/s11433-024-2479-4
5. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. LHAASO detection of very-high-energy γ-ray emission surrounding PSR J0248+6021[J]. Science China Physics Mechanics and Astronomy, 2025, 68(7): 279504. doi: 10.1007/s11433-024-2508-5
6. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. Observation of the γ-ray emission from W43 with LHAASO[J]. Science China Physics Mechanics and Astronomy, 2025, 68(7): 279502. doi: 10.1007/s11433-024-2477-9
7. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. Study of ultra-high-energy gamma-ray source 1LHAASO J0056+6346u and its possible origins[J]. Science China Physics Mechanics and Astronomy, 2025, 68(7): 279505. doi: 10.1007/s11433-024-2661-8
8. Calore, F., Eckner, C. Training School Lecture Notes: WISPs in gamma-ray astrophysics[J]. Proceedings of Science, 2025.
9. Qi, J.-C., Liu, J., Zhao, J. et al. Analysis and simulation of factors affecting accuracy in extensive air shower-based Cosmic Time Synchronization[J]. Journal of Instrumentation, 2025, 20(6): P06025. doi: 10.1088/1748-0221/20/06/P06025
10. Zhou, Q., Hou, Q., Feng, Y. et al. A novel energy reconstruction method for wide-field-of-view imaging atmospheric Cherenkov technique experiments[J]. Experimental Astronomy, 2025, 59(3): 39. doi: 10.1007/s10686-025-10007-x
11. Ye, X., Zhang, Y., He, J. et al. The influence of Sun's and Moon's shadows on cosmic-ray anisotropy[J]. Astroparticle Physics, 2025. doi: 10.1016/j.astropartphys.2025.103088
12. Kato, S., Alves Batista, R., Anzorena, M. et al. A Discussion on the Origin of the Sub-PeV Galactic Gamma-Ray Emission[J]. Astrophysical Journal, 2025, 984(2): 98. doi: 10.3847/1538-4357/adcb45
13. Bakalová, A., Conceição, R., Gibilisco, L. et al. Azimuthal fluctuations and number of muons at the ground in muon-depleted proton air showers at PeV energies[J]. Physical Review D, 2025, 111(8): 083036. doi: 10.1103/PhysRevD.111.083036
14. [No author name available]. Ultra-high-energy γ-ray emission associated with the tail of a bow-shock pulsar wind nebula[J]. Innovation, 2025, 6(4): 100802. doi: 10.1016/j.xinn.2025.100802
15. Luzquiños, D., Colán, A., Bazo, J. Gamma/hadron separation using the arrival time distribution of particle cascades at TeV energies for SWGO[J]. Journal of Instrumentation, 2025, 20(4): T04008. doi: 10.1088/1748-0221/20/04/T04008
16. Budnev, N.M., Astapov, I.I., Bezyazeekov, P.A. et al. The TAIGA Experiment—Current Status, Recent Results, and Development Prospects[J]. Physics of Atomic Nuclei, 2025, 88(2): 232-241. doi: 10.1134/S106377882570036X
17. Cao, Z., Aharonian, F., Bai, Y.X. et al. Broadband γ-Ray Spectrum of Supernova Remnant Cassiopeia A[J]. Astrophysical Journal Letters, 2025, 982(1): L33. doi: 10.3847/2041-8213/adb97c
18. Cao, Z., Aharonian, F., Bai, Y.X. et al. Constraining the Cosmic-Ray Energy Based on Observations of Nearby Galaxy Clusters by LHAASO[J]. Astrophysical Journal Letters, 2025, 982(1): L19. doi: 10.3847/2041-8213/adb97d
19. Yang, C., Zhou, X.X., He, H.H. et al. Effect of near-Earth thunderstorm electric fields on the flux of cosmic ray air showers in LHAASO-KM2A[J]. Physical Review D, 2025, 111(6): 063023. doi: 10.1103/PhysRevD.111.063023
20. Chen, S.. Status and recent high light results from the LHAASO experiment experiment[J]. EPJ Web of Conferences, 2025. doi: 10.1051/epjconf/202531902001
21. Alves Batista, R., Amelino-Camelia, G., Boncioli, D. et al. White paper and roadmap for quantum gravity phenomenology in the multi-messenger era[J]. Classical and Quantum Gravity, 2025, 42(3): 032001. doi: 10.1088/1361-6382/ad605a
22. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. An Enigmatic PeVatron in an Area around H ii Region G35.6−0.5[J]. Astrophysical Journal, 2025, 979(1): 70. doi: 10.3847/1538-4357/ad991d
23. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. Data quality control system and long-term performance monitor of LHAASO-KM2A[J]. Astroparticle Physics, 2025. doi: 10.1016/j.astropartphys.2024.103029
24. Li, J., Lv, H., Liu, Y. et al. Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation[J]. Astrophysical Journal Supplement Series, 2025, 276(1): 24. doi: 10.3847/1538-4365/ad9581
25. Sánchez-Ramírez, R., Lang, R.G., Pozanenko, A. et al. Early photometric and spectroscopic observations of the extraordinarily bright INTEGRAL-detected GRB 221009A[J]. Astronomy and Astrophysics, 2024. doi: 10.1051/0004-6361/202449783
26. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. Measurement of attenuation length of the muon content in extensive air showers from 0.3 to 30 PeV with LHAASO[J]. Physical Review D, 2024, 110(10): 103017. doi: 10.1103/PhysRevD.110.103017
27. [No author name available]. Monitoring the daily variation of Sun-Earth magnetic fields using galactic cosmic rays[J]. Innovation, 2024, 5(6): 100695. doi: 10.1016/j.xinn.2024.100695
28. Mitchell, A.M.W., Celli, S. Exploring the supernova remnant contribution to the first LHAASO source catalog via passively illuminated interstellar clouds[J]. Journal of High Energy Astrophysics, 2024. doi: 10.1016/j.jheap.2024.10.011
29. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. Detection of Very High-energy Gamma-Ray Emission from the Radio Galaxy M87 with LHAASO[J]. Astrophysical Journal Letters, 2024, 975(2): L44. doi: 10.3847/2041-8213/ad8921
30. Cao, Z., Aharonian, F., Axikegu, Zuo, X. et al. Evidence for particle acceleration approaching PeV energies in the W51 complex[J]. Science Bulletin, 2024, 69(18): 2833-2841. doi: 10.1016/j.scib.2024.07.017
31. Yudong, W., Suhong, C., Liqiao, Y. et al. Study on improving the gamma ray discrimination from cosmic ray detected by LHAASO[J]. Proceedings of Science, 2024.
32. Ma, L., Wang, L. Study the measurement of air depth of shower maximum for cosmic rays by LHAASO-WFCTA[J]. Proceedings of Science, 2024.
33. Yu, Y., Luo, Y. Observation the UHE Gamma-ray emission from the J1959+2850 with LHAASO[J]. Proceedings of Science, 2024.
34. Dai, C.-Y., Lei, M.-Y., Huang, X.-Y. et al. On the synchrotron origin of MeV-GeV emission of Crab Nebula: Constraints from LHAASO’s Observation[J]. Proceedings of Science, 2024.
35. Yang, Z., Gou, Q., Li, Z. et al. Constraining the Neutrino Flux of GRB 221009A with LHAASO Horizontal Events[J]. Proceedings of Science, 2024.
36. Subieta Vasquez, M.A., Anzorena, M., Blanco, D. et al. Overview status of the ALPACA experiment[J]. Proceedings of Science, 2024.
37. Hou, B.W., Zhang, R., Xi, S.Q. et al. Discovery of the Ultra-High-Energy gamma-ray source LHAASO J2002+3238 spatially associated with SNR G69.7+1.0[J]. Proceedings of Science, 2024.
38. Yin, L., Chen, S., Zhang, S. Measurement of the cosmic ray iron spectrum with energy from 100 TeV to 10 PeV by LHAASO[J]. Proceedings of Science, 2024.
39. Zhang, Y., He, J., Zhang, R. et al. Investigation of the Ultra-High-Energy gamma-ray emission from the Northern Fermi Bubble with LHAASO-KM2A[J]. Proceedings of Science, 2024.
40. Xiong, Z., Wu, S., He, H. Method to measure muon content of extensive air showers with LHAASO KM2A-WCDA synergy[J]. Proceedings of Science, 2024.
41. Xu, R.F., Wang, K. Possible VHE gamma emission from PWN tail of PSR J1740+1000[J]. Proceedings of Science, 2024.
42. Mariş, I.C., Gonzalez, N. On the possibility to measure galactic photons at the altitude of the Pierre Auger Observatory[J]. Proceedings of Science, 2024.
43. Yang, C., Chen, X., Cao, Z. et al. Flux variations of cosmic ray air showers detected by LHAASO-KM2A during thunderstorms[J]. Proceedings of Science, 2024.
44. Zhang, H., He, H., Feng, C. et al. The all-particle spectrum and mean logarithmic mass of cosmic rays in the knee region by LHAASO-KM2A[J]. Proceedings of Science, 2024.
45. He, J., Zhao, S., Guo, Y. et al. Observation of Large-Scale Anisotropy of Proton above Tens of TeV with LHAASO-KM2A[J]. Proceedings of Science, 2024.
46. Xi, S., Chen, S. LHAASO First >25 TeV Gamma-ray Source Catalog[J]. Proceedings of Science, 2024.
47. Nan, Y., Chen, S., Feng, C. Measurement of the Daily Interplanetary Magnetic Field Using the Cosmic-ray Sun Shadow by LHAASO-KM2A.[J]. Proceedings of Science, 2024.
48. Wu, S., Chen, S., Cao, Z. et al. Highlight of LHAASO science results on PeVatrons[J]. Proceedings of Science, 2024.
49. Cao, Z., Aharonian, F., An, Q. et al. LHAASO-KM2A detector simulation using Geant4[J]. Radiation Detection Technology and Methods, 2024, 8(3): 1437-1447. doi: 10.1007/s41605-024-00467-8
50. Albert, A., Alfaro, R., Alvarez, C. et al. Performance of the HAWC Observatory and TeV Gamma-Ray Measurements of the Crab Nebula with Improved Extensive Air Shower Reconstruction Algorithms[J]. Astrophysical Journal, 2024, 972(2): 144. doi: 10.3847/1538-4357/ad5f2d
51. Celli, S., Peron, G. Detection prospects of very and ultra high-energy gamma rays from extended sources with ASTRI, CTA, and LHAASO[J]. Astronomy and Astrophysics, 2024. doi: 10.1051/0004-6361/202449837
52. Tian, X., Li, Z., Gou, Q. et al. Approach for composition measurement of cosmic rays using the muon-to-electron ratio observed by LHAASO-KM2A[J]. Physical Review D, 2024, 110(4): 043030. doi: 10.1103/PhysRevD.110.043030
53. Cao, Z., Aharonian, F., An, Q. et al. Constraints on Ultraheavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations[J]. Physical Review Letters, 2024, 133(6): 061001. doi: 10.1103/PhysRevLett.133.061001
54. Zhang, Z., Yang, R., Zhang, S. et al. Prospects for joint reconstruction of imaging air Cherenkov telescope array and extensive air shower array[J]. Journal of High Energy Astrophysics, 2024. doi: 10.1016/j.jheap.2024.07.012
55. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. Discovery of Very High Energy Gamma-Ray Emissions from the Low-luminosity AGN NGC 4278 by LHAASO[J]. Astrophysical Journal Letters, 2024, 971(2): L45. doi: 10.3847/2041-8213/ad5e6d
56. Zheng, J.-H., Zhang, H.-M., Liu, R.-Y. et al. Probing the nova shock physics with future gamma-ray observations of the upcoming outburst from T Coronae Borealis[J]. Journal of High Energy Astrophysics, 2024. doi: 10.1016/j.jheap.2024.07.007
57. Conceição, R., Costa, P.J., Gibilisco, L. et al. High resolution gamma/hadron and composition discriminant variable for water-Cherenkov detector cosmic-ray observatories[J]. Physical Review D, 2024, 110(2): 023033. doi: 10.1103/PhysRevD.110.023033
58. Samoliga, V.S.. Observation of Astrophysical Objects with the TAIGA–HiSCORE Installation[J]. Technical Physics, 2024, 69(7): 2115-2117. doi: 10.1134/S1063784224070405
59. Reis, I., Moulin, E., Viana, A. et al. Sensitivity to sub-GeV dark matter from cosmic-ray scattering with very-high-energy gamma-ray observatories[J]. Journal of Cosmology and Astroparticle Physics, 2024, 2024(7): 012. doi: 10.1088/1475-7516/2024/07/012
60. Cao, Z., Aharonian, F., An, Q. et al. Optimization of performance of the KM2A full array using the Crab Nebula*[J]. Chinese Physics C, 2024, 48(6): 065001. doi: 10.1088/1674-1137/ad2e82
61. Wen, T., Chen, S., Dai, B. The Influence of the Sun and Moon on the Observation of Very High Energy Gamma-ray Sources Using EAS Arrays[J]. Research in Astronomy and Astrophysics, 2024, 24(6): 065020. doi: 10.1088/1674-4527/ad42d6
62. Aharonian, F., Ait Benkhali, F., Aschersleben, J. et al. Spectrum and extension of the inverse-Compton emission of the Crab Nebula from a combined Fermi -LAT and H.E.S.S. analysis[J]. Astronomy and Astrophysics, 2024. doi: 10.1051/0004-6361/202348651
63. Angüner, E.O., Ergin, T. The expected potential of hadronic PeVatron searches with spectral γ-ray data from the Southern Wide-field Gamma-ray Observatory[J]. Astroparticle Physics, 2024. doi: 10.1016/j.astropartphys.2024.102936
64. Song, S., Liu, J., He, H. A method of three-dimensional muon imaging using cosmic ray ground array[J]. Journal of Instrumentation, 2024, 19(5): P05077. doi: 10.1088/1748-0221/19/05/P05077
65. Cheng, A.-Y., Cai, H., Chen, S. et al. Application of deep learning methods combined with physical background in wide field of view imaging atmospheric Cherenkov telescopes[J]. Nuclear Science and Techniques, 2024, 35(4): 84. doi: 10.1007/s41365-024-01448-8
66. Mitchell, A.M.W.. LHAASO J2108+5157 as a molecular cloud illuminated by a supernova remnant[J]. Astronomy and Astrophysics, 2024. doi: 10.1051/0004-6361/202346473
67. de Oña Wilhelmi, E., López-Coto, R., Aharonian, F. et al. The hunt for PeVatrons as the origin of the most energetic photons observed in the Galaxy[J]. Nature Astronomy, 2024, 8(4): 425-431. doi: 10.1038/s41550-024-02224-9
68. Vercellone, S., Pittori, C., Tavani, M. Scientific Highlights of the AGILE Gamma-ray Mission[J]. Universe, 2024, 10(4): 153. doi: 10.3390/universe10040153
69. Cao, Z., Aharonian, F., Axikegu, Zou, Y.C. et al. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A[J]. Physical Review Letters, 2024, 132(13): 131002. doi: 10.1103/PhysRevLett.132.131002
70. Cao, Z., Aharonian, F., An, Q. et al. The First LHAASO Catalog of Gamma-Ray Sources[J]. Astrophysical Journal Supplement Series, 2024, 271(1): 25. doi: 10.3847/1538-4365/acfd29
71. Chiavassa, A.. SWGO: a wide-field of view gamma-ray observatory in the southern hemisphere[J]. Journal of Instrumentation, 2024, 19(2): C02065. doi: 10.1088/1748-0221/19/02/C02065
72. Cao, Z., Aharonian, F., An, Q. et al. Method to measure muon content of extensive air showers with LHAASO KM2A-WCDA synergy[J]. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 2024. doi: 10.1016/j.nima.2023.168958
73. Liu, J., Wu, H., Liu, Q. et al. Study of Angular Resolution Using Imaging Atmospheric Cherenkov Technique[J]. Universe, 2024, 10(2): 100. doi: 10.3390/universe10020100
74. Cao, Z., Aharonian, F., An, Q. et al. Does or Did the Supernova Remnant Cassiopeia A Operate as a PeVatron?[J]. Astrophysical Journal Letters, 2024, 961(2): L43. doi: 10.3847/2041-8213/ad1d62
75. Vercellone, S.. Science with the ASTRI Mini-Array: From Experiment to Open Observatory[J]. Universe, 2024, 10(2): 94. doi: 10.3390/universe10020094
76. Ma, X.-H., Cao, Z. The Large High-Altitude Air Shower Observatory (LHAASO)[J]. Pulsar Astronomy Unveiling Compact Stars with Chinas New Facilities, 2024. doi: 10.1142/9789819800643_0012
77. Chen, S., Cao, Z. Current Particle Detector Arrays in Gamma-Ray Astronomy[J]. Handbook of X Ray and Gamma Ray Astrophysics, 2024. doi: 10.1007/978-981-19-6960-7_66
78. Malyshev, D., Mohrmann, L. Analysis Methods for Gamma-Ray Astronomy[J]. Handbook of X Ray and Gamma Ray Astrophysics, 2024. doi: 10.1007/978-981-19-6960-7_177
79. Galanti, G., Nava, L., Roncadelli, M. et al. Observability of the Very-High-Energy Emission from GRB 221009A[J]. Physical Review Letters, 2023, 131(25): 251001. doi: 10.1103/PhysRevLett.131.251001
80. Amenomori, M., Asano, S., Bao, Y.W. et al. Observation of multi-ten TeV to sub-PeV gamma rays from the HESS J1843-033 region with the Tibet air shower array[J]. Proceedings of Science, 2023.
81. [No author name available]. Very high-energy gamma-ray emission beyond 10 TeV from GRB 221009A[J]. Science Advances, 2023, 9(46): eadj2778. doi: 10.1126/sciadv.adj2778
82. Cao, Z., Aharonian, F., An, Q. et al. Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A[J]. Physical Review Letters, 2023, 131(15): 151001. doi: 10.1103/PhysRevLett.131.151001
83. Conceição, R., Costa, P.J., Gibilisco, L. et al. The gamma/hadron discriminator LCm in realistic air shower array experiments[J]. European Physical Journal C, 2023, 83(10): 932. doi: 10.1140/epjc/s10052-023-12106-5
84. Zhang, F., Liu, H., Zhu, F. et al. Study of longitudinal development of air showers in the knee energy range[J]. Astroparticle Physics, 2023. doi: 10.1016/j.astropartphys.2023.102877
85. Cao, Z., Chen, S., Liu, R. et al. Ultra-High-Energy Gamma-Ray Astronomy[J]. Annual Review of Nuclear and Particle Science, 2023, 73(1): 341-363. doi: 10.1146/annurev-nucl-112822-025357
86. Bradascio, F., Rueda, H., Barrio, J.A. et al. The NectarCAM timing system[J]. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 2023. doi: 10.1016/j.nima.2023.168398
87. Acero, F., Acharyya, A., Adam, R. et al. Sensitivity of the Cherenkov Telescope Array to spectral signatures of hadronic PeVatrons with application to Galactic Supernova Remnants[J]. Astroparticle Physics, 2023. doi: 10.1016/j.astropartphys.2023.102850
88. Zhu, B.-Y., Liang, Y.-F. Prediction of using LHAASO's cosmic-ray electron measurements to constrain decaying heavy dark matter[J]. Physical Review D, 2023, 107(12): 123027. doi: 10.1103/PhysRevD.107.123027
89. Kunwar, S., Goksu, H., Hinton, J. et al. A double-layered Water Cherenkov Detector array for Gamma-ray astronomy[J]. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 2023. doi: 10.1016/j.nima.2023.168138
90. Okukawa, S., Anzorena, M., Asano, S. et al. Hadronic interaction model dependence in cosmic Gamma-ray flux estimation using an extensive air shower array with a muon detector[J]. Experimental Astronomy, 2023, 55(2): 325-342. doi: 10.1007/s10686-022-09883-4
91. Bandiera, R., Bucciantini, N., Martín, J. et al. Reverberation of pulsar wind nebulae - II. Anatomy of the 'thin-shell' evolution[J]. Monthly Notices of the Royal Astronomical Society, 2023, 520(2): 2451-2472. doi: 10.1093/mnras/stad134
92. Sako, T.K., Amenomori, M., Asano, S. et al. TIBET ASgamma: Dawn of sub-PeV gamma-ray astronomy[J]. Proceedings of Science, 2023.
93. Wang, L., Ma, L., Zhang, S. et al. Cosmic ray mass independent energy reconstruction method using Cherenkov light and muon content in LHAASO[J]. Physical Review D, 2023, 107(4): 043036. doi: 10.1103/PhysRevD.107.043036
94. Sudoh, T., Beacom, J.F. Where are Milky Way's hadronic PeVatrons[J]. Physical Review D, 2023, 107(4): 043002. doi: 10.1103/PhysRevD.107.043002
95. Aharonian, F., An, Q., None, A. et al. Flux variations of cosmic ray air showers detected by LHAASO-KM2A during a thunderstorm on June 10, 2021*[J]. Chinese Physics C, 2023, 47(1): 015001. doi: 10.1088/1674-1137/ac9371
96. Angüner, E.O.. Exploring the high energy frontiers of the Milky Way with ground-based gamma-ray astronomy: PeVatrons and the quest for the origin of Galactic cosmic-rays[J]. Turkish Journal of Physics, 2023, 47(2): 40-92. doi: 10.55730/1300-0101.2738
97. Allen, B.. Space Applications of CdZnTe and CdTe Detector Systems: Past, Present and Future[J]. X Ray Photon Processing Detectors Space Industrial and Medical Applications, 2023. doi: 10.1007/978-3-031-35241-6_9
98. Spitzer, S.A., Kornbleuth, M.Z., Opher, M. et al. Complementary interstellar detections from the heliotail[J]. Frontiers in Astronomy and Space Sciences, 2023. doi: 10.3389/fspas.2023.1163519
99. Cao, Z., Aharonian, F., An, Q. et al. Constraints on Heavy Decaying Dark Matter from 570 Days of LHAASO Observations[J]. Physical Review Letters, 2022, 129(26): 261103. doi: 10.1103/PhysRevLett.129.261103
100. Zhang, F., Liu, H., Zhu, F.-R. Properties of secondary components in extensive air shower of cosmic rays in knee energy region | [膝区宇宙线广延大气簇射次级成分的特征][J]. Wuli Xuebao Acta Physica Sinica, 2022, 71(24): 249601. doi: 10.7498/aps.71.20221556
101. Zhang, H., He, H., Feng, C. Approaches to composition independent energy reconstruction of cosmic rays based on the LHAASO-KM2A detector[J]. Physical Review D, 2022, 106(12): 123028. doi: 10.1103/PhysRevD.106.123028
102. Aharonian, F., An, Q., Axikegu, Zhu, K.J. et al. Self-calibration of LHAASO-KM2A electromagnetic particle detectors using single particles within extensive air showers[J]. Physical Review D, 2022, 106(12): 122004. doi: 10.1103/PhysRevD.106.122004
103. Aharonian, F., An, Q., Axikegu, Zhu, K.J. et al. Reconstruction of Cherenkov image by multiple telescopes of LHAASO-WFCTA[J]. Radiation Detection Technology and Methods, 2022, 6(4): 544-557. doi: 10.1007/s41605-022-00342-4
104. Albert, A., Alfaro, R., Arteaga-Velázquez, J.C. et al. Validation of standardized data formats and tools for ground-level particle-based gamma-ray observatories[J]. Astronomy and Astrophysics, 2022. doi: 10.1051/0004-6361/202243527
105. Brose, R., Sushch, I., Mackey, J. Core-collapse supernovae in dense environments - particle acceleration and non-thermal emission[J]. Monthly Notices of the Royal Astronomical Society, 2022, 516(1): 492-505. doi: 10.1093/mnras/stac2234
106. Kurahashi, N., Murase, K., Santander, M. High-Energy Extragalactic Neutrino Astrophysics[J]. Annual Review of Nuclear and Particle Science, 2022. doi: 10.1146/annurev-nucl-011122-061547
107. Huentemeyer, P.. Particle detection arrays for very-high-energy gamma-ray astrophysics[J]. Large Area Networked Detectors for Particle Astrophysics, 2022. doi: 10.1142/9781800612617_0008
108. Yan, K., Liu, R.-Y., Chen, S.Z. et al. Prospect of Detecting TeV Halos with LHAASO: In the Framework of the Anisotropic Diffusion Model[J]. Astrophysical Journal, 2022, 935(2): 65. doi: 10.3847/1538-4357/ac7feb
109. Vercellone, S., Bigongiari, C., Burtovoi, A. et al. ASTRI Mini-Array core science at the Observatorio del Teide[J]. Journal of High Energy Astrophysics, 2022. doi: 10.1016/j.jheap.2022.05.005
110. Pattanaik, D., Ahmad, S., Chakraborty, M. et al. Validating the improved angular resolution of the GRAPES-3 air shower array by observing the Moon shadow in cosmic rays[J]. Physical Review D, 2022, 106(2): 022009. doi: 10.1103/PhysRevD.106.022009
111. Axikegu, Bartoli, B., Bernardini, P., Zhu, F.R. et al. Cosmic ray shower rate variations detected by the ARGO-YBJ experiment during thunderstorms[J]. Physical Review D, 2022, 106(2): 022008. doi: 10.1103/PhysRevD.106.022008
112. Li, C., Ma, B.-Q. Testing Lorentz invariance of electrons with LHAASO observations of PeV gamma-rays from the Crab Nebula[J]. Physics Letters Section B Nuclear Elementary Particle and High Energy Physics, 2022. doi: 10.1016/j.physletb.2022.137034
113. Banik, P., Ghosh, S.K. Probing the Origin of Cosmic Rays in Cygnus Cocoon Using Ultrahigh-energy Gamma-Ray and Neutrino Observations[J]. Astrophysical Journal Letters, 2022, 931(2): L30. doi: 10.3847/2041-8213/ac7157
114. Cao, Z.. LHAASO: A milestone of the cosmic ray research | [LHAASO在宇宙线物理中的里程碑意义][J]. Kexue Tongbao Chinese Science Bulletin, 2022, 67(14): 1558-1566. doi: 10.1360/TB-2022-0160
115. Liu, R.-Y.. First results of LHAASO[J]. International Journal of Modern Physics D, 2022, 31(7): 2230011. doi: 10.1142/S0218271822300117
116. Liu, H., Cao, Z., Aharonian, F. et al. Study of longitudinal development of cosmic-ray induced air showers with LHAASO-WFCTA[J]. Proceedings of Science, 2022.
117. Lingling, M., Yudong, W., Liping, W. et al. Measurement of the cosmic ray proton and helium spectrum with energy above 100TeV by the LHAASO experiment[J]. Proceedings of Science, 2022.
118. Bai, L., Liu, H., Cao, Z. et al. Study of Energy Measurement of Cosmic Ray Nuclei with LHAASO[J]. Proceedings of Science, 2022.
119. Gou, Q., Li, Z., Giuseppe, D.S. et al. Observation of Horizontal Air Showers with LHAASO-KM2A[J]. Proceedings of Science, 2022.
120. Zha, M., Gao, C., Hu, S. et al. Data analysis and key science results of LHAASO-WCDA[J]. Proceedings of Science, 2022.
121. Cao, Z., Aharonian, F., An, Q. et al. A 3D Likelihood Analysis Tool for LHAASO-KM2A data[J]. Proceedings of Science, 2022.
122. Gao, W., Cao, Q., Chen, S. et al. Observation of large-scale anisotropy in the arrival directions of cosmic rays with LHAASO-KM2A[J]. Proceedings of Science, 2022.
123. Olivera-Nieto, L., Mitchell, A.M.W., Bernlöhr, K. et al. Muons as a tool for background rejection in imaging atmospheric Cherenkov telescope arrays[J]. Proceedings of Science, 2022.
124. Zhang, H., Feng, C., Feng, X. et al. Measurement of muon contents in cosmic ray shower with LHAASO-KM2A around knee region[J]. Proceedings of Science, 2022.
125. Chen, L., AXi, K., Zhou, X. et al. Lateral distribution of vertical and inclined showers during thunderstorms[J]. Proceedings of Science, 2022.
126. Shoushan, Z.. A Review of Cosmic Rays of LHAASO[J]. Proceedings of Science, 2022.
127. Abdalla, H., Abe, H., Abe, S. et al. The Cherenkov Telescope Array: layout, design and performance[J]. Proceedings of Science, 2022.
128. Huang, Z., Huang, D., Zhou, X. et al. Simulation Study on Scaler Mode in LHAASO-KM2A[J]. Proceedings of Science, 2022.
129. Li, C., Chen, S., Wu, S. et al. Detection of emission from Cygnus Cocoon above 100TeV with LHAASO[J]. Proceedings of Science, 2022.
130. Kato, S., Aceves de la Cruz, F., Asano, S. et al. A simulation study on the performance of the ALPAQUITA experiment[J]. Proceedings of Science, 2022.
131. Wang, L., Chen, S., Cao, Z. et al. The Crab Nebula: observations and a search for UHE γ-ray flares with LHAASO[J]. Proceedings of Science, 2022.
132. Budnev, N., Kuzmichev, L., Mirzoyan, R. et al. TAIGA - an advanced hybrid detector complex for astroparticle physics, cosmic ray physics and gamma-ray astronomy[J]. Proceedings of Science, 2022.
133. Axi, K.G., Zhou, X.X., Huang, Z.C. et al. Intensity variations of showers with different zenith angle ranges during thunderstorms[J]. Astrophysics and Space Science, 2022, 367(3): 30. doi: 10.1007/s10509-022-04056-3
134. Piazzoli, B.D., Liu, S.-M., Volpe, D.D. et al. Chapter 4 Cosmic-Ray Physics *[J]. Chinese Physics C, 2022, 46(3): 030004. doi: 10.1088/1674-1137/ac3faa
135. Cao, Z., Aharonian, F., An, Q. et al. Exploring Lorentz Invariance Violation from Ultrahigh-Energy γ Rays Observed by LHAASO[J]. Physical Review Letters, 2022, 128(5): 051102. doi: 10.1103/PhysRevLett.128.051102
136. Sitarek, J.. TeV Instrumentation: Current and Future[J]. Galaxies, 2022, 10(1): 21. doi: 10.3390/galaxies10010021
137. Nie, L., Liu, Y., Jiang, Z. et al. Ultra-high-energy Gamma-Ray Radiation from the Crab Pulsar Wind Nebula[J]. Astrophysical Journal, 2022, 924(2): 42. doi: 10.3847/1538-4357/ac348d
138. Bose, D., Chitnis, V.R., Majumdar, P. et al. Ground-based gamma-ray astronomy: history and development of techniques[J]. European Physical Journal Special Topics, 2022, 231(1): 3-26. doi: 10.1140/epjs/s11734-021-00396-3
139. Wang, Y.-P., Hou, C., Sheng, X.-D. et al. Testing and analysis of the plastic scintillator units for LHAASO-ED[J]. Radiation Detection Technology and Methods, 2021, 5(4): 513-519. doi: 10.1007/s41605-021-00274-5
140. Olivera-Nieto, L., Mitchell, A.M.W., Bernlöhr, K. et al. Muons as a tool for background rejection in imaging atmospheric Cherenkov telescope arrays[J]. European Physical Journal C, 2021, 81(12): 1101. doi: 10.1140/epjc/s10052-021-09869-0
141. Liang, Y.-F., Zhang, X.-F., Cheng, J.-G. et al. Effect of axion-like particles on the spectrum of the extragalactic gamma-ray background[J]. Journal of Cosmology and Astroparticle Physics, 2021, 2021(11): 030. doi: 10.1088/1475-7516/2021/11/030
142. Amato, E., Olmi, B. The crab pulsar and nebula as seen in gamma-rays[J]. Universe, 2021, 7(11): 448. doi: 10.3390/universe7110448
143. Huang, Z.-C., Zhou, X.-X., Huang, D.-H. et al. Simulation study of scaler mode at large high altitude air shower observatory | [高海拔宇宙线观测实验中scaler模式的模拟研究][J]. Wuli Xuebao Acta Physica Sinica, 2021, 70(19): 199301. doi: 10.7498/aps.70.20210632
144. Chen, L., Xiong, Z., Li, C. et al. Strong constraints on Lorentz violation using new γ-ray observations around PeV[J]. Chinese Physics C, 2021, 45(10): 105105. doi: 10.1088/1674-1137/ac1166
145. Cao, Z., Aharonian, F., An, Q. et al. Discovery of the Ultrahigh-energy Gamma-Ray Source LHAASO J2108+5157[J]. Astrophysical Journal Letters, 2021, 919(2): L22. doi: 10.3847/2041-8213/ac2579
146. Kato, S., Condori, C.A.H., Fuente, E. et al. Detectability of southern gamma-ray sources beyond 100 TeV with ALPAQUITA, the prototype experiment of ALPACA[J]. Experimental Astronomy, 2021, 52(1-2): 85-107. doi: 10.1007/s10686-021-09796-8
147. Bao, Y., Chen, Y. On the hard gamma-ray spectrum of the potential pevatron supernova remnant g106.3+2.7[J]. Astrophysical Journal, 2021, 919(1): 32. doi: 10.3847/1538-4357/ac1581
148. Aharonian, F., An, Q., Axikegu, Zhu, K.J. et al. Calibration of the air shower energy scale of the water and air Cherenkov techniques in the LHAASO experiment[J]. Physical Review D, 2021, 104(6): 062007. doi: 10.1103/PhysRevD.104.062007
149. Li, C., Ma, B.-Q. Ultrahigh-energy photons from LHAASO as probes of Lorentz symmetry violations[J]. Physical Review D, 2021, 104(6): 063012. doi: 10.1103/PhysRevD.104.063012
150. Cao, Z., Aharonian, F., An, Q. et al. Discovery of a New Gamma-Ray Source, LHAASO J0341+5258, with Emission up to 200 TeV[J]. Astrophysical Journal Letters, 2021, 917(1): L4. doi: 10.3847/2041-8213/ac0fd5
151. Cao, Z.. An ultra-high-energy γ-ray telescope at 4,410 m[J]. Nature Astronomy, 2021, 5(8): 849. doi: 10.1038/s41550-021-01453-6
152. Aharonian, F., An, Q., Axikegu, Zhu, K.J. et al. Performance of LHAASO-WCDA and observation of the Crab Nebula as a standard candle[J]. Chinese Physics C, 2021, 45(8): 085002. doi: 10.1088/1674-1137/ac041b
153. Ando, S., Barik, S.K., Feng, Z. et al. Decaying dark matter in dwarf spheroidal galaxies: Prospects for x-ray and gamma-ray telescopes[J]. Physical Review D, 2021, 104(2): 023022. doi: 10.1103/PhysRevD.104.023022
154. Aharonian, F., An, Q., Axikegu, Zuo, X. et al. Extended Very-High-Energy Gamma-Ray Emission Surrounding PSR J0622+3749 Observed by LHAASO-KM2A[J]. Physical Review Letters, 2021, 126(24): 241103. doi: 10.1103/PhysRevLett.126.241103
155. Aharonian, F., An, Q., Axikegu, Zhu, K.J. et al. Performance test of the electromagnetic particle detectors for the LHAASO experiment[J]. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 2021. doi: 10.1016/j.nima.2021.165193
156. Cao, Z., Aharonian, F.A., An, Q. et al. Ultrahigh-energy photons up to 1.4 petaelectronvolts from 12 γ-ray Galactic sources[J]. Nature, 2021, 594(7861): 33-36. doi: 10.1038/s41586-021-03498-z
157. Yang, R.. LHAASO and the origin of cosmic rays | [LHAASO与宇宙线起源][J]. Scientia Sinica Physica Mechanica Et Astronomica, 2021, 51(11): 0417. doi: 10.1360/SSPMA-2020-0417

Figures(17) / Tables(1)

Get Citation
F. Aharonian, Q. An, L.X. Bai, Y.X. Bai, Y.W. Bao, D. Bastieri, X.J. Bi, Y.J. Bi, H. Cai, J.T. Cai, Z. Cao, Z. Cao, J. Chang, J.F. Chang, X.C. Chang, B.M. Chen, J. Chen, L. Chen, L. Chen, L. Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Chen, N. Cheng, Y.D. Cheng, S.W. Cui, X.H. Cui, Y.D. Cui, B.Z. Dai, H.L. Dai, Z.G. Dai, D. della Volpe, B. D'Ettorre Piazzoli, X.J. Dong, J.H. Fan, Y.Z. Fan, Z.X. Fan, J. Fang, K. Fang, C.F. Feng, L. Feng, S.H. Feng, Y.L. Feng, B. Gao, C.D. Gao, Q. Gao, W. Gao, M.M. Ge, L.S. Geng, G.H. Gong, Q.B. Gou, M.H. Gu, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Guo, Y.A. Han, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. He, M. Heller, Y.K. Hor, C. Hou, X. Hou, H.B. Hu, S. Hu, S.C. Hu, X.J. Hu, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, Z.C. Huang, F. Ji, X.L. Ji, H.Y. Jia, K. Jiang, Z.J. Jiang, C. Jin, D. Kuleshov, K. Levochkin, B.B. Li, C. Li, C. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X. Li, X. Li, X.R. Li, Y. Li, Y.Z. Li, Z. Li, Z. Li, E.W. Liang, Y.F. Liang, S.J. Lin, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y.N. Liu, Z.X. Liu, W.J. Long, R. Lu, H.K. Lv, B.Q. Ma, L.L. Ma, X.H. Ma, J.R. Mao, A. Masood, W. Mitthumsiri, T. Montaruli, Y.C. Nan, B.Y. Pang, P. Pattarakijwanich, Z.Y. Pei, M.Y. Qi, D. Ruffolo, V. Rulev, A. Sáiz, L. Shao, O. Shchegolev, X.D. Sheng, J.R. Shi, H.C. Song, Yu.V. Stenkin, V. Stepanov, Q.N. Sun, X.N. Sun, Z.B. Sun, P.H.T. Tam, Z.B. Tang, W.W. Tian, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Wang, J.S. Wang, L.P. Wang, L.Y. Wang, R.N. Wang, W. Wang, W. Wang, X.G. Wang, X.J. Wang, X.Y. Wang, Y.D. Wang, Y.J. Wang, Y.P. Wang, Z. Wang, Z. Wang, Z.H. Wang, Z.X. Wang, D.M. Wei, J.J. Wei, Y.J. Wei, T. Wen, C.Y. Wu, H.R. Wu, S. Wu, W.X. Wu, X.F. Wu, S.Q. Xi, J. Xia, J.J. Xia, G.M. Xiang, G. Xiao, H.B. Xiao, G.G. Xin, Y.L. Xin, Y. Xing, D.L. Xu, R.X. Xu, L. Xue, D.H. Yan, C.W. Yang, F.F. Yang, J.Y. Yang, L.L. Yang, M.J. Yang, R.Z. Yang, S.B. Yang, Y.H. Yao, Z.G. Yao, Y.M. Ye, L.Q. Yin, N. Yin, X.H. You, Z.Y. You, Y.H. Yu, Q. Yuan, H.D. Zeng, T.X. Zeng, W. Zeng, Z.K. Zeng, M. Zha, X.X. Zhai, B.B. Zhang, H.M. Zhang, H.Y. Zhang, J.L. Zhang, J.W. Zhang, L. Zhang, L. Zhang, L.X. Zhang, P.F. Zhang, P.P. Zhang, R. Zhang, S.R. Zhang, S.S. Zhang, X. Zhang, X.P. Zhang, Y. Zhang, Y. Zhang, Y.F. Zhang, Y.L. Zhang, B. Zhao, J. Zhao, L. Zhao, L.Z. Zhao, S.P. Zhao, F. Zheng, Y. Zheng, B. Zhou, H. Zhou, J.N. Zhou, P. Zhou, R. Zhou, X.X. Zhou, C.G. Zhu, F.R. Zhu, H. Zhu, K.J. Zhu and X. Zuo. Observation of the Crab Nebula with LHAASO-KM2A − a Performance Study[J]. Chinese Physics C. doi: 10.1088/1674-1137/abd01b
F. Aharonian, Q. An, L.X. Bai, Y.X. Bai, Y.W. Bao, D. Bastieri, X.J. Bi, Y.J. Bi, H. Cai, J.T. Cai, Z. Cao, Z. Cao, J. Chang, J.F. Chang, X.C. Chang, B.M. Chen, J. Chen, L. Chen, L. Chen, L. Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Chen, N. Cheng, Y.D. Cheng, S.W. Cui, X.H. Cui, Y.D. Cui, B.Z. Dai, H.L. Dai, Z.G. Dai, D. della Volpe, B. D'Ettorre Piazzoli, X.J. Dong, J.H. Fan, Y.Z. Fan, Z.X. Fan, J. Fang, K. Fang, C.F. Feng, L. Feng, S.H. Feng, Y.L. Feng, B. Gao, C.D. Gao, Q. Gao, W. Gao, M.M. Ge, L.S. Geng, G.H. Gong, Q.B. Gou, M.H. Gu, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Guo, Y.A. Han, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. He, M. Heller, Y.K. Hor, C. Hou, X. Hou, H.B. Hu, S. Hu, S.C. Hu, X.J. Hu, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, Z.C. Huang, F. Ji, X.L. Ji, H.Y. Jia, K. Jiang, Z.J. Jiang, C. Jin, D. Kuleshov, K. Levochkin, B.B. Li, C. Li, C. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X. Li, X. Li, X.R. Li, Y. Li, Y.Z. Li, Z. Li, Z. Li, E.W. Liang, Y.F. Liang, S.J. Lin, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y.N. Liu, Z.X. Liu, W.J. Long, R. Lu, H.K. Lv, B.Q. Ma, L.L. Ma, X.H. Ma, J.R. Mao, A. Masood, W. Mitthumsiri, T. Montaruli, Y.C. Nan, B.Y. Pang, P. Pattarakijwanich, Z.Y. Pei, M.Y. Qi, D. Ruffolo, V. Rulev, A. Sáiz, L. Shao, O. Shchegolev, X.D. Sheng, J.R. Shi, H.C. Song, Yu.V. Stenkin, V. Stepanov, Q.N. Sun, X.N. Sun, Z.B. Sun, P.H.T. Tam, Z.B. Tang, W.W. Tian, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Wang, J.S. Wang, L.P. Wang, L.Y. Wang, R.N. Wang, W. Wang, W. Wang, X.G. Wang, X.J. Wang, X.Y. Wang, Y.D. Wang, Y.J. Wang, Y.P. Wang, Z. Wang, Z. Wang, Z.H. Wang, Z.X. Wang, D.M. Wei, J.J. Wei, Y.J. Wei, T. Wen, C.Y. Wu, H.R. Wu, S. Wu, W.X. Wu, X.F. Wu, S.Q. Xi, J. Xia, J.J. Xia, G.M. Xiang, G. Xiao, H.B. Xiao, G.G. Xin, Y.L. Xin, Y. Xing, D.L. Xu, R.X. Xu, L. Xue, D.H. Yan, C.W. Yang, F.F. Yang, J.Y. Yang, L.L. Yang, M.J. Yang, R.Z. Yang, S.B. Yang, Y.H. Yao, Z.G. Yao, Y.M. Ye, L.Q. Yin, N. Yin, X.H. You, Z.Y. You, Y.H. Yu, Q. Yuan, H.D. Zeng, T.X. Zeng, W. Zeng, Z.K. Zeng, M. Zha, X.X. Zhai, B.B. Zhang, H.M. Zhang, H.Y. Zhang, J.L. Zhang, J.W. Zhang, L. Zhang, L. Zhang, L.X. Zhang, P.F. Zhang, P.P. Zhang, R. Zhang, S.R. Zhang, S.S. Zhang, X. Zhang, X.P. Zhang, Y. Zhang, Y. Zhang, Y.F. Zhang, Y.L. Zhang, B. Zhao, J. Zhao, L. Zhao, L.Z. Zhao, S.P. Zhao, F. Zheng, Y. Zheng, B. Zhou, H. Zhou, J.N. Zhou, P. Zhou, R. Zhou, X.X. Zhou, C.G. Zhu, F.R. Zhu, H. Zhu, K.J. Zhu and X. Zuo. Observation of the Crab Nebula with LHAASO-KM2A − a Performance Study[J]. Chinese Physics C.  doi: 10.1088/1674-1137/abd01b shu
Milestone
Received: 2020-10-13
Article Metric

Article Views(5269)
PDF Downloads(177)
Cited by(157)
Policy on re-use
To reuse of subscription content published by CPC, the users need to request permission from CPC, unless the content was published under an Open Access license which automatically permits that type of reuse.
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Email This Article

Title:
Email:

Observation of the Crab Nebula with LHAASO-KM2A − a performance study

  • 1. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
  • 2. University of Chinese Academy of Sciences, 100049 Beijing, China
  • 3. TIANFU Cosmic Ray Research Center, Chengdu, Sichuan, China
  • 4. State Key Laboratory of Particle Detection and Electronics, China
  • 5. University of Science and Technology of China, 230026 Hefei, Anhui, China
  • 6. Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
  • 7. National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
  • 8. National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
  • 9. School of Physics, Peking University, 100871 Beijing, China
  • 10. Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
  • 11. School of Physics and Astronomy & School of Physics (Guangzhou), Sun Yat-sen University, 519000 Zhuhai, Guangdong, China
  • 12. School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
  • 13. Hebei Normal University, 050024 Shijiazhuang, Hebei, China
  • 14. School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
  • 15. School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
  • 16. Key Laboratory of Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
  • 17. Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
  • 18. Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
  • 19. Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
  • 20. School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
  • 21. College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
  • 22. Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
  • 23. School of Physics and Technology, Wuhan University, 430072 Wuhan, Hubei, China
  • 24. School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
  • 25. Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
  • 26. Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, 2 Dublin, Ireland
  • 27. Max-Planck-Institut for Nuclear Physics, P.O. Box 103980, 69029 Heidelberg, Germany
  • 28. Dipartimento di Fisica dell'Università di Napoli “Federico II”, Complesso Universitario di Monte Sant'Angelo, via Cinthia, 80126 Napoli, Italy
  • 29. Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
  • 30. Moscow Institute of Physics and Technology, 141700 Moscow, Russia
  • 31. Département de Physique Nucléaire et Corpusculaire, Faculté de Sciences, Université de Genève, 24 Quai Ernest Ansermet, 1211 Geneva, Switzerland
  • 32. Department of Physics, Faculty of Science, Mahidol University, 10400 Bangkok, Thailand

Abstract: A sub-array of the Large High Altitude Air Shower Observatory (LHAASO), KM2A is mainly designed to observe a large fraction of the northern sky to hunt for γ-ray sources at energies above 10 TeV. Even though the detector construction is still underway, half of the KM2A array has been operating stably since the end of 2019. In this paper, we present the KM2A data analysis pipeline and the first observation of the Crab Nebula, a standard candle in very high energy γ-ray astronomy. We detect γ-ray signals from the Crab Nebula in both energy ranges of 10100 TeV and >100 TeV with high significance, by analyzing the KM2A data of 136 live days between December 2019 and May 2020. With the observations, we test the detector performance, including angular resolution, pointing accuracy and cosmic-ray background rejection power. The energy spectrum of the Crab Nebula in the energy range 10-250 TeV fits well with a single power-law function dN/dE = (1.13±0.05stat±0.08sys)×1014(E/20 TeV)3.09±0.06stat±0.02sys cm2 s1 TeV1. It is consistent with previous measurements by other experiments. This opens a new window of γ-ray astronomy above 0.1 PeV through which new ultrahigh-energy γ-ray phenomena, such as cosmic PeVatrons, might be discovered.

    HTML

    I.   INTRODUCTION
    • The Crab Nebula (2 kpc from the Earth) is the remnant of a core-collapse supernova in 1054 AD, recorded in Chinese and Japanese chronicles [1]. Observations of the nebula have been carried out at every accessible wavelength, resulting in a remarkably well-determined spectral energy distribution (SED), making it a “standard candle” at several wavelengths up to very high energy (VHE). The Crab Nebula was the first VHE γ-ray source discovered by the Whipple Collaboration in 1989 [2]. To date, the VHE emission has been firmly detected by many ground-based experiments, including both air shower arrays [3-6] and imaging Cherenkov telescopes [7-9]. Although several GeV flares have been detected by AGILE and Fermi [10, 11], the γ-ray emission from the Crab Nebula is generally believed to be steady at higher energies. Recently, γ-rays with energy above 100 TeV have been detected from this source by HAWC [5] and Tibet ASγ [6]. The observed spectrum around 100 TeV is consistent with a smooth extrapolation of the lower-energy spectrum. As a reference VHE γ-ray source, the Crab Nebula is often used to check detector performance, including sensitivity, pointing accuracy, angular resolution, and so on.

      The non-thermal radiation of the Crab Nebula is characterized by an SED consisting of two components. The low-energy component extending from radio to γ-ray frequencies comes from synchrotron radiation by relativistic electrons. The high-energy component dominates the emission above 1 GeV and is produced via inverse Compton (IC) scattering of ambient seed photons by relativistic electrons [7]. The absence of a high-energy cutoff in the measured spectrum from the Crab Nebula up to about 400 TeV indicates that the primary electrons can reach at least sub-PeV energies [6].

      LHAASO (100.01E, 29.35N) is a large hybrid extensive air shower (EAS) array being constructed at Haizi Mountain, 4410 m a.s.l., in Daocheng, Sichuan province, China [12]. It is composed of three sub-arrays: a 1.3 km2 array (KM2A) for γ-ray astronomy above 10 TeV and cosmic ray physics, a 78000 m2 water Cherenkov detector array (WCDA) for TeV γ-ray astronomy, and 18 wide field-of-view air Cherenkov/fluorescence telescopes (WFCTA) for cosmic ray physics from 10 TeV to 1 EeV. A considerable proportion of the LHAASO detectors have been operating since 2019 and the whole array will be completed in 2021. KM2A has a wide field-of-view (FOV) of 2 sr and covers 60% of the sky within a diurnal observation. KM2A is unique for its unprecedented sensitivity at energies above 20 TeV. Even though only one half of KM2A has been operating for a few months, the sensitivity for γ-ray sources at energies above 50 TeV is already better than what has been achieved by previous observations.

      Here, we present the first observation of the “standard candle” Crab Nebula using the first 5 months of half-array LHAASO-KM2A data, from December 2019 to May 2020. Through this, the detector performance is thoroughly tested, including pointing accuracy, angular resolution, background rejection power, and flux determination.

    II.   KM2A AS AN ARRAY FOR EAS DETECTION

      A.   KM2A detector

    • The whole KM2A array will consist of 5195 electromagnetic detectors (EDs, 1 m2 each) and 1188 muon detectors (MDs, 36 m2 each), deployed over an area of 1.3 km2 as shown in Fig. 1. Within 575 m of the center of the array, EDs are distributed with a spacing of 15 m and MDs are distributed with a spacing of 30 m. Within the outskirt ring region of width 60 m, the spacing of EDs is enlarged to 30 m and these EDs are used to veto showers with cores located outside the central 1 km2. KM2A operates around the clock, since both EDs and MDs can work during both day and night.

      Figure 1.  (color online) Planned layout of all LHAASO-KM2A detectors. The red squares and blue circles indicate the EDs and MDs in operation, respectively. The area enclosed by the cyan line outlines the fiducial area of the current KM2A half-array used in this analysis. The central green squares indicate the LHAASO-WCDA array region.

      An ED consists of 4 plastic scintillation tiles (100 cm×25 cm×1 cm each). More details about the ED design can be found elsewhere [12]. The coated tile is covered by a 5-mm-thick lead plate to absorb low-energy charged particles in showers and convert γ-rays into electron-positron pairs, which can improve the angular and core position resolution of the array. Once high energy charged particles enter the scintillator, they lose energy and excite the scintillation medium to produce a large number of scintillation photons. The embedded wavelength-shifting fibers collect scintillation light and transmit it to a 1.5-inch photomultiplier tube (PMT). The PMT records the arrival time and number of the particles, based on which the shower parameters can be reconstructed. The detection efficiency of a typical ED for minimum ionization particles is about 98%. The time resolution of an ED is about 2 ns. The charge resolution of the particle counter is <25% for a single particle and the dynamic range is from 1 to 104 particles. The average single rate of an ED is about 1.7 kHz with a threshold equivalent to 1/3 particle at the LHAASO site.

      The MD is a pure water Cherenkov detector enclosed within a cylindrical concrete tank with an inner diameter of 6.8 m and height of 1.2 m. An 8-inch PMT is installed at the center of the top of the tank to collect the Cherenkov light produced by high energy particles as they pass through the water. More details about the MD design can be found elsewhere [12]. The whole detector is covered by a steel lid underneath soil. The thickness of the overburden soil is 2.5 m, to absorb the secondary electrons/positrons and γ-ray in showers. Thus the particles that can reach the water inside and produce Cherenkov signals are almost exclusively muons, except for those MDs located at the very central part of showers where some very high energy EM components may have a chance to punch through the screening soil layer. The detection efficiency of a typical MD to muons is >95%. The time resolution of an MD is about 10 ns. The charge resolution of the particle counter is <25% for a single muon and the dynamic range is from 1 to 104 particles. The average single rate of an MD is about 8 kHz with a threshold equivalent to 0.4 particles at the LHAASO site.

      The KM2A detectors were constructed and merged into the data acquisition system (DAQ) in stages. The first 33 EDs started operating in February 2018 and the partial array was then enlarged step by step. Nearly half of the KM2A array, including 2365 EDs and 578 MDs and covering an area of 432000 m2 as shown in Fig. 1, has been operating since 27 December 2019. The trigger logic of KM2A has been well tested and more details about it can be found elsewhere [13]. For the first half-array, at least 20 EDs firing within a window of 400 ns is required for a shower trigger, thus yielding a negligible random noise trigger rate. The event trigger rate is about 1 kHz. For each event, the DAQ records 10 μs of data from all EDs and MDs that have signals over the thresholds.

      The signal arrival time is measured by a time-to-digital converter (TDC) with a time precision of 1 ns and 2 ns for the EDs and MDs, respectively. The clock of each TDC node is synchronized via the so-called White Rabbit (WR) timing system with an accuracy of ±150 ps. To further calibrate the detector response time, an offline method using the time residuals with respect to a reconstructed shower front plane is applied. More details about this method can be found elsewhere [14]. Figure 2 shows the calibrated timing offset for the EDs and MDs. The average delay in the response of the MDs relative to that of EDs is 119 ns. The standard deviations are 0.82 ns for the EDs and 6.7 ns for the MDs, which can be taken to represent the timing uncertainty of individual detectors. The timing calibration parameters are very stable and only need to be updated about every one or two months.

      Figure 2.  Distribution of calibrated timing offset for the EDs and MDs using experimental data on 1st May 2020. The average delay in the response of the MDs relative to that of EDs is 119 ns..

      The signal charge is measured by an analog-to-digital converter (ADC). Based on the measurement of showers, the typical charge produced by a particle for each ED and MD was calibrated. With the charge calibration for each detector, the measured ADC counts were converted into the number of particles [14]. The calibration parameters for the EDs and MDS both vary with time because the system is affected by the temperature of the surroundings. The charge calibration parameters need to be updated every day. A variation of about 5% within each day remains uncorrected.

    • B.   Detector simulation

    • To estimate the properties of primary particles above the atmosphere, such as energy, composition, flux, etc, the simulation of the detector response is crucial. In this work, the cascade processes within the atmosphere were simulated via the CORSIKA code (version7.6400) [15]. To accurately simulate the KM2A detector response, a specific software, G4KM2A [16, 17], was developed in the framework of the Geant4 package (v4.10.00) [18]. De-correlated single rate noise and corresponding charges determined by the experimental data are also taken into account in this simulation. This software adopts a flexible strategy and can simulate the KM2A array with any configuration. The reliability of the detector simulation was verified via the partial KM2A array data [17]. Figure 3 shows the distribution of particle numbers recorded by a typical ED and MD. The simulation results are fairly consistent with experimental data. The change of the slope of the muon rate at Nμ>10 is caused by the contamination of punch-through high energy electromagnetic particles in the core region of the shower.

      Figure 3.  (color online) Comparison between MC simulation and experimental data of the daily averaged trigger rate distribution of a typical ED (upper panel) and MD (lower panel). The horizontal axes indicate the number of particles recorded by these detectors for the triggered events. Detectors with a particle number less than 0.2, as indicated by the vertical lines, are removed in both MC and experimental data reconstruction. The MC simulation, with five components, is normalized to the cosmic ray model of Ref. [19].

      In this work, a data sample with 2.222×108 γ-ray shower and 4.444×108 proton shower events was simulated. Both the γ-ray and proton events were sampled in the energy range from 1 TeV to 10 PeV following a power-law function with a spectral index of -2.0. The zenith angle is distributed from 0 to 70. The sample area is a circular region with a sufficiently large radius of 1000 m.

      For a specific astrophysical source, the response of KM2A depends both on the source emission spectrum and the zenith angle within the detector FOV. With the above data sample, we need to re-weight the distribution of the zenith angle for γ-rays to trace the trajectory of the astrophysical source. In this work, the simulation data sample has been re-weighted to the sky trajectory of the Crab Nebula. In general the same data sample can be re-weighted for any astrophysical source of interest within the KM2A FOV. The response of KM2A for a different emission spectrum can also be simulated via further re-weighting on the primary spectrum. For the simulation data sample, the same reconstruction and analysis algorithms as for experimental data are adopted to extract the relevant quantities.

    III.   EVENT RECONSTRUCTION
    • Each shower event is composed of many ED and MD hits, each of which has timing and charge information. In combination with the positions of these detectors, the primary direction and core location of the shower event can be reconstructed. For KM2A events, only the ED hits are used for direction, core location, and energy reconstruction. Both ED hits and MD hits are used for composition discrimination. For the experimental data selection, first the status of each detector is evaluated and abnormal EDs and MDs are removed. Then, each hit is calibrated for its timing and charge information to unify the detector response. Finally, both experimental and simulation events are processed through the same reconstruction pipeline.

      For the event reconstruction, firstly, a time window of 400 ns and a circular window with a radius of 100 m are adopted to select the most probable real secondary shower hits. With these selected hits, the core location is reconstructed using an optimized centroid method and the direction is reconstructed by fitting the shower plane. Secondly, only hits within [−30, 50] ns perpendicular to the shower plane and with a distance less than 200 m from the shower core are selected. Using these hits, the core location, number of particles (i.e., shower size, denoted as Nsize) and shower age (denoted as s) are reconstructed using a likelihood method. The direction is also updated. Finally, all the ED and MD hits within [-30, 50] ns perpendicular to the reconstructed shower plane are selected. The final surviving ED hits are used to count the number of electromagnetic particles (denoted as Ne). To reduce pollution from the punch-through high energy electromagnetic particles near the shower core, only MDs further than 15 m from the core are used to obtain the number of muons Nμ. The parameters Nμ and Ne are used to discriminate between γ-ray showers and cosmic ray showers.

      We show in Fig. 4 the pattern of a high energy γ-ray like shower (Nμ = 0) detected by KM2A from the Crab Nebula direction. Although there are many random noise hits during the recorded shower, the core location is evident from the distribution of particle density. The particle density and arrival time of the shower become clear after filtering out the noise hits via reconstruction.

      Figure 4.  (color online) A high-energy γ-ray-like shower detected by KM2A from the Crab Nebula. Left: the original particle number of detector units in KM2A. Middle: the particle number map after filtering out noise hits that are clearly irrelevant to the reconstructed shower front. The number of fired EDs after filtering out the noise hits for this event is 202. The color scale indicates the logarithm of the particle number. Right: the unit map of the arrival time. The color scale indicates the relative trigger time of the unit in ns. Erec denotes the reconstructed energy of the event. θ and ϕ denote the zenith angle and azimuth angle of the event, respectively. The magenta arrows shows the incident direction of the event. Δθ denotes the space angle between this event and the Crab Nebula direction.

      Before giving details about the core location, direction, energy reconstruction, effective area, and γ-ray/background discrimination, we list several data quality cuts: (1) the shower core is located in the fiducial area enclosed by the cyan lines in Fig. 1; (2) the zenith angle is less than 50; (3) the number of particles detected within 40 m from shower core is larger than that within 40100 m; (4) the number of EDs and the number of particles for the reconstruction are both greater than 10; (5) the shower age is between 0.6 and 2.4.

    • A.   Core reconstruction

    • In an air shower, most of the secondary particles are distributed along the trajectory of the original primary particle. The expected position of the primary particle on the ground is defined as the shower core. Determining the core location is crucial for direction reconstruction, which will use the core location as a fixed vertex when fitting the shower front to a conical shape. The simplest method to reconstruct the core position consists of calculating the average of the fired detector coordinates weighted with the number of particles (denoted as ne). This simple algorithm is called the centroid method. It is fast in computing time, but turns out to be inadequate to perform a good core reconstruction. More refined techniques are needed. In this work, an optimized centroid method is implemented first. The functions are:

      Corex=wixiwi,Corey=wiyiwi,Corez=wiziwi,

      (1)

      where wi = nee0.5(ri/15)2, (xi, yi, zi) are the ED coordinates, ri is the ED distance to the shower core, and the units are m. The calculation needs about 20 iterations before converging. The obtained core location is used to filter out noise hits and as an initial value for further core reconstruction.

      The core is further reconstructed by fitting the lateral distribution function of the shower. The lateral distribution of particle density measured by the KM2A array is fitted using the following modified Nishimura-Kamata-Greisen (NKG) function [20]:

      ρ(r)=Nsize2πr2mΓ(4.5s)Γ(s0.5)Γ(52s)(rrm)s2.5(1+rrm)s4.5,

      (2)

      where r is the distance to the air shower axis, Nsize is the total number of particles, s is the age of the shower, and rm is the Molière radius. rm is fixed at 136 m, following Ref. [21]. The reconstructed parameters are the core location, Nsize and s. The MINUIT package [22] is used to maximize the log likelihood by varying the parameters via two steps. Firstly, the core location is reconstructed with s = 1.2. Secondly, Nsize and s are reconstructed with the core location fixed at values obtained from the first step. Figure 5 shows the lateral distribution and corresponding fitting result of the γ-ray-like event shown in Fig. 4.

      Figure 5.  (color online) The solid curve shows the modified NKG function (2) that fits the data. The energy is 184±31 TeV.

      The core resolution is energy and zenith dependent. The core resolution for γ-ray events over various zenith angle ranges is shown in Fig. 6 as a function of the reconstructed energy. The resolution (denoted as R68, containing 68% of the events) is about 49 m at 20 TeV and 24 m at 100 TeV.

      Figure 6.  (color online) Core resolution of the KM2A half-array for simulated γ-ray showers over different zenith angle ranges.

    • B.   Direction reconstruction

    • The secondary particles of a shower travel roughly in a plane perpendicular to the direction of the primary particle, at the speed of light, as illustrated in Fig. 4. The direction can be reconstructed by fitting the shower plane. In fact, the shower front has a slightly conical shape, which needs to be accounted for when performing a good direction reconstruction. The arrival times of the particles are fitted by minimizing the following quantity:

      χ2=1Nhiti=Nhiti=1wi(tilxicmyicnzicαrict0),

      (3)

      where l = sinθcosϕ, m = sinθsinϕ, n = cosθ, θ and ϕ are the direction angles, α is the conical correction coefficient, and c = 0.2998 m/ns is the speed of light. The sum is over the triggered EDs, ti is the measured time of the ith ED, xi, yi, zi are the ED coordinates, ri is the ED distance from the core in the shower plane, and wi is a weight set according to the time residual and distance to the shower core, i.e., w = ζ(δt)ξ(r). It is known that the distribution of time residuals relative to the shower front is asymmetric. Multiple scattering can lead to a broader arrival time distribution for delayed particles. To optimize the fit, a specific asymmetric weight method is adopted in this work to reduce the effect of the delayed particles. The weight is set according to:

      ζ(δt)={1,(20ns<δt<0ns)e12(δt10)2,(δt>0ns,δt<20ns)δt=tilxicmyicnzicαrict0,

      (4)

      where the times are given in ns.

      It is also known that the error in the arrival time increases with the distance from the shower core. An empirical function in Ref. [23] is used in this work to calculate the weight according to the distance from the shower core. The weight is

      ξ(r)=11+(1.6(r30+1)1.5)2,

      (5)

      where r is given in m.

      The reconstructed parameters are the direction cosines l, m, α and the offset time t0. n can be determined using the parameters l and m during iteration. The zenith angle θ and azimuth angle ϕ of the shower can be derived from the parameters l and m. The angular resolution is energy and zenith angle dependent. The angular resolution for γ-ray events is shown in Fig. 7 as a function of the reconstructed energy over different zenith angle ranges. The resolution (denoted as ϕ68, containing 68% of the events) is 0.50.8 at 20 TeV and 0.24 0.3 at 100 TeV.

      Figure 7.  (color online) Angular resolution of the KM2A half-array for simulated γ-ray showers over different zenith angle ranges.

    • C.   Energy reconstruction

    • EAS arrays work by detecting the shower particles that reach ground level. A simple way to estimate a shower energy is to count the number of triggered detector elements, as used by the ARGO-YBJ experiment [24]. A robust estimator of a shower energy is to utilize the normalization of the lateral distribution function (LDF) of the shower as proposed by Ref. [25]. Usually, this is implemented by using the particle density at the optimal radius at which the uncertainty is minimized. This method has been used by Tibet ASγ [26] and HAWC [5].

      The particle density at r = 50 m (denoted as ρ50) evaluated using Eq. (2) is used to estimate the γ-ray energy in this work. The energy resolution values using densities from ρ40 to ρ70 are almost the same. Because the atmospheric depth over which the shower develops is proportional to sec(θ), the zenith angle effect has to be taken into account in the energy reconstruction. The final response function between ρ50 and the primary energy is given by:

      log(Erec/TeV)=a(θ)(log(ρ50))2+b(θ)log(ρ50)+c(θ),

      (6)

      where a(θ), b(θ) and c(θ) are known constants, which have been given as functions of sec(θ). The shower illustrated in Fig. 4 is estimated to have energy 184±31 TeV using Eq. (6).

      The energy resolution is energy and zenith angle dependent. Figure 8 shows the relation between the reconstructed energy (Erec) and the primary true energy (Etrue) over zenith angles 0-50. As the energy of the primary γ-ray increases, the shower maximum becomes closer to the altitude of the observatory, leading to better energy resolution. As the zenith angle increases, the shower maximum becomes higher, leading to a worse energy resolution. A slight asymmetry is visible in the reconstructed energy compared to the true energy. This is caused by the underestimation of energy for a fraction of showers, due to large fluctuations during the cascade process or large core reconstruction error. In this work, events with reconstructed energy above 10 TeV are divided into five bins per decade. The energy resolution for each energy bin over different zenith angles is shown in Fig. 8. For showers with zenith angle less than 20, the resolution is about 24% at 20 TeV and 13% at 100 TeV.

      Figure 8.  (color online) Left: event-by-event comparison of the primary true energy and the reconstructed energy for simulated γ-ray events over zenith angles 0-50. The color represents the log probability density within each Erec bin. The dotted line is the identity line. Middle: energy resolution function of showers in the energy range 1001000 TeV with zenith angle 0-20. Right: dependence of energy resolution, defined as the half 68% width of the resolution function, on each reconstructed energy bin. The three colors indicate the resolutions over different zenith angle ranges.

    • D.   γ-ray/background discrimination

    • Most of the events recorded by KM2A are cosmic ray induced showers, which constitute the chief background for γ-ray observations. Considering that γ-ray induced showers are muon-poor and cosmic ray induced showers are muon-rich, the ratio between the measured muons and electrons is used to discriminate primary γ-rays from cosmic nuclei. The ratio is defined as:

      R=log(Nμ+0.0001Ne),

      (7)

      where Nμ and Ne are defined at the start of Sec. III, and 0.0001 is used to show the cases with Nμ = 0. Figure 9 shows the ratio as a function of the reconstructed energy for γ-rays and protons. According to Fig. 9, the distributions of R for γ-rays and protons partly overlap at low energies due to wide Nμ and Ne fluctuations. The separation between γ-rays and protons becomes clearer at higher energies. For proton showers, the number of electrons detected by EDs is about 10 times the number of muons detected by MDs. This factor is about 1000 for γ-ray showers. The shower illustrated in Fig. 4 is a γ-ray-like event with Nμ = 0.

      Figure 9.  (color online) Scatter plot of R as defined in Eq. (7) vs. reconstructed energy using simulated γ-ray-induced (upper panel) and proton-induced (lower panel) air showers, respectively. The color represents the log probability density within each Erec bin. The dotted lines indicate the γ-ray/background discrimination cuts used in this work.

      γ-ray-like events are selected using simple cuts on the parameter R. These cuts depend on energy and are optimized to maximize the detection significance (defined by the Li-Ma formula, Eq. (17) of Ref. [27]) for a typical Crab-like source. This optimization consists of a mixture of γ-ray simulation and real off-source data recorded by KM2A, which are taken to represent the cosmic ray background. These cuts are shown in Fig. 9. Figure 10 shows the survival fraction for γ-ray showers (from simulation) along with the measured survival fraction for the cosmic ray background (from observational data) under these cuts. The fraction for γ-ray showers varies from 48% to 93%. The rejection power of cosmic ray induced showers is better than 4×103 at energies above 100 TeV.

      Figure 10.  (color online) Survival fraction of γ-ray (according to simulation) and cosmic ray background events (according to observational data) in different energy bins after the discrimination cuts.

      It is worth noting that these cuts are optimized for point-like sources. The rejection power can be improved using stricter cuts. For example, if the survival fraction for γ-ray showers were restricted to 60%, the rejection power for cosmic rays would be better than 2×104 at energies above 100 TeV.

    • E.   Effective area

    • The effective area of the KM2A for detecting γ-ray showers is calculated using the simulation. It is energy and zenith angle dependent. Figure 11 shows the effective areas at four zenith angles θ=10, 30, 40 and 50. The data quality and γ-ray/background discrimination cuts have been applied here. The effective area increases with energy and gradually reaches a constant value at energies above 30 TeV for zenith angles less than 30. The effective area is about 3×105 m2 at 20 TeV for a zenith angle of 10.

      Figure 11.  (color online) Effective area of the KM2A for γ-ray showers at four zenith angles after applying the data quality and γ-ray/background discrimination cuts.

    IV.   ANALYSIS AND RESULTS

      A.   Background estimation

    • For the analysis presented in this paper, only events with zenith angles less than 50 degrees and energies above 10 TeV are used. The data quality cuts and the γ-ray/background discrimination cuts discussed in the previous section are applied. The data sets are divided into five groups per decade according to the reconstructed energy. For the data set in each group, the sky map in celestial coordinates (right ascension and declination) is divided into a grid of 0.1×0.1 pixels which are filled with the number of the detected events according to their reconstructed arrival direction (event map). To obtain the excess of γ-induced showers in each pixel, the “direct integration method” [28] is adopted to estimate the number of cosmic ray background events in the bin. The direct integration method uses events with the same direction in local coordinates but different arrival times to estimate the background. In this work, we integrated 24 hours of data to estimate the detector acceptance for different directions. The integral acceptance combined with the event rate is used to estimate the number of background events in each pixel (background map). This method is widely used for the ARGO-YBJ [3] and HAWC [5] experiments.

      The background map is then subtracted from the event map to obtain the source map which is used to extract the γ-ray signal from any specific source. The events in a circular area centered on each pixel within an angular radius of the KM2 point spread function (PSF) are summed. The number of excess events centered on the Crab Nebula in each energy bin is used to estimate its γ-ray spectrum.

    • B.   Data selection and significance

    • The LHAASO-KM2A data used in this analysis were collected from 27th December 2019 to 28th May 2020. As this was the beginning of operation, some detectors still needed debugging during this period. To obtain a reliable data sample, some quality selections have been applied according to the data status. The main selection is to require the number of live EDs >2100 and number of live MDs >500. Figure 12 shows the daily duty cycle after these selections. The average duty cycle is 87.7% during this period. The total effective observation time is 136.0 days. With a trigger rate of about 900 Hz, the number of events recorded by KM2A is 1.0×1010. After the data quality cuts and the γ-ray/background discrimination cuts, the number of events used in this work is 6×107.

      Figure 12.  (color online) Daily duty cycle of 1/2 KM2A operation during the period from December 2019 to May 2020.

      Using these data, the sky is surveyed in the region with declination within 15<Dec<75. In order to extract a smooth significance map, the likelihood method (see Eq. 2.5 in Ref. [29]) is adopted to estimate the significance of the γ-ray signal. A 2-dimensional Gaussian is used to approximately describe the PSF of the KM2A detector. The width of the Gaussian is set to be σR = ϕ68/1.51, which is obtained using the simulation sample. A likelihood ratio test is performed between the background-only model and the one-source model. The test statistic (TS) is used to estimate the significance S = TS. This method is realized using the MINUIT package. The pre-trial significance distribution in the whole sky region at energies above 25 TeV is shown in Fig. 13. The distribution closely follows a standard Gaussian distribution, except for a tail with large positive values, due to excesses from γ-ray emission from the Galactic Plane including the Crab Nebula. After excluding the Galactic region with latitude |b|<12, the distribution, with a mean value of −0.05 and σ = 1.007, closely follows a standard normal distribution.

      Figure 13.  (color online) Pre-trial significance distribution of events with Erec>25 TeV for the whole KM2A sky region (blue) and the portion of the sky outside the Galactic Plane region with |b|>12 (red), which represents diffuse background events.

      Focusing on the Crab Nebula region, a clear signal is observed in different energy ranges, i.e., 19.2 σ at 10-25 TeV, 28.0 σ at 25-100 TeV and 14.7 σ at >100 TeV (see Fig. 14). A signal with such a level of significance allows us to estimate the pointing error of the detector, the angular resolution for γ-ray showers, and the γ-ray spectrum from the Crab Nebula.

      Figure 14.  (color online) Significance maps centered on the Crab Nebula at three energy ranges. σS is the sigma of the 2-dimension Gaussian taken according to the PSF of KM2A. The color represents the significance. S is the maximum value in the map.

    • C.   Pointing accuracy

    • To estimate the position of the γ-ray signal around the Crab Nebula direction at different energy bins, a 2-dimensional Gaussian is used to fit the event excess map. The positions obtained, in right ascension (R.A.) and declination (Dec) relative to the known Crab position (R.A. = 83.63, Dec = 22.02, J2000.0 epoch) are shown in Fig. 15. The last energy point in Fig. 15 is obtained using the bins with 100 TeV<Erec<1 PeV. When a constant value is used to fit the positions at all energies, we obtain ΔR.A. = −0.024±0.016, ΔDec = 0.035±0.014.

      Figure 15.  (color online) The centroid of the significance map around the Crab Nebula in R.A. and Dec directions as a function of energy. The dashed lines show constant values that fit the centroid for all energies.

      The Crab Nebula can be observed by KM2A for about 7.4 hr per day with a zenith angle less than 50, culminating at 7. The observation time for zenith angle less than 30 is 4.3 hr per day. To check for a possible systematic pointing error at large zenith angles, the observation of the Crab Nebula at zenith angles higher than 30 is analyzed separately. At energies >25 TeV, the achieved significance is 12σ, and the obtained position relative to the known Crab position is ΔR.A. = −0.073±0.042, ΔDec = 0.074±0.032. This result is roughly consistent with that obtained using all data within statistical errors.

      According to these observations of the Crab Nebula, the pointing error of KM2A for γ-ray events can be demonstrated to be less than 0.1.

    • D.   Angular resolution

    • According to a recent HESS measurement [30], the intrinsic extension of TeV γ-ray emission from the Crab Nebula is about 0.014. Compared with the PSF of the KM2A detector, the intrinsic extension is negligible. Therefore, the angular distribution of γ-rays detected by KM2A from the Crab Nebula should be mainly due to the detector angular resolution. Figure 16 shows the measured angular distribution in KM2A data in two energy ranges. The solid-angle density of recorded events in the vicinity of the Crab Nebula is shown as a function of θ2, where θ is the angle to the Crab direction. The distribution is generally consistent with the angular resolution obtained using MC simulations. For each energy bin, a Gaussian function is used to fit the angular distribution shown in the left-hand and middle panels of Fig. 16. The resulting σPSF from Crab data is consistent with simulations, as shown in the right-hand panel of Fig. 16.

      Figure 16.  (color online) Distribution of events as a function of the square of the angle to the Crab direction for both experimental data and MC simulation. Two energy ranges, i.e., 2540 TeV (left) and 1001000 TeV (middle) are shown. The right-hand panel shows the σPSF as a function of energy.

    • E.   Spectral energy distribution

    • The γ-ray flux from the Crab Nebula is estimated using the number of excess events (Ns) and the corresponding statistical uncertainty (σNs) in each energy bin. The γ-ray emission from the Crab Nebula is assumed to follow a power-law spectrum f(E)=JEα. The response of the KM2A detector was simulated by tracing the trajectory of the Crab Nebula within the FOV of KM2A. The best-fit values of J and α are obtained by minimizing a χ2 function for 7 energy bins:

      χ2=7i=1(NsiNMCi(J,α)σNsi)2.

      (8)

      Since the fit of the spectrum is forward-folded, the biases and energy resolution in the energy assignments are taken into account. The influence coming from the asymmetry in energy resolution shown in Fig. 8 can be neglected. The resulting differential flux (TeV1 cm2 s1) in the energy range from 10 to 250 TeV is:

      f(E)=(1.13±0.05stat±0.08sys)×1014(E20TeV)3.09±0.06stat±0.02sys.

      (9)

      The χ2 of the fit is 1.8 for 5 degrees of freedom, which favors a pure power-law description of the spectrum. The SED is shown in Fig. 17 and is also listed in Table 1. The SED obtained in this work is in agreement with previous observations by other detectors, such as HEGRA [7], HAWC [5] and Tibet AS-γ [6]. The discrepancy between this result and that measured by H.E.S.S. [8] and MAGIC [9] in the overlapping region may be due to their large systematic uncertainties at the highest energy region.

      log(Erec/TeV) Emiddle Non Nb Differential Flux
      (TeV) (TeV1 cm2 s1))
      [1.0, 1.2] 12.6 10810 9620 (4.52±0.40)×1014
      [1.2, 1.4] 20.0 2513 1902 (1.13±0.09)×1014
      [1.4, 1.6] 31.6 294 81 (2.98±0.24)×1015
      [1.6, 1.8] 50.1 91 9.3 (6.64±0.78)×1016
      [1.8, 2.0] 79.4 47 4.0 (1.43±0.23)×1016
      [2.0, 2.2] 126 21 0.50 (4.05±0.91)×1017
      [2.2, 2.4] 200 7 0.11 (8.0+3.83.2)×1018

      Table 1.  Energy and differential flux as shown in Fig. 17

      Figure 17.  (color online) Spectrum of the Crab Nebula measured by KM2A, in red, with spectra measured by other experiments, in various colors as indicated in the legend. The dotted line indicates the best-fit result using a power-law function. References for other experiments are: HEGRA [7], H.E.S.S. [8], MAGIC [9], ARGO-YBJ [3], HAWC [5], Tibet AS-γ [6].

    • F.   Systematic uncertainties

    • The systematic errors affecting the SED have been investigated by studying the variation of the Crab Nebula spectrum under various assumptions. During the period of interest, a few percent of detector units were being debugged. The number of operating units varied with time. A typical layout is taken into account in the detector simulation to mimic the status of the array. The uncertainty is estimated by using different configurations in the detector simulation. The variation of detector number affects the γ-ray/background separation, while the impact on γ-rays is weaker than on the background. The maximum variation in flux introduced by detector layout is less than 2%. The main systematic error comes from the atmospheric model used in the Monte Carlo simulations. The atmospheric density profile in reality always deviates from the model provided in Ref. [15], due to seasonal and daily changes. According to the variation of event rate during the operational period, the total systematic uncertainty is estimated to be 7% for the flux and 0.02 for the spectral index.

    V.   SUMMARY
    • Using the first five months of data from the KM2A half-array, a standard candle at very high energy − the Crab Nebula − has been observed to investigate the detector performance and corresponding data analysis pipeline for γ-rays. The statistical significance of the γ-ray signal from Crab Nebula is 28.0 σ at 25-100 TeV and 14.7 σ at >100 TeV. The γ-ray angular distributions around the source are fairly consistent with the point spread function obtained by simulations. According to measurement of the centroids of the significance maps of the Crab Nebula at different energies, the pointing error of KM2A is found to be less than 0.1. The spectrum from 10 TeV to 250 TeV can be fitted well with a power-law function with a spectral index of 3.09±0.06stat±0.02sys. This result is quite consistent with previous measurements by other experiments. The overall systematic error of KM2A on spectral measurement is estimated to be 7% in flux and 0.02 in spectral index.

      The KM2A data analysis pipeline presented in this work is not specifically designed for the Crab Nebula but is also generally useful for surveying the whole sky in the range of declination from −15 to 75 and the corresponding measurements for source morphology and energy spectra. This opens a new window of γ-ray astronomy above 0.1 PeV. A new era of ultrahigh-energy γ-ray astronomy foreshadows a fruitful future for fundamental discoveries.

    ACKNOWLEDGEMENTS
    • The authors would like to thank all staff members who work at the LHAASO site above 4400 meters above sea level year-round to maintain the detector and keep the electrical power supply and other components of the experiment operating smoothly. We are grateful to the Chengdu Management Committee of Tianfu New Area for their constant financial support of research with LHAASO data.

Reference (30)

目录

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return