15 results on '"Kukec Mezek, Gasper"'
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2. The CoMET multiperspective event tracker for wide field-of-view gamma-ray astronomy
- Author
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Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, Vercellone, Stefano, Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, and Vercellone, Stefano
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
3. Expected performance of the ALTO particle detector array designed for 200 GeV - 50 TeV gamma-ray astronomy
- Author
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Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, Ernenwein, Jean-Pierre, Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, and Ernenwein, Jean-Pierre
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
4. Expected performance of the ALTO particle detector array designed for 200 GeV - 50 TeV gamma-ray astronomy
- Author
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Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, Ernenwein, Jean-Pierre, Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, and Ernenwein, Jean-Pierre
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
5. The CoMET multiperspective event tracker for wide field-of-view gamma-ray astronomy
- Author
-
Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, Vercellone, Stefano, Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, and Vercellone, Stefano
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
6. Expected performance of the ALTO particle detector array designed for 200 GeV - 50 TeV gamma-ray astronomy
- Author
-
Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, Ernenwein, Jean-Pierre, Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, and Ernenwein, Jean-Pierre
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
7. The CoMET multiperspective event tracker for wide field-of-view gamma-ray astronomy
- Author
-
Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, Vercellone, Stefano, Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, and Vercellone, Stefano
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
8. Expected performance of the ALTO particle detector array designed for 200 GeV - 50 TeV gamma-ray astronomy
- Author
-
Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, Ernenwein, Jean-Pierre, Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, and Ernenwein, Jean-Pierre
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
9. The CoMET multiperspective event tracker for wide field-of-view gamma-ray astronomy
- Author
-
Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, Vercellone, Stefano, Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, and Vercellone, Stefano
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
10. Expected performance of the ALTO particle detector array designed for 200 GeV - 50 TeV gamma-ray astronomy
- Author
-
Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, Ernenwein, Jean-Pierre, Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Bylund, Tomas, Kukec Mezek, Gasper, and Ernenwein, Jean-Pierre
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
11. The CoMET multiperspective event tracker for wide field-of-view gamma-ray astronomy
- Author
-
Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, Vercellone, Stefano, Becherini, Yvonne, Bylund, Tomas, Ernenwein, Jean-Pierre, Kukec Mezek, Gasper, Punch, Michael, Romano, Patrizia, Saleh, Ahmed, Senniappan, Mohanraj, Thoudam, Satyendra, Tluczykont, Martin, and Vercellone, Stefano
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2022
12. Studies of Gamma-Ray Shower Reconstruction UsingDeep Learning
- Author
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Bylund, Tomas, Kukec Mezek, Gasper, Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, Ernenwein, Jean-Pierre, Bylund, Tomas, Kukec Mezek, Gasper, Senniappan, Mohanraj, Becherini, Yvonne, Punch, Michael, Thoudam, Satyendra, and Ernenwein, Jean-Pierre
- Abstract
The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
- Published
- 2021
13. Search for Dark Matter Annihilation Signals from Unidentified Fermi-LAT Objects with HESS
- Author
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Abdalla, H., Aharonian, F., Ait Benkhali, F., Anguner, E. O., Arcaro, C., Armand, C., Armstrong, T., Ashkar, H., Backes, M., Baghmanyan, V., Barbosa-Martins, V., Barnacka, A., Barnard, M., Becherini, Yvonne, Berge, D., Bernloehr, K., Bi, B., Bottcher, M., Boisson, C., Bolmont, J., de Bony de Lavergne, M., Breuhaus, M., Brose, R., Brun, F., Bulik, T., Bylund, Tomas, Cangemi, F., Caroff, S., Casanova, S., Chambery, P., Catalano, J., Chand, T., Chen, A., Cotter, G., Curylo, M., Dalgleish, H., Mbarubucyeye, J. Damascene, Davids, I. D., Davies, J., Devin, J., Djannati-Atai, A., Dmytriiev, A., Donath, A., Doroshenko, V., Dreyer, L., du Plessis, L., Duffy, C., Egberts, K., Einecke, S., Emery, G., Ernenwein, J. -P, Feijen, K., Fegan, S., Fiasson, A., de Clairfontaine, G. Fichet, Fontaine, G., Funk, S., Fuessling, M., Gabici, S., Gallant, Y. A., Ghafourizade, S., Giavitto, G., Giunti, L., Glawion, D., Glicenstein, J. F., Grondin, M. -H, Hattingh, S., Haupt, M., Hermann, G., Hinton, J. A., Hofmann, W., Hoischen, C., Holch, T. L., Holler, M., Horbe, M., Horns, D., Huang, Z., Huber, D., Jamrozy, M., Jankowsky, D., Jankowsky, F., Joshi, V., Jung-Richardt, I., Kasai, E., Katarzynski, K., Katz, U., Khangulyan, D., Khelifi, B., Klepser, S., Kluzniak, W., Komin, Nu., Konno, R., Kosack, K., Kostunin, D., Kreter, M., Kukec Mezek, Gasper, Kundu, A., Lamanna, G., Le Stum, S., Lemiere, A., Lemoine-Goumard, M., Lenain, J. -P, Leuschner, F., Levy, C., Luashvili, A., Lohse, T., Lypova, I., Mackey, J., Majumdar, J., Malyshev, D., Marandon, V., Marchegiani, P., Marcowith, A., Mares, A., Marti-Devesa, G., Marx, R., Maurin, G., Meintjes, P. J., Meyer, M., Mitchell, A., Moderski, R., Mohrmann, L., Montanari, A., Moore, C., Morris, P., Moulin, E., Muller, J., Murach, T., Nakashima, K., Nayerhoda, A., de Naurois, M., Ndiyavala, H., Niemiec, J., Noel, A., Oberholzer, L., O'Brien, P., Ohm, S., Olivera-Nieto, L., de Ona Wilhelmi, E., Ostrowski, M., Panter, M., Panny, S., Parsons, R. D., Peron, G., Pita, S., Poireau, V., Prokhorov, D. A., Prokoph, H., Puhlhoefer, G., Punch, Michael, Quirrenbach, A., Reichherzer, P., Reimer, A., Reimer, O., Remy, Q., Renaud, M., Rieger, F., Romoli, C., Rowell, G., Rudak, B., Rueda Ricarte, H., Ruiz-Velasco, E., Sahakian, V., Sailer, S., Salzmann, H., Sanchez, D. A., Santangelo, A., Sasaki, M., Schaefer, J., Schussler, F., Schutte, H. M., Schwanke, U., Senniappan, Mohanraj, Seyffert, A. S., Shapopi, J. N. S., Shiningayamwe, K., Simoni, R., Sinha, A., Spackman, H., Sol, H., Specovius, A., Spencer, S., Spir-Jacob, M., Stawarz, L., Sun, L., Steenkamp, R., Stegmann, C., Steinmassl, S., Steppa, C., Takahashi, T., Tanaka, T., Tavernier, T., Taylor, A. M., Terrier, R., Thorpe-Morgan, C., Thiersen, J. H. E., Tluczykont, M., Tomankova, L., Tsirou, M., Tsuji, M., Tuffs, R., Uchiyama, Y., van der Walt, D. J., van Eldik, C., van Rensburg, C., van Soelen, B., Vasileiadis, G., Veh, J., Venter, C., Vincent, P., Viana, A., Vink, J., Voelk, H. J., Wagner, S. J., Werner, F., White, R., Wierzcholska, A., Wong, Yu Wun, Yassin, H., Yusafzai, A., Zacharias, M., Zanin, R., Zargaryan, D., Zdziarski, A. A., Zech, A., Zhu, S. J., Zmija, A., Zorn, J., Zouari, S., Zywucka, N., Abdalla, H., Aharonian, F., Ait Benkhali, F., Anguner, E. O., Arcaro, C., Armand, C., Armstrong, T., Ashkar, H., Backes, M., Baghmanyan, V., Barbosa-Martins, V., Barnacka, A., Barnard, M., Becherini, Yvonne, Berge, D., Bernloehr, K., Bi, B., Bottcher, M., Boisson, C., Bolmont, J., de Bony de Lavergne, M., Breuhaus, M., Brose, R., Brun, F., Bulik, T., Bylund, Tomas, Cangemi, F., Caroff, S., Casanova, S., Chambery, P., Catalano, J., Chand, T., Chen, A., Cotter, G., Curylo, M., Dalgleish, H., Mbarubucyeye, J. Damascene, Davids, I. D., Davies, J., Devin, J., Djannati-Atai, A., Dmytriiev, A., Donath, A., Doroshenko, V., Dreyer, L., du Plessis, L., Duffy, C., Egberts, K., Einecke, S., Emery, G., Ernenwein, J. -P, Feijen, K., Fegan, S., Fiasson, A., de Clairfontaine, G. Fichet, Fontaine, G., Funk, S., Fuessling, M., Gabici, S., Gallant, Y. A., Ghafourizade, S., Giavitto, G., Giunti, L., Glawion, D., Glicenstein, J. F., Grondin, M. -H, Hattingh, S., Haupt, M., Hermann, G., Hinton, J. A., Hofmann, W., Hoischen, C., Holch, T. L., Holler, M., Horbe, M., Horns, D., Huang, Z., Huber, D., Jamrozy, M., Jankowsky, D., Jankowsky, F., Joshi, V., Jung-Richardt, I., Kasai, E., Katarzynski, K., Katz, U., Khangulyan, D., Khelifi, B., Klepser, S., Kluzniak, W., Komin, Nu., Konno, R., Kosack, K., Kostunin, D., Kreter, M., Kukec Mezek, Gasper, Kundu, A., Lamanna, G., Le Stum, S., Lemiere, A., Lemoine-Goumard, M., Lenain, J. -P, Leuschner, F., Levy, C., Luashvili, A., Lohse, T., Lypova, I., Mackey, J., Majumdar, J., Malyshev, D., Marandon, V., Marchegiani, P., Marcowith, A., Mares, A., Marti-Devesa, G., Marx, R., Maurin, G., Meintjes, P. J., Meyer, M., Mitchell, A., Moderski, R., Mohrmann, L., Montanari, A., Moore, C., Morris, P., Moulin, E., Muller, J., Murach, T., Nakashima, K., Nayerhoda, A., de Naurois, M., Ndiyavala, H., Niemiec, J., Noel, A., Oberholzer, L., O'Brien, P., Ohm, S., Olivera-Nieto, L., de Ona Wilhelmi, E., Ostrowski, M., Panter, M., Panny, S., Parsons, R. D., Peron, G., Pita, S., Poireau, V., Prokhorov, D. A., Prokoph, H., Puhlhoefer, G., Punch, Michael, Quirrenbach, A., Reichherzer, P., Reimer, A., Reimer, O., Remy, Q., Renaud, M., Rieger, F., Romoli, C., Rowell, G., Rudak, B., Rueda Ricarte, H., Ruiz-Velasco, E., Sahakian, V., Sailer, S., Salzmann, H., Sanchez, D. A., Santangelo, A., Sasaki, M., Schaefer, J., Schussler, F., Schutte, H. M., Schwanke, U., Senniappan, Mohanraj, Seyffert, A. S., Shapopi, J. N. S., Shiningayamwe, K., Simoni, R., Sinha, A., Spackman, H., Sol, H., Specovius, A., Spencer, S., Spir-Jacob, M., Stawarz, L., Sun, L., Steenkamp, R., Stegmann, C., Steinmassl, S., Steppa, C., Takahashi, T., Tanaka, T., Tavernier, T., Taylor, A. M., Terrier, R., Thorpe-Morgan, C., Thiersen, J. H. E., Tluczykont, M., Tomankova, L., Tsirou, M., Tsuji, M., Tuffs, R., Uchiyama, Y., van der Walt, D. J., van Eldik, C., van Rensburg, C., van Soelen, B., Vasileiadis, G., Veh, J., Venter, C., Vincent, P., Viana, A., Vink, J., Voelk, H. J., Wagner, S. J., Werner, F., White, R., Wierzcholska, A., Wong, Yu Wun, Yassin, H., Yusafzai, A., Zacharias, M., Zanin, R., Zargaryan, D., Zdziarski, A. A., Zech, A., Zhu, S. J., Zmija, A., Zorn, J., Zouari, S., and Zywucka, N.
- Abstract
Cosmological N-body simulations show that Milky Way-sized galaxies harbor a population of unmerged dark matter (DM) subhalos. These subhalos could shine in gamma-rays and eventually be detected in gamma-ray surveys as unidentified sources. We performed a thorough selection among unidentified Fermi-Large Area Telescope Objects (UFOs) to identify them as possible tera-electron-volt-scale DM subhalo candidates. We search for very-high-energy (E greater than or similar to 100 GeV) gamma-ray emissions using H.E.S.S. observations toward four selected UFOs. Since no significant very-high-energy gamma-ray emission is detected in any data set of the four observed UFOs or in the combined UFO data set, strong constraints are derived on the product of the velocity-weighted annihilation cross section sigma v by the J factor for the DM models. The 95% confidence level observed upper limits derived from combined H.E.S.S. observations reach sigma vJ values of 3.7 x 10(-5) and 8.1 x 10(-6) GeV(2 )cm(-2 )s(-1) in the W (+) W (-) and tau (+) tau (-) channels, respectively, for a 1 TeV DM mass. Focusing on thermal weakly interacting massive particles, the H.E.S.S. constraints restrict the J factors to lie in the range 6.1 x 10(19)-2.0 x 10(21) GeV(2 )cm(-5) and the masses to lie between 0.2 and 6 TeV in the W (+) W (-) channel. For the tau (+) tau (-) channel, the J factors lie in the range 7.0 x 10(19)-7.1 x 10(20) GeV(2 )cm(-5) and the masses lie between 0.2 and 0.5 TeV. Assuming model-dependent predictions from cosmological N-body simulations on the J-factor distribution for Milky Way-sized galaxies, the DM models with masses >0.3 TeV for the UFO emissions can be ruled out at high confidence level.
- Published
- 2021
- Full Text
- View/download PDF
14. LMC N132D : A mature supernova remnant with a power-law gamma-ray spectrum extending beyond 8 TeV
- Author
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Abdalla, H., Aharonian, F., Benkhali, F. Ait, Anguner, E. O., Arcaro, C., Armand, C., Armstrong, T., Ashkar, H., Backes, M., Baghmanyan, V, Martins, V. Barbosa, Barnacka, A., Barnard, M., Batzofin, R., Becherini, Yvonne, Berge, D., Bernloehr, K., Bi, B., Boettcher, M., Boisson, C., Bolmont, J., de Lavergne, M. de Bony, Breuhaus, M., Brose, R., Brun, F., Bulik, T., Bylund, Tomas, Cangemi, F., Caroff, S., Casanova, S., Catalano, J., Chambery, P., Chand, T., Chen, A., Cotter, G., Curylo, M., Mbarubucyeye, J. Damascene, Davids, I. D., Davies, J., Devin, J., Djannati-Atai, A., Dmytriiev, A., Donath, A., Doroshenko, V, Dreyer, L., Du Plessis, L., Duffy, C., Egberts, K., Einecke, S., Ernenwein, J-P, Fegan, S., Feijen, K., Fiasson, A., de Clairfontaine, G. Fichet, Fontaine, G., Lott, F., Fussling, M., Funk, S., Gabici, S., Gallant, Y. A., Giavitto, G., Giunti, L., Glawion, D., Glicenstein, J. F., Grondin, M-H, Hattingh, S., Haupt, M., Hermann, G., Hinton, J. A., Hofmann, W., Hoischen, C., Holch, T. L., Holler, M., Hoerbe, M., Horns, D., Huang, Zhiqiu, Huber, D., Jamrozy, M., Jankowsky, F., Joshi, V, Jung-Richardt, I, Kasai, E., Katarzynski, K., Katz, U., Khangulyan, D., Khelifi, B., Klepser, S., Kluzniak, W., Komin, Nu, Konno, R., Kosack, K., Kostunin, D., Kreter, M., Kukec Mezek, Gasper, Kundu, A., Lamanna, G., Le Stum, S., Lemiere, A., Lemoine-Goumard, M., Lenain, J-P, Leuschner, F., Levy, C., Lohse, T., Luashvili, A., Lypova, I, Mackey, J., Majumdar, J., Malyshev, D., Marandon, V, Marchegiani, P., Marcowith, A., Mares, A., Marti-Devesa, G., Marx, R., Maurin, G., Meintjes, P. J., Meyer, M., Mitchell, A., Moderski, R., Mohrmann, L., Montanari, A., Moore, C., Moulin, E., Muller, J., Murach, T., Nakashima, K., de Naurois, M., Nayerhoda, A., Ndiyavala, H., Niemiec, J., Noel, A. Priyana, O'Brien, P., Oberholzer, L., Odaka, H., Ohm, S., Olivera-Nieto, L., Wilhelmi, E. de Ona, Ostrowski, M., Panny, S., Panter, M., Parsons, R. D., Peron, G., Pita, S., Poireau, V, Prokhorov, D. A., Prokoph, H., Puehlhofer, G., Punch, Michael, Quirrenbach, A., Reichherzer, P., Reimer, A., Reimer, O., Remy, Q., Renaud, M., Reville, B., Rieger, F., Romoli, C., Rowell, G., Rudak, B., Ricarte, H. Rueda, Ruiz-Velasco, E., Sahakian, V, Sailer, S., Salzmann, H., Sanchez, D. A., Santangelo, A., Sasaki, M., Schaefer, J., Schuessler, F., Schutte, H. M., Schwanke, U., Senniappan, Mohanraj, Seyffert, A. S., Shapopi, J. N. S., Shiningayamwe, K., Simoni, R., Sinha, A., Sol, H., Specovius, A., Spencer, S., Spir-Jacob, M., Stawarz, L., Steenkamp, R., Stegmann, C., Steinmassl, S., Steppa, C., Sun, L., Takahashi, T., Tanaka, T., Tavernier, T., Taylor, A. M., Terrier, R., Thiersen, J. H. E., Thorpe-Morgan, C., Tluczykont, M., Tomankova, L., Tsirou, M., Tsuji, N., Tuffs, R., Uchiyama, Y., van der Walt, D. J., van Eldik, C., van Rensburg, C., van Soelen, B., Vasileiadis, G., Veh, J., Venter, C., Vincent, P., Vink, J., Voelk, H. J., Wagner, S. J., Watson, J., Werner, F., White, R., Wierzcholska, A., Wong, Yu Wun, Yassin, H., Yusafzai, A., Zacharias, M., Zanin, R., Zargaryan, D., Zdziarski, A. A., Zech, A., Zhu, S. J., Zmija, A., Zouari, S., Zywucka, N., Abdalla, H., Aharonian, F., Benkhali, F. Ait, Anguner, E. O., Arcaro, C., Armand, C., Armstrong, T., Ashkar, H., Backes, M., Baghmanyan, V, Martins, V. Barbosa, Barnacka, A., Barnard, M., Batzofin, R., Becherini, Yvonne, Berge, D., Bernloehr, K., Bi, B., Boettcher, M., Boisson, C., Bolmont, J., de Lavergne, M. de Bony, Breuhaus, M., Brose, R., Brun, F., Bulik, T., Bylund, Tomas, Cangemi, F., Caroff, S., Casanova, S., Catalano, J., Chambery, P., Chand, T., Chen, A., Cotter, G., Curylo, M., Mbarubucyeye, J. Damascene, Davids, I. D., Davies, J., Devin, J., Djannati-Atai, A., Dmytriiev, A., Donath, A., Doroshenko, V, Dreyer, L., Du Plessis, L., Duffy, C., Egberts, K., Einecke, S., Ernenwein, J-P, Fegan, S., Feijen, K., Fiasson, A., de Clairfontaine, G. Fichet, Fontaine, G., Lott, F., Fussling, M., Funk, S., Gabici, S., Gallant, Y. A., Giavitto, G., Giunti, L., Glawion, D., Glicenstein, J. F., Grondin, M-H, Hattingh, S., Haupt, M., Hermann, G., Hinton, J. A., Hofmann, W., Hoischen, C., Holch, T. L., Holler, M., Hoerbe, M., Horns, D., Huang, Zhiqiu, Huber, D., Jamrozy, M., Jankowsky, F., Joshi, V, Jung-Richardt, I, Kasai, E., Katarzynski, K., Katz, U., Khangulyan, D., Khelifi, B., Klepser, S., Kluzniak, W., Komin, Nu, Konno, R., Kosack, K., Kostunin, D., Kreter, M., Kukec Mezek, Gasper, Kundu, A., Lamanna, G., Le Stum, S., Lemiere, A., Lemoine-Goumard, M., Lenain, J-P, Leuschner, F., Levy, C., Lohse, T., Luashvili, A., Lypova, I, Mackey, J., Majumdar, J., Malyshev, D., Marandon, V, Marchegiani, P., Marcowith, A., Mares, A., Marti-Devesa, G., Marx, R., Maurin, G., Meintjes, P. J., Meyer, M., Mitchell, A., Moderski, R., Mohrmann, L., Montanari, A., Moore, C., Moulin, E., Muller, J., Murach, T., Nakashima, K., de Naurois, M., Nayerhoda, A., Ndiyavala, H., Niemiec, J., Noel, A. Priyana, O'Brien, P., Oberholzer, L., Odaka, H., Ohm, S., Olivera-Nieto, L., Wilhelmi, E. de Ona, Ostrowski, M., Panny, S., Panter, M., Parsons, R. D., Peron, G., Pita, S., Poireau, V, Prokhorov, D. A., Prokoph, H., Puehlhofer, G., Punch, Michael, Quirrenbach, A., Reichherzer, P., Reimer, A., Reimer, O., Remy, Q., Renaud, M., Reville, B., Rieger, F., Romoli, C., Rowell, G., Rudak, B., Ricarte, H. Rueda, Ruiz-Velasco, E., Sahakian, V, Sailer, S., Salzmann, H., Sanchez, D. A., Santangelo, A., Sasaki, M., Schaefer, J., Schuessler, F., Schutte, H. M., Schwanke, U., Senniappan, Mohanraj, Seyffert, A. S., Shapopi, J. N. S., Shiningayamwe, K., Simoni, R., Sinha, A., Sol, H., Specovius, A., Spencer, S., Spir-Jacob, M., Stawarz, L., Steenkamp, R., Stegmann, C., Steinmassl, S., Steppa, C., Sun, L., Takahashi, T., Tanaka, T., Tavernier, T., Taylor, A. M., Terrier, R., Thiersen, J. H. E., Thorpe-Morgan, C., Tluczykont, M., Tomankova, L., Tsirou, M., Tsuji, N., Tuffs, R., Uchiyama, Y., van der Walt, D. J., van Eldik, C., van Rensburg, C., van Soelen, B., Vasileiadis, G., Veh, J., Venter, C., Vincent, P., Vink, J., Voelk, H. J., Wagner, S. J., Watson, J., Werner, F., White, R., Wierzcholska, A., Wong, Yu Wun, Yassin, H., Yusafzai, A., Zacharias, M., Zanin, R., Zargaryan, D., Zdziarski, A. A., Zech, A., Zhu, S. J., Zmija, A., Zouari, S., and Zywucka, N.
- Abstract
Context. Supernova remnants (SNRs) are commonly thought to be the dominant sources of Galactic cosmic rays up to the knee of the cosmic-ray spectrum at a few PeV. Imaging Atmospheric Cherenkov Telescopes have revealed young SNRs as very-high-energy (VHE, >100 GeV) gamma-ray sources, but for only a few SNRs the hadronic cosmic-ray origin of their gamma-ray emission is indisputably established. In all these cases, the gamma-ray spectra exhibit a spectral cutoff at energies much below 100 TeV and thus do not reach the PeVatron regime. Aims. The aim of this work was to achieve a firm detection for the oxygen-rich SNR LMC N132D in the VHE gamma-ray domain with an extended set of data, and to clarify the spectral characteristics and the localization of the gamma-ray emission from this exceptionally powerful gamma-ray-emitting SNR. Methods. We analyzed 252 h of High Energy Stereoscopic System (H.E.S.S.) observations towards SNR N132D that were accumulated between December 2004 and March 2016 during a deep survey of the Large Magellanic Cloud, adding 104 h of observations to the previously published data set to ensure a > 5 sigma detection. To broaden the gamma-ray spectral coverage required for modeling the spectral energy distribution, an analysis of Fermi-LAT Pass 8 data was also included. Results. We unambiguously detect N132D at VHE with a significance of 5.7 sigma. We report the results of a detailed analysis of its spectrum and localization based on the extended H.E.S.S. data set. The joint analysis of the extended H.E.S.S and Fermi-LAT data results in a spectral energy distribution in the energy range from 1.7 GeV to 14.8 TeV, which suggests a high luminosity of N132D at GeV and TeV energies. We set a lower limit on a gamma-ray cutoff energy of 8 TeV with a confidence level of 95%. The new gamma-ray spectrum as well as multiwavelength observations of N132D when compared to physical models suggests a hadronic origin of the VHE gamma-ray emission. Conclusions. S
- Published
- 2021
- Full Text
- View/download PDF
15. Searching for TeV Gamma-Ray Emission from SGR 1935+2154 during Its 2020 X-Ray and Radio Bursting Phase
- Author
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Abdalla, H., Aharonian, F., Benkhali, F. Ait, Anguner, E. O., Arcaro, C., Armand, C., Armstrong, T., Ashkar, H., Backes, M., Baghmanyan, V, Martins, V. Barbosa, Barnacka, A., Barnard, M., Becherini, Yvonne, Berge, D., Bernloehr, K., Bi, B., Bottcher, M., Boisson, C., Bolmont, J., de Lavergne, M. de Bony, Breuhaus, M., Brose, R., Brun, F., Brun, P., Bryan, M., Buechele, M., Bulik, T., Bylund, Tomas, Cangemi, F., Caroff, S., Carosi, A., Casanova, S., Chambery, P., Chand, T., Chandra, S., Chen, A., Cotter, G., Curylo, M., Mbarubucyeye, J. Damascene, Davids, I. D., Davies, J., Deil, C., Devin, J., Dirson, L., Djannati-Atai, A., Dmytriiev, A., Donath, A., Doroshenko, V, Dreyer, L., Duffy, C., Du Plessis, L., Dyks, J., Egberts, K., Eichhorn, F., Einecke, S., Emery, G., Ernenwein, J-P, Feijen, K., Fegan, S., Fiasson, A., de Clairfontaine, G. Fichet, Fontaine, G., Funk, S., Fuessling, M., Gabici, S., Gallant, Y. A., Ghafourizade, S., Giavitto, G., Giunti, L., Glawion, D., Glicenstein, J. F., Grondin, M-H, Hahn, J., Haupt, M., Hattingh, S., Hermann, G., Hinton, J. A., Hofmann, W., Hoischen, C., Holch, T. L., Holler, M., Horbe, M., Horns, D., Huang, Z., Huber, D., Jamrozy, M., Jankowsky, D., Jankowsky, F., Jardin-Blicq, A., Joshi, V, Jung-Richardt, I, Kasai, E., Kastendieck, M. A., Katarzynski, K., Katz, U., Khangulyan, D., Khelifi, B., Klepser, S., Kluzniak, W., Komin, Nu, Konno, R., Kosack, K., Kostunin, D., Kreter, M., Kukec Mezek, Gasper, Kundu, A., Lamanna, G., Lemiere, A., Lemoine-Goumard, M., Lenain, J-P, Le Stum, S., Leuschner, F., Levy, C., Lohse, T., Luashvili, A., Lypova, I, Mackey, J., Majumdar, J., Malyshev, D., Marandon, V, Marchegiani, P., Marcowith, A., Mares, A., Marti-Devesa, G., Marx, R., Maurin, G., Meintjes, P. J., Meyer, M., Mitchell, A., Moderski, R., Mohrmann, L., Montanari, A., Moore, C., Morris, P., Moulin, E., Muller, J., Murach, T., Nakashima, K., Nayerhoda, A., de Naurois, M., Ndiyavala, H., Niemiec, J., Oakes, L., O'Brien, P., Odaka, H., Ohm, S., Olivera-Nieto, L., Wilhelmi, E. de Ona, Ostrowski, M., Panny, S., Panter, M., Parsons, R. D., Peron, G., Peyaud, B., Piel, Q., Pita, S., Poireau, V, Noel, A. Priyana, Prokhorov, D. A., Prokoph, H., Puehlhofer, G., Punch, Michael, Quirrenbach, A., Raab, S., Rauth, R., Reichherzer, P., Reimer, A., Reimer, O., Remy, Q., Renaud, M., Reville, B., Rieger, F., Rinchiuso, L., Romoli, C., Rowell, G., Rudak, B., Ricarte, H. Rueda, Ruiz-Velasco, E., Sahakian, V, Sailer, S., Salzmann, H., Sanchez, D. A., Santangelo, A., Sasaki, M., Schaefer, J., Schussler, F., Schutte, H. M., Schwanke, U., Seglar-Arroyo, M., Senniappan, Mohanraj, Seyffert, A. S., Shafi, N., Shapopi, J. N. S., Shiningayamwe, K., Simoni, R., Sinha, A., Sol, H., Spackman, H., Specovius, A., Spencer, S., Spir-Jacob, M., Stawarz, L., Sun, L., Steenkamp, R., Stegmann, C., Steinmassl, S., Steppa, C., Takahashi, T., Tanaka, T., Tavernier, T., Taylor, A. M., Terrier, R., Thiersen, J. H. E., Thorpe-Morgan, C., Tiziani, D., Tluczykont, M., Tomankova, L., Trichard, C., Tsirou, M., Tsuji, N., Tuffs, R., Uchiyama, Y., van der Walt, D. J., van Eldik, C., van Rensburg, C., van Soelen, B., Vasileiadis, G., Veh, J., Venter, C., Vincent, P., Vink, J., Voelk, H. J., Wadiasingh, Z., Wagner, S. J., Watson, J., Werner, F., White, R., Wierzcholska, A., DeWilt, P., Wong, Yu Wun, Yassin, H., Yusafzai, A., Zacharias, M., Zanin, R., Zargaryan, D., Zdziarski, A. A., Zech, A., Zhu, S. J., Zorn, J., Zouari, S., Zywucka, N., Abdalla, H., Aharonian, F., Benkhali, F. Ait, Anguner, E. O., Arcaro, C., Armand, C., Armstrong, T., Ashkar, H., Backes, M., Baghmanyan, V, Martins, V. Barbosa, Barnacka, A., Barnard, M., Becherini, Yvonne, Berge, D., Bernloehr, K., Bi, B., Bottcher, M., Boisson, C., Bolmont, J., de Lavergne, M. de Bony, Breuhaus, M., Brose, R., Brun, F., Brun, P., Bryan, M., Buechele, M., Bulik, T., Bylund, Tomas, Cangemi, F., Caroff, S., Carosi, A., Casanova, S., Chambery, P., Chand, T., Chandra, S., Chen, A., Cotter, G., Curylo, M., Mbarubucyeye, J. Damascene, Davids, I. D., Davies, J., Deil, C., Devin, J., Dirson, L., Djannati-Atai, A., Dmytriiev, A., Donath, A., Doroshenko, V, Dreyer, L., Duffy, C., Du Plessis, L., Dyks, J., Egberts, K., Eichhorn, F., Einecke, S., Emery, G., Ernenwein, J-P, Feijen, K., Fegan, S., Fiasson, A., de Clairfontaine, G. Fichet, Fontaine, G., Funk, S., Fuessling, M., Gabici, S., Gallant, Y. A., Ghafourizade, S., Giavitto, G., Giunti, L., Glawion, D., Glicenstein, J. F., Grondin, M-H, Hahn, J., Haupt, M., Hattingh, S., Hermann, G., Hinton, J. A., Hofmann, W., Hoischen, C., Holch, T. L., Holler, M., Horbe, M., Horns, D., Huang, Z., Huber, D., Jamrozy, M., Jankowsky, D., Jankowsky, F., Jardin-Blicq, A., Joshi, V, Jung-Richardt, I, Kasai, E., Kastendieck, M. A., Katarzynski, K., Katz, U., Khangulyan, D., Khelifi, B., Klepser, S., Kluzniak, W., Komin, Nu, Konno, R., Kosack, K., Kostunin, D., Kreter, M., Kukec Mezek, Gasper, Kundu, A., Lamanna, G., Lemiere, A., Lemoine-Goumard, M., Lenain, J-P, Le Stum, S., Leuschner, F., Levy, C., Lohse, T., Luashvili, A., Lypova, I, Mackey, J., Majumdar, J., Malyshev, D., Marandon, V, Marchegiani, P., Marcowith, A., Mares, A., Marti-Devesa, G., Marx, R., Maurin, G., Meintjes, P. J., Meyer, M., Mitchell, A., Moderski, R., Mohrmann, L., Montanari, A., Moore, C., Morris, P., Moulin, E., Muller, J., Murach, T., Nakashima, K., Nayerhoda, A., de Naurois, M., Ndiyavala, H., Niemiec, J., Oakes, L., O'Brien, P., Odaka, H., Ohm, S., Olivera-Nieto, L., Wilhelmi, E. de Ona, Ostrowski, M., Panny, S., Panter, M., Parsons, R. D., Peron, G., Peyaud, B., Piel, Q., Pita, S., Poireau, V, Noel, A. Priyana, Prokhorov, D. A., Prokoph, H., Puehlhofer, G., Punch, Michael, Quirrenbach, A., Raab, S., Rauth, R., Reichherzer, P., Reimer, A., Reimer, O., Remy, Q., Renaud, M., Reville, B., Rieger, F., Rinchiuso, L., Romoli, C., Rowell, G., Rudak, B., Ricarte, H. Rueda, Ruiz-Velasco, E., Sahakian, V, Sailer, S., Salzmann, H., Sanchez, D. A., Santangelo, A., Sasaki, M., Schaefer, J., Schussler, F., Schutte, H. M., Schwanke, U., Seglar-Arroyo, M., Senniappan, Mohanraj, Seyffert, A. S., Shafi, N., Shapopi, J. N. S., Shiningayamwe, K., Simoni, R., Sinha, A., Sol, H., Spackman, H., Specovius, A., Spencer, S., Spir-Jacob, M., Stawarz, L., Sun, L., Steenkamp, R., Stegmann, C., Steinmassl, S., Steppa, C., Takahashi, T., Tanaka, T., Tavernier, T., Taylor, A. M., Terrier, R., Thiersen, J. H. E., Thorpe-Morgan, C., Tiziani, D., Tluczykont, M., Tomankova, L., Trichard, C., Tsirou, M., Tsuji, N., Tuffs, R., Uchiyama, Y., van der Walt, D. J., van Eldik, C., van Rensburg, C., van Soelen, B., Vasileiadis, G., Veh, J., Venter, C., Vincent, P., Vink, J., Voelk, H. J., Wadiasingh, Z., Wagner, S. J., Watson, J., Werner, F., White, R., Wierzcholska, A., DeWilt, P., Wong, Yu Wun, Yassin, H., Yusafzai, A., Zacharias, M., Zanin, R., Zargaryan, D., Zdziarski, A. A., Zech, A., Zhu, S. J., Zorn, J., Zouari, S., and Zywucka, N.
- Abstract
Magnetar hyperflares are the most plausible explanation for fast radio bursts (FRBs)-enigmatic powerful radio pulses with durations of several milliseconds and high brightness temperatures. The first observational evidence for this scenario was obtained in 2020 April when an FRB was detected from the direction of the Galactic magnetar and soft gamma-ray repeater SGR 1935+2154. The FRB was preceded by two gamma-ray outburst alerts by the BAT instrument aboard the Swift satellite, which triggered follow-up observations by the High Energy Stereoscopic System (H.E.S.S.). H.E.S.S. observed SGR 1935+2154 for 2 hr on 2020 April 28. The observations are coincident with X-ray bursts from the magnetar detected by INTEGRAL and Fermi-GBM, thus providing the first very high energy gamma-ray observations of a magnetar in a flaring state. High-quality data acquired during these follow-up observations allow us to perform a search for short-time transients. No significant signal at energies E > 0.6 TeV is found, and upper limits on the persistent and transient emission are derived. We here present the analysis of these observations and discuss the obtained results and prospects of the H.E.S.S. follow-up program for soft gamma-ray repeaters.
- Published
- 2021
- Full Text
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