McElroy, T.
Observation of high-energy neutrinos from the Galactic plane
Abbasi, R., Ackermann, M., Adams, J., Aguilar, J. A., Ahlers, M., Ahrens, M., Alameddine, J. M., Alves, A. A. Jr., Amin, N. M., Andeen, K., Anderson, T., Anton, G., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S., Bai, X., V., A. Balagopal, Barwick, S. W., Basu, V., Baur, S., Bay, R., Beatty, J. J., Becker, K. -H., Tjus, J. Becker, Beise, J., Bellenghi, C., Benda, S., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Binder, G., Bindig, D., Blaufuss, E., Blot, S., Boddenberg, M., Bontempo, F., Book, J. Y., Borowka, J., Böser, S., Botner, O., Böttcher, J., Bourbeau, E., Bradascio, F., Braun, J., Brinson, B., Bron, S., Brostean-Kaiser, J., Burley, R. T., Busse, R. S., Campana, M. A., Carnie-Bronca, E. G., Chen, C., Chen, Z., Chirkin, D., Choi, K., Clark, B. A., Clark, K., Classen, L., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Correa, P., Cowen, D. F., Cross, R., Dappen, C., Dave, P., De Clercq, C., DeLaunay, J. J., López, D. Delgado, Dembinski, H., Deoskar, K., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dittmer, M., Dujmovic, H., Dunkman, M., DuVernois, M. A., Ehrhardt, T., Eller, P., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Fienberg, A. T., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Friedman, E., Fritz, A., Fürst, P., Gaisser, T. K., Gallagher, J., Ganster, E., Garcia, A., Garrappa, S., Gerhardt, L., Ghadimi, A., Glaser, C., Glauch, T., Glüsenkamp, T., Goehlke, N., Goldschmidt, A., Gonzalez, J. G., Goswami, S., Grant, D., Grégoire, T., Griswold, S., Günther, C., Gutjahr, P., Haack, C., Hallgren, A., Halliday, R., Halve, L., Halzen, F., Minh, M. Ha, Hanson, K., Hardin, J., Harnisch, A. A., Haungs, A., Helbing, K., Henningsen, F., Hettinger, E. C., Hickford, S., Hignight, J., Hill, C., Hill, G. C., Hoffman, K. D., Hoshina, K., Hou, W., Huang, F., Huber, M., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., In, S., Iovine, N., Ishihara, A., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katz, U., Kauer, M., Kellermann, M., Kelley, J. L., Kheirandish, A., Kin, K., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Kopper, S., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krupczak, E., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lanfranchi, J. L., Larson, M. J., Lauber, F., Lazar, J. P., Lee, J. W., Leonard, K., Leszczyńska, A., Li, Y., Lincetto, M., Liu, Q. R., Liubarska, M., Lohfink, E., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Ludwig, A., Luszczak, W., Lyu, Y., Ma, W. Y., Madsen, J., Mahn, K. B. M., Makino, Y., Mancina, S., Mariş, I. C., Martinez-Soler, I., Maruyama, R., McCarthy, S., McElroy, T., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Meighen-Berger, S., Merckx, Y., Micallef, J., Mockler, D., Montaruli, T., Moore, R. W., Morik, K., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nahnhauer, R., Naumann, U., Necker, J., Nguyen, L. V., Niederhausen, H., Nisa, M. U., Nowicki, S. C., Nygren, D., Pollmann, A. Obertacke, Oehler, M., Oeyen, B., Olivas, A., O'Sullivan, E., Pandya, H., Pankova, D. V., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Peters, L., Peterson, J., Philippen, S., Pieper, S., Pizzuto, A., Plum, M., Popovych, Y., Porcelli, A., Rodriguez, M. Prado, Pries, B., Przybylski, G. T., Raab, C., Rack-Helleis, J., Raissi, A., Rameez, M., Rawlins, K., Rea, I. C., Rechav, Z., Rehman, A., Reichherzer, P., Reimann, R., Renzi, G., Resconi, E., Reusch, S., Rhode, W., Richman, M., Riedel, B., Roberts, E. J., Robertson, S., Roellinghoff, G., Rongen, M., Rott, C., Ruhe, T., Ryckbosch, D., Cantu, D. Rysewyk, Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Herrera, S. E. Sanchez, Sandrock, A., Santander, M., Sarkar, S., Sarkar, S., Satalecka, K., Schaufel, M., Schieler, H., Schindler, S., Schmidt, T., Schneider, A., Schneider, J., Schröder, F. G., Schumacher, L., Schwefer, G., Sclafani, S., Seckel, D., Seunarine, S., Sharma, A., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Sogaard, A., Soldin, D., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stein, R., Stettner, J., Stezelberger, T., Stokstad, B., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Thwaites, J., Tilav, S., Tischbein, F., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Tselengidou, M., Tung, C. F., Turcati, A., Turcotte, R., Turley, C. F., Twagirayezu, J. P., Ty, B., Elorrieta, M. A. Unland, Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Veitch-Michaelis, J., Verpoest, S., Walck, C., Wang, W., Watson, T. B., Weaver, C., Weigel, P., Weindl, A., Weiss, M. J., Weldert, J., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Willey, N., Williams, D. R., Wolf, M., Wrede, G., Wulff, J., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P.
The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, has been a mystery for over a century. Due to deflection in interstellar magnetic fields, cosmic rays from the Milky Way arrive at Earth from random directions. However, near their sources and during propagation, cosmic rays interact with matter and produce high-energy neutrinos. We search for neutrino emission using machine learning techniques applied to ten years of data from the IceCube Neutrino Observatory. We identify neutrino emission from the Galactic plane at the 4.5$\sigma$ level of significance, by comparing diffuse emission models to a background-only hypothesis. The signal is consistent with modeled diffuse emission from the Galactic plane, but could also arise from a population of unresolved point sources.
Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200
Li, S., Ostrovskiy, I., Li, Z., Yang, L., Kharusi, S. Al, Anton, G., Badhrees, I., Barbeau, P. S., Beck, D., Belov, V., Bhatta, T., Breidenbach, M., Brunner, T., Cao, G. F., Cen, W. R., Chambers, C., Cleveland, B., Coon, M., Craycraft, A., Daniels, T., Darroch, L., Daugherty, S. J., Davis, J., Delaquis, S., Der Mesrobian-Kabakian, A., DeVoe, R., Dilling, J., Dolgolenko, A., Dolinski, M. J., Echevers, J., Fairbank, W. Jr., Fairbank, D., Farine, J., Feyzbakhsh, S., Fierlinger, P., Fu, Y. S., Fudenberg, D., Gautam, P., Gornea, R., Gratta, G., Hall, C., Hansen, E. V., Hoessl, J., Hufschmidt, P., Hughes, M., Iverson, A., Jamil, A., Jessiman, C., Jewell, M. J., Johnson, A., Karelin, A., Kaufman, L. J., Koffas, T., Krücken, R., Kuchenkov, A., Kumar, K. S., Lan, Y., Larson, A., Lenardo, B. G., Leonard, D. S., Li, G. S., Licciardi, C., Lin, Y. H., MacLellan, R., McElroy, T., Michel, T., Mong, B., Moore, D. C., Murray, K., Njoya, O., Nusair, O., Odian, A., Perna, A., Piepke, A., Pocar, A., Retière, F., Robinson, A. L., Rowson, P. C., Runge, J., Schmidt, S., Sinclair, D., Skarpaas, K., Soma, A. K., Stekhanov, V., Tarka, M., Thibado, S., Todd, J., Tolba, T., Totev, T. I., Tsang, R.
Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.