| Title: |
Citizen science for IceCube: Name that Neutrino |
| Authors: |
Abbasi, R; Ackermann, M; Adams, J; Agarwalla, SK; Aguilar, JA; Ahlers, M; Alameddine, JM; Amin, NM; Andeen, K; Anton, G; Argüelles, C; Ashida, Y; Athanasiadou, S; Ausborm, L; Axani, SN; Bai, X; Balagopal V., A; Baricevic, M; Barwick, SW; Basu, V; Bay, R; Beatty, JJ; Becker Tjus, J; Beise, J; Bellenghi, C; Benning, C; BenZvi, S; Berley, D; Bernardini, E; Besson, DZ; Blaufuss, E; Blot, S; Bontempo, F; Book, JY; Boscolo Meneguolo, C; Böser, S; Botner, O; Böttcher, J; Braun, J; Brinson, B; Brostean-Kaiser, J; Brusa, L; Burley, RT; Busse, RS; Butterfield, D; Campana, MA; Caracas, I; Carloni, K; Carpio, J; Chattopadhyay, S; Chau, N; Chen, C; Chen, Z; Chirkin, D; Choi, S; Clark, BA; Coleman, A; Collin, GH; Connolly, A; Conrad, JM; Coppin, P; Corley, R; Correa, P; Cowen, DF; Dave, P; De Clercq, C; DeLaunay, JJ; Delgado, D; Deng, S; Deoskar, K; Desai, A; Desiati, P; de Vries, KD; de Wasseige, G; DeYoung, T; Diaz, A; Díaz-Vélez, JC; Dittmer, M; Domi, A; Draper, L; Dujmovic, H; DuVernois, MA; Ehrhardt, T; Eimer, A; Eller, P; Ellinger, E; El Mentawi, S; Elsässer, D; Engel, R; Erpenbeck, H; Evans, J; Evenson, PA; Fan, KL; Fang, K; Farrag, K; Fazely, AR; Fedynitch, A; Feigl, N; Fiedlschuster, S; Finley, C |
| Source: |
The European Physical Journal Plus, vol 139, iss 6 |
| Publisher Information: |
eScholarship, University of California |
| Publication Year: |
2024 |
| Collection: |
University of California: eScholarship |
| Subject Terms: |
5106 Nuclear and Plasma Physics (for-2020); 5107 Particle and High Energy Physics (for-2020); 51 Physical Sciences (for-2020); Networking and Information Technology R&D (NITRD) (rcdc); Machine Learning and Artificial Intelligence (rcdc); 49 Mathematical sciences (for-2020) |
| Description: |
Name that Neutrino is a citizen science project where volunteers aid in classification of events for the IceCube Neutrino Observatory, an immense particle detector at the geographic South Pole. From March 2023 to September 2023, volunteers did classifications of videos produced from simulated data of both neutrino signal and background interactions. Name that Neutrino obtained more than 128,000 classifications by over 1800 registered volunteers that were compared to results obtained by a deep neural network machine-learning algorithm. Possible improvements for both Name that Neutrino and the deep neural network are discussed. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
qt7c73x29g; https://escholarship.org/uc/item/7c73x29g; https://escholarship.org/content/qt7c73x29g/qt7c73x29g.pdf |
| DOI: |
10.1140/epjp/s13360-024-05179-y |
| Availability: |
https://escholarship.org/uc/item/7c73x29g; https://escholarship.org/content/qt7c73x29g/qt7c73x29g.pdf; https://doi.org/10.1140/epjp/s13360-024-05179-y |
| Rights: |
CC-BY |
| Accession Number: |
edsbas.BC685F70 |
| Database: |
BASE |