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024 7 _ |a 10.1088/1361-6471/ad307f
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024 7 _ |a 1361-6471
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024 7 _ |a 10.3204/PUBDB-2025-00684
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082 _ _ |a 530
100 1 _ |a Acero, M. A.
|0 0000-0002-0835-0641
|b 0
245 _ _ |a White paper on light sterile neutrino searches and related phenomenology
260 _ _ |a Bristol
|c 2024
|b IOP Publ.
336 7 _ |a article
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336 7 _ |a Journal Article
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336 7 _ |a ARTICLE
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520 _ _ |a This white paper provides a comprehensive review of our present understanding of experimental neutrino anomalies that remain unresolved, charting the progress achieved over the last decade at the experimental and phenomenological level, and sets the stage for future programmatic prospects in addressing those anomalies. It is purposed to serve as a guiding and motivational "encyclopedic" reference, with emphasis on needs and options for future exploration that may lead to the ultimate resolution of the anomalies. We see the main experimental, analysis, and theory-driven thrusts that will be essential to achieving this goal being: 1) Cover all anomaly sectors -- given the unresolved nature of all four canonical anomalies, it is imperative to support all pillars of a diverse experimental portfolio, source, reactor, decay-at-rest, decay-in-flight, and other methods/sources, to provide complementary probes of and increased precision for new physics explanations; 2) Pursue diverse signatures -- it is imperative that experiments make design and analysis choices that maximize sensitivity to as broad an array of these potential new physics signatures as possible; 3) Deepen theoretical engagement -- priority in the theory community should be placed on development of standard and beyond standard models relevant to all four short-baseline anomalies and the development of tools for efficient tests of these models with existing and future experimental datasets; 4) Openly share data -- Fluid communication between the experimental and theory communities will be required, which implies that both experimental data releases and theoretical calculations should be publicly available; and 5) Apply robust analysis techniques -- Appropriate statistical treatment is crucial to assess the compatibility of data sets within the context of any given model.
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700 1 _ |a Argüelles, C. A.
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700 1 _ |a Hostert, M.
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700 1 _ |a Kalra, D.
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700 1 _ |a Karagiorgi, G.
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700 1 _ |a Kelly, K. J.
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700 1 _ |a Littlejohn, B. R.
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700 1 _ |a Machado, P.
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700 1 _ |a Pettus, W.
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700 1 _ |a Toups, M.
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700 1 _ |a Ross-Lonergan, M.
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700 1 _ |a Sousa, Celia
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700 1 _ |a Surukuchi, P. T.
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700 1 _ |a Wong, Y. Y. Y.
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700 1 _ |a Abdallah, W.
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700 1 _ |a Abdullahi, A. M.
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700 1 _ |a Akutsu, R.
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700 1 _ |a Alvarez-Ruso, L.
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700 1 _ |a Alves, D. S. M.
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700 1 _ |a Aurisano, A.
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700 1 _ |a Balantekin, A. B.
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700 1 _ |a Berryman, J. M.
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700 1 _ |a Bertólez-Martínez, T.
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700 1 _ |a Brunner, J.
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700 1 _ |a Blennow, M.
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700 1 _ |a Bolognesi, S.
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700 1 _ |a Borusinski, M.
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700 1 _ |a Chen, T. Y.
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700 1 _ |a Cianci, D.
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700 1 _ |a Collin, G.
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700 1 _ |a Conrad, J. M.
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700 1 _ |a Crow, B.
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700 1 _ |a Denton, P. B.
|b 32
700 1 _ |a Duvall, M.
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700 1 _ |a Fernández-Martinez, E.
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700 1 _ |a Fong, C. S.
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700 1 _ |a Foppiani, N.
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700 1 _ |a Forero, D. V.
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700 1 _ |a Friend, M.
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700 1 _ |a García-Soto, A.
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700 1 _ |a Giganti, C.
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700 1 _ |a Giunti, C.
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700 1 _ |a Gandhi, R.
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700 1 _ |a Ghosh, M.
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700 1 _ |a Hardin, J.
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700 1 _ |a Heeger, K. M.
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700 1 _ |a Ishitsuka, M.
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700 1 _ |a Izmaylov, A.
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700 1 _ |a Jones, B. J. P.
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700 1 _ |a Jordan, J. R.
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700 1 _ |a Kamp, N. W.
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700 1 _ |a Katori, T.
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700 1 _ |a Kim, S. B.
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700 1 _ |a Koerner, L. W.
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700 1 _ |a Lamoureux, M.
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700 1 _ |a Lasserre, T.
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700 1 _ |a Leach, K. G.
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700 1 _ |a Learned, J.
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700 1 _ |a Li, Y. F.
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700 1 _ |a Link, J. M.
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700 1 _ |a Mahn, K.
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700 1 _ |a Meyers, P. D.
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700 1 _ |a Maricic, J.
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700 1 _ |a Markoff, D.
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700 1 _ |a Maruyama, T.
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700 1 _ |a Mertens, S.
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700 1 _ |a Minakata, H.
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700 1 _ |a Mocioiu, I.
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700 1 _ |a Mooney, M.
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700 1 _ |a Moulai, M. H.
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700 1 _ |a Nunokawa, H.
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700 1 _ |a Ochoa-Ricoux, J. P.
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700 1 _ |a Oh, Y. M.
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700 1 _ |a Ohlsson, T.
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700 1 _ |a Päs, H.
|b 74
700 1 _ |a Pershey, D.
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700 1 _ |a Robertson, R. G. H.
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700 1 _ |a Rosauro-Alcaraz, S.
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700 1 _ |a Rott, C.
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700 1 _ |a Roy, S.
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700 1 _ |a Salvado, J.
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700 1 _ |a Scott, M.
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700 1 _ |a Seo, S. H.
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700 1 _ |a Shaevitz, M. H.
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700 1 _ |a Smiley, M.
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700 1 _ |a Stachurska, J.
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700 1 _ |a Tammaro, M.
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700 1 _ |a Thakore, T.
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700 1 _ |a Ternes, C. A.
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700 1 _ |a Thompson, A.
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700 1 _ |a Tseng, S.
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700 1 _ |a Vogelaar, B.
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700 1 _ |a Weiss, T.
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700 1 _ |a Wendell, R. A.
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700 1 _ |a Wilson, R. J.
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700 1 _ |a Xin, Z.
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700 1 _ |a Yang, B. S.
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700 1 _ |a Yoo, J.
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700 1 _ |a Zennamo, J.
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700 1 _ |a Zettlemoyer, J.
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700 1 _ |a Zupan, J.
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700 1 _ |a Ahmad, S.
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700 1 _ |a Arrieta-Diaz, E.
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700 1 _ |a Caratelli, D.
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700 1 _ |a Chang, C. V.
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700 1 _ |a Convery, M.
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700 1 _ |a Ho, S.
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700 1 _ |a Hoefken Zink, J.
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700 1 _ |a Jones, R. S.
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700 1 _ |a Kunkle, P.
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700 1 _ |a Li, J-Y
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700 1 _ |a Li, S. C.
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700 1 _ |a Luo, X.
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700 1 _ |a Malyshkin, Yu
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700 1 _ |a Martoff, C. J.
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700 1 _ |a Massaro, D.
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700 1 _ |a Petrov, A. A.
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700 1 _ |a Qian, X.
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700 1 _ |a Rajaoalisoa, M.
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700 1 _ |a Razafinime, S. H.
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700 1 _ |a Roca, C.
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700 1 _ |a Schmitz, D. W.
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773 _ _ |a 10.1088/1361-6471/ad307f
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