Bertolez-Martinez, T., Arguelles, C., Esteban, I., Lopez-Pavon, J., Martinez-Soler, I., & Salvado, J. (2023). IceCube and the origin of ANITA-IV events. J. High Energy Phys., 07(7), 005–24pp.
Abstract: Recently, the ANITA collaboration announced the detection of new, unsettling upgoing Ultra-High-Energy (UHE) events. Understanding their origin is pressing to ensure success of the incoming UHE neutrino program. In this work, we study their internal consistency and the implications of the lack of similar events in IceCube. We introduce a generic, simple parametrization to study the compatibility between these two observatories in Standard Model-like and Beyond Standard Model scenarios: an incoming flux of particles that interact with Earth nucleons with cross section sigma, producing particle showers along with long-lived particles that decay with lifetime iota and generate a shower that explains ANITA observations. We find that the ANITA angular distribution imposes significant constraints, and when including null observations from IceCube only iota similar to 10(-3)-10(-2) s and sigma similar to 10(-33) -10(-32) cm(2) can explain the data. This hypothesis is testable with future IceCube data. Finally, we discuss a specific model that can realize this scenario. Our analysis highlights the importance of simultaneous observations by high-energy optical neutrino telescopes and new UHE radio detectors to uncover cosmogenic neutrinos or discover new physics.
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Caron, S., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? J. High Energy Phys., 03(3), 004–37pp.
Abstract: Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.
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