TY  - JOUR
AU  - Caron, S.
AU  - Ruiz de Austri, R.
AU  - Zhang, Z. Y.
PY  - 2023
DA  - 2023//
TI  - Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories?
T2  - J. High Energy Phys.
JO  - Journal of High Energy Physics
SP  - 004
EP  - 37pp
VL  - 03
IS  - 3
PB  - Springer
KW  - Specific BSM Phenomenology
KW  - Supersymmetry
AB  - 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.
SN  - 1029-8479
UR  - https://arxiv.org/abs/2207.07631
UR  - https://doi.org/10.1007/JHEP03(2023)004
DO  - 10.1007/JHEP03(2023)004
LA  - English
N1  - WOS:000943095100001
ID  - Caron_etal2023
ER  -