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We explore the possibility of directly detecting light, long-lived hidden sector particles at the IceCube neutrino telescope. Such particles frequently arise in nonminimal hidden sectors that couple to the Standard Model through portal operators. We consider two distinct scenarios. In the first scenario, which arises from a neutrino portal interaction, a hidden sector particle is produced inside the detector by the collision of an energetic neutrino with a nucleon, giving rise to a visible cascade. This new state then decays into a hidden sector daughter, which can naturally be long-lived. The eventual decay of the daughter particle back to Standard Model states gives rise to a second cascade inside the detector. This scenario therefore gives rise to a characteristic “double bang” signal arising from the two distinct cascades. In the second scenario, which arises from a hypercharge portal interaction, a hidden sector particle is produced outside the detector by the collision of an atmospheric muon with a nucleon. This new state promptly decays into a pair of hidden sector daughters that are long-lived. If both daughters decay into Standard Model states inside the detector, we again obtain a double bang signal from the two distinct cascades. We explore the reach of IceCube for these two scenarios and show that it has the potential to significantly improve the sensitivity to hidden sector models in the mass range from about 1 GeV to about 20 GeV.
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Applying machine learning techniques to intermediate-length cascade decays
In the collider phenomenology of extensions of the Standard Model with partner particles, cascade decays occur generically, and they can be challenging to discover when the spectrum of new particles is compressed and the signal cross section is low. Achieving discovery-level significance and measuring the properties of the new particles appearing as intermediate states in the cascade decays is a longstanding problem, with analysis techniques for some decay topologies already optimized. We focus our attention on a benchmark decay topology with four final state particles where there is room for improvement, and where multidimensional analysis techniques have been shown to be effective in the past. Using machine learning techniques, we identify the optimal kinematic observables for discovery, spin determination and mass measurement. In agreement with past work, we confirm that the kinematic observable Δ4 is highly effective. We quantify the achievable accuracy for spin determination and for the precision for mass measurements as a function of the signal size.
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- Award ID(s):
- 2210562
- PAR ID:
- 10525382
- Publisher / Repository:
- Physical Review Journal D
- Date Published:
- Journal Name:
- Physical review D
- ISSN:
- 2470-0029
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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