Beyond lepton number violation at the HL-LHC: Resolving heavy neutrino-antineutrino oscillations
Stefan Antusch, Jan Hajer, and Johannes Rosskopp

TL;DR
This paper investigates the potential to observe heavy neutrino-antineutrino oscillations at the HL-LHC, using detailed simulations to identify conditions under which such oscillations could be detected, especially for small mass splittings.
Contribution
First full Monte Carlo simulation of heavy neutrino-antineutrino oscillations at the HL-LHC, demonstrating discovery prospects for small mass splittings.
Findings
Oscillations detectable for mass splittings below 100 μeV.
Simulation framework integrating FeynRules, MadGraph, and Delphes.
Statistical analysis shows promising discovery potential.
Abstract
Collider testable low-scale seesaw models predict pseudo-Dirac heavy neutrinos, that can produce an oscillating pattern of lepton number conserving and lepton number violating events. We explore if such heavy neutrino-antineutrino oscillations can be resolved at the HL-LHC. To that end, we employ the first ever full Monte Carlo simulation of the oscillations, for several example benchmark points, and show under which conditions the CMS experiment is able to discover them. The workflow builds on a FeynRules model file for the phenomenological symmetry protected seesaw scenario (pSPSS) and a patched version of MadGraph , able to simulate heavy neutrino-antineutrino oscillations. We use the fast detector simulation Delphes and present a statistical analysis capable of inferring the significance of oscillations in the simulated data. Our results demonstrate that, for heavy neutrino mass…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Particle Detector Development and Performance
