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Searching for Axionlike Particles with Ultraperipheral Heavy-Ion Collisions
Simon Knapen1, Tongyan Lin1, Hou Keong Lou1
1Department of Physics, University of California, Berkeley, California 94720, USA and Theoretical Physics Group, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Physical Review Letters
|May 13, 2017
Summary
Ultraperipheral heavy-ion collisions at the Large Hadron Collider (LHC) can detect axionlike particles. This study provides stringent new limits for axionlike particle searches, improving upon existing literature for a wide mass range.
Area of Science:
- Particle Physics
- High-Energy Physics
- Cosmology
Background:
- Axionlike particles are hypothetical particles that could explain dark matter.
- Searches for these particles are ongoing in various experimental settings.
- Previous searches have provided limited constraints on their properties.
Purpose of the Study:
- To explore the potential of ultraperipheral heavy-ion collisions at the LHC for detecting axionlike particles.
- To establish new and improved exclusion limits for axionlike particles within a specific mass range.
- To leverage existing multiphoton search data from LEP II and the LHC.
Main Methods:
- Utilizing the Z^4 enhanced photon-photon luminosity in heavy-ion collisions.
- Analyzing exclusive production of back-to-back photon pairs with no other detector activity.
- Recasting existing multiphoton search data from LEP II and the LHC.
Main Results:
- Demonstrated the feasibility of using LHC heavy-ion collisions for axionlike particle searches.
- Achieved new, more stringent exclusion limits for axionlike particles from 100 MeV to 100 GeV.
- Improved upon existing limits in the specified mass range.
Conclusions:
- Ultraperipheral heavy-ion collisions offer a promising channel for discovering axionlike particles.
- The study significantly advances the search for axionlike particles by providing stronger constraints.
- This research opens new avenues for exploring fundamental physics beyond the Standard Model.
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