1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Machine learning approach to MDI optimization for 3 TeV c.o.m. Muon Collider

MOPM092
2 Jun 2025, 16:00
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC1.A09 Muon Accelerators, Neutrino Factories, Muon Monday Poster Session

Speaker

Luca Castelli (Sapienza University of Rome)

Description

The Muon Collider is a proposed future accelerator for very high energy muon collision. Since muons are heavier than electrons, the synchrotron radiation is negligible at this high energy, allowing to build a compact machine able to deliver Multi-TeV c.o.m. energy collisions, enabling precision measurements of the Standard Model quantities and search for new physics. A challenge of a muon beam is the Beam-Induced Background (BIB), a flux of particles in the detector generated by secondary interaction of muon decay products with the accelerator components.
To deliver the required physics performance, the Machine Detector Interface design needs to include a shielding for the BIB. The proposed solution consists of cone-shaped tungsten shields inside the detector area. The nozzles reduce the BIB to a manageable level at the cost of reducing the detector acceptance. A careful optimization of the geometry is necessary to further mitigate the BIB and improving the detector acceptance to maximize the physics potential. This contribution aims at discussing the optimization achieved with machine learning algorithms in combination with FLUKA simulations for a 3 TeV c.o.m. Muon Collider.

Region represented Europe
Paper preparation format LaTeX

Author

Luca Castelli (Sapienza University of Rome)

Co-authors

Anton Lechner (European Organization for Nuclear Research) Daniele Calzolari (European Organization for Nuclear Research) Donatella Lucchesi (INFN- Sez. di Padova) Francesco Collamati (Istituto Nazionale di Fisica Nucleare)

Presentation materials

There are no materials yet.