MAIA: A new detector concept for a 10 TeV muon collider

TL;DR

This paper presents MAIA, a novel silicon-based detector design optimized for 10 TeV muon collider experiments, demonstrating high efficiency and resolution even with background noise.

Contribution

Introduction of the MAIA detector concept featuring all-silicon tracking and high-granularity calorimeters tailored for 10 TeV muon collisions, with performance evaluation under realistic conditions.

Findings

Reconstruction efficiencies exceed 95% for energetic particles with background.

MAIA maintains high resolution and efficiency in simulated beam-induced background conditions.

The design supports advanced flavor and boosted object tagging.

Abstract

Muon colliders offer a compelling opportunity to explore the TeV scale and conduct precision tests of the Standard Model, all within a relatively compact geographical footprint. This paper introduces a new detector concept, MAIA (Muon Accelerator Instrumented Apparatus), optimized for $\sqrt{s}=10$ TeV $\mu\mu$ collisions. The detector features an all-silicon tracker immersed in a 5T solenoid field. High-granularity silicon-tungsten and iron-scintillator calorimeters surrounding the solenoid capture high-energy electronic and hadronic showers, respectively, and support particle-flow reconstruction. The outermost subsystem comprises an air-gap muon spectrometer, which enables standalone track reconstruction for high-momentum muons. The performance of the MAIA detector is evaluated in terms of differential particle reconstruction efficiencies and resolutions. Beam-induced background (BIB) simulations generated in FLUKA are overlaid with single particle gun samples to assess detector reconstruction capabilities under realistic experimental conditions. Even with BIB, reconstruction efficiencies exceed 95% for energetic tracks, photons, and neutrons in the central region of the detector. This paper outlines promising avenues of future work, including forward region optimization and opportunities for enhanced flavor/boosted object tagging, and addresses the technological assumptions needed to achieve the desired detector performance.