[Paper Review] Promising Technologies and R&D Directions for the Future Muon Collider Detectors

S. Jindariani, F. Meloni+59 more|arXiv (Cornell University)|Mar 14, 2022

Particle Detector Development and Performance4 citations

TL;DR

This paper proposes a streaming data acquisition (DAQ) system for future muon collider detectors, addressing beam-induced background (BIB) challenges through high-granularity, high-time-resolution detectors and advanced trigger algorithms. It demonstrates that with aggressive filtering and modern computing (GPU/FPGA), per-event processing can be reduced to a few seconds, enabling viable DAQ performance despite extreme BIB levels similar to HL-LHC.

ABSTRACT

Among the post-LHC generation of particle accelerators, the muon collider represents a unique machine with capability to provide very high energy leptonic collisions and to open the path to a vast and mostly unexplored physics programme. However, on the experimental side, such great physics potential is accompanied by unprecedented technological challenges, due to the fact that muons are unstable particles. Their decay products interact with the machine elements and produce an intense flux of background particles that eventually reach the detector and may degrade its performance. In this paper, we present technologies that have a potential to match the challenging specifications of a muon collider detector and outline a path forward for the future R&D efforts.

Motivation & Objective

• Address the extreme beam-induced background (BIB) in muon collider detectors, which exceeds that of $e^+e^-$ colliders due to muon decay products.
• Design a scalable, high-bandwidth data acquisition (DAQ) system capable of handling high data rates while maintaining low processing latency.
• Identify and evaluate emerging detector technologies with high granularity, timing resolution, and radiation tolerance to mitigate BIB effects.
• Optimize trigger and high-level trigger (HLT) algorithms to reduce data rates and enable real-time event reconstruction within seconds.
• Assess the feasibility of using existing and near-future computing technologies (GPU, FPGA, ASIC) for HLT processing under stringent timing constraints.

Proposed method

• Propose a streaming DAQ architecture with front-end filtering to reduce data rates before HLT processing.
• Use a Hough Transform-based algorithm to identify candidate tracks with $p_T > 2.5$ GeV to reject background events.
• Apply independent timing or pointing-based filtering to reduce BIB hit rate by a factor of four, improving event rejection to ~50.
• Estimate data flow using 20 Gb/s optical links, requiring ~10,000 links to transport data from detector to electronics area.
• Model the back-end system with a full-mesh hardware or software Event Builder network capable of ~60 Tb/s aggregate bandwidth.
• Explore FPGA and GPU acceleration to achieve per-event processing times in the few-second range, even with 44 million cells per beam crossing.

Experimental results

Research questions

• RQ1 Can a streaming DAQ architecture effectively manage the extreme beam-induced background (BIB) levels expected at a muon collider?
• RQ2 What level of filtering and trigger efficiency is required to reduce data rates to a manageable level for HLT processing?
• RQ3 Can modern computing technologies (GPU, FPGA, ASIC) achieve the required per-event processing time of a few seconds under high data load?
• RQ4 How does the combination of high granularity, timing resolution, and pointing-based filtering impact BIB suppression and data rate reduction?
• RQ5 What are the bandwidth and system architecture requirements for a scalable, high-throughput DAQ system in a muon collider experiment?

Key findings

• Preliminary simulations indicate that a streaming DAQ architecture is viable for muon collider experiments despite large beam-induced background (BIB).
• Filtering based on timing or pointing information can reduce data rates by up to a factor of eight, significantly easing system demands.
• A single candidate track with $p_T > 2.5$ GeV, combined with a four-fold BIB rate reduction, achieves an event rejection factor of approximately 50.
• Processing 44 million cells per beam crossing in a few seconds is feasible with current and near-future CPUs, GPUs, and FPGAs.
• The system requires approximately 10,000 high-speed (20 Gb/s) optical links to transport data, with a back-end network capable of ~60 Tb/s aggregate bandwidth.
• Even in the most aggressive filtering scenarios, output bandwidth to storage remains below 100 Gb/s, ensuring scalability.