AURORA: A High Performance DAQ Framework for Next-Generation Rare-Event Search Experiments

TL;DR

AURORA is a scalable, high-performance DAQ framework designed for next-generation rare-event search experiments, capable of handling over 3 GB/s data throughput with modular architecture and modern technologies.

Contribution

The paper introduces AURORA, a novel distributed DAQ framework optimized for high throughput, low latency, and scalability in large-scale physics experiments.

Findings

Achieves over 3 GB/s throughput in benchmarks

Supports over 3,000 readout channels at 500 MSa/s

Designed to be adaptable to various large-scale experiments

Abstract

The upcoming PandaX-xT experiment will deploy over 3,000 readout channels operating at a 500 MSa/s sampling rate, generating a sustained data bandwidth up to 1.6 GB/s. To meet this demanding requirement, we present AURORA, a high-performance, distributed data acquisition (DAQ) framework designed for scalability, low latency, and efficient resource utilization. Built on a modular architecture and leveraging modern I/O and networking technologies, including multi-level buffering, deferred and asynchronous processing, AURORA achieves a projected throughput of over 3 GB/s on the aggregation node in benchmark tests. While developed to support PandaX-xT, the framework is experiment-agnostic and readily adaptable to other large-scale particle and nuclear physics experiments.