# Innovations in trigger and data acquisition systems for next-generation physics facilities

arXiv: https://arxiv.org/abs/2203.07620 · HTML: https://mlsanity.com/paper/2203.07620/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 6 of 6)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [Teaching Categories to Human Learners with Visual Explanations](https://mlsanity.com/paper/1802.06924/) (2018)
- [A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber](https://mlsanity.com/paper/1808.07269/) (2018)

## Built on by (most cited first, 2 of 2)

- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Learning Before Filtering: Real-Time Hardware Learning at the Detector Level](https://mlsanity.com/paper/2506.11981/) (2025)

## Abstract (arXiv)

Data-intensive physics facilities are increasingly reliant on heterogeneous and large-scale data processing and computational systems in order to collect, distribute, process, filter, and analyze the ever increasing huge volumes of data being collected. Moreover, these tasks are often performed in hard real-time or quasi real-time processing pipelines that place extreme constraints on various parameters and design choices for those systems. Consequently, a large number and variety of challenges are faced to design, construct, and operate such facilities. This is especially true at the energy and intensity frontiers of particle physics where bandwidths of raw data can exceed 100 TB/s of heterogeneous, high-dimensional data sourced from 300M+ individual sensors. Data filtering and compression algorithms deployed at these facilities often operate at the level of 1 part in 105, and once executed, these algorithms drive the data curation process, further highlighting the critical roles that these systems have in the physics impact of those endeavors. This White Paper aims to highlight the challenges that these facilities face in the design of the trigger and data acquisition instrumentation and systems, as well as in their installation, commissioning, integration and operation, and in building the domain knowledge and technical expertise required to do so.