Publications
Publication Information
| Title | Streaming readout for next generation electron scattering experiments |
| Authors | Mariangela Bondi, Fabrizio Ameli, Marco Battaglieri, Vladimir Berdnikov, Sergey Boyarinov, Nathan Brei, A. Celentano, Laura Cappelli, Tommaso Chiarusi, Raffaella De Vita, Cristiano Fanelli, Vardan Gyurjyan, David Lawrence, Patrick Mora, Paolo Musico, Carmelo Pellegrino, Alessandro Pilloni, B. Raydo, Carl Timmer, Murizio Ungaro, Simone Vallarino |
| JLAB number | JLAB-PHY-22-3556 |
| LANL number | arXiv:2202.03085 |
| Other number | DOE/OR/23177-5419 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | Yes |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for European Physical Journal Plus Volume 137 Page(s) 958 Refereed | |
| Publication Abstract: | Current and future experiments at the high intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now pos- sible to replace the standard 'triggered data acquisi- tion systems with a new, simplified and outperform- ing scheme. 'Streaming readout' (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that consid- ers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper we report results of the on-beam validation of the Jefferson Lab SRO framework. We exposed different de- tectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron- positron secondary beam and to the Hall-B produc- tion electron beam, with increasingly complex exper- imental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Fur- thermore, we provide evidence of its superiority in im- plementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction. |
| Experiment Numbers: | other |
| Group: | Data Acquisition |
| Document: | |
| DOI: | https://doi.org/10.1140/epjp/s13360-022-03146-z |
| Accepted Manuscript: | StreamingRO_EPJPlus.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |