Speaker
Description
Maintaining high data quality in modern Nuclear and High Energy
Physics experiments increasingly requires scalable, automated solutions
as data rates and detector complexity continue to grow. Traditionally, hu-
mans monitored data quality with varying skill sets and expertise, while
any automation was typically overly bespoke, covering only specific de-
tector systems or processes. These human-driven methods do not scale
well as experimentation scales. To solve this Jefferson Lab has devel-
oped Hydra, a scalable open-source framework for training and managing
Artificial Intelligence (AI) models for near real-time monitoring. Hydra
enables the training, validation, and management of vision models that
operate directly on detector monitoring images, providing consistent and
scalable assessment of data quality across all of Jefferson Lab’s experi-
mental halls. Hydra is integrated into a web-based interface built using a
React front end and Flask backend and is deployed lab-wide to support
continuous experimental operations. This allows shift crews and experts
to rapidly interpret model outputs, validate findings, and focus attention
on anomalous conditions rather than routine inspection. Hydra enhances
operational efficiency, improves consistency in data quality assessment,
and provides quantitative insight into detector and human performance.
This talk will highlight the software, scalability, and proven reliability
in 24/7 operations, along with its extensibility toward future vision and
multi-modal based monitoring applications.Hydra: A scalable, open-source vision system for
continual data quality monitoring
Thomas Britton, Torri Jeske, Raiqa Rasool, Nataliia Matsiuk, Darren Upton, Jordan O’Kron
December 2025
Abstract
Maintaining high data quality in modern Nuclear and High Energy
Physics experiments increasingly requires scalable, automated solutions
as data rates and detector complexity continue to grow. Traditionally, hu-
mans monitored data quality with varying skill sets and expertise, while
any automation was typically overly bespoke, covering only specific de-
tector systems or processes. These human-driven methods do not scale
well as experimentation scales. To solve this Jefferson Lab has devel-
oped Hydra, a scalable open-source framework for training and managing
Artificial Intelligence (AI) models for near real-time monitoring. Hydra
enables the training, validation, and management of vision models that
operate directly on detector monitoring images, providing consistent and
scalable assessment of data quality across all of Jefferson Lab’s experi-
mental halls. Hydra is integrated into a web-based interface built using a
React front end and Flask backend and is deployed lab-wide to support
continuous experimental operations. This allows shift crews and experts
to rapidly interpret model outputs, validate findings, and focus attention
on anomalous conditions rather than routine inspection. Hydra enhances
operational efficiency, improves consistency in data quality assessment,
and provides quantitative insight into detector and human performance.
This talk will highlight the software, scalability, and proven reliability
in 24/7 operations, along with its extensibility toward future vision and
multi-modal based monitoring applications.