Deep learning level-3 electron trigger for CLAS12

Tyson, R.; Gavalian, G.; Ireland, D. G.; McKinnon, B.

doi:10.1016/j.cpc.2023.108783

Cited by 3 publications

(10 citation statements)

References 11 publications

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“…As shown, the AI trigger can greatly improve on the conventional trigger. Not shown here for brevity, the efficiency of the AI trigger is also stable around 99.5% as a function of beam current [3]. The purity is also shown for the AI trigger with and without the denoiser.…”

Section: Performance and Deploymentmentioning

confidence: 65%

“…The negative sample is composed of all other sectors including those from data taken with different triggers. The AI trigger is called on a sector by sector basis [3]. To decrease the amount of data required for the AI trigger, the choice was made to only use data from the DC and the ECAL.…”

Section: Trainingmentioning

confidence: 99%

“…An example of this array is shown in Figure 5. An example of an ECAL image containing all ECAL hits in a given sector for a single event [3]. The colour axis represents the energy deposited in each strip divided by three.…”

Section: Trainingmentioning

confidence: 99%

“…7. The accuracy, efficiency and purity and the product of the purity and efficiency of the AI trigger as a function of the threshold cut on the CNN response [3]. Here the trigger is used without the denoiser.…”

Section: Trainingmentioning

confidence: 99%

“…It defines True (False) Positives as events with (without) an electron selected by the trigger and True (False) Negatives as events without (with) an electron rejected The conventional CLAS12 electron trigger has a high efficiency measured above 99.5% [4] but in 2018 had a purity below 40%. Given that machine learning algorithms are known to be well suited to classification tasks such as electron identification, the decision was then made to test convolutional neural networks [3,5,6] as a way to improve the CLAS12 trigger purity. This machine learning based trigger is referred to as the AI trigger.…”

Section: Introductionmentioning

confidence: 99%

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Level-3 Trigger for CLAS12 with Artificial Intelligence

Tyson,

Gavalian,

McKinnon

et al. 2024

EPJ Web of Conf.

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Fast, efficient and accurate triggers are a critical requirement for modern high energy physics experiments given the increasingly large quantities of data that they produce. The CEBAF Large Acceptance Spectrometer (CLAS12) employs a highly efficient electron trigger to filter the amount of data recorded by requiring at least one electron candidate in each event, at the cost of a low purity in electron identification. However, machine learning algorithms are increasingly employed for classification tasks such as particle identification due to their high accuracy and fast processing times. In this proceeding we present recently published work that showed how a convolutional neural network could be deployed as a Level 3 electron trigger at CLAS12. We demonstrate that this AI trigger would achieve a significant data reduction compared to the conventional CLAS12 electron trigger, whilst preserving a 99.5% electron identification efficiency, at nominal CLAS12 beam currents.

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Section: Performance and Deploymentmentioning

confidence: 65%

Section: Trainingmentioning

confidence: 99%

Section: Trainingmentioning

confidence: 99%

Section: Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Level-3 Trigger for CLAS12 with Artificial Intelligence

Tyson,

Gavalian,

McKinnon

et al. 2024

EPJ Web of Conf.

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Advances in nuclear detection and readout techniques

He,

Niu,

Wang

et al. 2023

NUCL SCI TECH

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Abstract“A Craftsman Must Sharpen His Tools to Do His Job,” said Confucius. Nuclear detection and readout techniques are the foundation of particle physics, nuclear physics, and particle astrophysics to reveal the nature of the universe. Also, they are being increasingly used in other disciplines like nuclear power generation, life sciences, environmental sciences, medical sciences, etc. The article reviews the short history, recent development, and trend of nuclear detection and readout techniques, covering Semiconductor Detector, Gaseous Detector, Scintillation Detector, Cherenkov Detector, Transition Radiation Detector, and Readout Techniques. By explaining the principle and using examples, we hope to help the interested reader underst and this research field and bring exciting information to the community.

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