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Cluster-Locating Algorithm Based on Deep Learning for Silicon Pixel Sensors.

Fatai Mai^1,2, Haibo Yang^1,2,3, Dong Wang⁴

¹University of Chinese Academy of Sciences, Beijing 100049, China.

Sensors (Basel, Switzerland)

|May 13, 2023

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This summary is machine-generated.

Deep learning object detection improves particle cluster location in silicon pixel sensors. This technology enhances data compression and detection speed for particle physics experiments.

Area of Science:

Particle physics instrumentation
Deep learning applications
Sensor technology

Background:

Silicon pixel sensors offer high signal-to-noise ratio, spatial resolution, and readout speed crucial for particle physics.
High-performance cluster-locating technology is essential for data compression and enhancing particle detection accuracy and speed in CMOS-sensor-based systems.

Purpose of the Study:

To evaluate and compare the performance of deep learning object detection algorithms for particle cluster location in silicon pixel sensor data.
To assess both one-stage (YOLO) and two-stage (RCNN) detection frameworks, alongside CNN and transformer-based backbones.

Main Methods:

Constructed and compared YOLO and RCNN deep learning models for object detection.
Utilized a dataset from heavy-ion tests on a Topmetal-M silicon pixel sensor at HIRFL for training and validation.

Keywords:

RCNN YOLO cluster locating deep learning particle physics transformer

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Evaluated performance using metrics such as average precision (AP) and frames per second (FPS).

Main Results:

Achieved state-of-the-art performance with 68.0% AP at 10.04 FPS on Tesla V100.
Demonstrated detection efficiency comparable to the traditional Selective Search approach.
Significantly improved detection speed compared to conventional methods.

Conclusions:

Deep learning object detection, particularly using frameworks like YOLO and RCNN, shows significant potential for high-performance particle cluster location.
The developed methods offer a faster and efficient alternative for data processing in silicon pixel sensor applications.
This approach advances data compression and detection capabilities in particle physics experiments.