Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection Article Swipe
J Schueler
,
H. M. Araújo
,
S. Balashov
,
Jeremy Borg
,
C. Brew
,
F. Brunbauer
,
Carlo Cazzaniga
,
A. Cottle
,
Christopher Frost
,
F. García
,
David Hunt
,
A.C Kaboth
,
Maria Kastriotou
,
I. Katsioulas
,
A. Khazov
,
P. Knights
,
H. Kraus
,
V. A. Kudryavtsev
,
S. Lilley
,
A. Lindote
,
M. Lisowska
,
D. Loomba
,
I. Lopes
,
E. López Asamar
,
P. Luna Dapica
,
P. A. Majewski
,
Tom Marley
,
C. McCabe
,
L. Millins
,
A. F. Mills
,
M. Nakhostin
,
Rajalakshmi Nandakumar
,
T. J. Neep
,
F. Neves
,
K. Nikolopoulos
,
E. Oliveri
,
L. Ropelewski
,
V. N. Solovov
,
T. J. Sumner
,
J. Tarrant
,
E. Tilly
,
R. Turnley
,
R. Veenhof
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.07538
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.07538
Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for real-time rare event searches for the Migdal effect, using high-resolution image data from a state-of-the-art scientific CMOS camera in the MIGDAL experiment. The Migdal effect in nuclear scattering, crucial for sub-GeV dark matter searches, has yet to be experimentally confirmed, making its detection a primary goal of the MIGDAL experiment. Our pipeline employs the YOLOv8 object detection algorithm and is trained on real data to enhance the detection efficiency of nuclear and electronic recoils, particularly those exhibiting overlapping tracks that are indicative of the Migdal effect. When deployed online on the MIGDAL readout PC, we demonstrate our pipeline to process and perform the rare event search on 2D image data faster than the peak 120 frame per second acquisition rate of the CMOS camera. Applying these same steps offline, we demonstrate that we can reduce a sample of 20 million camera frames to around 1000 frames while maintaining nearly all signal that YOLOv8 is able to detect, thereby transforming a rare search into a much more manageable search. Our studies highlight the potential of pipelines similar to ours significantly improving the detection capabilities of experiments requiring rapid and precise object identification in high-throughput data environments.
Related Topics
Concepts
Pipeline (software)
Event (particle physics)
Computer science
Artificial intelligence
Frame rate
Frame (networking)
Object detection
Object (grammar)
Computer vision
Data acquisition
Process (computing)
Real-time computing
Pattern recognition (psychology)
Physics
Programming language
Quantum mechanics
Telecommunications
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.07538
- https://arxiv.org/pdf/2406.07538
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399598094
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399598094Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2406.07538Digital Object Identifier
- Title
-
Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-11Full publication date if available
- Authors
-
J Schueler, H. M. Araújo, S. Balashov, Jeremy Borg, C. Brew, F. Brunbauer, Carlo Cazzaniga, A. Cottle, Christopher Frost, F. García, David Hunt, A.C Kaboth, Maria Kastriotou, I. Katsioulas, A. Khazov, P. Knights, H. Kraus, V. A. Kudryavtsev, S. Lilley, A. Lindote, M. Lisowska, D. Loomba, I. Lopes, E. López Asamar, P. Luna Dapica, P. A. Majewski, Tom Marley, C. McCabe, L. Millins, A. F. Mills, M. Nakhostin, Rajalakshmi Nandakumar, T. J. Neep, F. Neves, K. Nikolopoulos, E. Oliveri, L. Ropelewski, V. N. Solovov, T. J. Sumner, J. Tarrant, E. Tilly, R. Turnley, R. VeenhofList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.07538Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.07538Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2406.07538Direct OA link when available
- Concepts
-
Pipeline (software), Event (particle physics), Computer science, Artificial intelligence, Frame rate, Frame (networking), Object detection, Object (grammar), Computer vision, Data acquisition, Process (computing), Real-time computing, Pattern recognition (psychology), Physics, Programming language, Quantum mechanics, Telecommunications, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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