BibTex format
@article{Schueler:2025:10.1103/PhysRevD.111.072004,
author = {Schueler, J and Araújo, HM and Balashov, SN and Borg, JE and Brew, C and Brunbauer, FM and Cazzaniga, C and Cottle, A and Frost, CD and Garcia, F and Hunt, D and Kaboth, AC and Kastriotou, M and Katsioulas, I and Khazov, A and Knights, P and Kraus, H and Kudryavtsev, VA and Lilley, S and Lindote, A and Lisowska, M and Loomba, D and Lopes, MI and Lopez, Asamar E and Luna, Dapica P and Majewski, PA and Marley, T and McCabe, C and Millins, L and Mills, AF and Nakhostin, M and Nandakumar, R and Neep, T and Neves, F and Nikolopoulos, K and Oliveri, E and Ropelewski, L and Solovov, VN and Sumner, TJ and Tarrant, J and Tilly, E and Turnley, R and Veenhof, R},
doi = {10.1103/PhysRevD.111.072004},
journal = {Physical Review D},
title = {Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection},
url = {http://dx.doi.org/10.1103/PhysRevD.111.072004},
volume = {111},
year = {2025}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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 rare event searches for the Migdal effect, at real-time speeds, using high-resolution image data from the scientific CMOS camera readout of the MIGDAL experiment. The Migdal effect in nuclear scattering, critical for sub-GeV dark matter searches, has yet to be experimentally confirmed, making its detection a primary goal of the MIGDAL experiment. The Migdal effect forms a composite rare event signal topology consisting of an electronic and nuclear recoil sharing the same vertex. Crucially, both recoil species are commonly observed in isolation in the MIGDAL experiment, enabling us to train YOLOv8, a state-of-the-art object detection algorithm, on real data. Topologies indicative of the Migdal effect can then be identified in science data via pairs of neighboring or overlapping electron and nuclear recoils. Applying selections to real data that retain 99.7% signal acceptance in simulations, we demonstrate our pipeline to reduce a sample of 20 million recorded images to fewer than 1000 frames, thereby transforming a rare search into a much more manageable search. More broadly, we discuss the applicability of using object detection to enable data-driven machine learning training for other rare event search applications such as neutrinoless double beta decay searches and experiments imaging exotic nuclear decays.
AU - Schueler,J
AU - Araújo,HM
AU - Balashov,SN
AU - Borg,JE
AU - Brew,C
AU - Brunbauer,FM
AU - Cazzaniga,C
AU - Cottle,A
AU - Frost,CD
AU - Garcia,F
AU - Hunt,D
AU - Kaboth,AC
AU - Kastriotou,M
AU - Katsioulas,I
AU - Khazov,A
AU - Knights,P
AU - Kraus,H
AU - Kudryavtsev,VA
AU - Lilley,S
AU - Lindote,A
AU - Lisowska,M
AU - Loomba,D
AU - Lopes,MI
AU - Lopez,Asamar E
AU - Luna,Dapica P
AU - Majewski,PA
AU - Marley,T
AU - McCabe,C
AU - Millins,L
AU - Mills,AF
AU - Nakhostin,M
AU - Nandakumar,R
AU - Neep,T
AU - Neves,F
AU - Nikolopoulos,K
AU - Oliveri,E
AU - Ropelewski,L
AU - Solovov,VN
AU - Sumner,TJ
AU - Tarrant,J
AU - Tilly,E
AU - Turnley,R
AU - Veenhof,R
DO - 10.1103/PhysRevD.111.072004
PY - 2025///
SN - 2470-0010
TI - Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection
T2 - Physical Review D
UR - http://dx.doi.org/10.1103/PhysRevD.111.072004
VL - 111
ER -