Demonstration of sub-micron UCN position resolution using room-temperature CMOS sensor

S. Lin; J. K. Baldwin; M. Blatnik; S. M. Clayton; C. Cude-Woods; S. A. Currie; B. Filippone; E. M. Fries; P. Geltenbort; A. T. Holley; W. Li; C. Y. Liu; M. Makela; C. L. Morris; R. Musedinovic; C. O'Shaughnessy; R. W. Pattie; D. J. Salvat; A. Saunders; E. I. Sharapov; M. Singh; X. Sun; Z. Tang; W. Uhrich; W. Wei; B. Wolfe; A. R. Young; Z. Wang

doi:10.1016/j.nima.2023.168769

Demonstration of sub-micron UCN position resolution using room-temperature CMOS sensor

S. Lin, J. K. Baldwin, M. Blatnik, S. M. Clayton, C. Cude-Woods, S. A. Currie, B. Filippone, E. M. Fries, P. Geltenbort, A. T. Holley, W. Li, C. Y. Liu, M. Makela, C. L. Morris, R. Musedinovic, C. O'Shaughnessy, R. W. Pattie, D. J. Salvat, A. Saunders, E. I. SharapovM. Singh, X. Sun, Z. Tang, W. Uhrich, W. Wei, B. Wolfe, A. R. Young, Z. Wang

Neutron Symmetries

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

High spatial resolution of ultracold neutron (UCN) measurement is of growing interest to UCN experiments such as UCN spectrometers, UCN polarimeters, quantum physics of UCNs, and quantum gravity. Here we utilize physics-informed deep learning to enhance the experimental position resolution and to demonstrate sub-micron spatial resolutions for UCN position measurements obtained using a room-temperature CMOS sensor, extending our previous work (Kuk et al., 2021; Yue et al., 2023) that demonstrated a position uncertainty of 1.5μm. We explore the use of the open-source software Allpix Squared to generate experiment-like synthetic hit images with ground-truth position labels. We use physics-informed deep learning by training a fully-connected neural network (FCNN) to learn a mapping from input hit images to output hit position. The automated analysis for sub-micron position resolution in UCN detection combined with the fast data rates of current and next generation UCN sources will enable improved precision for future UCN research and applications.

Original language	English
Article number	168769
Journal	Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Volume	1057
DOIs	https://doi.org/10.1016/j.nima.2023.168769
State	Published - Dec 2023

Funding

SL and ZW wish to thank Dr. Don Groom and Dr. Steve Holland, both from Lawrence Berkeley National Laboratory, for bCCD simulating discussions. SL and ZW wish to thank Dr. Simon Spannagel for help with Allpix Squared. This work is supported in part by the LANL LDRD program. This work is supported in part by the LANL LDRD program .

Keywords

B nanometer thin film
Deep learning
Direct detection
Solid state detector
Sub-micron position resolution
Ultracold neutrons

Access to Document

10.1016/j.nima.2023.168769

Cite this

Lin, S., Baldwin, J. K., Blatnik, M., Clayton, S. M., Cude-Woods, C., Currie, S. A., Filippone, B., Fries, E. M., Geltenbort, P., Holley, A. T., Li, W., Liu, C. Y., Makela, M., Morris, C. L., Musedinovic, R., O'Shaughnessy, C., Pattie, R. W., Salvat, D. J., Saunders, A., ... Wang, Z. (2023). Demonstration of sub-micron UCN position resolution using room-temperature CMOS sensor. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1057, Article 168769. https://doi.org/10.1016/j.nima.2023.168769

@article{6118a11ea6f845738bad20b356d5b00b,

title = "Demonstration of sub-micron UCN position resolution using room-temperature CMOS sensor",

abstract = "High spatial resolution of ultracold neutron (UCN) measurement is of growing interest to UCN experiments such as UCN spectrometers, UCN polarimeters, quantum physics of UCNs, and quantum gravity. Here we utilize physics-informed deep learning to enhance the experimental position resolution and to demonstrate sub-micron spatial resolutions for UCN position measurements obtained using a room-temperature CMOS sensor, extending our previous work (Kuk et al., 2021; Yue et al., 2023) that demonstrated a position uncertainty of 1.5μm. We explore the use of the open-source software Allpix Squared to generate experiment-like synthetic hit images with ground-truth position labels. We use physics-informed deep learning by training a fully-connected neural network (FCNN) to learn a mapping from input hit images to output hit position. The automated analysis for sub-micron position resolution in UCN detection combined with the fast data rates of current and next generation UCN sources will enable improved precision for future UCN research and applications.",

keywords = "B nanometer thin film, Deep learning, Direct detection, Solid state detector, Sub-micron position resolution, Ultracold neutrons",

author = "S. Lin and Baldwin, {J. K.} and M. Blatnik and Clayton, {S. M.} and C. Cude-Woods and Currie, {S. A.} and B. Filippone and Fries, {E. M.} and P. Geltenbort and Holley, {A. T.} and W. Li and Liu, {C. Y.} and M. Makela and Morris, {C. L.} and R. Musedinovic and C. O'Shaughnessy and Pattie, {R. W.} and Salvat, {D. J.} and A. Saunders and Sharapov, {E. I.} and M. Singh and X. Sun and Z. Tang and W. Uhrich and W. Wei and B. Wolfe and Young, {A. R.} and Z. Wang",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = dec,

doi = "10.1016/j.nima.2023.168769",

language = "English",

volume = "1057",

journal = "Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment",

issn = "0168-9002",

publisher = "Elsevier",

}

Lin, S, Baldwin, JK, Blatnik, M, Clayton, SM, Cude-Woods, C, Currie, SA, Filippone, B, Fries, EM, Geltenbort, P, Holley, AT, Li, W, Liu, CY, Makela, M, Morris, CL, Musedinovic, R, O'Shaughnessy, C, Pattie, RW, Salvat, DJ, Saunders, A, Sharapov, EI, Singh, M, Sun, X, Tang, Z, Uhrich, W, Wei, W, Wolfe, B, Young, AR & Wang, Z 2023, 'Demonstration of sub-micron UCN position resolution using room-temperature CMOS sensor', Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 1057, 168769. https://doi.org/10.1016/j.nima.2023.168769

TY - JOUR

T1 - Demonstration of sub-micron UCN position resolution using room-temperature CMOS sensor

AU - Lin, S.

AU - Baldwin, J. K.

AU - Blatnik, M.

AU - Clayton, S. M.

AU - Cude-Woods, C.

AU - Currie, S. A.

AU - Filippone, B.

AU - Fries, E. M.

AU - Geltenbort, P.

AU - Holley, A. T.

AU - Li, W.

AU - Liu, C. Y.

AU - Makela, M.

AU - Morris, C. L.

AU - Musedinovic, R.

AU - O'Shaughnessy, C.

AU - Pattie, R. W.

AU - Salvat, D. J.

AU - Saunders, A.

AU - Sharapov, E. I.

AU - Singh, M.

AU - Sun, X.

AU - Tang, Z.

AU - Uhrich, W.

AU - Wei, W.

AU - Wolfe, B.

AU - Young, A. R.

AU - Wang, Z.

PY - 2023/12

Y1 - 2023/12

N2 - High spatial resolution of ultracold neutron (UCN) measurement is of growing interest to UCN experiments such as UCN spectrometers, UCN polarimeters, quantum physics of UCNs, and quantum gravity. Here we utilize physics-informed deep learning to enhance the experimental position resolution and to demonstrate sub-micron spatial resolutions for UCN position measurements obtained using a room-temperature CMOS sensor, extending our previous work (Kuk et al., 2021; Yue et al., 2023) that demonstrated a position uncertainty of 1.5μm. We explore the use of the open-source software Allpix Squared to generate experiment-like synthetic hit images with ground-truth position labels. We use physics-informed deep learning by training a fully-connected neural network (FCNN) to learn a mapping from input hit images to output hit position. The automated analysis for sub-micron position resolution in UCN detection combined with the fast data rates of current and next generation UCN sources will enable improved precision for future UCN research and applications.

AB - High spatial resolution of ultracold neutron (UCN) measurement is of growing interest to UCN experiments such as UCN spectrometers, UCN polarimeters, quantum physics of UCNs, and quantum gravity. Here we utilize physics-informed deep learning to enhance the experimental position resolution and to demonstrate sub-micron spatial resolutions for UCN position measurements obtained using a room-temperature CMOS sensor, extending our previous work (Kuk et al., 2021; Yue et al., 2023) that demonstrated a position uncertainty of 1.5μm. We explore the use of the open-source software Allpix Squared to generate experiment-like synthetic hit images with ground-truth position labels. We use physics-informed deep learning by training a fully-connected neural network (FCNN) to learn a mapping from input hit images to output hit position. The automated analysis for sub-micron position resolution in UCN detection combined with the fast data rates of current and next generation UCN sources will enable improved precision for future UCN research and applications.

KW - B nanometer thin film

KW - Deep learning

KW - Direct detection

KW - Solid state detector

KW - Sub-micron position resolution

KW - Ultracold neutrons

UR - http://www.scopus.com/inward/record.url?scp=85174385254&partnerID=8YFLogxK

U2 - 10.1016/j.nima.2023.168769

DO - 10.1016/j.nima.2023.168769

M3 - Article

AN - SCOPUS:85174385254

SN - 0168-9002

VL - 1057

JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

M1 - 168769

ER -

Demonstration of sub-micron UCN position resolution using room-temperature CMOS sensor

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this