The PROBIES diagnostic is a new, highly flexible, imaging and energy spectrometer designed for laser-accelerated protons. The diagnostic can detect low-mode spatial variations in the proton beam profile while resolving multiple energies on a single detector or more. When a radiochromic film stack is employed for “single-shot mode,” the energy resolution of the stack can be greatly increased while reducing the need for large numbers of films; for example, a recently deployed version allowed for 180 unique energy measurements spanning ∼3 to 75 MeV with <0.4 MeV resolution using just 20 films vs 180 for a comparable traditional film and filter stack. When utilized with a scintillator, the diagnostic can be run in high-rep-rate (>Hz rate) mode to recover nine proton energy bins. We also demonstrate a deep learning-based method to analyze data from synthetic PROBIES images with greater than 95% accuracy on sub-millisecond timescales and retrained with experimental data to analyze real-world images on sub-millisecond time-scales with comparable accuracy.
REFERENCES
1.
M.
Borghesi
et al., “Proton imaging detection of transient electromagnetic fields in laser-plasma interactions
,” Rev. Sci. Instrum.
74
, 1688
–1693
(2003
).2.
R. A.
Simpson
et al., “Demonstration of TNSA proton radiography on the national ignition facility advanced radiographic capability (NIF-ARC) laser
,” Plasma Phys. Controlled Fusion
63
, 124006
(2021
).3.
P.
Patel
et al., “Isochoric heating of solid-density matter with an ultrafast proton beam
,” Phys. Rev. Lett.
91
, 125004
(2003
).4.
C.
McGuffey
et al., “Focussing protons from a kilojoule laser for intense beam heating using proximal target structures
,” Sci. Rep.
10
, 9415
(2020
).5.
S. C.
Wilks
et al., “Energetic proton generation in ultra-intense laser–solid interactions
,” Phys. Plasmas
8
, 542
–549
(2001
).6.
J.
Fuchs
et al., “Laser-driven proton scaling laws and new paths towards energy increase
,” Nat. Phys.
2
, 48
–54
(2005
).7.
K.
Flippo
et al., “Omega EP, laser scalings and the 60 MeV barrier: First observations of ion acceleration performance in the 10 picosecond kilojoule short-pulse regime
,” J. Phys.: Conf. Ser.
244
, 022033
(2010
).8.
R. A.
Simpson
et al., “Scaling of laser-driven electron and proton acceleration as a function of laser pulse duration, energy, and intensity in the multi-picosecond regime
,” Phys. Plasmas
28
, 013108
(2021
).9.
D. S.
Hey
et al., “Use of GafChromic film to diagnose laser generated proton beams
,” Rev. Sci. Instrum.
79
, 053501
(2008
).10.
H.
Chen
et al., “High performance compact magnetic spectrometers for energetic ion and electron measurement in ultraintense short pulse laser solid interactions
,” Rev. Sci. Instrum.
79
, 10E533
(2008
).11.
D.
Mariscal
et al., “Calibration of proton dispersion for the nif electron positron proton spectrometer (NEPPS) for short-pulse laser experiments on the NIF ARC
,” Rev. Sci. Instrum.
89
, 10I145
(2018
).12.
G. J.
Williams
et al., “Production of relativistic electrons at subrelativistic laser intensities
,” Phys. Rev. E
101
, 031201
(2020
).13.
D.
Mariscal
et al., “First demonstration of arc-accelerated proton beams at the national ignition facility
,” Phys. Plasmas
26
, 043110
(2019
).14.
A. G.
MacPhee
et al., “Enhanced laser–plasma interactions using non-imaging optical concentrator targets
,” Optica
7
, 129
–130
(2020
).15.
J. F.
Ziegler
and J. P.
Biersack
, “The stopping and range of ions in matter
,” in Treatise on Heavy-Ion Science
(Springer
, 1985
), pp. 93
–129
.16.
S. N.
Chen
et al., “Absolute dosimetric characterization of Gafchromic EBT3 and HDv2 films using commercial flat-bed scanners and evaluation of the scanner response function variability
,” Rev. Sci. Instrum.
87
, 073301
(2016
).17.
D. G.
Lowe
, “Distinctive image features from scale-invariant keypoints
,” Int. J. Comput. Vision
60
, 91
–110
(2004
).18.
J.
Schindelin
et al., “Fiji: An open-source platform for biological-image analysis
,” Nat. Methods
9
, 676
–682
(2012
).19.
M.
Newville
et al., “Lmfit: Non-linear least-square minimization and curve-fitting for python
,” Astrophysics
Source Code Library, ascl–1606, 2016
.20.
F.
Nürnberg
et al., “Radiochromic film imaging spectroscopy of laser-accelerated proton beams
,” Rev. Sci. Instrum.
80
, 033301
(2009
).21.
Y.
Wang
et al., “0.85 PW laser operation at 3.3 Hz and high-contrast ultrahigh-intensity λ = 400 nm second-harmonic beamline
,” Opt. Lett.
42
, 3828
–3831
(2017
).22.
E.
Sistrunk
et al., “All diode-pumped, high-repetition-rate advanced petawatt laser system (HAPLS)
,” in CLEO: Science and Innovations
(Optical Society of America
, 2017
), p. STh1L
-2
.23.
J.
Metzkes
et al., “A scintillator-based online detector for the angularly resolved measurement of laser-accelerated proton spectra
,” Rev. Sci. Instrum.
83
, 123301
(2012
).24.
N. P.
Dover
et al., “Scintillator-based transverse proton beam profiler for laser-plasma ion sources
,” Rev. Sci. Instrum.
88
, 073304
(2017
).25.
T.
Ma
et al., “Accelerating the rate of discovery: Toward high-repetition-rate HED science
,” Plasma Phys. Controlled Fusion
63
, 104003
(2021
).26.
S.
Dann
et al., “Laser wakefield acceleration with active feedback at 5 Hz
,” Phys. Rev. Accel. Beams
22
, 041303
(2019
).27.
R. J.
Shalloo
et al., “Automation and control of laser wakefield accelerators using Bayesian optimization
,” Nat. Commun.
11
, 6355
–6358
(2020
).28.
M. J.-E.
Manuel
et al., “Intrinsic resolution limits of monolithic organic scintillators for use in rep-rated proton imaging
,” Nucl. Instrum. Methods Phys. Res., Sect. A
913
, 103
–106
(2019
).29.
H.
Tang
et al., “Scintillator detector characterization for laser-driven proton beam imaging
,” Rev. Sci. Instrum.
91
, 123304
(2020
).30.
R. A.
Simpson
et al., “Development of a deep learning based automated data analysis for step-filter x-ray spectrometers in support of high-repetition rate short-pulse laser-driven acceleration experiments
,” Rev. Sci. Instrum.
92
, 075101
(2021
).31.
D. A.
Mariscal
et al., “Design of flexible proton beam imaging energy spectrometers (PROBIES)
,” Plasma Phys. Controlled Fusion
63
, 114003
(2021
).32.
B. K.
Spears
et al., “Deep learning: A guide for practitioners in the physical sciences
,” Phys. Plasmas
25
, 080901
(2018
).33.
P.
Virtanen
et al., SciPy 1.0 Contributors
, “SciPy 1.0: Fundamental algorithms for scientific computing in python
,” Nat. Methods
17
, 261
–272
(2020
).34.
M.
Abadi
et al., “Tensorflow: A system for large-scale machine learning
,” in 12th USENIX Symposium on Operating Systems Design and Implementation
(OSDI
, 16), 2016
), pp. 265
–283
.35.
S.
Trummer
et al., “Automated repair of laser damage on national ignition facility optics using machine learning
,” Proc. SPIE
10805
, 108050L
(2018
).© 2023 Author(s). Published under an exclusive license by AIP Publishing.
2023
Author(s)
You do not currently have access to this content.
