For many decades, the running joke in fusion research has been that 'fusion' is thirty years away and always will be. Yet, these past few years we find ourselves in a position where we can now talk about the milestones of burning plasmas, fusion ignition, and target energy gain greater than unity (scientific breakeven) in the past tense. Fusion is no longer a joke! Yet getting to fusion ignition, the tipping-point of thermonuclear instability resulting in an explosive increase in ion thermal temperature and fusion reaction-rate, and scientific breakeven (target gain, G(target)= fusion yield/deposited laser energy >1, in the laser-driven inertial confinement fusion context) has not been easy. In this publication we discuss our present understanding of the physics and technological challenges surrounding ignition and Gain as well as highlight some outstanding problems that still need resolution.

Ignition and gain greater than unity has been achieved in inertial confinement fusion (ICF) implosions at the National Ignition Facility (NIF). These accomplishments required implosions that produced high hotspot pressures that are inertially confined by a dense shell of DT fuel. However, even in the burning and igniting plasma regime, 3D asymmetries can reduce the coupling of shell kinetic energy to the hotspot harming the overall implosion performance and truncating burn. Likewise, the overall scale of the implosion can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Recent experiments commonly show signs of significant 3D asymmetry that manifest as high hotspot velocity or asymmetry in the self-emission and scattered neutron images. While modeling 3D asymmetries in implosion with full scale hydrodynamic simulations is often performed, it is labor intensive and computationally costly. Therefore, 3D simulation is applied only in special cases like experiments of particular interest. To enable a wider survey of 3D post-shot analysis, an approximate but computationally inexpensive approach is applied by using multiple rocket-pistons discretizing the spherical implosion. These rocket-pistons are coupled together through the central hotspot pressure using the power balance equations. The approach is similar to that reported by Springer [Springer et al., Nuclear Fusion 59 (3) (2019)] with the inclusion of an approximate hohlraum model beginning at the rocket-implosion stage and post-processing of realistic synthetic diagnostic data at the stagnation and peak burn. This rocket piston tool can provide approximate 3D image and diagnostic data that can then be compared quantitively with data enabling new techniques in iterative, forward fitting, and machine learning to interpreting measurements.

Neutron imaging is important in optimizing analysis of inertial confinement fusion (ICF) events such as those at the National Ignition Facility (NIF) and improving current and future ICF platforms. However, images of neutron sources are often degraded by various types of noise. Most commonly, Gaussian and Poisson noise often coexist within one image, obscuring fine details and blurring edges. These noise types often overlap, making them difficult to distinguish and remove using conventional filtering and thresholding methods. As a result, noise removal techniques that preserve image fidelity are important for analyzing and interpreting images of a neutron source. Current solutions include a combination of filtering and thresholding methodologies. In the past, machine learning approaches were rarely implemented due to a lack of ground truth neutron imaging data for ICF processes. However, recent advances in synthetic data production, particularly in the fusion imaging field, have opened opportunities to investigate new denoising procedures using both supervised and unsupervised machine learning methods. In this study, we implement an unsupervised autoencoder with a Cohen-Daubechies- Feauveau (CDF 97) wavelet transform in the latent space for mixed Gaussian-Poisson denoising. The network successfully denoises neutron imaging data. Additionally, it demonstrates lower reconstruction error and superior edge preservation metrics when benchmarked with data generated by a forward model and compared to non-ML-based filtering mechanisms such as Block-matching and 3D filtering (BM3D). This approach presents a promising advancement in neutron image noise reduction and three-dimensional reconstruction analysis of ICF experiments.

the remaining ablator and potential contamination of the hot spot—a powerful diagnostic window into the capsule assembly during burn.

X-ray radiography is a ubiquitous diagnostic technique in high energy density (HED) physics, with point projection backlighting commonly used for characterizing static and dynamic objects at high spatial and temporal resolutions. These are typically constrained in attainable resolution by their decrease in brightness, which is a limiting factor for high-Z HED experiments, such as double-shell implosions at the National Ignition Facility (NIF) requiring MeV-scale bremsstrahlung sources at high (<50μm) resolution. Coded source imaging is a technique using multiple point-projection sources to produce multiple overlapping radiographs, which are then decoded as a function of the source positions in a process akin to coded aperture imaging. Here, we discuss a new approach to coded source generation using multiple individual small-diameter wire targets within the footprint of a defocused large-scale a0 ≃ 1 laser to produce an MeV-scale high-resolution bright combined source for x-ray radiography. We outline optimal source designs with NIF-Advanced Radiography Capability as the case study, highlight the need for iterative reconstruction decoding, and discuss the research required to demonstrate a robust physical proof-of-concept.

To benchmark the accuracy of the models and improve the predictive capability of future experiments, the National Ignition Facility requires measurements of the physical conditions inside inertial confinement fusion hohlraums. The ion temperature and bulk motion velocity of the gas-filled regions of the hohlraum can be obtained by replacing the helium tamping gas in the hohlraum with deuterium-tritium (DT) gas and measuring the Doppler broadening and Doppler shift of the neutron spectrum produced by nuclear reactions in the hohlraum. To understand the spatial distribution of the neutron production inside the hohlraum, we have developed a new penumbral neutron imager with a 12 mm diameter field of view using a simple tungsten alloy spindle. We performed the first experiment using this imager on a DT gas-filled hohlraum and successfully obtained the spatial distribution of neutron production in the hohlraum plasma. We will report on the design of the spindle, characterization of the detectors, and methodology of the image reconstruction.

We describe our current understanding of the variability and degradation mechanisms observed through a series of five indirectly driven inertial fusion implosions fielded at the National Ignition Facility in the fall of 2021, four of which attempted to reproduce the first experiment to achieve Lawson's criterion for ignition with a thermonuclear yield of 1.35 MJ on August 8, 2021. A large number of absolutely calibrated (imaging, time-resolved, and spectrally resolved) x-ray and neutron diagnostics are fielded on the NIF along multiple lines of sight for each experiment. This allows for a reconstruction of the DT fuel and ablator mix injected into the hotspot around peak burn. We show that nuclear yield variations are well reproduced by numerical modeling when the measured low mode asymmetries and mix mass are included. Furthermore, these observed perturbations during burn are linked to small variations in laser delivery and capsule defects. Stringent specifications are then set to achieve robust ignition with the implosion design studied in this paper. (C) 2024 Author(s).

Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.

The nuclear imaging system has been capturing neutron images of inertial confinement fusion (ICF) driven implosions for over a decade at the National Ignition Facility. This imaging system has evolved from one to three nearly orthogonal lines-of-sight, allowing for the study of three-dimensional shape characteristics of ignition shots. Limited-view tomography algorithms help visualize the burning hotspot in 3D and assess neutron source geometry using Legendre mode parameters. With its neutron, gamma-ray, and x-ray image reconstruction capabilities, NIS has provided critical insight into mechanisms that have limited implosion performance, such as fill tube diameter for ignition-type targets. This comprehensive diagnostic suite opens a window into the shape characteristics of ignition shots and how symmetry affects ICF implosion performance. In more recent ignition shots, neutron yields have visibly increased. Analyzing the shape and size of the reconstructed neutron source has shown an expansion of the burn volume, which is indicative of more efficient alpha heating during the implosion process.

3D asymmetries are major degradation mechanisms in inertial-confinement fusion implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed and reconstructed with the neutron imaging system (NIS) on three lines of sight around the NIF target chamber. Conventional tomographic reconstructions are used to reconstruct the 3D morphology of the implosion using NIS [Volegov et al., J. Appl. Phys. 127, 083301 (2020)], but the problem is ill-posed with only three imaging lines of sight. Asymmetries can also be diagnosed with the real-time neutron activation diagnostics (RTNAD) and the neutron time-of-flight (nToF) suite. Since the NIS, RTNAD, and nToF each sample a different part of the implosion using different physical principles, we propose that it is possible to overcome the limitations of too few imaging lines of sight by performing 3D reconstructions that combine information from all three heterogeneous data sources. This work presents a new machine learning-based reconstruction technique to do just this. By using a simple physics model and group of neural networks to map 3D morphologies to data, this technique can easily account for data of multiple different types. A simple proof-of-principle is presented, demonstrating that this technique can accurately reconstruct a hot-spot shape using synthetic primary neutron images and a hot-spot velocity vector. In particular, the hot-spot's asymmetry, quantified as spherical harmonic coefficients, is reconstructed to within ±4% of the radius in 90% of test cases. In the future, this technique will be applied to actual NIS, RTNAD, and nToF data to better understand 3D asymmetries at the NIF.

The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium-tritium shots.

Magnetized Liner Inertial Fusion experiments have been performed at the Z facility at Sandia National Laboratories. These experiments use deuterium fuel, which produces 2.45 MeV neutrons on reaching thermonuclear conditions. To study the spatial structure of neutron production, the one-dimensional imager of neutrons diagnostic was fielded to record axial resolved neutron images. In this diagnostic, neutrons passing through a rolled edge aperture form an image on a CR-39-based solid state nuclear track detector. Here, we present a modified generalized expectation–maximization algorithm to reconstruct an axial neutron emission profile of the stagnated fusion plasma. We validate the approach by comparing the reconstructed neutron emission profile to an x-ray emission profile provided by a time-integrated pinhole camera.

We present findings from an experimental tuning campaign aimed at igniting larger DT cryogenic layered implosions using a dual frustum shaped hohlraum, denoted "frustraum". The frustraum's distinctive shape reduces hohlraum wall losses while concurrently enhancing minimum capsule clearance with the hohlraum wall and sensitivity to pointing changes. Compared to current cylindrical hohlraum (6.4 x 11.24 mm), the frustraum has a wall area approximately 20 % smaller, resulting in a measured improvement in efficiency of around 12 %. Consequently, 12 % less laser energy is required to implode a capsule within the same acceleration timeframe. Conversely, directing the same laser energy into the frustraum yields higher ion temperatures within symmetry capsules, along with increased radiation temperatures and reduced implosion acceleration times compared to current cylindrical hohlraums.

Inertial confinement fusion and inertial fusion energy experiments diagnose the geometry of the fusion region through imaging of the neutrons released through fusion reactions. Pinhole arrays typically used for such imaging require thick substrates to obtain high contrast along with a small pinhole diameter to obtain high resolution capability, resulting in pinholes that have large aspect ratios. This leads to expensive pinhole arrays that have small solid angles and are difficult to align. Here, we propose a coded aperture with scatter and partial attenuation (CASPA) for fusion neutron imaging that relaxes the thick substrate requirement for good image contrast. These coded apertures are expected to scale to larger solid angles and are easier to align without sacrificing imaging resolution or throughput. We use Monte Carlo simulations (Geant4) to explore a coded aperture design to measure neutron implosion asymmetries on fusion experiments at the National Ignition Facility (NIF) and discuss the viability of this technique, matching the current nominal resolution of 10 µm. The results show that a 10 mm thick tungsten CASPA can image NIF implosions with neutron yields above 1014 with quality comparable to unprocessed data from a current NIF neutron imaging aperture. This CASPA substrate is 20 times thinner than the current aperture arrays for fusion neutron imaging and less than one mean free-path of 14.1 MeV neutrons through the substrate. Since the resolution, solid angle, and throughput are decoupled in coded aperture imaging, the resolution and solid angle achievable with future designs will be limited primarily by manufacturing capability.

We have recently demonstrated significant improvements to the resolution and sensitivity of the NIF gamma imaging system by replacing the existing EJ262 plastic scintillator with the Ce-doped gadolinium garnet transparent ceramic scintillator GYGAG. Penumbral imaging of inelastic gammas emitted during inertial confinement fusion (ICF) experiments at NIF can be used to recover the time integrated spatial distribution of the remaining shell during the fusion burn, the technique is therefore a critical diagnostic for understanding the failure modes and quality of NIF implosions. In this work we discuss GEANT4 calculations of the relative sensitivities of GYGAG and EJ262 as well as rolled edge measurements made on NIF shot N221204 in December 2022, for the purpose of directly comparing the spatial resolution of each scintillator in-situ.