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Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%. Laser wakefield accelerators are compact sources of ultra-relativistic electrons which are highly sensitive to many control parameters. Here the authors present an automated machine learning based method for the efficient multi-dimensional optimization of these plasma-based particle accelerators.

The reflectivity and optical quality of laser pulses reflected off 125μm Kapton™ tape was investigated for use as a repeatable plasma mirror. Reflectivities of around 70% were measured at ≈2 × 10 21Wm−2 while maintaining the quality of the laser beam. The ability of the reflected pulse to drive a laser wakefield accelerator was investigated. Self-guided propagation of the reflected beam through a gas cell was observed and shown to depend on the plasma density. Particle-in-cell simulations showed that a wakefield would have been generated, driven solely by the energy within the central high intensity feature of the focal spot. The use of plasma mirrors at these elevated intensities would open up the possibilities of extremely compact configurations for laser wakefield accelerator stages.

Laser-plasma acceleration of protons offers a compact, ultra-fast alternative to conventional acceleration techniques, and is being widely pursued for potential applications in medicine, industry and fundamental science. Creating a stable, collimated beam of protons at high repetition rates presents a key challenge. Here, we demonstrate the generation of multi-MeV proton beams from a fast-replenishing ambient-temperature liquid sheet. The beam has an unprecedentedly low divergence of 1° (≤20 mrad), resulting from magnetic self-guiding of the proton beam during propagation through a low density vapour. The proton beams, generated at a repetition rate of 5 Hz using only 190 mJ of laser energy, exhibit a hundred-fold increase in flux compared to beams from a solid target. Coupled with the high shot-to-shot stability of this source, this represents a crucial step towards applications. Applications of laser-plasma accelerated protons in fundamental, applied and medical sciences crucially depend on the creation of stable collimated beams with high repetition rates. Here the authors demonstrate the generation of multi-MeV protons at 5 Hz, with low (degree-level) proton beam divergence from a laser pulse focused onto a water sheet target, potentially mitigating the need for beam capturing techniques.

Selective inhibition of BCL-2 is expected to enhance therapeutic vulnerability in luminal estrogen receptor-positive breast cancers. We show here that the BCL-2 dependency of luminal tumor cells is nevertheless mitigated by breast cancer-associated fibroblasts (bCAFs) in a manner that defines MCL-1 as another critical therapeutic target. bCAFs favor MCL-1 expression and apoptotic resistance in luminal cancer cells in a IL-6 dependent manner while their own, robust, survival also relies on MCL-1. Studies based on ex vivo cultures of human luminal breast cancer tissues further argue that the contribution of stroma-derived signals to MCL-1 expression shapes BCL-2 dependency. Thus, MCL-1 inhibitors are beneficial for targeted apoptosis of breast tumor ecosystems, even in a subtype where MCL-1 dependency is not intrinsically driven by oncogenic pathways.

Laser-wakefield accelerators (LWFAs) are high acceleration-gradient plasma-based particle accelerators capable of producing ultra-relativistic electron beams. Within the strong focusing fields of the wakefield, accelerated electrons undergo betatron oscillations, emitting a bright pulse of X-rays with a micrometer-scale source size that may be used for imaging applications. Non-destructive X-ray phase contrast imaging and tomography of heterogeneous materials can provide insight into their processing, structure, and performance. To demonstrate the imaging capability of X-rays from an LWFA we have examined an irregular eutectic in the aluminum-silicon (Al-Si) system. The lamellar spacing of the Al-Si eutectic microstructure is on the order of a few micrometers, thus requiring high spatial resolution. We present comparisons between the sharpness and spatial resolution in phase contrast images of this eutectic alloy obtained v ia X-ray phase contrast imaging at the Swiss Light Source (SLS) synchrotron and X-ray projection microscopy via an LWFA source. An upper bound on the resolving power of 2.7 ± 0.3  μ m of the LWFA source in this experiment was measured. These results indicate that betatron X-rays from laser wakefield acceleration can provide an alternative to conventional synchrotron sources for high resolution imaging of eutectics and, more broadly, complex microstructures.

Hydrodynamic optically-field-ionized (HOFI) plasma channels up to 100 mm long are investigated. Optical guiding is demonstrated of laser pulses with a peak input intensity of6×1017Wcm−2through 100 mm long plasma channels with on-axis densities measured interferometrically to be as low asne0=(1.0±0.3)×1017cm−3. Guiding is also observed at lower axial densities, which are inferred from magneto-hydrodynamic simulations to be approximately7×1016cm−3. Measurements of the power attenuation lengths of the channels are shown to be in good agreement with those calculated from the measured transverse electron density profiles. To our knowledge, the plasma channels investigated in this work are the longest, and have the lowest on-axis density, of any free-standing waveguide demonstrated to guide laser pulses with intensities above>1017Wcm−2.

We describe the first demonstration of plasma mirrors made using freely suspended, ultra-thin films formed dynamically and in - situ . We also present novel particle-in-cell simulations that for the first time incorporate multiphoton ionization and dielectric models that are necessary for describing plasma mirrors. Dielectric plasma mirrors are a crucial component for high intensity laser applications such as ion acceleration and solid target high harmonic generation because they greatly improve pulse contrast. We use the liquid crystal 8CB and introduce an innovative dynamic film formation device that can tune the film thickness so that it acts as its own antireflection coating. Films can be formed at a prolonged, high repetition rate without the need for subsequent realignment. High intensity reflectance above 75% and low-field reflectance below 0.2% are demonstrated, as well as initial ion acceleration experimental results that demonstrate increased ion energy and yield on shots cleaned with these plasma mirrors.

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

We present the development and characterization of a high-stability, multi-material, multi-thickness tape-drive target for laser-driven acceleration at repetition rates of up to 100 Hz. The tape surface position was measured to be stable on the sub-micrometre scale, compatible with the high-numerical aperture focusing geometries required to achieve relativistic intensity interactions with the pulse energy available in current multi-Hz and near-future higher repetition-rate lasers ( $>$ kHz). Long-term drift was characterized at 100 Hz demonstrating suitability for operation over extended periods. The target was continuously operated at up to 5 Hz in a recent experiment for 70,000 shots without intervention by the experimental team, with the exception of tape replacement, producing the largest data-set of relativistically intense laser–solid foil measurements to date. This tape drive provides robust targetry for the generation and study of high-repetition-rate ion beams using next-generation high-power laser systems, also enabling wider applications of laser-driven proton sources.

A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.