Compact spectroscopy of keV to MeV X-rays from a laser wakefield accelerator

Machine learning based unfolding of x-ray spectra from filter stack spectrometer data

We demonstrate the application of neural networks to perform x-ray spectra unfolding from data collected by filter stack spectrometers. A filter stack spectrometer consists of a series of filter-detector pairs, where the detectors behind each filter measure the energy deposition through each layer as photo-stimulated luminescence (PSL). The network is trained on synthetic data, assuming x-rays of energies <1 MeV and of two different distribution functions (Maxwellian and Gaussian) and the corresponding measured PSL values obtained from five different filter stack spectrometer designs. Predicted unfolds of single distributions are near identical reproductions of the ground truth spectra, with differences in the values lower than 20% at the higher energy end in some cases. The neural network has also demonstrated robustness to experimental measurement errors of <5% and some capability of performing unfolds for linear combinations of the two distributions without previous training. The network can perform unfolds at rates >1 Hz, ideal for application to some high-repetition-rate systems.

Robust unfolding of MeV x-ray spectra from filter stack spectrometer data

We present an inversion method capable of robustly unfolding MeV x-ray spectra from filter stack spectrometer (FSS) data without requiring an a priori specification of a spectral shape or arbitrary termination of the algorithm. Our inversion method is based upon the perturbative minimization (PM) algorithm, which has previously been shown to be capable of unfolding x-ray transmission data, albeit for a limited regime in which the x-ray mass attenuation coefficient of the filter material increases monotonically with x-ray energy. Our inversion method improves upon the PM algorithm through regular smoothing of the candidate spectrum and by adding stochasticity to the search. With these additions, the inversion method does not require a physics model for an initial guess, fitting, or user-selected termination of the search. Instead, the only assumption made by the inversion method is that the x-ray spectrum should be near a smooth curve. Testing with synthetic data shows that the inversion method can successfully recover the primary large-scale features of MeV x-ray spectra, including the number of x-rays in energy bins of several-MeV widths to within 10%. Fine-scale features, however, are more difficult to recover accurately. Examples of unfolding experimental FSS data obtained at the Texas Petawatt Laser Facility and the OMEGA EP laser facility are also presented.

In Situ Measurement of Electron Energy Evolution in a Laser-Plasma Accelerator

We report on a novel, noninvasive method applying Thomson scattering to measure the evolution of the electron beam energy inside a laser-plasma accelerator with high spatial resolution. The determination of the local electron energy enabled the in-situ detection of the acting acceleration fields without altering the final beam state. In this Letter we demonstrate that the accelerating fields evolve from (265±119)  GV/m to (9±4)  GV/m in a plasma density ramp. The presented data show excellent agreement with particle-in-cell simulations. This method provides new possibilities for detecting the dynamics of plasma-based accelerators and their optimization.

Calibration of image plate and back illuminated charge coupled device detectors at the thermal emission band of high Z target laser produced plasmas (80-800 eV)

We present a systematic method to absolutely calibrate detector efficiency vs photon energy using a laser produced plasma broadband x-ray source, a gold standard calibrated detector, and transmission gratings (TGs) as dispersive elements. Calibration uses one calibrated TG and a calibrated gold standard detector on one channel and a second calibrated TG and a detector to be calibrated on the other channel. Both channels simultaneously view the laser-produced plasma x-ray source from the same angle with respect to the laser beam and the planar target normal. Image plate detectors are calibrated for the first time at photon energies below 700 eV. Single shot simultaneous calibration of several detectors is possible, making this method an efficient and practical way to periodically calibrate detectors, using in-house capabilities of laser laboratories.

Calorimeter with Bayesian unfolding of spectra of high-flux broadband x rays

We report the development of a multipurpose differential x-ray calorimeter with a broad energy bandwidth. The absorber architecture is combined with a Bayesian unfolding algorithm to unfold high energy x-ray spectra generated in high-intensity laser-matter interactions. Particularly, we show how to extract absolute energy spectra and how our unfolding algorithm can reconstruct features not included in the initial guess. The performance of the calorimeter is evaluated via Monte Carlo generated data. The method accuracy to reconstruct electron temperatures from bremsstrahlung is shown to be 5% for electron temperatures from 1 to 50 MeV. We study bremsstrahlung generated in solid target interaction showing an electron temperature of 0.56 ± 0.04 MeV for a 700 μm Ti titanium target and 0.53 ± 0.03 MeV for a 50 μm target. We investigate bremsstrahlung from a target irradiated by laser-wakefield accelerated electrons showing an endpoint energy of 551 ± 5 MeV, inverse Compton generated x rays with a peak energy of 1.1 MeV, and calibrated radioactive sources. The total energy range covered by all these sources ranges from 10 keV to 551 MeV.