Shikha Bhadoria

The impact of radiation reaction and Breit-Wheeler pair production on the acceleration of fully ionized carbon ions driven by an intense linearly polarized laser pulse has been investigated in the ultra-relativistic transparency regime. Against initial expectations, the radiation reaction and pair production at ultra-high laser intensities are found to enhance the energy gained by the ions. The electrons lose most of their transverse momentum, and the additionally produced pair plasma of Breit-Wheeler electrons and positrons co-streams in the forward direction as opposed to the existing electrons streaming at an angle above zero degree. We discuss how these observations could be explained by the changes in the phase velocity of the Buneman instability, which is known to aid ion acceleration in the breakout afterburner regime, by tapping the free energy in the relative electron and ion streams. We present evidence that these non-classical effects can further improve the highest carbon ion energies in this transparency regime.

Acceleration of electrons in vacuum directly by intense laser fields, often termed vacuum laser acceleration (VLA), holds great promise for the creation of compact sources of high-charge, ultrashort, relativistic electron bunches. However, while the energy gain is expected to be higher with tighter focusing (i.e. stronger electric field), this does not account for the reduced acceleration range, which is limited by diffraction. Here, we present the results of an experimental investigation of VLA, using tungsten nanotips driven by relativistic-intensity few-cycle laser pulses. We demonstrate the acceleration of relativistic electron beams with typical charge of 100s pC to 15 MeV energies. Two different focusing geometries (tight and loose, with f-numbers one and three respectively) produced comparable results, despite a factor of ten difference in the peak intensities, which is evidence for the importance of post-injection acceleration mechanisms around the focus. Our results are in good agreement with the results of full-scale, three-dimensional particle-in-cell simulations.

The impact of radiation reaction and Breit-Wheeler pair production on acceleration of fully ionized Carbon ions driven by an intense linearly-polarized laser pulse has been investigated in the ultra-relativistic transparency regime. Against initial expectations radiation reaction and pair production at ultra-high laser intensities is found to enhance the energy gained by the ions. The electrons lose most of their transverse momentum and the additionally produced pair plasma of Breit-Wheeler electrons and positrons co-stream in the forward direction as opposed to the existing electrons streaming at an angle above zero. We discuss how these observations could be explained by the changes in the phase velocity of the Buneman instability, that is known to aid ion acceleration in the Breakout-Afterburner regime, by tapping the free energy in the relative electron and ion streams. We present evidence that these non-classical effects can further improve the highest Carbon ion energies in this transparency regime.

Visible or near infrared light can be manipulated to produce bursts of coherent extreme ultraviolet or x rays via the relativistic high-order harmonic generation process when a laser irradiates a solid plasma target. The intensity of the spectral components of the reflected signal decays with the increase in harmonic order and the efficiency of this non-linear process largely hinges on how prompt this decay is. This is governed by the conditions of the laser–plasma interaction for which various models have been proposed. At relativistic intensities, a spectrum exhibiting a power-law decay with an exponent of 8/3 or 4/3 is often stated. Here, we analyze the dependence of this exponent on interaction parameters, including the angle of incidence, the carrier envelope phase, intensity of the laser, and the pre-plasma length, and discuss opportunities for optimization. Our simulations show that, rather than there being one universal exponent, the spectral decay is a continuous function of the laser–plasma interaction parameters.

Visible or near infra-red light can be manipulated to produce bursts of coherent extreme ultraviolet (XUV) or X-rays via the relativistic high-order harmonic generation process when a laser irradiates a solid plasma target. The intensity of the spectral components of the reflected signal decays with increase of harmonic order and the efficiency of this non-linear process largely hinges on how prompt this decay is. This is governed by the conditions of the laser-plasma interaction for which various models have been proposed. At relativistic intensities, a spectrum exhibiting a power-law decay with an exponent of 8/3 or 4/3 is often stated. Here, we analyse the dependence of this exponent on interaction parameters including the angle of incidence, the carrier envelope phase, intensity of the laser and the pre-plasma length, and discuss opportunities for optimization. Our simulations show that, rather than there being one universal exponent, the spectral decay is a continuous function of the laser-plasma interaction parameters.

The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, the differences between the two can pose an obstacle to reliable data extraction. Here we report on the development of ML-based diagnostics for experiments on high-intensity laser–matter interactions. With the intention to accentuate robust, physics-governed features, the presence of which is tolerant to such differences, we test the application of principal component analysis, data augmentation and training with data that has superimposed noise of gradually increasing amplitude. Using synthetic data of simulated experiments, we identify that the approach based on the noise of increasing amplitude yields the most accurate ML models and thus is likely to be useful in similar projects on ML-based diagnostics.