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%.

We study the nature of the faint radio source population detected in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Early Science data in the COSMOS field, focusing on the properties of the radio-loud active galactic nuclei (AGNs). Using the extensive multiwavelength data available in the field, we are able to classify 88 per cent of the 5223 radio sources in the field with host galaxy identifications as AGNs (35 per cent) or star-forming galaxies (54 per cent). We select a sample of radio-loud AGNs with redshifts out to z ∼ 6 and radio luminosities 10²⁰ < L_1.4 GHz/W Hz⁻¹ < 10²⁷ and classify them as high-excitation and low-excitation radio galaxies (HERGs and LERGs). The classification catalogue is released with this work. We find no significant difference in the host galaxy properties of the HERGs and LERGs in our sample. In contrast to previous work, we find that the HERGs and LERGs have very similar Eddington-scaled accretion rates; in particular we identify a population of very slowly accreting AGNs that are formally classified as HERGs at these low radio luminosities, where separating into HERGs and LERGs possibly becomes redundant. We investigate how black hole mass affects jet power, and find that a black hole mass ≳ 10^7.8 M_⊙ is required to power a jet with mechanical power greater than the radiative luminosity of the AGN (L_mech/L_bol > 1). We discuss that both a high black hole mass and black hole spin may be necessary to launch and sustain a dominant radio jet.

Using a sample of 67 galaxies from the MeerKAT International GigaHertz Tiered Extragalactic Exploration Survey Early Science data, we study the H i-based baryonic Tully–Fisher relation (bTFr), covering a period of ∼1 billion years (0 ≤ z ≤ 0.081). We consider the bTFr based on two different rotational velocity measures: the width of the global H i profile and V_out, measured as the outermost rotational velocity from the resolved H i rotation curves. Both relations exhibit very low intrinsic scatter orthogonal to the best-fitting relation (σ_⊥ = 0.07 ± 0.01), comparable to the SPARC sample at z ≃ 0. The slopes of the relations are similar and consistent with the z ≃ 0 studies (⁠|$3.66^{+0.35}_{-0.29}$| for W₅₀ and |$3.47^{+0.37}_{-0.30}$| for V_out). We find no evidence that the bTFr has evolved over the last billion years, and all galaxies in our sample are consistent with the same relation independent of redshift and the rotational velocity measure. Our results set-up a reference for all future studies of the H i-based bTFr as a function of redshift that will be conducted with the ongoing deep SKA pathfinders surveys.

We present deep 1.4 GHz source counts from ∼5 deg² of the continuum Early Science data release of the MeerKAT International Gigahertz Tiered Extragalactic Exploration (MIGHTEE) survey down to S_1.4GHz ∼15 μJy. Using observations over two extragalactic fields (COSMOS and XMM-LSS), we provide a comprehensive investigation into correcting the incompleteness of the raw source counts within the survey to understand the true underlying source count population. We use a variety of simulations that account for: errors in source detection and characterisation, clustering, and variations in the assumed source model used to simulate sources within the field and characterise source count incompleteness. We present these deep source count distributions and use them to investigate the contribution of extragalactic sources to the sky background temperature at 1.4 GHz using a relatively large sky area. We then use the wealth of ancillary data covering a subset of the COSMOS field to investigate the specific contributions from both active galactic nuclei (AGN) and star forming galaxies (SFGs) to the source counts and sky background temperature. We find, similar to previous deep studies, that we are unable to reconcile the sky temperature observed by the ARCADE 2 experiment. We show that AGN provide the majority contribution to the sky temperature contribution from radio sources, but the relative contribution of SFGs rises sharply below 1 mJy, reaching an approximate 15-25 per cent contribution to the total sky background temperature (T_b ∼100 mK) at ∼15 μJy.

The design of inertial confinement fusion (ICF) experiments, alongside improving the development of energy density physics theory and experimental methods, is one of the key challenges in the quest for nuclear fusion as a viable energy source [O. A. Hurricane, J. Phys.: Conf. Ser. 717, 012005 (2016)]. Recent challenges in achieving a high-yield implosion at the National Ignition Facility (NIF) have led to new interest in considering a much wider design parameter space than normally studied [J. L. Peterson et al., Phys. Plasmas 24, 032702 (2017)]. Here, we report an algorithmic approach that can produce reasonable ICF designs with minimal assumptions. In particular, we use the genetic algorithm metaheuristic, in which “populations” of implosions are simulated, the design of the capsule is described by a “genome,” natural selection removes poor designs, high quality designs are “mated” with each other based on their yield, and designs undergo “mutations” to introduce new ideas. We show that it takes ∼5 × 10⁴ simulations for the algorithm to find an original NIF design. We also link this method to other parts of the design process and look toward a completely automated ICF experiment design process—changing ICF from an experiment design problem to an algorithm design problem.