National Astronomy Meeting Poster Exhibition

The Vera C. Rubin Observatory, under construction, will undertake the ten-year Legacy Survey of Space and Time (LSST) beginning in 2023. A significant fraction of observed galaxies will overlap at least one other galaxy along the same line of sight, and so their images must be "deblended" to infer properties of the separate underlying galaxies. Commonly used deblenders rely on an initial estimate of the total number of galaxies participating in a given blend, and this estimate is traditionally made by counting up the number of intensity peaks in a smoothed image of the neighborhood around a blend. However, the reliability of this procedure for LSST images has not yet been comprehensively studied, and the method of peak counting does not naturally assign probabilities to its estimates. Both of these issues are addressed here, the first by constructing a realistic simulation of blends for evaluation, and the second by developing a novel classifier based on a Gaussian process model. This model is shown to have competitive performance compared to the standard peak counting method for identifying blends in i-band images.

Binary neutron star (BNS) mergers are thought to be the dominant, or one of the dominant, production sites of rapid-neutron capture (r-process) elements in the Universe. Approximately half of all elements heavier than iron are synthesised via this process. Despite the r-process being extremely important for heavy element production, there is much we still do not understand about this production mechanism in BNS mergers. To date, we have observed one confirmed optical counterpart to a BNS merger - the kilonova (KN), AT2017gfo. Since its discovery in 2017, there has been several attempts to directly identify different r-process species in the observed spectra (strontium, caesium and tellurium). This proves to be difficult, due to the lack of complete atomic data for the heavy elements that would be of interest in this type of study. Additionally, the works to date have not considered realistic compositions from theoretical nucleosynthesis calculations. In this talk, I will present the results of our modelling of AT2017gfo, with compositions obtained from such a calculation. From this, we can make more robust predictions about the composition of the ejecta. Additionally, I will present the results of an analysis we performed with a new atomic dataset we generated for neutral, singly- and doubly-ionised platinum and gold (both 3rd r-peak elements). From these types of study, we can attempt to directly identify features corresponding to individual species. From this, we can infer properties about the KN ejecta, such as electron fraction, which helps to inform us about which r-peak is favoured in the elemental synthesis in KN ejecta.

Coronal null points are locations where the magnetic field, and hence the local Alfven speed, is zero. The behaviours of all three MHD wave modes, i.e. fast and slow magnetoacoustic waves and the Alfven wave, have been investigated in the neighbourhood of 2D, 2.5D and (to a certain extent) 3D magnetic null points, for a variety of assumptions, configurations and geometries. These studies contribute to our understanding of MHD wave propagation in inhomogeneous media, and this talk will detail some specific findings in this area, in particular the results that have led to critical insights into mode-coupling, mode-conversion, reconnection, and quasi-periodic pulsations.

A major trend in astronomy and healthcare is the rapid adoption of Bayesian statistics for data analysis and modeling. With modern data-sets growing by orders of magnitude in size, the focus is now on developing methods capable of applying contemporary inference techniques to extremely large datasets. To this aim, I present PyAutoFit (https://github.com/rhayes777/PyAutoFit), an open-source probabilistic programming language for automated Bayesian inference.

I will present PyAutoFit’s multi-level modeling framework, which allows a user to compose and fit hierarchical models to extremely large datasets. In an Astronomy setting, I will show how a multi-level model can constrain the dark matter particle, by modeling individual images of strong lens galaxies at the lowest level and the Universe’s cosmological parameters at the top. Next, I will describe a multi-level model of cancer treatment we are building with the healthcare company Roche, where the inner components are how specific genetic or epigenetic profiles of cancers respond to treatments and the higher levels represent tumour dynamics and patient outcomes. Finally, I will discuss how this framework can overcome a major challenge for both Astronomy and healthcare datasets: missing data.

Strong galaxy-galaxy gravitational lensing is the distortion of the paths of light rays from a background galaxy into arcs or rings as viewed from Earth, caused by the gravitational field of an intervening foreground lens galaxy. The vast quantity of strong lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate Bayesian convolutional neural network (CNN) to predict mass profile parameters and associated uncertainties, and compare its accuracy to that of conventional parametric modelling. In addition, we present a method for combining the CNN with conventional modelling in an automated fashion, where the CNN provides initial priors on the latter's parameters. These methods are tested on a range of increasingly complex lensing systems, from standard smooth parametric mass and light profiles to images containing hydrodynamical EAGLE galaxies, Hubble Ultra Deep Field source galaxies and the inclusion of foreground mass structures. While the CNN is undoubtedly the fastest modelling method, the combination of the two increases the speed of conventional modelling, especially when priors include CNN-predicted uncertainties. This, combined with significantly improved accuracy, highlights the benefits one can obtain through combining neural networks with conventional techniques in order to achieve an efficient automated modelling approach.

Simulations and observations have recently shown that reconnecting current sheets are present in the transition region of Earth's bow shock. Current sheets are also observed in the magnetosheath as a consequence of turbulent reconnection. However, the link between shock and magnetosheath reconnection is not yet known. To investigate this link, we use observations of a crossing of Earth's quasi-parallel bow shock by Magnetospheric Multiscale (MMS) on 18/04/2020. For this event, we characterise magnetic fluctuations as the spacecraft crosses the extended transition region of the shock, and continues downstream into the magnetosheath. Making use of the Fluxgate-Searchcoil Merged dataset, the ~11 minute burst interval is split into consecutive or sliding windows of ~3.7s each. We quantify changes in fluctuations across the transition region; including in the slope of the magnetic spectrum, the scales of breaks in spectral power laws, and the correlation length of the fluctuations. Furthermore, we examine how these quantities change across the inertial, ion and electron scales. This provides insight into the evolution of turbulence and reconnection in the solar wind, through the shock transition layer, and then towards the magnetosheath.
We find that the ion scale magnetic spectra remain minimally affected through the shock, while strong steepening of the spectra at electron scales may be linked to changes in dissipation mechanisms at those scales, such as onset of electron-only reconnection. Before the shock, in the solar wind, the slopes of the magnetic spectra are steepest in the ion range, yet at the shock crossing, the electron scale spectrum steepens considerably to match the ion slope. Furthermore, the spectral break between ion and electron scales becomes much less significant.

The presence of quasi-periodic pulsations in solar flares, temporary periodic signatures in emitted radiation at different wavelengths, may reveal important information about the energy release process but the origin of this phenomenon is not well understood. One approach to addressing this issue is to develop models of oscillatory reconnection and to explore how reconnection itself may generate oscillatory behavior and waves. To this end, we present the results of 2D magnetohydrodynamic simulations of magnetic reconnection between two twisted magnetic flux ropes, leading to the merger of the flux ropes, and discuss the origins of the resulting periodic pulsations and the waves which are emitted.

The astrophysical oxygen-15 alpha capture reaction [1] is a key breakout route from the Hot CNO cycle leading to explosive nucleosynthesis via the rp-process on the surface of neutron stars in binary systems. Determining an accurate cross section for the relevant states is critical for a better understanding of the X-ray burst energy production and light-curves [2], as well as other novel binary stellar systems involving neutron stars and their potential impact on nucleosynthesis [3].

An indirect alpha transfer reaction in inverse kinematics has been performed, populating the relevant states at temperatures up to 1GK. In this, we take advantage of the 15O Radioactive Ion Beam provided at GANIL and the state-of-the art detection system VAMOS + AGATA + MUGAST coupled together for the first time [4], allowing us an unrivalled selectivity for detecting triple coincidences in this reaction. We will present the experimental set-up and analysis, as well as preliminary results for the strongest populated resonances in 19Ne.

[1] M. R. Hall et al. Phys. Rev. C 99, 035805 (2019)
[2] R. H. Cyburt et al. Astrophys. J. 830, 55 (2016)
[3] J. Keegans et al. MNRAS, V. 485, Issue 1, Pages 620–639 (2019)
[4] M. Assié et al. Accepted for publication in NIMA (2021)

Environment plays a key role in influencing the evolution of galaxies, and clusters provide ‘laboratories’ in which to study galaxy interactions and the effect of environment on these. Meanwhile, the VISTA Magellanic Clouds Survey (VMC) provides a high-resolution near-infrared view covering the Magellanic Clouds and Magellanic Bridge. Due to its high resolution and sensitivity, the VMC also contains information about many background galaxies, most of which have not been captured by past surveys. Therefore, it is now possible to investigate galaxy clusters and groups that would previously have been obscured by foreground stars, providing us with new ‘laboratories’ in which to study galaxy evolution. We use VMC data in combination with data from optical and infrared surveys and new radio continuum images from the Australian SKA Pathfinder to study the evolution of galaxies in clusters and groups. To this end, we map galaxy clusters in the VMC survey using photometric colours and redshift estimates. This allows us to quantify the environments in which background galaxies reside and to study their properties by using near-infrared as a tracer of stellar mass and AGN dust and using radio lobes as a probe of the intracluster medium. We approach the problem of mapping background galaxies in areas of high stellar contamination using an automated method that we intend to be transferrable to other near-infrared surveys that present similar challenges to the VMC, such as those covering the galactic plane of the Milky Way.

AtLAST is a concept for a next generation, 50-meter class single-dish astronomical observatory. Operating at sub-millimetre and millimetre wavelengths, it will deliver a much needed combination of spatial and spectral resolution, with high mapping speeds and sensitivity to large scale structures that current facilities cannot achieve. With a high throughput, 2 degree field of view and a full complement of advanced instrumentation, including highly multiplexed high-resolution spectrometers, continuum cameras and Integral Field Units, AtLAST will have mapping speeds thousands of times greater than any current or planned facility. It will reach confusion limits below L*, allowing for SDSS-style surveys at long wavelengths and enabling a fundamentally new understanding of the sub-millimetre universe at unprecedented depths.