Collaborative planning session to discuss upcoming research directions, project coordination, and semester schedule planning.

Clear communication is a cornerstone of scientific success, and it’s a skill that can be refined with practice. In this session, Dr. Rebecca McElroy will share strategies for delivering impactful journal club presentations, ensuring your research group gains maximum value from every discussion. This session is highly recommended for all students and early career researchers.

In this workshop, I will be presenting an overview of web design, detailing the essential elements to include in an academic portfolio, as well as providing a template for you to get started right away. The session will focus on modern, low-maintenance tools that allow researchers to maintain a professional digital presence without a massive time investment. We will cover how to easily link your publications and highlight your work. No prior web design expertise is needed! This session is highly recommended for all students and early career researchers.

Collaboration is a vital driver of scientific innovation. In this meeting, speakers from each university will present an overview of the research conducted at their institution, offering a comprehensive look at the breadth of work across South-East Queensland. This session provides a key opportunity to identify potential avenues for future collaboration between neighboring institutions and to foster connections within our local astrophysics community.

With the recent release of the the LIGO-Virgo-KAGRA Collaboration's fifth gravitational-wave transient catalog (GWTC-5), the number of detected binary-black hole mergers has increased to more than 300. The growing catalog of these events has increased the number of so-called 'exceptional' events, which defy standard binary evolution channels (e.g., by having extreme masses or spins), as well as enabled investigations into population-level properties of these objects (e.g., their mass or spin distribution). I'll discuss what we've learned from several of these exceptional mergers, the populations they come from, and how we can use these objects to test general relativity on a population level.

In this workshop, Hugh McDougall will present a brief and painless overview of the basics of Bayesian statistics. The session will focus on providing a foundational, intuitive understanding of the core operating principles behind this modeling approach, avoiding overly granular details. In the first half, Hugh will cover conditional probabilities, likelihoods, and priors, and the difference between Bayesian and frequentist statistics. In the second half, the session will explore practical numerical methods used to solve high-dimensional problems, introducing common techniques like Markov Chain Monte Carlo (MCMC) and nested sampling. This talk is specifically designed for students and researchers who are new to the field and want to build a conceptual foundation.

Galaxy spectra encode detailed information including redshift, ionisation conditions, metallicity and stellar content, but the features needed to recover these quantities are often faint, blended or unmeasurable in faint and distant sources, and labelled training data remains limited. We present SpecML, a self supervised foundation model that learns general representations of galaxy spectra from unlabelled data and transfers them to tasks where labels are scarce. SpecML adapts the OmniSpectra architecture for use with JWST NIRSpec prism spectra drawn from the DAWN archive. Each spectrum is tokenised into overlapping flux patches, and a sinusoidal encoding of mean patch wavelength supplies global positional information. The model is pretrained with a masked reconstruction objective using a validity aware transformer that adds local structure through a depthwise convolution, allowing it to process variable length spectra at native resolution and to reconstruct masked regions from surrounding context. We assess the learned representations with downstream probes. SpecML recovers redshift with a coefficient of determination of 0.936 and a NMAD of 0.035 across 10746 sources, with the largest scatter confined to the low redshift population. We extend the same representations to source classification and galaxy property estimation, and test whether SpecML can predict the evolution of star forming galaxies and active galactic nuclei on the BPT diagram beyond z = 3, where the diagnostic lines typically become difficult to measure directly. These results show that self supervised pretraining recovers physically meaningful information that would otherwise require strong individual line detections.