Talks

UniverseTBD is an interdisciplinary community of astronomers, AI researchers, engineers, artists and enthusiasts aligned on a bold mission to democratise Science for everyone. From releasing the first large language model in Astronomy, AstroLLaMA-1, to the AI-enabled literature discovery tool Pathfinder, and through our research with AstroPT and HypoGen, our team has pushed the boundaries of AI for Science for the past two years. In this talk, I discuss for the first time how UniverseTBD came to be, our vision, our values, and what drives us and has enabled us to scale our team projects in our commitment to share our learnings with the broader scientific community. I also briefly discuss our latest results with hypothesis generation (HypoGen), multimodal language models (AstroLlaVA-1) and agentic AI (AstroCoder). I conclude with a vision for the future where AI teams up with human researchers to "help us understand the Universe".

The newest large-language reasoning models are for the first time powerful enough to perform mathematical reasoning in theoretical physics at graduate level. In the mathematics community, data sets such as FrontierMath are being used to drive progress and evaluate models, but theoretical physics has so far received less attention. In this talk I will present our dataset TPBench (arxiv:2502.15815, tpbench.org), which was constructed to benchmark and improve AI models specifically for theoretical physics. We find extremely rapid progress of models over the last months, but also significant challenges at research level difficulty. I will also briefly outline strategies to improve these models for theoretical physics.

Recent advancements have positioned Large Language Models (LLMs) as transformative tools for scientific research, capable of addressing complex tasks that require reasoning, problem-solving, and decision-making. Their exceptional capabilities suggest their potential as scientific research assistants, but also highlight the need for holistic, rigorous, and domain-specific evaluation to assess effectiveness in real-world scientific applications. This talk describes a multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory. This methodology incorporates four primary classes of evaluations. 1) Multiple Choice Questions to assess factual recall; 2) Open Response to evaluate advanced reasoning and problem-solving skills; 3) Lab-Style Experiments involving detailed analysis of capabilities as research assistants in controlled environments; and 4) Field-Style Experiments to capture researcher-LLM interactions at scale in a wide range of scientific domains and applications. These complementary methods enable a comprehensive analysis of LLM strengths and weaknesses with respect to their scientific knowledge, reasoning abilities, and adaptability. Recognizing the rapid pace of LLM advancements, we designed the methodology to evolve and adapt so as to ensure its continued relevance and applicability. This talk describes the current methodology's state. Although developed within a subset of scientific domains, the methodology is designed to be generalizable to a wide range of scientific domains.

Morphological summary statistics, such as Minkowski functionals, Betti numbers and Minkowski tensors, provide a route to carry out cosmological data analysis which is complementary to traditional mode decomposition based statistics such as the power spectrum. We summarize the properties of these statistics and demonstrate their information content by using the gravitational evolution of matter and 21-cm brightness temperature as physical examples.

The formation of the first stars in the Universe remains a key challenge in cosmology, requiring a multi-wavelength observational strategy across a range of facilities. Telescopes like the James Webb Space Telescope (JWST) enable direct observation of early galaxies, while upcoming radio telescopes such as the SKA will capture the hydrogen signal from the early Universe. Interpreting the extensive datasets from these facilities demands sophisticated theoretical models that balance detailed physics with computational efficiency, supported by advanced statistical methods to probe the parameter space. In this talk, we will discuss recent advancements in modelling the high-redshift Universe, along with the evolution of machine learning-driven statistical techniques. Together, these developments are essential for bridging the gap between theoretical predictions and observations, enabling a deeper understanding of cosmic evolution with next-generation observational capabilities.

In this talk I will describe recent work done by the MIST Global 21-cm experiment. The focus will be on sky observations conducted from the Canadian High Arctic and on efforts to calibrate these observations with high accuracy.