The Handley Research Group stands at the forefront of cosmological exploration, pioneering novel approaches that fuse fundamental physics with the transformative power of artificial intelligence. We are a dynamic team of researchers, including PhD students, postdoctoral fellows, and project students, based at the University of Cambridge. Our mission is to unravel the mysteries of the Universe, from its earliest moments to its present-day structure and ultimate fate. We tackle fundamental questions in cosmology and astrophysics, with a particular focus on leveraging advanced Bayesian statistical methods and AI to push the frontiers of scientific discovery. Our research spans a wide array of topics, including the primordial Universe, inflation, the nature of dark energy and dark matter, 21-cm cosmology, the Cosmic Microwave Background (CMB), and gravitational wave astrophysics.

Our Research Approach: Innovation at the Intersection of Physics and AI

At The Handley Research Group, we develop and apply cutting-edge computational techniques to analyze complex astronomical datasets. Our work is characterized by a deep commitment to principled Bayesian inference and the innovative application of artificial intelligence (AI) and machine learning (ML).

Key Research Themes:

• Cosmology: We investigate the early Universe, including quantum initial conditions for inflation and the generation of primordial power spectra. We explore the enigmatic nature of dark energy, using methods like non-parametric reconstructions, and search for new insights into dark matter. A significant portion of our efforts is dedicated to 21-cm cosmology, aiming to detect faint signals from the Cosmic Dawn and the Epoch of Reionization.
• Gravitational Wave Astrophysics: We develop methods for analyzing gravitational wave signals, extracting information about extreme astrophysical events and fundamental physics.
• Bayesian Methods & AI for Physical Sciences: A core component of our research is the development of novel statistical and AI-driven methodologies. This includes advancing nested sampling techniques (e.g., PolyChord, dynamic nested sampling, and accelerated nested sampling with $\beta$-flows), creating powerful simulation-based inference (SBI) frameworks, and employing machine learning for tasks such as radiometer calibration, cosmological emulation, and mitigating radio frequency interference. We also explore the potential of foundation models for scientific discovery.

Technical Contributions: Our group has a strong track record of developing widely-used scientific software. Notable examples include:

• PolyChord: A next-generation nested sampling algorithm for Bayesian computation.
• anesthetic: A Python package for processing and visualizing nested sampling runs.
• GLOBALEMU: An emulator for the sky-averaged 21-cm signal.
• maxsmooth: A tool for rapid maximally smooth function fitting.
• margarine: For marginal Bayesian statistics using normalizing flows and KDEs.
• fgivenx: A package for functional posterior plotting.
• nestcheck: Diagnostic tests for nested sampling calculations.

Impact and Discoveries

Our research has led to significant advancements in cosmological data analysis and yielded new insights into the Universe. Key achievements include:

• Pioneering the development and application of advanced Bayesian inference tools, such as PolyChord, which has become a cornerstone for cosmological parameter estimation and model comparison globally.
• Making significant contributions to the analysis of major cosmological datasets, including the Planck mission, providing some of the tightest constraints on cosmological parameters and models of inflation.
• Developing novel AI-driven approaches for astrophysical challenges, such as using machine learning for radiometer calibration in 21-cm experiments and simulation-based inference for extracting cosmological information from galaxy clusters.
• Probing the nature of dark energy through innovative non-parametric reconstructions of its equation of state from combined datasets.
• Advancing our understanding of the early Universe through detailed studies of 21-cm signals from the Cosmic Dawn and Epoch of Reionization, including the development of sophisticated foreground modelling techniques and emulators like GLOBALEMU.
• Developing new statistical methods for quantifying tensions between cosmological datasets (Quantifying tensions in cosmological parameters: Interpreting the DES evidence ratio) and for robust Bayesian model selection (Bayesian model selection without evidences: application to the dark energy equation-of-state).
• Exploring fundamental physics questions such as potential parity violation in the Large-Scale Structure using machine learning.

Charting the Future: AI-Powered Cosmological Discovery

The Handley Research Group is poised to lead a new era of cosmological analysis, driven by the explosive growth in data from next-generation observatories and transformative advances in artificial intelligence. Our future ambitions are centred on harnessing these capabilities to address the most pressing questions in fundamental physics.

Strategic Research Pillars:

• Next-Generation Simulation-Based Inference (SBI): We are developing advanced SBI frameworks to move beyond traditional likelihood-based analyses. This involves creating sophisticated codes for simulating Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillation (BAO) datasets from surveys like DESI and 4MOST, incorporating realistic astrophysical effects and systematic uncertainties. Our AI initiatives in this area focus on developing and implementing cutting-edge SBI algorithms, particularly neural ratio estimation (NRE) methods, to enable robust and scalable inference from these complex simulations.
• Probing Fundamental Physics: Our enhanced analytical toolkit will be deployed to test the standard cosmological model ($\Lambda$CDM) with unprecedented precision and to explore extensions to Einstein’s General Relativity. We aim to constrain a wide range of theoretical models, from modified gravity to the nature of dark matter and dark energy. This includes leveraging data from upcoming gravitational wave observatories like LISA, alongside CMB and large-scale structure surveys from facilities such as Euclid and JWST.
• Synergies with Particle Physics: We will continue to strengthen the connection between cosmology and particle physics by expanding the GAMBIT framework to interface with our new SBI tools. This will facilitate joint analyses of cosmological and particle physics data, providing a holistic approach to understanding the Universe’s fundamental constituents.
• AI-Driven Theoretical Exploration: We are pioneering the use of AI, including large language models and symbolic computation, to automate and accelerate the process of theoretical model building and testing. This innovative approach will allow us to explore a broader landscape of physical theories and derive new constraints from diverse astrophysical datasets, such as those from GAIA.

Our overarching goal is to remain at the forefront of scientific discovery by integrating the latest AI advancements into every stage of our research, from theoretical modeling to data analysis and interpretation. We are excited by the prospect of using these powerful new tools to unlock the secrets of the cosmos.

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