Title: Better Together: Combining the Strengths of Rubin, LS4 and Euclid for Time-domain Science and Cosmology

Abstract: We have entered an exciting decade for survey science with such space-based surveys as Euclid and such ground-based surveys as LSST and LS4 providing overlapping imaging datasets across the optical and IR.  LSST will identify thousands of tidal disruption events (TDEs) and millions of AGN and supernovae, including hundreds of gravitationally lensed supernovae (gLSNe) from which we can measure the Hubble Constant. I will discuss how joint analysis of ground and space-based data via multi-resolution forward modeling methods will enable us to search for TDEs from non-nuclear massive black holes, undertake time delay cosmography with populations of gLSNe and study the environments of supernovae and dwarf galaxy AGN.

NB: non-standard time!

Title: Symmetry-weighted ensemble averaging from TQFT gravity

Abstract: In a recently proposed framework of TQFT gravity (2310.13044, 2405.20366) -- a toy model of AdS3 gravity -- a bulk 3d TQFT summed over all topologies is shown to be dual to a unitary ensemble of boundary 2d CFTs. I will show that the CFTs in this ensemble are weighted by the inverse of the order of their symmetry group (relative to the categorical symmetry provided by the bulk TQFT as a SymTFT). Mathematically, this is the natural measure over the groupoid of the TQFT Lagrangian algebras that construct the CFTs, and the holographic duality then provides a generalization of the Siegel-Weil formula beyond averaging over bosonic lattice-CFTs. I will also discuss some examples for rational CFTs as well as implications to noncompact TQFTs and pure gravity.

Dr. Matthew Bayliss, University of Cincinnati

Title: Taking Galaxies Apart and Putting Them Back Together Again

Abstract: Understanding the growth and evolution of stars and galaxies across cosmic time is a cornerstone of modern observational cosmology. After Cosmic Dawn, the first generation of galaxies powered much of cosmic re-ionization. Later, the global star-formation density accelerated toward its peak at Cosmic Noon, when most of the stellar mass in the Universe was formed. The industry standard is to use individual galaxies as the de facto measurement unit. There are practical reasons for counting galaxy-by-galaxy: galaxies grow and reside in dark matter haloes that map back to primordial mass over-densities, and even space-based observatories can only marginally resolve galaxies in the distant universe. However, the physical processes that drive galaxy growth and evolution -- cloud collapse, star formation, feedback, etc. -- operate on scales much smaller than a galaxy. I will present ongoing work using bright, strongly lensed galaxies to zoom in on the scales of individual star clusters to resolve the physics of what's happening inside distant galaxies. 

Title: From chiral effective field theory to perturbative QCD: A Bayesian model mixing approach to neutron star matter

Abstract: Constraining the equation of state (EOS) of strongly interacting, dense matter is the focus of significant experimental, observational, and theoretical effort. While chiral effective field theory (EFT) can describe the EOS between the typical densities of nuclei and those in the outer cores of neutron stars, perturbative QCD (pQCD) can be applied to properties of deconfined quark matter, both with quantified theoretical uncertainties.

However, describing the full range of densities in between with a single EOS that has well-quantified uncertainties is a challenging problem. Bayesian model mixing (BMM) can help bridge the gap between the two theories.

In this talk, I will present a BMM framework that can combine EOS constraints from different density regions in a principled way to construct a globally predictive, composite EOS model based on Gaussian processes (GPs). I will discuss applications of this BMM framework to the EOS and structure of neutron stars, as well as the statistical uncertainty quantification of the underlying microscopic EOS calculations.

Title: Some results from my research career and recent work on a sample size problem for free-ranging wildlife.

Abstract: I will begin by briefly discussing some aspects of my Ph.D. dissertation and research at the University of Kentucky, which focused on an estimation technique for Markov processes. I will then review results from three papers I have co-authored involving, respectively, an epidemic model, the double bootstrap and generalized linear mixed models.

The main part of my talk concerns a recently proposed two-parameter model and a Bayesian statistical framework for estimating prevalence and determining sample size requirements for detecting disease in free-ranging wildlife. Well-known sample size formulas assume that animals contract disease independently, an assumption that is unlikely to hold in many practical settings. The presence of correlation has implications for sample size requirements and sampling design.

Booth et al. (2023) “Sample size for estimating disease prevalence in free-ranging wildlife populations: a Bayesian modeling approach”, Journal of Agricultural, Biological and Environmental Statistics, 29(3):438-454

Booth et al. (2025) “Management agencies can leverage animal social structure for wildlife disease surveillance”, Journal of Wildlife Diseases, 61(2):472-476