Cosmology and Gravitation

Over the last few decades, observations of diffuse gamma-ray emission in the Milky Way– in particular, the excess of GeV gamma-rays detected in the Milky Way’s galactic center, and the massive gamma-ray bubbles (the “Fermi bubbles”) centered about the Milky Way’s disk– have challenged astrophysical models. Nearly all past studies of galactic gamma-ray emission make simplifying assumptions about cosmic ray (CR) propagation that may not be valid (e.g., steady-state equilibrium), but recent numerical breakthroughs have enabled fully time-dependent dynamical evolution of CRs in magnetohydrodynamic (MHD) simulations with resolved, multi-phase small-scale structure in the interstellar medium (ISM), allowing self-consistent comparisons to the Milky Way observations. In this talk, I will present new work in which we model diffuse gamma-ray emission in simulations of Milky Way-mass galaxies with fully-resolved, multi-species CR spectra. We find that the gamma-ray spectrum in the galactic center can fluctuate by up to an order of magnitude on million-year timescales due to highly variable star formation and losses from variable structure in the turbulent ISM, with some fluctuations consistent with the Fermi-LAT galactic center excess. I will also show that Fermi bubble-like features arise from stellar feedback in these simulations. Finally, I will present the first results from a new suite of cosmological simulations in which a dark sector with an ultra-light mediator gives rise to a long-range (kiloparsec-scale) self-interaction. The addition of a long-range dark matter self-interaction has dramatic effects on the formation of galaxies and their host halos, and will be testable by current and upcoming astronomical surveys.

Weak lensing of galaxies and of the CMB provide direct probes of the cosmic matter density field, but are sensitive to different redshift ranges and different survey systematics. The combination of these probes provides a way to calibrate systematics and test LCDM across cosmic time. I will talk about the cross-correlation of cosmic shear and CMB lensing using DES Y3 and new lensing reconstructions from SPT-3G. The main result of this analysis is a first high-significance measurement of the lensing-shear cross-correlation using polarization-only lensing maps, allowing us to sidestep the issue of extragalactic foregrounds without losing much constraining power on large scales. Additionally, using a pure blue shear sample and a variety of foreground-mitigated CMB lensing reconstructions we are able to conduct data-driven tests to ensure our results are robust to both galaxy intrinsic alignments and foregrounds in the CMB.

Weak gravitational lensing of the CMB has been established as a robust and powerful observable for precision cosmology. By mapping the matter distribution, it provides a clean window to measure structure growth and key cosmological parameters such as the neutrino mass. In this talk, I will present the major improvements to the Atacama Cosmology Telescope lensing measurement following the latest public data release (DR6). In particular, I will discuss both the inclusion of DR6 data collected during the day (which has never before been used in any lensing analysis) and the prospect of accessing wider sky areas thanks to Galactic foreground mitigation strategies. These improvements will dramatically increase the forecasted power of CMB lensing measurements with the Simons Observatory.

The first and smallest systems of particle dark matter gravitationally condensed directly out of the smooth mass distribution of the early universe. This formation mechanism left these "prompt cusps" with uniquely compact r^-1.5 density profiles and linked their properties tightly with the cosmic initial conditions. Prompt cusps are the densest and most abundant dark matter systems. I will present their basis in simulations and theory, and I will show how they bring new opportunities to test the physics of dark matter and the early universe. For example, the measured kinematics of dwarf galaxies strongly constrain dark matter models that were primordially warm, while gamma rays from galaxy clusters strongly constrain dark matter models with matter-antimatter symmetry. I will also discuss small-scale structure formation for an alternative dark matter candidate – primordial black holes (PBHs) – based on a new simulation that fully resolves the inter-PBH dynamics. For example, gravitational interactions involving PBH binaries can eject PBHs at extreme speeds, making a component of "hot dark matter" that suppresses the growth rate of structure up to galaxy scales.

Multiprobe techniques are at the forefront of modern cosmological analysis. In this talk, I will demonstrate the advantages of the multiprobe approach for assessing internal consistency, mitigating systematic effects, and breaking parameter degeneracies across datasets spanning different scales and redshifts. I will showcase these benefits using a comprehensive multiprobe pipeline, developed over a series of analyses, that combines cosmic microwave background (CMB) and large-scale structure (LSS) observations, including all possible cross-correlations between probes. In the second half of the talk, I will present recent results from the pipeline demonstrating how multiprobe analysis can address fundamental questions such as the nature of dark energy and the sum of neutrino masses. Finally, I will look forward to applications with upcoming CMB and LSS surveys. I will highlight how the multiprobe pipeline, combined with modular tools I have developed for effective field theory of large-scale structure (EFTofLSS) modelling and beyond-Limber angular power spectrum computation, positions this work for natural extension to next-generation (Stage-IV) datasets.

The Hubble tension, recently reaching as high as 7σ, increasingly presents a challenge to the ΛCDM model. A key question is whether this tension stems from our use of the sound horizon as a standard ruler. In this talk, I will present a new, sub-2% measurement of the Hubble constant (H₀) that is independent of the sound horizon scale, using data from the first data release of the Dark Energy Spectroscopic Instrument (DESI). The analysis employs a power spectrum rescaling technique that marginalizes over the sound horizon information, drawing instead on the matter-radiation equality scale as a standard ruler. Combining DESI’s full-shape galaxy clustering with uncalibrated post-reconstruction BAO and the CMB acoustic scale θ*, along with various external sound horizon-free datasets, results in highly robust constraints that are the most precise to date from LSS. Looking ahead, this measurement offers a new window on beyond-ΛCDM physics and the physical origin of the Hubble tension.

Maximizing our chances of discovering new physics from next-generation cosmological surveys requires making robust theoretical predictions of the nonlinear processes governing both the early Universe and the late-time large-scale structure. In this talk, I will present advances in two complementary methods for constraining the physics of the early Universe with cosmological survey data: forward simulation from inflation to late-time observables, and machine-learning-assisted Bayesian reconstruction of primordial initial conditions. In the first approach, we study the early Universe by directly simulating its dynamics. I have developed the first code for simulating multifield inflationary theories on a discrete lattice with enough precision to robustly predict primordial non-Gaussianity in higher-order N-point correlation functions. Focusing on axion-U(1) inflation, we accurately characterize the rich phenomenology predicted by this model, including modifications to the power spectrum, bispectrum, and higher-order correlation functions that violate parity. Using the output of these inflation simulations as initial conditions for N-body simulations, we effectively simulate the entire history of the Universe from inflation to late-time large-scale structure, finally revealing what inflation actually predicts about the origin of structures in the Universe. In the second approach, we reconstruct the initial conditions of the Universe using Bayesian inference with a fast, accurate, differentiable model of large-scale structure formation. I developed the first field-level emulator for large-scale structure, training a convolutional neural network to map linear initial conditions directly to the late-time matter field as modelled by computationally expensive N-body simulations. The model accelerates predictions by a factor of 1000 over full N-body simulations and achieves percent-level accuracy at deeply nonlinear scales (1 Mpc), outperforming fast particle-mesh simulations. This field-level emulator opens the possibility of reconstructing the initial conditions of the Universe using Bayesian inference techniques that include information from late-time nonlinear scales, as I will demonstrate with an application to simulated data. This highly nontrivial task involved exploring a million-dimensional parameter space using Hamiltonian Monte Carlo sampling and was only possible due to the combined speed, accuracy, and differentiability of the emulator.

Primordial non-Gaussianities (PNGs) are imprints in the density field caused by particle interactions during the inflationary epoch. A subclass of these signatures ("cosmological colliders") encode information about the interactions and properties of any additional particle fields present during inflation. I introduce a novel method for producing cosmological simulations with arbitrary collider signals, and present signatures from over thirty collider models in the deeply non-linear regime of the density field. I show how weak lensing surveys can offer competitive constraints that are complementary to other probes of PNGs in the late Universe. This simulation framework expands the probes of nonlinear structure (beyond lensing) that are usable in constraining primordial particle physics, which in turn expands the space of testable models and enhances the precision with which we constrain them.

Lambda Cold Dark Matter (LCDM) is the most successful theory for the formation of structure in the Universe. Although its predictions have been verified on large scales, they are still contested on the scale of dwarf galaxies, whose dynamical properties are often cited as evidence for the need to revise some of LCDM’s basic tenets. In this context, I will discuss the recent discovery of the faintest galaxies known to date, and how their properties may be used to place constraints on the clustering of dark matter on the smallest galactic and sub-galactic scales, as well as on the viability of some of the proposed alternatives to LCDM.

Modern telescopes such as Hubble and JWST deliver exquisitely detailed images that encode rich information about galaxies, stars, and dark matter. Extracting this information requires solving high-dimensional inverse problems in which each pixel represents a parameter of the sky. In this talk, I will present a Bayesian framework that uses diffusion-based generative models to perform inference directly in these high-dimensional spaces. By learning expressive priors over galaxy morphologies and likelihoods over complex, non-Gaussian noise distributions, this approach enables the reconstruction of astronomical images at unprecedented fidelity. This talk will cover a wide range of applications where we have applied these techniques, from the recovery of detailed images of protoplanetary discs using ALMA data and high redshift galaxies using Hubble data and strong gravitational lensing to a discussion about the misscalibration of the generative model. The generative model often needs to be trained on low redshift galaxies or using simulations, all of which could introduce biases in our inference. To address this, I will present a framework which can update the parameters of the generative model using corrupted data only. Together, these results illustrate how generative modeling can be used in a principled statistical framework for astronomy.