arXiv subjects

Drew Jamieson

Publications and source records attributed to Drew Jamieson.

At least 19 recordsLinked to original sources

arXiv2026

Ab Initio Cosmological Simulations: From Inflation to Present-Day Structure Formation

The cosmic web preserves a record of the physics that shaped the universe in its earliest moments, the period of exponential expansion known as cosmic inflation. However, if inflation involves significant nonlinear interactions, there are no direct theoretical predictions for the resulting cosmic web. We present the first simulation of the entire history of the universe, from deep in the inflationary epoch to the present-day cosmic structure. Applying this to axion-U(1) inflation, we find that early-universe interactions enhance small-scale structure at high redshift, imprinting the matter power spectrum and the mass function of dark matter halos with signatures of a modified primordial curvature power spectrum and non-Gaussianity. These signatures make high-redshift galaxy surveys, 21-cm observations, and line-intensity mapping promising probes of inflationary physics. More broadly, this ab initio approach provides a novel framework for mapping the signatures of nonlinear inflationary dynamics onto observable cosmic structures across cosmic time.

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arXiv2026

Field-Level Inference of Primordial Non-Gaussianity with the Quijote Simulation Suite

Local primordial non-Gaussianity, parameterised as $f_{\rm NL}^{\rm local}$, will be stringently constrained using state-of-the-art methods applied to next-generation galaxy redshift survey data. In this paper, in preparation for the upcoming data sets, we demonstrate for the first time the joint field-level inference of $f_{\rm NL}^{\rm local}$, nuisance parameters, and the initial conditions in realistic halo catalogues, ones which are generated through full dark-matter-only $N$-body simulations. The field-level inference algorithm optimally constrains $f_{\rm NL}^{\rm local}$ through a Bayesian forward-modelling approach at the field level, which outperforms traditional methods by leveraging the full statistical power of the data at the scales considered. In addition, we assess its performance under various design choices in the forward model, including tests of the structure formation model and resolution. We demonstrate the robustness of our approach by applying it to a subset of the \textit{Quijote} simulation suite, performing the inference at scales down to $k_{\rm max} \approx 0.1 h \rm{Mpc}^{-1}$. Compared with a power spectrum and bispectrum estimator, we find a $\sim1.3$ improvement in $\sigma(f_{\rm NL}^{\rm local})$ when applying \borg{}, while marginalising over the initial conditions and bias parameters. From the small-scale information sensitivity tests, we show that the constraints on $f_{\rm NL}^{\rm local}$ improve as we increase the resolution of the inference. These findings underscore the transformative potential of field-level inference to leverage the information available in ongoing surveys such as \textit{Euclid}, providing accurate insights into the physics of cosmic inflation and the number of fields driving it.

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arXiv2025

Nonlinear Evolution of the Matter Trispectrum with Primordial Parity Violation

Parity-odd four-point correlation functions, or trispectra, of cosmic matter density fields provide a unique probe of fundamental symmetries in cosmology. Trispectra of primordial matter density fluctuations produced in the early universe are modified by the subsequent nonlinear structure formation. In this paper, we compute the nonlinear evolution of the parity-odd matter trispectrum to one-loop order, i.e., to third order in density fluctuations, within the framework of effective field theory of the large-scale structure of the universe. By analyzing the different terms in the perturbation series, we demonstrate the structure of infrared divergence cancellations, as required by the equivalence principle. We also derive the forms of the counterterms required to renormalize the ultraviolet divergences. Adopting a specific model for a primordial parity-odd trispectrum, we numerically compute the leading-order effects of nonlinear gravitational evolution and study its impact on baryonic acoustic oscillations within the signal. These calculations are essential for comparing the observed trispectra of nonlinear cosmic density fields with theoretical expectations.

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arXiv2025

Parity Violation in Galaxy Shapes: Primordial Non-Gaussianity

We present a comprehensive study of galaxy intrinsic alignment (IA) as a probe of parity-violating primordial non-Gaussianity (PNG). Within the effective field theory (EFT) framework, we show that the parity-odd IA power spectrum is sensitive to the collapsed limit of the parity-odd primordial trispectrum. For a $U(1)$-gauge inflationary model, the IA power spectrum is proportional to the power spectrum of the curvature perturbation, $P_\zeta(k) \propto k^{-3}$. However, the proportionality constants contain not only the PNG amplitude but also undetermined EFT bias parameters. We use $N$-body simulations to determine the bias parameters for dark matter halos. Using these bias parameters, we forecast IA's constraining power, assuming data from the Dark Energy Spectroscopic Instrument (DESI) and the Rubin Observatory Legacy Survey of Space and Time (LSST). We find that the IA power spectrum can improve the current limits on the amplitude of parity-violating PNG derived from galaxy four-point correlation and CMB trispectrum analyses. Moreover, galaxy shapes are complementary to these probes as they are sensitive to different scales and trispectrum configurations. Beyond galaxy shapes, we develop a new method to generate initial conditions for simulations and forward models from the parity-odd trispectrum with an enhanced collapsed limit.

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arXiv2025

Where does non-Universality in Assembly Bias come from?

Constraints on local primordial non-Gaussianity (LPnG) obtained from galaxy power spectra are limited by the perfect degeneracy between the LPnG parameter $f_{\rm NL}$ and the bias parameter $b_{\phi}$ which encodes the response of galaxy clustering to a change in the amplitude of primordial curvature fluctuations. For galaxies observed by galaxy surveys, the relation between $b_{\phi}$ and the galaxy bias $b_{g}$ is poorly understood and differs significantly from the universal mass function ansatz. In this paper, we investigate this non-universality in the context of dark-matter halos using the Separate Universe framework, focussing on dark-matter halos selected by mass and/or concentration. We show that the Separate Universe framework provides a natural explanation of the observed universality in the bias of dark-matter halos selected purely by their mass, independent of the spherical collapse picture of halo formation. We further propose an explanation for the observed non-universality in halos selected by concentration and corroborate it with $N$-body simulations in scale-free (EdS) and $\Lambda\text{CDM}$ cosmologies. In particular, we show that the relation between $b_{\phi}$ and halo bias $b_{h}$ for halos selected by concentration in matter-dominated cosmologies tends towards universality at the highest halo masses due to such halos gravitationally dominating their environment throughout their evolution. We also argue that concentration-selected halos of lower masses exhibit non-universality due to their mass accretion being significantly affected by the gravitational influence of neighbouring, more massive halos. Our results suggest that any non-universality in high redshift ($z\gtrsim 3$), high-bias objects observed by realistic galaxy surveys is entirely an artifact of the associated selection function.

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arXiv2025

Primordial Power Spectrum and Bispectrum from Lattice Simulations of Axion-U(1) Inflation

We present primordial non-Gaussianity predictions from a new high-precision code for simulating axion-U(1) inflation on a discrete lattice. We measure the primordial scalar curvature power spectrum and bispectrum from our simulations, determining their dependence on both scale and axion-gauge coupling strength. Both the gauge-sourced power spectrum and the bispectrum exhibit a strong blue tilt due to our choice of an $\alpha$-attractor inflaton potential. We provide fitting functions for the power spectrum and bispectrum that accurately reproduce these statistics across a wide range of scales and coupling strengths. While our fitting function for the bispectrum has a separable form, results from high-resolution simulations demonstrate that the full shape is not separable. Thus, our simulations generate realizations of primordial curvature perturbations with nontrivial correlators that cannot be generated using standard techniques for primordial non-Gaussianity. We derive bounds on the axion-gauge coupling strength based on the bispectrum constraints from the cosmic microwave background, demonstrating a new method for constraining inflationary primordial non-Gaussianity by simulating the nonlinear dynamics.

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arXiv2025

Learning the Universe: $3\ h^{-1}{\rm Gpc}$ Tests of a Field Level $N$-body Simulation Emulator

We apply and test a field-level emulator for non-linear cosmic structure formation in a volume matching next-generation surveys. Inferring the cosmological parameters and initial conditions from which the particular galaxy distribution of our Universe was seeded can be achieved by comparing simulated data to observational data. Previous work has focused on building accelerated forward models that efficiently mimic these simulations. One of these accelerated forward models uses machine learning to apply a non-linear correction to the linear $z=0$ Zeldovich approximation (ZA) fields, closely matching the cosmological statistics in the $N$-body simulation. This emulator was trained and tested at $(h^{-1}{\rm Gpc})^3$ volumes, although cosmological inference requires significantly larger volumes. We test this emulator at $(3\ h^{-1}{\rm Gpc})^3$ by comparing emulator outputs to $N$-body simulations for eight unique cosmologies. We consider several summary statistics, applied to both the raw particle fields and the dark matter (DM) haloes. We find that the power spectrum, bispectrum and wavelet statistics of the raw particle fields agree with the $N$-body simulations within ${\sim} 5 \%$ at most scales. For the haloes, we find a similar agreement between the emulator and the $N$-body for power spectrum and bispectrum, though a comparison of the stacked profiles of haloes shows that the emulator has slight errors in the positions of particles in the highly non-linear interior of the halo. At these large $(3\ h^{-1}{\rm Gpc})^3$ volumes, the emulator can create $z=0$ particle fields in a thousandth of the time required for $N$-body simulations and will be a useful tool for large-scale cosmological inference. This is a Learning the Universe publication.

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arXiv2024

A semi-analytic estimate for the effective sound speed counterterm in the EFTofLSS

The Effective Field Theory of Large Scale Structure (EFTofLSS) has found tremendous success as a perturbative framework for the evolution of large scale structure, and it is now routinely used to compare theoretical predictions against cosmological observations. The model for the total matter field includes one nuisance parameter at 1-loop order, the effective sound speed, which can be extracted by matching the EFT to full N-body simulations. In this work we first leverage the Layzer-Irvine cosmic energy equation to show that the equation of state can be exactly computed with knowledge of the fully nonlinear power spectrum. When augmented with separate universe methods, we show one can estimate the effective sound speed. This estimate is in good agreement with simulation results, with errors at the few tens of percent level. We apply our method to investigate the cosmology dependence of the effective sound speed and to shed light on what cosmic structures shape its value.

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arXiv2024

Can Baryon Acoustic Oscillations Illuminate the Parity-Violating Galaxy 4PCF?

Measurements of the galaxy 4-Point Correlation Function (4PCF) from theSloan Digital Sky Survey Baryon Oscillation Spectroscopic Survey (SDSS BOSS) have recently found strong statistical evidence for parity violation. If this signal is of genuine physical origin, it must stem from beyond-Standard Model physics, most likely during the very early Universe, prior to decoupling ($z$$\sim$$1,020$). Since the Baryon Acoustic Oscillation (BAO) features imprint at decoupling, they are expected in the parity-odd galaxy 4PCF, and so detecting them would be an additional piece of evidence that the signal is genuine. We demonstrate in a toy parity-violating model how the BAO imprint on the parity-odd 4PCF. We then outline how to perform a model-independent search for BAO in the odd 4PCF, desirable since, if the signal is real, we may not know for some time what model of e.g. inflation is producing it. If BAO are detected in the parity-odd sector, they can be used as a standard ruler as is already done in the 2PCF and 3PCF. We derive a simple formula relating the expected precision on the BAO scale to the overall parity-odd detection significance. Pursuing BAO in the odd 4PCF of future redshift surveys such as DESI, Euclid, Spherex, and Roman will be a valuable additional avenue to determine if parity violation in the distribution of galaxies is of genuine cosmological origin.

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arXiv2024

Field-level Emulation of Cosmic Structure Formation with Cosmology and Redshift Dependence

We present a field-level emulator for large-scale structure, capturing the cosmology dependence and the time evolution of cosmic structure formation. The emulator maps linear displacement fields to their corresponding nonlinear displacements from N-body simulations at specific redshifts. Designed as a neural network, the emulator incorporates style parameters that encode dependencies on $\Omega_{\rm m}$ and the linear growth factor $D(z)$ at redshift $z$. We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model's displacement outputs. This innovation results in significant improvements in training efficiency and model accuracy. Tested on diverse cosmologies and redshifts not seen during training, the emulator achieves percent-level accuracy on scales of $k\sim~1~{\rm Mpc}^{-1}~h$ at $z=0$, with improved performance at higher redshifts. We compare predicted structure formation histories with N-body simulations via merger trees, finding consistent merger event sequences and statistical properties.

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arXiv2024

Parity-Odd Power Spectra: Concise Statistics for Cosmological Parity Violation

We introduce the Parity-Odd Power (POP) spectra, a novel set of observables for probing parity violation in cosmological $N$-point statistics. POP spectra are derived from composite fields obtained by applying nonlinear transformations, involving also gradients, curls, and filtering functions, to a scalar field. This compresses the parity-odd trispectrum into a power spectrum. These new statistics offer several advantages: they are computationally fast to construct, estimating their covariance is less demanding compared to estimating that of the full parity-odd trispectrum, and they are simple to model theoretically. We measure the POP spectra on simulations of a scalar field with a specific parity-odd trispectrum shape. We compare these measurements to semi-analytic theoretical calculations and find agreement. We also explore extensions and generalizations of these parity-odd observables.

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arXiv2024

Hybrid-bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space

Recently, hybrid bias expansions have emerged as a powerful approach to modelling the way in which galaxies are distributed in the Universe. Similarly, field-level emulators have recently become possible thanks to advances in machine learning and $N$-body simulations. In this paper we explore whether both techniques can be combined to provide a field-level model for the clustering of galaxies in real and redshift space. Specifically, here we will demonstrate that field-level emulators are able to accurately predict all the operators of a $2^{\rm nd}$-order hybrid bias expansion. The precision achieved in real and redshift space is similar to that obtained for the nonlinear matter power spectrum. This translates to roughly 1-2\% precision for the power spectrum of a BOSS and a Euclid-like galaxy sample up to $k\sim 0.6 h^{-1}$Mpc. Remarkably, this combined approach also delivers precise predictions for field-level galaxy statistics. Despite all these promising results, we detect several areas where further improvements are required. Therefore, this work serves as a road-map for the developments required for a more complete exploitation of upcoming large-scale structure surveys.

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arXiv2024

Differentiable Cosmological Simulation with Adjoint Method

Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and computational accelerators like GPUs have facilitated forward modeling of the Universe with differentiable simulations. Based on analytic or automatic backpropagation, current differentiable cosmological simulations are limited by memory, and thus are subject to a trade-off between time and space/mass resolution, usually sacrificing both. We present a new approach free of such constraints, using the adjoint method and reverse time integration. It enables larger and more accurate forward modeling at the field level, and will improve gradient based optimization and inference. We implement it in an open-source particle-mesh (PM) $N$-body library pmwd (particle-mesh with derivatives). Based on the powerful AD system JAX, pmwd is fully differentiable, and is highly performant on GPUs.

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arXiv2024

Quijote-PNG: The Information Content of the Halo Mass Function

We study signatures of primordial non-Gaussianity (PNG) in the redshift-space halo field on non-linear scales, using a combination of three summary statistics, namely the halo mass function (HMF), power spectrum, and bispectrum. The choice of adding the HMF to our previous joint analysis of power spectrum and bispectrum is driven by a preliminary field-level analysis, in which we train graph neural networks on halo catalogues to infer the PNG $f_\mathrm{NL}$ parameter. The covariance matrix and the responses of our summaries to changes in model parameters are extracted from a suite of halo catalogues constructed from the Quijote-PNG N-body simulations. We consider the three main types of PNG: local, equilateral and orthogonal. Adding the HMF to our previous joint analysis of power spectrum and bispectrum produces two main effects. First, it reduces the equilateral $f_\mathrm{NL}$ predicted errors by roughly a factor $2$, while also producing notable, although smaller, improvements for orthogonal PNG. Second, it helps break the degeneracy between the local PNG amplitude, $f_\mathrm{NL}^\mathrm{local}$, and assembly bias, $b_ϕ$, without relying on any external prior assumption. Our final forecasts for PNG parameters are $Δf_\mathrm{NL}^\mathrm{local} = 40$, $Δf_\mathrm{NL}^\mathrm{equil} = 210$, $Δf_\mathrm{NL}^\mathrm{ortho} = 91$, on a cubic volume of $1 \left(h^{-1}{\rm Gpc}\right)^3$, with a halo number density of $\bar{n}\sim 5.1 \times 10^{-5}~h^3\mathrm{Mpc}^{-3}$, at $z = 1$, and considering scales up to $k_\mathrm{max} = 0.5~h\,\mathrm{Mpc}^{-1}$.

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arXiv2023

Predicting the Initial Conditions of the Universe using a Deterministic Neural Network

Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive. Recently, deep learning has emerged as a surrogate for N-body simulations by directly learning the mapping between the linear input of an N-body simulation and the final nonlinear output from the simulation, significantly accelerating the forward modeling. However, this still does not reduce the search space for initial conditions. In this work, we pioneer the use of a deterministic convolutional neural network for learning the reverse mapping and show that it accurately recovers the initial linear displacement field over a wide range of scales ($<1$-$2\%$ error up to nearly $k\simeq0.8$-$0.9 \text{ Mpc}^{-1}h$), despite the one-to-many mapping of the inverse problem (due to the divergent backward trajectories at smaller scales). Specifically, we train a V-Net architecture, which outputs the linear displacement of an N-body simulation, given the nonlinear displacement at redshift $z=0$ and the cosmological parameters. The results of our method suggest that a simple deterministic neural network is sufficient for accurately approximating the initial linear states, potentially obviating the need for the more complex and computationally demanding backward modeling methods that were recently proposed.

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arXiv2023

Bayesian Inference of Initial Conditions from Non-Linear Cosmic Structures using Field-Level Emulators

Analysing next-generation cosmological data requires balancing accurate modeling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full $N$-body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from $N$-body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of $1.95h^{-1}$ Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency $k_\mathrm{N} \approx 2.79h \; \mathrm{Mpc}^{-1}$. Posterior resimulations - using the inferred initial conditions for $N$-body simulations - show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate $M_{200\mathrm{c}}$ halo mass function and stacked density profiles of haloes in different mass bins $[0.853,16]\times 10^{14}M_{\odot}h^{-1}$. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modeling, indicating the potential for extracting additional information at smaller scales.

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arXiv2023

Statistics of thermal gas pressure as a probe of cosmology and galaxy formation

The statistics of thermal gas pressure are a new and promising probe of cosmology and astrophysics. The large-scale cross-correlation between galaxies and the thermal Sunyaev-Zeldovich effect gives the bias-weighted mean electron pressure, $\langle b_\mathrm{h}P_e\rangle$. In this paper, we show that $\langle b_\mathrm{h}P_e\rangle$ is sensitive to the amplitude of fluctuations in matter density, for example $\langle b_\mathrm{h}P_e\rangle\propto \left(σ_8Ω_\mathrm{m}^{0.81}h^{0.67}\right)^{3.14}$ at redshift $z=0$. We find that at $z<0.5$ the observed $\langle b_\mathrm{h}P_e\rangle$ is smaller than that predicted by the state-of-the-art hydrodynamical simulations of galaxy formation, MillenniumTNG, by a factor of $0.93$. This can be explained by a lower value of $σ_8$ and $Ω_\mathrm{m}$, similar to the so-called "$S_8$ tension'' seen in the gravitational lensing effect, although the influence of astrophysics cannot be completely excluded. The difference between Magneticum and MillenniumTNG at $z<2$ is small, indicating that the difference in the galaxy formation models used by these simulations has little impact on $\langle b_\mathrm{h}P_e\rangle$ at this redshift range. At higher $z$, we find that both simulations are in a modest tension with the existing upper bounds on $\langle b_\mathrm{h}P_e\rangle$. We also find a significant difference between these simulations there, which we attribute to a larger sensitivity to the galaxy formation models in the high redshift regime. Therefore, more precise measurements of $\langle b_\mathrm{h}P_e\rangle$ at all redshifts will provide a new test of our understanding of cosmology and galaxy formation.

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arXiv2023

Quijote-PNG: Simulations of primordial non-Gaussianity and the information content of the matter field power spectrum and bispectrum

Primordial non-Gaussianity (PNG) is one of the most powerful probes of the early Universe and measurements of the large scale structure of the Universe have the potential to transform our understanding of this area. However relating measurements of the late time Universe to the primordial perturbations is challenging due to the non-linear processes that govern the evolution of the Universe. To help address this issue we release a large suite of N-body simulations containing four types of PNG: \textsc{quijote-png}. These simulations were designed to augment the \textsc{quijote} suite of simulations that explored the impact of various cosmological parameters on large scale structure observables. Using these simulations we investigate how much information on PNG can be extracted by extending power spectrum and bispectrum measurements beyond the perturbative regime at $z=0.0$. This is the first joint analysis of the PNG and cosmological information content accessible with power spectrum and bispectrum measurements of the non-linear scales. We find that the constraining power improves significantly up to $k_\mathrm{max}\approx 0.3 h/{\rm Mpc}$, with diminishing returns beyond as the statistical probes signal-to-noise ratios saturate. This saturation emphasizes the importance of accurately modelling all the contributions to the covariance matrix. Further we find that combining the two probes is a powerful method of breaking the degeneracies with the $Λ$CDM parameters.

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