arXiv subjects

Michael W. Toomey

Publications and source records attributed to Michael W. Toomey.

At least 19 recordsLinked to original sources

arXiv2026

Heterotic Warm Inflation

We propose a two-field model of warm inflation motivated by a heterotic string construction, involving an axion and a dilaton-like scalar field with non-trivial kinetic mixing. Gauge-field interactions generate dissipation and thermal corrections affecting both fields. A systematic numerical analysis reveals a range of dynamical regimes, including effectively single-field and multi-field behavior. We find that warm inflation is typically realized along the axion direction, while thermal corrections tend to hinder sustained dilaton-driven inflation over most of the parameter space. Although configurations exist in which the dilaton becomes dynamically relevant, particularly near the end of inflation, the majority of viable solutions are effectively single-field and axion-dominated. These results point to a dynamical mechanism in heterotic-inspired models that naturally favors axion-driven warm inflation while limiting the role of the dilaton.

hep-th↗

arXiv2025

Kinetic Mixing and the Phantom Illusion: Axion-Dilaton Quintessence in Light of DESI DR2

Recent results from DESI BAO analyses suggest that dark energy may not be a cosmological constant and is in fact dynamical. Furthermore, the data suggest that the equation of state may have been in the phantom regime in the distant past, recently undergoing a phantom crossing. In this work, we investigate whether this preference can be realized within a kinetically mixed axion-dilaton (KMIX) quintessence model, a string-motivated system in which an axion-like field couples exponentially to a dilaton-like (moduli) field. Crucially, KMIX can appear phantom in a standard Chevallier-Polarski-Linder (CPL) based analysis. To confront the model with data, we develop a fast pipeline based on normalizing flows that (i) learns a theory-informed prior on $(w_0,w_a)$ from KMIX realizations and (ii) provides an inverse mapping from CPL parameters back to the physical KMIX parameters. By importance-sampling pre-computed CPL chains using this framework, we effectively transform generic phenomenological constraints into direct, computationally efficient constraints on the underlying KMIX theory, avoiding the prohibitive cost of full parameter space exploration. Applied to Planck+DESI DR2 BAO measurements, our framework finds support for KMIX at $2.5σ$ compared to the base CPL fit at $3.1σ$, demonstrating that KMIX may account for the DESI preference without invoking true phantom behavior. When additionally including Type Ia supernovae data, we find that the preference remains above $3σ$ for Union3 and DES Y5, but drops to $2.1σ$ with Pantheon+. The latter, combined with the DESI full-shape power spectrum and bispectrum data, further reduces the preference to $1.7σ$. Ultimately, should the DESI deviation persist with future data, KMIX may offer a theoretically well-motivated explanation for the phantom-like signatures inferred from phenomenological fits.

astro-ph.CO↗

arXiv2025

FlowLensing: Simulating Gravitational Lensing with Flow Matching

Gravitational lensing is one of the most powerful probes of dark matter, yet creating high-fidelity lensed images at scale remains a bottleneck. Existing tools rely on ray-tracing or forward-modeling pipelines that, while precise, are prohibitively slow. We introduce FlowLensing, a Diffusion Transformer-based compact and efficient flow-matching model for strong gravitational lensing simulation. FlowLensing operates in both discrete and continuous regimes, handling classes such as different dark matter models as well as continuous model parameters ensuring physical consistency. By enabling scalable simulations, our model can advance dark matter studies, specifically for probing dark matter substructure in cosmological surveys. We find that our model achieves a speedup of over 200$\times$ compared to classical simulators for intensive dark matter models, with high fidelity and low inference latency. FlowLensing enables rapid, scalable, and physically consistent image synthesis, offering a practical alternative to traditional forward-modeling pipelines.

astro-ph.IM↗

arXiv2025

How Theory-Informed Priors Affect DESI Evidence for Evolving Dark Energy

Recent measurements of baryon acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI) have been interpreted to suggest that dark energy may be evolving. In this work, we examine how prior choices affect such conclusions. Specifically, we study the biases introduced by the customary use of uniform priors on the Chevallier-Polarski-Linder (CPL) parameters, $w_0$ and $w_a$, when assessing evidence for evolving dark energy. To do so, we construct theory-informed priors on $(w_0, w_a)$ using a normalizing flow (NF), trained on two representative quintessence models, which learns the distribution of these parameters conditional on the underlying $Λ$CDM parameters. In the combined $\textit{Planck}$ CMB + DESI BAO analysis we find that the apparent tension with a cosmological constant in the CPL framework can be reduced from $\sim 3.1σ$ to $\sim 1.3σ$ once theory-informed priors are applied, rendering the result effectively consistent with $Λ$CDM. For completeness, we also analyze combinations that include Type Ia supernova data, showing similar shifts toward the $Λ$CDM limit. Taken together, the observed sensitivity to prior choices in these analyses arises because uniform priors - often mischaracterized as "uninformative" - can actually bias inferences toward unphysical parameter regions. Consequently, our results underscore the importance of adopting physically motivated priors to ensure robust cosmological inferences, especially when evaluating new hypotheses with only marginal statistical support. Lastly, our NF-based framework achieves these results by post-processing existing MCMC chains, requiring $\approx 1$ hour of additional CPU compute time on top of the base analysis - a dramatic speedup over direct model sampling that highlights the scalability of this approach for testing diverse theoretical models.

astro-ph.CO↗

arXiv2025

Kinetically Coupled Dark Matter Condensates

Dark matter consisting of ultralight bosons can form a macroscopic Bose-Einstein condensate with distinctive observational signatures. While this possibility has been extensively studied for axions and axion-like particles $-$ pseudoscalars with masses protected by shift symmetry $-$ realistic models from string theory and other higher-dimensional theories predict more complex structures. Here we investigate a two-field generalization where an axion couples to a moduli field through its kinetic term, representing the phase and radial modes of a complex scalar field. We demonstrate that when this system forms a gravitationally bound Bose-Einstein condensate, the kinetic coupling produces dramatic modifications to cosmological evolution compared to the canonical single-field case. Most notably, the axion Jeans scale becomes dynamically dependent on the moduli field's evolution, fundamentally altering structure formation. By mapping existing observational constraints from canonical axion models to our two-field scenario, we identify regions of parameter space that are already excluded by current observations. In particular, consistency with observations requires that the moduli field must take on small field values, $χ/M_{\rm pl} \ll 1$, throughout most of cosmic history for this class of axions to remain a viable description of all dark matter.

astro-ph.CO↗

arXiv2025

Full-shape analysis with simulation-based priors: cosmological parameters and the structure growth anomaly

We explore full-shape analysis with simulation-based priors, which is the simplest approach to galaxy clustering data analysis that combines effective field theory (EFT) on large scales and numerical simulations on small scales. The core ingredient of our approach is the prior density of EFT parameters which we extract from a suite of 10500 galaxy simulations based on the halo occupation distribution (HOD) model. We measure the EFT parameters with the field-level forward model, which enables us to cancel cosmic variance. On the theory side, we develop a new efficient approach to calculate field-level transfer functions using time-sliced perturbation theory and the logarithmic fast Fourier transform. We find that the cosmology dependence of EFT parameters of galaxies is approximately degenerate with the HOD parameters, and hence it can be ignored for the purpose of prior generation. We use neural density estimation to model the measured distribution of EFT parameters. Our distribution model is then used as a prior in a reanalysis of the BOSS full-shape galaxy power spectrum data. Assuming the $Λ$CDM model, we find significant ($\approx 30\%$ and $\approx 60\%$) improvements for the matter density fraction and the mass fluctuation amplitude, which are constrained to $Ω_{m}= 0.315 \pm 0.010$ and $σ_8 = 0.671 \pm 0.027$. The value of the Hubble constant does not change, $H_0= 68.7\pm 1.1$~km/s/Mpc. This reaffirms earlier reports of the structure growth tension from the BOSS data. Finally, we use the measured EFT parameters to constrain the galaxy-dark matter connection.

astro-ph.CO↗

arXiv2025

Fundamental physics with the Lyman-alpha forest: constraints on the growth of structure and neutrino masses from SDSS with effective field theory

We present an effective field theory (EFT) approach to extract fundamental cosmological parameters from the Lyman-alpha forest flux fluctuations as an alternative to the standard simulation-based techniques. As a first application, we re-analyze the publicly available one-dimensional Lyman-alpha flux power spectrum data from the Sloan Digital Sky Survey. Our analysis relies on informative priors on EFT parameters which we extract from a combination of public hydrodynamic simulation and emulator data. Assuming the concordance cosmological model, our one-parameter analysis yields a $2\%$ measurement of the late time mass fluctuation amplitude $σ_8 = 0.841\pm 0.017$, or equivalently, the structure growth parameter $S_8 = 0.852\pm 0.017$, consistent with the standard cosmology. This result is obtained assuming that non-linear EFT parameters are cosmology-independent functions of the linear bias parameter. When this assumption is loosened, the limit degrades by a factor of 3, suggesting that informative priors are necessary for competitive constraints. Combining our EFT likelihood with Planck + baryon acoustic oscillation data, we find a new constraint on the total neutrino mass, $\sum m_ν<$ 0.08 eV (at 95\% CL). Our study defines priorities for the development of EFT methods and sets the benchmark for cosmological analyses of the Lyman-alpha forest data from the Dark Energy Spectroscopic Instrument.

astro-ph.CO↗

arXiv2024

The Millennium and Astrid galaxies in effective field theory: comparison with galaxy-halo connection models at the field level

Cosmological analyses of redshift space clustering data are primarily based on using luminous ``red'' galaxies (LRGs) and ``blue'' emission line galaxies (ELGs) to trace underlying dark matter. Using the large high-fidelity high-resolution MillenniumTNG (MTNG) and Astrid simulations, we study these galaxies with the effective field theory (EFT)-based field level forward model. We confirm that both red and blue galaxies can be accurately modeled with EFT at the field level and their parameters match those of the phenomenological halo-based models. Specifically, we consider the state of the art Halo Occupation Distribution (HOD) and High Mass Quenched (HMQ) models for the red and blue galaxies, respectively. Our results explicitly confirm the validity of the halo-based models on large scales beyond the two-point statistics. In addition, we validate the field-level HOD/HMQ-based priors for EFT full-shape analysis. We find that the local bias parameters of the ELGs are in tension with the predictions of the LRG-like HOD models and present a simple analytic argument explaining this phenomenology. We also confirm that ELGs exhibit weaker non-linear redshift-space distortions (``fingers-of-God''), suggesting that a significant fraction of their data should be perturbative. We find that the response of EFT parameters to galaxy selection is sensitive to assumptions about baryonic feedback, suggesting that a detailed understanding of feedback processes is necessary for robust predictions of EFT parameters. Finally, using neural density estimation based on paired HOD-EFT parameter samples, we obtain optimal HOD models that reproduce the clustering of Astrid and MTNG galaxies.

astro-ph.CO↗

arXiv2024

Domain adaptation in application to gravitational lens finding

The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need to be developed. In this work, we assess the performance of three domain adaptation techniques -- Adversarial Discriminative Domain Adaptation (ADDA), Wasserstein Distance Guided Representation Learning (WDGRL), and Supervised Domain Adaptation (SDA) -- in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. We find that WDGRL combined with an ENN-based encoder provides the best performance in an unsupervised setting and that supervised domain adaptation is able to enhance the model's ability to distinguish between lenses and common similar-looking false positives, such as spiral galaxies, which is crucial for future lens surveys.

astro-ph.IM↗

arXiv2024

Learning Theory Informed Priors for Bayesian Inference: A Case Study with Early Dark Energy

Cosmological models are often motivated and formulated in the language of particle physics, using quantities such as the axion decay constant, but tested against data using ostensibly physical quantities, such as energy density ratios, assuming uniform priors on the latter. This approach neglects priors on the model from fundamental theory, including from particle physics and string theory, such as the preference for sub-Planckian axion decay constants. We introduce a novel approach to learning theory-informed priors for Bayesian inference using normalizing flows (NF), a flexible generative machine learning technique that generates priors on model parameters when analytic expressions are unavailable or difficult to compute. As a test case, we focus on early dark energy (EDE), a model designed to address the Hubble tension. Rather than using uniform priors on the $\textit{phenomenological}$ EDE parameters $f_{\rm EDE}$ and $z_c$, we train a NF on EDE cosmologies informed by theory expectations for axion masses and decay constants. Our method recovers known constraints in this representation while being $\sim 300,000$ times more efficient in terms of total CPU compute time. Applying our NF to $\textit{Planck}$ and BOSS data, we obtain the first theory-informed constraints on EDE, finding $f_{\rm EDE} \lesssim 0.02$ at $95\%$ confidence with an $H_0$ consistent with $\textit{Planck}$, but in $\sim 6σ$ tension with SH0ES. This yields the strongest constraints on EDE to date, additionally challenging its role in resolving the Hubble tension.

astro-ph.CO↗

arXiv2024

Full-shape analysis with simulation-based priors: constraints on single field inflation from BOSS

Perturbative, or effective field theory (EFT)-based, full-shape analyses of galaxy clustering data involve ``nuisance parameters'' to capture various observational effects such as the galaxy-dark matter connection (galaxy bias). We present an efficient approach to set informative physically motivated priors on these parameters. We extract these priors from simulated galaxy catalogs based on halo occupation distribution (HOD) models. First, we build a joint distribution between EFT galaxy bias and HOD parameters from a set of 10,500 HOD mock catalogs. We use the field level EFT technique that allows for cosmic variance cancellation, enabling a precision calibration of EFT parameters from computationally inexpensive small-volume simulations. Second, we use neural density estimators -- normalizing flows -- to model the marginal probability density of the EFT parameters, which can be used as a prior distribution in full shape analyses. As a first application, we use our HOD-based priors in a new analysis of galaxy power spectra and bispectra from the BOSS survey in the context of single field primordial non-Gaussianity. We find that our priors lead to a reduction of the posterior volume of bias parameters by an order of magnitude. We also find $f_{\rm NL}^{\rm equil} = 320\pm 300$ and $f_{\rm NL}^{\rm ortho} = 100\pm 130$ (at 68\% CL) in a combined two-template analysis, representing a $\approx 40\%$ improvement in constraints on single field primordial non-Gaussianity, equivalent to doubling the survey volume.

astro-ph.CO↗

arXiv2023

Observational constraints on early dark energy

We review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 and PR4 cosmic microwave background (CMB) data and large-scale structure (LSS) data sets including galaxy clustering and weak lensing data from the Dark Energy Survey, Subaru Hyper Suprime-Cam, and KiDS+VIKING-450, as well as BOSS/eBOSS galaxy clustering and Lyman-$α$ forest data. We detail the fit to CMB data, and perform the first analyses of EDE using the CAMSPEC and Hillipop likelihoods for Planck CMB data, rather than Plik, both of which yield a tighter upper bound on the allowed EDE fraction than that found with Plik. We then supplement CMB data with large-scale structure data in a series of new analyses. All these analyses are concordant in their Bayesian preference for $Λ$CDM over EDE, as indicated by marginalized posterior distributions. We perform a series of tests of the impact of priors in these results, and compare with frequentist analyses based on the profile likelihood, finding qualitative agreement with the Bayesian results. All these tests suggest prior volume effects are not a determining factor in analyses of EDE. This work provides both a review of existing constraints and several new analyses.

astro-ph.CO↗

arXiv2023

Addressing the Hubble and $S_8$ Tensions with a Kinetically Mixed Dark Sector

We present a kinetically mixed dark sector (KMIX) model to address the Hubble and $S_8$ tensions. Inspired from string theory, our model includes two fields: an axion, which plays a role similar to the scalar field in early dark energy models, and a dilaton. This theory differs from other axio-dilaton models aimed at the Hubble tension in that there is necessarily kinetic mixing between the two fields which allows for efficient energy transfer from the axion into the dilaton which has $w\approx1$. As a direct consequence of these dynamics, we find the model does not need to resort to a fine-tuned potential to solve the Hubble tension and naturally accommodates a standard axion potential. Furthermore, the axion will necessarily makeup a small (fuzzy) fraction of $Ω_{\rm cdm}$ once it begins to oscillate at the bottom of its potential and will suppress the growth of perturbations on scales sensitive to $S_8$. Interestingly, the scale of the potential for the dilaton has to be small, $\lesssim \mathcal{O}(10~{\rm meV})^4$, suggesting the possibility for a connection to dark energy. Implementing the dynamics for the background and perturbations in a modified Boltzmann code we calculate the CMB and matter power spectra for our theory. Exploring the parameter space of our model, we find regions which can accommodate a $\sim 10\%$ increase in $H_0$ from the Planck inferred value and $S_8$ values that are consistent with large-scale structure constraints.

astro-ph.CO↗

arXiv2023

Quantum Gravity Signatures in the Late-Universe

We calculate deviations in cosmological observables as a function of parameters in a class of connection-based models of quantum gravity. In this theory non-trivial modifications to the background cosmology can occur due to a distortion of the wave function of the Universe at the transition from matter to dark energy domination (which acts as a "reflection" in connection space). We are able to exclude some regions of parameter space and show with projected constraints that future experiments like DESI will be able to further constrain these models. An interesting feature of this theory is that there exists a region of parameter space that could naturally alleviate the $S_8$ tension.

astro-ph.CO↗

arXiv2021

Domain Adaptation for Simulation-Based Dark Matter Searches Using Strong Gravitational Lensing

Clues to the identity of dark matter have remained surprisingly elusive, given the scope of experimental programs aimed at its identification. While terrestrial experiments may be able to nail down a model, an alternative, and equally promising, method is to identify dark matter based on astrophysical or cosmological signatures. A particularly sensitive approach is based on the unique signature of dark matter substructure on galaxy-galaxy strong lensing images. Machine learning applications have been explored in detail for extracting just this signal. With limited availability of high quality strong lensing data, these approaches have exclusively relied on simulations. Naturally, due to the differences with the real instrumental data, machine learning models trained on simulations are expected to lose accuracy when applied to real data. This is where domain adaptation can serve as a crucial bridge between simulations and real data applications. In this work, we demonstrate the power of domain adaptation techniques applied to strong gravitational lensing data with dark matter substructure. We show with simulated data sets of varying complexity, that domain adaptation can significantly mitigate the losses in the model performance. This technique can help domain experts build and apply better machine learning models for extracting useful information from strong gravitational lensing data expected from the upcoming surveys.

astro-ph.CO↗

arXiv2021

The Physics of Machine Learning: An Intuitive Introduction for the Physical Scientist

This article is intended for physical scientists who wish to gain deeper insights into machine learning algorithms which we present via the domain they know best, physics. We begin with a review of two energy-based machine learning algorithms, Hopfield networks and Boltzmann machines, and their connection to the Ising model. This serves as a foundation to understand the phenomenon of learning more generally. Equipped with this intuition we then delve into additional, more "practical," machine learning architectures including feedforward neural networks, convolutional neural networks, and autoencoders. We also provide code that explicitly demonstrates training a neural network with gradient descent.

cond-mat.dis-nn↗

arXiv2021

Deep learning the astrometric signature of dark matter substructure

We study the application of machine learning techniques for the detection of the astrometric signature of dark matter substructure. In this proof of principle a population of dark matter subhalos in the Milky Way will act as lenses for sources of extragalactic origin such as quasars. We train {\it ResNet-18}, a state-of-the-art convolutional neural network to classify angular velocity maps of a population of quasars into lensed and no lensed classes. We show that an SKA -like survey with extended operational baseline can be used to probe the substructure content of the Milky Way, and demonstrate how axiomatic attribution can be used to localize substructures in lensing maps.

astro-ph.CO↗

arXiv2021

Black hole and cosmological analysis of BF sequestered gravity

We study a minimal extension of a recently proposed modification of general relativity that draws on concepts from topological field theory to resolve aspects of the cosmological constant problem. In the original model, the field content of general relativity was augmented to include a gauge field and an adjoint-valued two-form without modifying the classical gravitational dynamics. Here we include a kinetic term for the gauge field which sources fluctuations of the cosmological constant. We then study spherically symmetric black holes and a simple homogeneous, isotropic cosmological model predicted by the extended theory. For the black hole case, we observe deviations near the event horizon as well as a ``charge"-like behavior induced by the gauge field. In the cosmological case, $\dot{H}$ is always positive and some solutions asymptote to a constant $H$.

gr-qc↗