arXiv subjects

Pedro G. Ferreira

Publications and source records attributed to Pedro G. Ferreira.

At least 37 records · Page 2Linked to original sources

arXiv2025

Robustness of dark energy phenomenology across different parameterizations

The recent evidence for dynamical dark energy from DESI, in combination with other cosmological data, has generated significant interest in understanding the nature of dark energy and its underlying microphysics. However, interpreting these results critically depends on how dark energy is parameterized. This paper examines the robustness of conclusions about the viability of particular kinds of dynamical dark energy models to the choice of parameterization, focusing on four popular two-parameter models: the Chevallier-Polarski-Linder (CPL), Jassal-Bagla-Padmanabhan (JBP), Barboza-Alcaniz (BA), and exponential (EXP) parameterizations. We find that conclusions regarding the viability of minimally and non-minimally coupled quintessence models are independent of the parameterization adopted. We demonstrate this both by mapping these dark energy models into the $(w_0, w_a)$ parameter space defined by these various parameterizations and by showing that all of these parameterizations can equivalently account for the phenomenology predicted by these dark energy models to a high degree of accuracy.

astro-ph.CO↗

arXiv2025

Black hole merger simulations in wave dark matter environments

The interaction of binary black hole mergers with their environments can be studied using numerical relativity simulations. These start only a short finite time before merger, at which point appropriate initial conditions must be imposed. A key task is therefore to identify the configuration that is appropriate for the binary and its environment at this stage of the evolution. In this work we study the behaviour of wave dark matter around equal mass black hole binaries, finding that there is a preferred, quasi-stationary profile that persists and grows over multiple orbits, in contrast to heavier mass dark matter where any overdensity tends to be dispersed by the binary motion. Whilst different initial configurations converge to the preferred quasi-stationary one after several orbits, unwanted transient oscillations are generated in the process, which may impact on the signal in short simulation runs. We also point out that naively superimposing the matter onto a circular binary results in artificially eccentric orbits due to the matter backreaction, which is an effect of the initial conditions and not a signature of dark matter. We discuss the further work required so that comparison of waveforms obtained with environments to vacuum cases can be done in a meaningful way.

gr-qc↗

arXiv2025

Matching current observational constraints with nonminimally coupled dark energy

We show that a Universe with a nonminimally coupled scalar field can fit current measurements of the expansion rate of the Universe better than the standard $Λ$-Cold Dark Matter ($Λ$CDM) model or other minimally coupled dark energy models. In particular, the nonminimal coupling in this model allows for the dark energy model to exhibit stable phantom crossing behavior, which seems to be suggested by the constraints on the dark energy equation of state coming from the most recent data. While we find a clear improvement in the goodness of fit for this dark energy model with respect to others that have been considered in the recent literature, using information theoretic criteria, we show that the evidence for it is still inconclusive.

astro-ph.CO↗

arXiv2025

Scant evidence for thawing quintessence

New constraints on the expansion rate of the Universe seem to favor evolving dark energy in the form of thawing quintessence models, i.e., models for which a canonical, minimally coupled scalar field has, at late times, begun to evolve away from potential energy domination. We scrutinize the evidence for thawing quintessence by exploring what it predicts for the equation of state. We show that, in terms of the usual Chevalier-Polarski-Linder parameters, ($w_0$, $w_a$), thawing quintessence is, in fact, only marginally consistent with a compilation of the current data. Despite this, we embrace the possibility that thawing quintessence is dark energy and find constraints on the microphysics of this scenario. We do so in terms of the effective mass $m^2$ and energy scale $V_0$ of the scalar field potential. We are particularly careful to enforce un-informative, flat priors on these parameters so as to minimize their effect on the final posteriors. While the current data favors a large and negative value of $m^2$, when we compare these models to the standard $Λ$CDM model we find that there is scant evidence for thawing quintessence.

astro-ph.CO↗

arXiv2024

Underdetermination of dark energy

There is compelling evidence that the Universe is undergoing a late phase of accelerated expansion. One of the simplest explanations for this behaviour is the presence of dark energy. A plethora of microphysical models for dark energy have been proposed. The hope is that, with the ever increasing precision of cosmological surveys, it will be possible to precisely pin down the model. We show that this is unlikely and that, at best, we will have a phenomenological description for the microphysics of dark energy. Furthermore, we argue that the current phenomenological prescriptions are ill-equipped for shedding light on the fundamental theory of dark energy.

astro-ph.CO↗

arXiv2024

syren-new: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy

Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symbolic approximations to the linear and nonlinear matter power spectra as a function of cosmological parameters in extended $Λ$CDM models which contain massive neutrinos and non-constant equations of state for dark energy. This constitutes an extension of the syren-halofit emulators to incorporate these two effects, which we call syren-new (SYmbolic-Regression-ENhanced power spectrum emulator with NEutrinos and $W_0-w_a$). We also obtain a simple approximation to the derived parameter $σ_8$ as a function of the cosmological parameters for these models. Our results for the linear power spectrum are designed to emulate CLASS, whereas for the nonlinear case we aim to match the results of EuclidEmulator2. We compare our results to existing emulators and $N$-body simulations. Our analytic emulators for $σ_8$, the linear and nonlinear power spectra achieve root mean squared errors of 0.1%, 0.3% and 1.3%, respectively, across a wide range of cosmological parameters, redshifts and wavenumbers. We verify that emulator-related discrepancies are subdominant compared to observational errors and other modelling uncertainties when computing shear power spectra for LSST-like surveys. Our expressions have similar accuracy to existing (numerical) emulators, but are at least an order of magnitude faster, both on a CPU and GPU. Our work greatly improves the accuracy, speed and range of applicability of current symbolic approximations to the linear and nonlinear matter power spectra. We provide publicly available code for all symbolic approximations found.

astro-ph.CO↗

arXiv2024

Self-interacting scalar dark matter around binary black holes

Gravitational waves can provide crucial insights about the environments in which black holes live. In this work, we use numerical relativity simulations to study the behaviour of self-interacting scalar (wave-like) dark matter clouds accreting onto isolated and binary black holes. We find that repulsive self-interactions smoothen the ``spike'' of an isolated black hole and saturate the density. Attractive self-interactions enhance the growth and result in more cuspy profiles, but can become unstable and undergo explosions akin to the superradiant bosenova that reduce the local cloud density. We quantify the impact of self-interactions on an equal-mass black hole merger by computing the dephasing of the gravitational-wave signal for a range of couplings. We find that repulsive self-interactions saturate the density of the cloud, thereby reducing the dephasing. For attractive self-interactions, the dephasing may be larger, but if these interactions dominate prior to the merger, the dark matter can undergo bosenova during the inspiral phase, disrupting the cloud and subsequently reducing the dephasing.

gr-qc↗

arXiv2024

Tomographic constraints on the production rate of gravitational waves from astrophysical sources

Using an optimal quadratic estimator, we measure the large-scale cross-correlation between maps of the stochastic gravitational-wave intensity, constructed from the first three LIGO-Virgo observing runs, and a suite of tomographic samples of galaxies covering the redshift range $z\lesssim 2$. We do not detect any statistically significant cross-correlation, but the tomographic nature of the data allows us to place constraints on the (bias-weighted) production rate density of gravitational waves by astrophysical sources as a function of cosmic time. Our constraints range from $\langle b\dotΩ_{\rm GW}\rangle<3.0\times10^{-9}\,{\rm Gyr}^{-1}$ at $z\sim0.06$ to $\langle b\dotΩ_{\rm GW}\rangle<2.7\times10^{-7}\,{\rm Gyr}^{-1}$ at $z\sim1.5$ (95\% C.L.), assuming a frequency spectrum of the form $f^{2/3}$ (corresponding to an astrophysical background of binary mergers), and a reference frequency $f_{\rm ref}=25\,{\rm Hz}$. Although these constraints are $\sim2$ orders of magnitude higher than the expected signal, we show that a detection may be possible with future experiments.

astro-ph.CO↗

arXiv2024

The effect of wave dark matter on equal mass black hole mergers

For dark matter to be detectable with gravitational waves from binary black holes, it must reach higher than average densities in their vicinity. In the case of light (wave-like) dark matter, the density of dark matter between the binary can be significantly enhanced by accretion from the surrounding environment. Here we show that the resulting dephasing effect on the last ten orbits of an equal mass binary is maximized when the Compton wavelength of the scalar particle is comparable to the orbital separation, $2π/μ\sim d$. The phenomenology of the effect is different from the channels that are usually discussed, where dynamical friction (along the orbital path) and radiation of energy and angular momentum drive the dephasing, and is rather dominated by the radial force (the spacetime curvature in the radial direction) towards the overdensity between the black holes. Whilst our numerical studies limit us to scales of the same order, this effect may persist at larger separations and/or particle masses, playing a significant role in the merger history of binaries.

gr-qc↗

arXiv2024

A precise symbolic emulator of the linear matter power spectrum

Computing the matter power spectrum, $P(k)$, as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desirable. Previous analytic approximations are insufficiently accurate for modern applications, so black-box, uninterpretable emulators are often used. We utilise an efficient genetic programming based symbolic regression framework to explore the space of potential mathematical expressions which can approximate the power spectrum and $σ_8$. We learn the ratio between an existing low-accuracy fitting function for $P(k)$ and that obtained by solving the Boltzmann equations and thus still incorporate the physics which motivated this earlier approximation. We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0.2% between $k = 9\times10^{-3} - 9 \, h{\rm \, Mpc^{-1}}$ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression. Our analytic approximation is 950 times faster to evaluate than camb and 36 times faster than the neural network based matter power spectrum emulator BACCO. We also provide a simple analytic approximation for $σ_8$ with a similar accuracy, with a root mean squared fractional error of just 0.1% when evaluated across the same range of cosmologies. This function is easily invertible to obtain $A_{\rm s}$ as a function of $σ_8$ and the other cosmological parameters, if preferred. It is possible to obtain symbolic approximations to a seemingly complex function at a precision required for current and future cosmological analyses without resorting to deep-learning techniques, thus avoiding their black-box nature and large number of parameters. Our emulator will be usable long after the codes on which numerical approximations are built become outdated.

astro-ph.CO↗

arXiv2024

syren-halofit: A fast, interpretable, high-precision formula for the $Λ$CDM nonlinear matter power spectrum

Rapid and accurate evaluation of the nonlinear matter power spectrum, $P(k)$, as a function of cosmological parameters and redshift is of fundamental importance in cosmology. Analytic approximations provide an interpretable solution, yet current approximations are neither fast nor accurate relative to numerical emulators. We use symbolic regression to obtain simple analytic approximations to the nonlinear scale, $k_σ$, the effective spectral index, $n_{\rm eff}$, and the curvature, $C$, which are required for the halofit model. We then re-optimise the coefficients of halofit to fit a wide range of cosmologies and redshifts. We explore the space of analytic expressions to fit the residuals between $P(k)$ and the optimised predictions of halofit. Our results are designed to match the predictions of EuclidEmulator2, but are validated against $N$-body simulations. Our symbolic expressions for $k_σ$, $n_{\rm eff}$ and $C$ have root mean squared fractional errors of 0.8%, 0.2% and 0.3%, respectively, for redshifts below 3 and a wide range of cosmologies. The re-optimised halofit parameters reduce the root mean squared fractional error (compared to EuclidEmulator2) from 3% to below 2% for wavenumbers $k=9\times10^{-3}-9 \, h{\rm Mpc^{-1}}$. We introduce syren-halofit (symbolic-regression-enhanced halofit), an extension to halofit containing a short symbolic correction which improves this error to 1%. Our method is 2350 and 3170 times faster than current halofit and hmcode implementations, respectively, and 2680 and 64 times faster than EuclidEmulator2 (which requires running class) and the BACCO emulator. We obtain comparable accuracy to EuclidEmulator2 and BACCO when tested on $N$-body simulations. Our work greatly increases the speed and accuracy of symbolic approximations to $P(k)$, making them significantly faster than their numerical counterparts without loss of accuracy.

astro-ph.CO↗

arXiv2024

Optimal Inflationary Potentials

Inflation is a highly favoured theory for the early Universe. It is compatible with current observations of the cosmic microwave background and large scale structure and is a driver in the quest to detect primordial gravitational waves. It is also, given the current quality of the data, highly under-determined with a large number of candidate implementations. We use a new method in symbolic regression to generate all possible simple scalar field potentials for one of two possible basis sets of operators. Treating these as single-field, slow-roll inflationary models we then score them with an information-theoretic metric ("minimum description length") that quantifies their efficiency in compressing the information in current data. We explore two possible priors on the parameter space of potentials, one related to the functions' structural complexity and one that uses a Katz back-off language model to prefer functions that may be theoretically motivated. This enables us to identify the inflaton potentials that optimally balance simplicity with accuracy at explaining current data, which may subsequently find theoretical motivation. Our exploratory study opens the door to extraction of fundamental physics directly from data, and may be augmented with more refined theoretical priors in the quest for a complete understanding of the early Universe.

astro-ph.CO↗

arXiv2024

Symmetry restoration and vacuum decay from accretion around black holes

Vacuum decay and symmetry breaking play an important role in the fundamental structure of the matter and the evolution of the universe. In this work we study how the purely classical effect of accretion of fundamental fields onto black holes can lead to shells of symmetry restoration in the midst of a symmetry broken phase. We also show how it can catalyze vacuum decay, forming a bubble that expands asymptotically at the speed of light. These effects offer an alternative, purely classical mechanism to quantum tunnelling for seeding phase transitions in the universe.

gr-qc↗

arXiv2024

Relativistic drag forces on black holes from scalar dark matter clouds of all sizes

We use numerical simulations of scalar field dark matter evolving on a moving black hole background to confirm the regime of validity of (semi-)analytic expressions derived from first principles for both dynamical friction and momentum accretion in the relativistic regime. We cover both small and large clouds (relative to the de Broglie wavelength of the scalars), and light and heavy particle masses (relative to the BH size). In the case of a small dark matter cloud, the effect of accretion is a non-negligible contribution to the total force on the black hole, even for small scalar masses. We confirm that this momentum accretion transitions between two regimes (wave- and particle-like) and we identify the mass of the scalar at which the transition between regimes occurs.

gr-qc↗

arXiv2023

Priors for symbolic regression

When choosing between competing symbolic models for a data set, a human will naturally prefer the "simpler" expression or the one which more closely resembles equations previously seen in a similar context. This suggests a non-uniform prior on functions, which is, however, rarely considered within a symbolic regression (SR) framework. In this paper we develop methods to incorporate detailed prior information on both functions and their parameters into SR. Our prior on the structure of a function is based on a $n$-gram language model, which is sensitive to the arrangement of operators relative to one another in addition to the frequency of occurrence of each operator. We also develop a formalism based on the Fractional Bayes Factor to treat numerical parameter priors in such a way that models may be fairly compared though the Bayesian evidence, and explicitly compare Bayesian, Minimum Description Length and heuristic methods for model selection. We demonstrate the performance of our priors relative to literature standards on benchmarks and a real-world dataset from the field of cosmology.

cs.LG↗

arXiv2023

Exhaustive Symbolic Regression

Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data accurately and in a highly interpretable manner. Conventional SR suffers from two fundamental issues which we address here. First, these methods search the space stochastically (typically using genetic programming) and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and subjective. To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations -- made with a given basis set of operators and up to a specified maximum complexity -- and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints. We implement the minimum description length principle as a rigorous method for combining these preferences into a single objective. To illustrate the power of ESR we apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding $\sim$40 functions (out of 5.2 million trial functions) that fit the data more economically than the Friedmann equation. These low-redshift data therefore do not uniquely prefer the expansion history of the standard model of cosmology. We make our code and full equation sets publicly available.

astro-ph.CO↗

arXiv2023

On the functional form of the radial acceleration relation

We apply a new method for learning equations from data -- Exhaustive Symbolic Regression (ESR) -- to late-type galaxy dynamics as encapsulated in the radial acceleration relation (RAR). Relating the centripetal acceleration due to baryons, $g_\text{bar}$, to the total dynamical acceleration, $g_\text{obs}$, the RAR has been claimed to manifest a new law of nature due to its regularity and tightness, in agreement with Modified Newtonian Dynamics (MOND). Fits to this relation have been restricted by prior expectations to particular functional forms, while ESR affords an exhaustive and nearly prior-free search through functional parameter space to identify the equations optimally trading accuracy with simplicity. Working with the SPARC data, we find the best functions typically satisfy $g_\text{obs} \propto g_\text{bar}$ at high $g_\text{bar}$, although the coefficient of proportionality is not clearly unity and the deep-MOND limit $g_\text{obs} \propto \sqrt{g_\text{bar}}$ as $g_\text{bar} \to 0$ is little evident at all. By generating mock data according to MOND with or without the external field effect, we find that symbolic regression would not be expected to identify the generating function or reconstruct successfully the asymptotic slopes. We conclude that the limited dynamical range and significant uncertainties of the SPARC RAR preclude a definitive statement of its functional form, and hence that this data alone can neither demonstrate nor rule out law-like gravitational behaviour.

astro-ph.GA↗

arXiv2023

Analytical marginalisation over photometric redshift uncertainties in cosmic shear analyses

As the statistical power of imaging surveys grows, it is crucial to account for all systematic uncertainties. This is normally done by constructing a model of these uncertainties and then marginalizing over the additional model parameters. The resulting high dimensionality of the total parameter spaces makes inferring the cosmological parameters significantly more costly using traditional Monte-Carlo sampling methods. A particularly relevant example is the redshift distribution, $p(z)$, of the source samples, which may require tens of parameters to describe fully. However, relatively tight priors can be usually placed on these parameters through calibration of the associated systematics. In this paper we show, quantitatively, that a linearisation of the theoretical prediction with respect to these calibratable systematic parameters allows us to analytically marginalise over these extra parameters, leading to a factor $\sim30$ reduction in the time needed for parameter inference, while accurately recovering the same posterior distributions for the cosmological parameters that would be obtained through a full numerical marginalisation over 160 $p(z)$ parameters. We demonstrate that this is feasible not only with current data and current achievable calibration priors but also for future Stage-IV datasets.

astro-ph.CO↗