arXiv subjects

Harry Desmond

Publications and source records attributed to Harry Desmond.

At least 73 records · Page 4Linked to original sources

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

On the tension between the Radial Acceleration Relation and Solar System quadrupole in modified gravity MOND

Modified Newtonian Dynamics (MOND), postulating a breakdown of Newtonian mechanics at low accelerations, has considerable success at explaining galaxy kinematics. However, the quadrupole of the gravitational field of the Solar System (SS) provides a strong constraint on the way in which Newtonian gravity can be modified. In this paper we assess the extent to which the AQUAL and QUMOND modified gravity formulations of MOND are capable of accounting simultaneously for the Radial Acceleration Relation (RAR), the Cassini measurement of the SS quadrupole and the kinematics of wide binaries in the Solar neighbourhood. We achieve this by inferring the location and sharpness of the MOND transition from the SPARC RAR under broad assumptions for the behaviour of the interpolating function and external field effect. We constrain the same quantities from the SS quadrupole, finding that this requires a significantly sharper transition between the deep-MOND and Newtonian regimes than is allowed by the RAR (an 8.7$σ$ tension under fiducial model assumptions). This may be relieved somewhat by allowing additional freedom in galaxies' mass-to-light ratios -- which also improves the RAR fit -- and more significantly (to 1.9$σ$) by removing galaxies with bulges. For the first time, we also apply to the SPARC RAR fit an AQUAL correction for flattened systems, obtaining similar results. Finally we show that the SS quadrupole constraint implies, to high precision, no deviation from Newtonian gravity in nearby wide binaries, and speculate on possible resolutions of this tension between SS and galaxy data within the MOND paradigm.

astro-ph.GA↗

arXiv2024

The information on halo properties contained in spectroscopic observations of late-type galaxies

Rotation curves are the key observational manifestation of the dark matter distribution around late-type galaxies. In a halo model context, the precision of constraints on halo parameters is a complex function of the properties of the measurements as well as properties of the galaxy itself. Forthcoming surveys will resolve rotation curves to varying degrees of precision, or measure their integrated effect in the HI linewidth. To ascertain the relative significance of the relevant quantities for constraining halo properties, we study the information on halo mass and concentration as quantified by the Kullback-Leibler divergence of the kinematics-informed posterior from the uninformative prior. We calculate this divergence as a function of the different types of spectroscopic observation, properties of the measurement, galaxy properties and auxiliary observational data on the baryonic components. Using the SPARC sample, we find that fits to the full rotation curve exhibit a large variation in information gain between galaxies, ranging from ~1 to ~11 bits. The variation is predominantly caused by the vast differences in the number of data points and the size of velocity uncertainties between the SPARC galaxies. We also study the relative importance of the minimum HI surface density probed and the size of velocity uncertainties on the constraining power on the inner halo slope, finding the latter to be significantly more important. We spell out the implications of these results for optimising the strategy of galaxy surveys aiming to constrain galaxies' dark matter distributions, highlighting spectroscopic precision as the most important factor.

astro-ph.GA↗

arXiv2023

Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors

The history of the seemingly simple problem of straight line fitting in the presence of both $x$ and $y$ errors has been fraught with misadventure, with statistically ad hoc and poorly tested methods abounding in the literature. The problem stems from the emergence of latent variables describing the "true" values of the independent variables, the priors on which have a significant impact on the regression result. By analytic calculation of maximum a posteriori values and biases, and comprehensive numerical mock tests, we assess the quality of possible priors. In the presence of intrinsic scatter, the only prior that we find to give reliably unbiased results in general is a mixture of one or more Gaussians with means and variances determined as part of the inference. We find that a single Gaussian is typically sufficient and dub this model Marginalised Normal Regression (MNR). We illustrate the necessity for MNR by comparing it to alternative methods on an important linear relation in cosmology, and extend it to nonlinear regression and an arbitrary covariance matrix linking $x$ and $y$. We publicly release a Python/Jax implementation of MNR and its Gaussian mixture model extension that is coupled to Hamiltonian Monte Carlo for efficient sampling, which we call ROXY (Regression and Optimisation with X and Y errors).

stat.ME↗

arXiv2023

The underlying radial acceleration relation

The radial acceleration relation (RAR) of late-type galaxies relates their dynamical acceleration, $g_\text{obs}$, to that sourced by baryons alone, $g_\text{bar}$, across their rotation curves. Literature fits to the RAR have fixed the galaxy parameters on which the relation depends -- distance, inclination, luminosity and mass-to-light ratios -- to their maximum a priori values with an uncorrelated Gaussian contribution to the uncertainties on $g_\text{bar}$ and $g_\text{obs}$. In reality these are free parameters of the fit, contributing systematic rather than statistical error. Assuming a range of possible functional forms for the relation with or without intrinsic scatter (motivated by Modified Newtonian Dynamics with or without the external field effect), I use Hamiltonian Monte Carlo to perform the full joint inference of RAR and galaxy parameters for the Spitzer Photometry and Accurate Rotation Curves (SPARC) dataset. This reveals the intrinsic RAR underlying that observed. I find an acceleration scale $a_0=(1.19 \pm 0.04 \, \text{(stat)} \pm 0.09 \, \text{(sys)}) \: \times \: 10^{-10}$ m s$^{-2}$, an intrinsic scatter $σ_\text{int}=(0.034 \pm 0.01 \, \text{(stat)} \pm 0.01 \, \text{(sys)})$ dex (assuming the SPARC error model is reliable) and weak evidence for the external field effect. I make summary statistics of all my analyses publicly available for future SPARC studies or applications of a calibrated RAR, for example redshift-independent distance measurement.

astro-ph.GA↗

arXiv2023

On the fundamentality of the radial acceleration relation for late-type galaxy dynamics

Galaxies have been observed to exhibit a level of simplicity unexpected in the complex galaxy formation scenario posited by standard cosmology. This is particularly apparent in their dynamics, where scaling relations display much regularity and little intrinsic scatter. However, the parameters responsible for this simplicity have not been identified. Using the Spitzer Photometry & Accurate Rotation Curves galaxy catalogue, we argue that the radial acceleration relation (RAR) between galaxies' baryonic and total dynamical accelerations is the fundamental $1$-dimensional correlation governing the radial (in-disk) dynamics of late-type galaxies. In particular, we show that the RAR cannot be tightened by the inclusion of any other available galaxy property, that it is the strongest projection of galaxies' radial dynamical parameter space, and that all other statistical radial dynamical correlations stem from the RAR plus the non-dynamical correlations present in our sample. We further provide evidence that the RAR's fundamentality is unique in that the second most significant dynamical relation does not possess any of these features. Our analysis reveals the root cause of the correlations present in galaxies' radial dynamics: they are nothing but facets of the RAR. These results have important ramifications for galaxy formation theory because they imply that to explain statistically late-type galaxy dynamics within the disk it is necessary and sufficient to explain the RAR and lack of any significant, partially independent correlation. While simple in some modified dynamics models, this poses a challenge to standard cosmology.

astro-ph.GA↗

arXiv2023

Inferring dark matter halo properties for HI-selected galaxies

We set constraints on the dark matter halo mass and concentration of ~22,000 individual galaxies visible both in HI (from the ALFALFA survey) and optical light (from the SDSS). This is achieved by combining two Bayesian models, one for the HI line width as a function of the stellar and neutral hydrogen mass distributions in a galaxy using kinematic modelling, and the other for the galaxy's total baryonic mass using the technique of inverse subhalo abundance matching. We hence quantify the constraining power on halo properties of spectroscopic and photometric observations, and assess their consistency. We find good agreement between the two sets of posteriors, although there is a sizeable population of low-line width galaxies that favour significantly smaller dynamical masses than expected from abundance matching (especially for cuspy halo profiles). Abundance matching provides significantly more stringent bounds on halo properties than the HI line width, even with a mass--concentration prior included, although combining the two provides a mean gain of 40% for the sample when fitting an NFW profile. We also use our kinematic posteriors to construct a baryonic mass--halo mass relation, which we find to be near power-law, and with a somewhat shallower slope than expected from abundance matching. Our method demonstrates the potential of combining photometric and spectroscopic observations to precisely map out the dark matter distribution at the galaxy scale using upcoming HI surveys such as the SKA.

astro-ph.GA↗

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↗

arXiv2022

Constraints on dark matter annihilation and decay from the large-scale structure of the nearby universe

Decaying or annihilating dark matter particles could be detected through gamma-ray emission from the species they decay or annihilate into. This is usually done by modelling the flux from specific dark matter-rich objects such as the Milky Way halo, Local Group dwarfs, and nearby groups. However, these objects are expected to have significant emission from baryonic processes as well, and the analyses discard gamma-ray data over most of the sky. Here we construct full-sky templates for gamma-ray flux from the large-scale structure within $\sim$200 Mpc by means of a suite of constrained $N$-body simulations (CSiBORG) produced using the Bayesian Origin Reconstruction from Galaxies algorithm. Marginalising over uncertainties in this reconstruction, small-scale structure, and parameters describing astrophysical contributions to the observed gamma-ray sky, we compare to observations from the Fermi Large Area Telescope to constrain dark matter annihilation cross sections and decay rates through a Markov Chain Monte Carlo analysis. We rule out the thermal relic cross section for $s$-wave annihilation for all $m_χ\lesssim 7 {\rm \, GeV}/c^2$ at 95\% confidence if the annihilation produces gluons or quarks less massive than the bottom quark. We infer a contribution to the gamma-ray sky with the same spatial distribution as dark matter decay at $3.3σ$. Although this could be due to dark matter decay via these channels with a decay rate $Γ\approx 6 \times 10^{-28} {\rm \, s^{-1}}$, we find that a power-law spectrum of index $p=-2.75^{+0.71}_{-0.46}$, likely of baryonic origin, is preferred by the data.

astro-ph.CO↗

arXiv2022

Testing Modified Gravity Theories with Numerical Solutions of the External Field Effect in Rotationally Supported Galaxies

The strong equivalence principle is violated by gravity theories of Milgromian dynamics (MOND) through the action of the external field effect. We test two different Lagrangian theories AQUAL and QUMOND based on their numerical solutions of the external field effect, by comparing two independent estimates of the mean external field strength of the nearby universe: a theory-deduced value from fitting the outer rotation curves of 114 galaxies and an empirical value from the large-scale distribution of cosmic baryons. The AQUAL-deduced external field strength from rotation curves agrees with that from the large-scale cosmic environment, while QUMOND-deduced value is somewhat higher. This suggests that AQUAL is likely to be preferred over QUMOND as an effective non-relativistic limit of a potential relativistic modified gravity theory.

astro-ph.GA↗

arXiv2022

The effect of local universe constraints on halo abundance and clustering

Cosmological $N$-body simulations of the dark matter component of the universe typically use initial conditions with a fixed power spectrum and random phases of the density field, leading to structure consistent with the local distribution of galaxies only in a statistical sense. It is, however, possible to infer the initial phases which lead to the configuration of galaxies and clusters that we see around us. We analyse the CSiBORG suite of 101 simulations, formed by constraining the density field within 155 Mpc$/h$ with dark matter particle mass $4.38\times10^9 M_\odot$, to quantify the degree to which constraints imposed on 2.65 Mpc$/h$ scales reduce variance in the halo mass function and halo-halo cross-correlation function on a range of scales. This is achieved by contrasting CSiBORG with a subset of the unconstrained Quijote simulations and expectations for the $Λ$CDM average. Using the FOF, PHEW and HOP halofinders, we show that the CSiBORG suite beats cosmic variance at large mass scales ($\gtrsim 10^{14}M_{\odot}/h$), which are most strongly constrained by the initial conditions, and exhibits a significant halo-halo cross-correlation out to $\sim30$ Mpc$/h$. Moreover, the effect of the constraints percolates down to lower mass objects and to scales below those on which they are imposed. Finally, we develop an algorithm to "twin" halos between realisations and show that approximately 50% of halos with mass greater than $10^{15}M_{\odot}/h$ can be identified in all realisations of the CSiBORG suite. We make the CSiBORG halo catalogues publicly available for future applications requiring knowledge of the local halo field.

astro-ph.CO↗

arXiv2022

The scatter in the galaxy-halo connection: a machine learning analysis

We apply machine learning, a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy-halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional machine learning models are deterministic and hence cannot capture its intrinsic scatter. To overcome this limitation, we design an ensemble of neural networks with a Gaussian loss function that predict probability distributions, allowing us to model statistical uncertainties in the galaxy-halo connection as well as its best-fit trends. We extract a number of galaxy and halo variables from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent to which knowledge of some subset of one enables prediction of the other. This allows us to identify the key features of the galaxy-halo connection and investigate the origin of its scatter in various projections. We find that while halo properties beyond mass account for up to 50 per cent of the scatter in the halo-to-stellar mass relation, the prediction of stellar half-mass radius or total gas mass is not substantially improved by adding further halo properties. We also use these results to investigate semi-analytic models for galaxy size in the two simulations, finding that assumptions relating galaxy size to halo size or spin are not successful.

astro-ph.GA↗

arXiv2022

Testing Screened Modified Gravity

Long range scalar fields with a coupling to matter appear to violate known bounds on gravitation in the solar system and the laboratory. This is evaded thanks to screening mechanisms. In this short review, we shall present the various screening mechanisms from an effective field theory point of view. We then investigate how they can and will be tested in the laboratory and on astrophysical and cosmological scales.

gr-qc↗

arXiv2022

Catalogues of voids as antihalos in the local Universe

A recently-proposed algorithm identifies voids in simulations as the regions associated with halos when the initial overdensity field is negated. We apply this method to the real Universe by running a suite of constrained simulations of the 2M++ volume with initial conditions inferred by the BORG algorithm, along with the corresponding inverted set. Our 101 inverted and uninverted simulations, spanning the BORG posterior, each identify ~150,000 "voids as antihalos" with mass exceeding $4.38\times10^{11} \: \mathrm{M_\odot}$ (100 particles) at $z=0$ in a full-sky sphere of radius 155 Mpc/h around the Milky Way. We calculate the size function, volume filling fraction, ellipticity, central density, specific angular momentum, clustering and stacked density profile of the voids, and cross-correlate them with those produced by VIDE on the same simulations. We make our antihalo and VIDE catalogues publicly available.

astro-ph.CO↗

arXiv2021

Constraints on quantum gravity and the photon mass from gamma ray bursts

Lorentz invariance violation in quantum gravity (QG) models or a nonzero photon mass, $m_γ$, would lead to an energy-dependent propagation speed for photons, such that photons of different energies from a distant source would arrive at different times, even if they were emitted simultaneously. By developing source-by-source, Monte Carlo-based forward models for such time delays from gamma ray bursts, and marginalising over empirical noise models describing other contributions to the time delay, we derive constraints on $m_γ$ and the QG length scale, $\ell_{\rm QG}$, using spectral lag data from the BATSE satellite. We find $m_γ< 4.0 \times 10^{-5} \, h \, {\rm eV}/c^2$ and $\ell_{\rm QG} < 5.3 \times 10^{-18} \, h \, {\rm \, GeV^{-1}}$ at 95% confidence, and demonstrate that these constraints are robust to the choice of noise model. The QG constraint is among the tightest from studies which consider multiple gamma ray bursts and the constraint on $m_γ$, although weaker than from using radio data, provides an independent constraint which is less sensitive to the effects of dispersion by electrons.

gr-qc↗