arXiv subjects

Linda Blot

Publications and source records attributed to Linda Blot.

15 recordsLinked to original sources

arXiv2026

AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology I: Literature Review

We investigate how well large language models (LLMs) can assist with literature reviews for scientific research. We perform a controlled study of eight expert-conceived research projects across the areas of physics, astrophysics, and cosmology. Each project has a defined background and goal, and human experts and AI prompters are asked to perform identical literature review tasks in parallel. We compare the relevant literature selected by humans with that selected by mid-2025 LLMs (ChatGPT-4o, ChatGPT Deep Research, and Gemini). We find the overlap between human- and AI-selected references to be small ($<$6\%), indicating that AI models do not yet reproduce a competent expert search on their own, though they have the potential to complement literature searches by humans. We then assess the reliability and completeness of AI-generated candidate references, distinguishing two types of hallucination: fabrications (references to nonexistent papers) and metadata mismatches (real papers with one or more incorrect fields). We find that while fabricated references make up 3\% of the AI-generated references, 64\% are real papers with at least one incorrect field (title, author, year, journal, DOI, or link), indicating that the mid-2025 models require systematic verification. However, the performance is significantly improved for the 2026 model ChatGPT Pro 5.5, with a single-project test showing zero fabrication or metadata mismatches.

astro-ph.IM↗

arXiv2026

AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology II: Project Planning and Proposal Evaluation

We investigate how well large language models (LLMs) can assist scientific project planning and proposal evaluation. One-page project plans were independently generated for eight expert-conceived research projects in physics, astrophysics, and cosmology by human researchers and three contemporary LLMs (ChatGPT, Claude, and DeepSeek; mid-2025 models, used with their default tool access). The resulting 32 proposals were blindly evaluated by four human reviewers and two newer frontier LLMs (Claude Opus 4.8 and ChatGPT Pro 5.5) using a four-aspect evaluation rubric. Reviewers were also asked to identify whether each proposal was written by a human or an AI. Human reviewers rated human- and AI-written proposals similarly overall, whereas both AI reviewers scored AI-written proposals about one point higher (on a five-point scale) than human-written proposals. Human reviewers correctly identified human- and AI-written proposals 72% and 79% of the time, respectively, while both AI reviewers correctly classified all 32 proposals (100%). These results suggest that current LLMs can produce project plans comparable to human-written ones in the eyes of human reviewers, but that AI reviewers show a systematic preference for AI-generated proposals. Our results suggest caution when deploying LLMs widely in proposal preparation and evaluation.

cs.CL↗

arXiv2026

The Environmental Dependence of Halo Intrinsic Alignments: Stronger Signals in Underdense Regions

The intrinsic alignment (IA) of galaxies and the dark matter haloes that host them is one of the leading astrophysical systematics for weak-lensing cosmology, yet how the IA signal depends on the large-scale environment in which haloes reside is not yet fully characterised. We use the high-resolution $N$-body simulations of the Dark Quest suite to measure the environmental dependence of the IA of dark matter haloes over the redshift range $z=0.1$--$1.5$. We quantify each halo's environment through the overdensity $\delta_8$, defined from the number of neighbouring haloes within $8\, h^{-1}Mpc$, and we isolate the environmental effect from its degeneracy with the halo-mass dependence by comparing the most overdense and most underdense haloes constructed to share the same halo-mass distribution. We find that haloes in underdense environments exhibit systematically larger IA amplitudes $A_{\rm IA}$ than haloes of the same mass in overdense environments, by a factor of $\sim1.5$--$1.8$, and that this trend persists across the mass and redshift ranges probed, strengthening towards low redshift. Using an orientation-only (unit-ellipticity) estimator, we further show that this environmental contrast is driven by a combination of two effects: haloes in underdense regions are both intrinsically less spherical and more strongly aligned with the large-scale tidal field than their overdense counterparts of the same mass. These results indicate that the large-scale environment is a non-negligible variable in modelling halo and galaxy alignments, and may be a particularly important factor for beyond-two-point weak-lensing analyses.

astro-ph.CO↗

arXiv2026

Cosmological hydrodynamical simulations of clustering dark energy with Nefertiti

We present the first cosmological simulations that consistently include nonlinear clustering dark energy evolved as a fluid with the numerical hydrodynamics code Nefertiti. Dark energy perturbations become fully nonlinear on small scales, developing significant density fluctuations without exhibiting the catastrophic instabilities previously reported. We show results for the density distribution, power spectrum, and halo profiles of dark energy. Clustering dark energy contributes to the total density perturbation at the $\sim 10\%$ level inside and around massive halos in our simulations with constant $w=-0.9$, a significant potential signal for lensing and dynamical probes. These simulations pave the way to robust constraints on the speed of sound of dark energy perturbations from large-scale structure data.

astro-ph.CO↗

arXiv2025

Impact of Simulation Box Size for Weak Lensing: Replication and Super-Sample Effects

We quantify the bias caused by small simulation box size on weak lensing observables and covariances, considering both replication and super-sample effects for a range of higher-order statistics. Using two simulation suites -- one comprising large boxes ($3750\,h^{-1}{\rm Mpc}$) and another constructed by tiling small boxes ($625\,h^{-1}{\rm Mpc}$) -- we generate full-sky convergence maps and extract $10^\circ \times 10^\circ$ patches via a Fibonacci grid. We consider biases in the mean and covariance of the angular power spectrum, bispectrum (up to $\ell=3000$), PDF, peak/minima counts, and Minkowski functionals. By first identifying lines of sight that are impacted by replications, we find that replication causes a O$(10\%)$ bias in the PDF and Minkowski functionals, and a O$(1\%)$ bias in other summary statistics. Replication also causes a O$(10\%)$ bias in the covariances, increasing with source redshift and $\ell$, reaching $\sim25\%$ for $z_s=2.5$. We additionally show that replication leads to heavy biases (up to O$(100\%)$ at high redshift) when performing gnomonic projection on a patch that is centered along a direction of replication. We then identify the lines of sight that are minimally affected by replication, and use the corresponding patches to isolate and study super-sample effects, finding that, while the mean values agree to within $1\%$, the variances differ by O$(10\%)$ for $z_s\leq2.5$. We show that these effects remain in the presence of noise and smoothing scales typical of the DES, KiDS, HSC, LSST, Euclid, and Roman surveys. We also discuss how these effects scale as a function of box size. Our results highlight the importance of large simulation volumes for accurate lensing statistics and covariance estimation.

astro-ph.CO↗

arXiv2023

Non-linear Eulerian Hydrodynamics of Dark Energy: Riemann problem and Finite Volume Schemes

Upcoming large-scale-structure surveys can shed new light on the properties of dark energy. In particular, if dark energy is a dynamical component, it must have spatial perturbations. Their behaviour is regulated by the speed of sound parameter, which is currently unconstrained. In this work we present the numerical methods that will allow to perform cosmological simulations of inhomogeneous dark energy scenarios where the speed of sound is small and non-vanishing. We treat the dark energy component as an effective fluid and build upon established numerical methods for hydrodynamics to construct a numerical solution of the effective continuity and Euler equations. In particular, we develop conservative finite volume schemes that rely on the solution of the Riemann problem, which we provide here in both exact and approximate forms for the case of a dark energy fluid.

astro-ph.CO↗

arXiv2020

Cosmological Model Parameter Dependence of the Matter Power Spectrum Covariance from the DEUS-PUR $Cosmo$ Simulations

Future galaxy surveys will provide accurate measurements of the matter power spectrum across an unprecedented range of scales and redshifts. The analysis of these data will require one to accurately model the imprint of non-linearities of the matter density field. In particular, these induce a non-Gaussian contribution to the data covariance that needs to be properly taken into account to realise unbiased cosmological parameter inference analyses. Here, we study the cosmological dependence of the matter power spectrum covariance using a dedicated suite of N-body simulations, the Dark Energy Universe Simulation - Parallel Universe Runs (DEUS-PUR) {\it Cosmo}. These consist of 512 realizations for 10 different cosmologies where we vary the matter density $Ω_m$, the amplitude of density fluctuations $σ_8$, the reduced Hubble parameter $h$ and a constant dark energy equation of state $w$ by approximately $10\%$. We use these data to evaluate the first and second derivatives of the power spectrum covariance with respect to a fiducial $Λ$CDM cosmology. We find that the variations can be as large as $150\%$ depending on the scale, redshift and model parameter considered. By performing a Fisher matrix analysis we explore the impact of different choices in modelling the cosmological dependence of the covariance. Our results suggest that fixing the covariance to a fiducial cosmology can significantly affect the recovered parameter errors and that modelling the cosmological dependence of the variance while keeping the correlation coefficient fixed can alleviate the impact of this effect.

astro-ph.CO↗

arXiv2019

Comparing approximate methods for mock catalogues and covariance matrices I: correlation function

This paper is the first in a set that analyses the covariance matrices of clustering statistics obtained from several approximate methods for gravitational structure formation. We focus here on the covariance matrices of anisotropic two-point correlation function measurements. Our comparison includes seven approximate methods, which can be divided into three categories: predictive methods that follow the evolution of the linear density field deterministically (ICE-COLA, Peak Patch, and Pinocchio), methods that require a calibration with N-body simulations (Patchy and Halogen), and simpler recipes based on assumptions regarding the shape of the probability distribution function (PDF) of density fluctuations (log-normal and Gaussian density fields). We analyse the impact of using covariance estimates obtained from these approximate methods on cosmological analyses of galaxy clustering measurements, using as a reference the covariances inferred from a set of full N-body simulations. We find that all approximate methods can accurately recover the mean parameter values inferred using the N-body covariances. The obtained parameter uncertainties typically agree with the corresponding N-body results within 5% for our lower mass threshold, and 10% for our higher mass threshold. Furthermore, we find that the constraints for some methods can differ by up to 20% depending on whether the halo samples used to define the covariance matrices are defined by matching the mass, number density, or clustering amplitude of the parent N-body samples. The results of our configuration-space analysis indicate that most approximate methods provide similar results, with no single method clearly outperforming the others.

astro-ph.CO↗

arXiv2019

Euclid preparation: II. The EuclidEmulator -- A tool to compute the cosmology dependence of the nonlinear matter power spectrum

We present a new power spectrum emulator named EuclidEmulator that estimates the nonlinear correction to the linear dark matter power spectrum. It is based on a spectral decomposition method called polynomial chaos expansion. All steps in the construction of the emulator have been tested and optimized: the large high-resolution N-body simulations carried out with PKDGRAV3 were validated using a simulation from the Euclid Flagship campaign and demonstrated to have converged up to wavenumbers $k\approx 5\,h\,{\rm Mpc}^{-1}$ for redshifts $z\leq 5$. The emulator is constructed using the uncertainty quantification software UQLab and it has been optimized first by creating mock emulators based on Takahashi's HALOFIT. We show that it is possible to successfully predict the performance of the final emulator in this way prior to performing any N-body simulations. We provide a C-code to calculate the nonlinear correction at a relative accuracy of $\sim0.3\%$ with respect to N-body simulations within 50 ms. The absolute accuracy of the final nonlinear power spectrum is comparable to one obtained with N-body simulations, i.e. $\sim 1\%$ for $k\lesssim 1\,h\,{\rm Mpc}^{-1}$ and $z\lesssim 3.5$. This enables efficient forward modeling in the nonlinear regime allowing for maximum likelihood estimation of cosmological parameters. EuclidEmulator has been compared to HALOFIT and CosmicEmu, an alternative emulator based on the Mira-Titan Universe, and shown to be more accurate than these other approaches. This work paves a new way for optimal construction of future emulators that also consider other cosmological observables, use higher resolution input simulations and investigate higher dimensional cosmological parameter spaces.

astro-ph.CO↗

arXiv2019

Comparing approximate methods for mock catalogues and covariance matrices II: Power spectrum multipoles

We study the accuracy of several approximate methods for gravitational dynamics in terms of halo power spectrum multipoles and their estimated covariance matrix. We propagate the differences in covariances into parameter constrains related to growth rate of structure, Alcock-Paczynski distortions and biasing. We consider seven methods in three broad categories: algorithms that solve for halo density evolution deterministically using Lagrangian trajectories (ICE-COLA, Pinocchio and PeakPatch), methods that rely on halo assignment schemes onto dark-matter overdensities calibrated with a target N-body run (Halogen, Patchy) and two standard assumptions about the full density PDF (Gaussian and Lognormal). We benchmark their performance against a set of three hundred N-body simulations, running similar sets of approximate simulations with matched initial conditions, for each method. We find that most methods reproduce the monopole to within $5\%$, while residuals for the quadrupole are sometimes larger and scale dependent. The variance of the multipoles is typically reproduced within $10\%$. Overall, we find that covariances built from approximate simulations yield errors on model parameters within $10\%$ of those from the N-body based covariance.

astro-ph.CO↗

arXiv2018

Comparing approximate methods for mock catalogues and covariance matrices III: Bispectrum

We compare the measurements of the bispectrum and the estimate of its covariance obtained from a set of different methods for the efficient generation of approximate dark matter halo catalogs to the same quantities obtained from full N-body simulations. To this purpose we employ a large set of three-hundred realisations of the same cosmology for each method, run with matching initial conditions in order to reduce the contribution of cosmic variance to the comparison. In addition, we compare how the error on cosmological parameters such as linear and nonlinear bias parameters depends on the approximate method used for the determination of the bispectrum variance. As general result, most methods provide errors within 10% of the errors estimated from N-body simulations. Exceptions are those methods requiring calibration of the clustering amplitude but restrict this to two-point statistics. Finally we test how our results are affected by being limited to a few hundreds measurements from N-body simulation, and therefore to the bispectrum variance, by comparing with a larger set of several thousands realisations performed with one approximate method.

astro-ph.CO↗

arXiv2018

The modified gravity lightcone simulation project I: Statistics of matter and halo distributions

We introduce a set of four very high resolution cosmological simulations for exploring $f(R)$-gravity, with $2048^3$ particles in $768\,h^{-1}\textrm{Mpc}$ and $1536\,h^{-1}\textrm{Mpc}$ simulation boxes, both for a $|\overline{f_{R0}}| = 10^{-5}$ model and a $Λ$CDM comparison universe, making the set the largest simulations of $f(R)$-gravity to date. In order to mimic real observations, the simulations include a continuous 2D and 3D lightcone output which is dedicated to study lensing and clustering statistics in modified gravity. In this work, we present a detailed analysis and resolution study for the matter power spectrum in $f(R)$-gravity over a wide range of scales. We also analyse the angular matter power spectrum and lensing convergence on the lightcone. In addition, we investigate the impact of modified gravity on the halo mass function, matter and halo auto-correlation functions, linear halo bias and the concentration-mass relation. We find that the impact of $f(R)$-gravity is generally larger on smaller scales and increases with decreasing redshift. Comparing our simulations to state-of-the-art hydrodynamical simulations we confirm a degeneracy between $f(R)$-gravity and baryonic feedback in the matter power spectrum on small scales, but also find that scales around $k = 1\, h\, {\rm Mpc}^{-1}$ are promising to distinguish both effects. The lensing convergence power spectrum is increased in $f(R)$-gravity. Interestingly available numerical fits are in good agreement overall with our simulations for both standard and modified gravity, but tend to overestimate their relative difference on non-linear scales by few percent. We also find that the halo bias is lower in $f(R)$-gravity compared to general relativity, whereas halo concentrations are increased for unscreened halos.

astro-ph.CO↗

arXiv2017

Assessment of the Information Content of the Power Spectrum and Bispectrum

The covariance matrix of the matter and halo power spectrum and bispectrum are studied. Using a large suite of simulations, we find that the non-Gaussianity in the covariance is significant already at mildly nonlinear scales. We compute the leading disconnected non-Gaussian correction to the matter bispectrum covariance using perturbation theory, and find that the corrections result in good agreement in the mildly nonlinear regime. The shot noise contribution to the halo power spectrum and bispectrum covariance is computed using the Poisson model, and the model yields decent agreement with simulation results. However, when the shot noise is estimated from the individual realization, which is usually done in reality, we find that the halo covariance is substantially reduced and gets close to the Gaussian covariance. This is because most of the non-Gaussianity in the covariance arises from the fluctuations in the Poisson shot noise. We use the measured non-Gaussian covariance to access the information content of the power spectrum and bispectrum. The signal-to-noise ratio, S/N, of the matter and halo power spectrum levels off in the mildly nonlinear regime, $k \sim 0.1 - 0.2 \,\mathrm{Mpc} h^{-1}$. In the nonlinear regime the S/N of the matter and halo bispectrum increases but much slower than the Gaussian results suggest. We find that both the S/N for power spectrum and bispectrum are overestimated by the Gaussian covariances, but the problem being much more serious for the bispectrum. Because the bispectrum is affected strongly by nonlinearity and shot noise, inclusion of the bispectrum only adds modest amount of S/N compared to that of the power spectrum.

astro-ph.CO↗

arXiv2016

Non-Linear Matter Power Spectrum Covariance Matrix Errors and Cosmological Parameter Uncertainties

The covariance matrix of the matter power spectrum is a key element of the statistical analysis of galaxy clustering data. Independent realisations of observational measurements can be used to sample the covariance, nevertheless statistical sampling errors will propagate into the cosmological parameter inference potentially limiting the capabilities of the upcoming generation of galaxy surveys. The impact of these errors as function of the number of independent realisations has been previously evaluated for Gaussian distributed data. However, non-linearities in the late time clustering of matter cause departures from Gaussian statistics. Here, we address the impact of non-Gaussian errors on the sample covariance and precision matrix errors using a large ensemble of numerical N-body simulations. In the range of modes where finite volume effects are negligible ($0.1\lesssim k\,[h\,{\rm Mpc^{-1}}]\lesssim 1.2$) we find deviations of the estimated variance of the sample covariance with respect to Gaussian predictions above $\sim 10\%$ level. These reduce to about $\sim 5\%$ in the case of the precision matrix. Finally, we perform a Fisher analysis to estimate the effect of covariance errors on the cosmological parameter constraints. In particular, assuming Euclid-like survey characteristics we find that a number of independent realisation larger than $\gtrsim 5000$ is necessary to reduce the contribution of sample covariance errors to the cosmological parameter uncertainties at sub-percent level. We also show that restricting the analysis to large scales $k\lesssim0.2\,h\,{\rm Mpc^{-1}}$ results in a considerable loss in constraining power, while using the linear covariance to include smaller scales leads to an underestimation of the errors on the cosmological parameters.

astro-ph.CO↗

arXiv2014

Matter Power Spectrum Covariance Matrix from the DEUS-PUR ΛCDM simulations: Mass Resolution and non-Gaussian Errors

The upcoming generation of galaxy surveys will probe the distribution of matter in the universe with unprecedented accuracy. Measurements of the matter power spectrum at different scales and redshifts will provide stringent constraints on the cosmological parameters. However, on non-linear scales this will require an accurate evaluation of the covariance matrix. Here, we compute the covariance matrix of the 3D matter density power spectrum for the concordance $Λ$CDM cosmology from an ensemble of N-body simulations of the Dark Energy Universe Simulation - Parallel Universe Runs (DEUS-PUR). This consists of 12288 realisations of a $(656\,h^{-1}\,\textrm{Mpc})^3$ simulation box with $256^3$ particles. We combine this set with an auxiliary sample of 96 simulations of the same volume with $1024^3$ particles. We find N-body mass resolution effect to be an important source of systematic errors on the covariance at high redshift and small intermediate scales. We correct for this effect by introducing an empirical statistical method which provide an accurate determination of the covariance matrix over a wide range of scales including the Baryon Oscillations interval. Contrary to previous studies that used smaller N-body ensembles, we find the power spectrum distribution to significantly deviate from expectations of a Gaussian random density field at $k\gtrsim 0.25\,h\,\textrm{Mpc}^{-1}$ and $z<0.5$. This suggests that in the case of finite volume surveys an unbiased estimate of the ensemble averaged band power at these scales and redshifts may require a careful assessment of non-Gaussian errors more than previously considered.

astro-ph.CO↗