arXiv subjects

Felicitas Keil

Publications and source records attributed to Felicitas Keil.

6 recordsLinked to original sources

arXiv2026

Growth, geometry, and early-universe split of the matter density parameter $\Omega_{\rm m}$

While the $\Lambda$ cold dark matter ($\Lambda$CDM) model can successfully reproduce the measurements of many cosmological probes, some discrepancies have recently emerged. Therefore, it is necessary to test the standard cosmological model for consistency. An important stress test is to separate the influence of different cosmological regimes on the parameter inference. We treat three regimes separately here: geometry, growth, and the early universe. The geometrical regime concerns the expansion and curvature history, while the growth regime governs structure formation and the early-universe regime affects physics prior to recombination. Previous analyses have performed the split between geometry and growth, whereas we also consider the early universe influence separately. We perform this split for the present day matter density parameter $\Omega_{\rm m}$ using multiple cosmological observables. The used data are galaxy clustering and weak lensing statistics (3x2pt) from the Dark Energy Survey (DES), cosmic microwave background (CMB) data from Planck, spectroscopic baryon acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI), type-Ia supernovae (SNe Ia) samples from Pantheon+, and redshift-space distortions (RSD) from a collection of galaxy surveys. For each of these probes, we introduce a phenomenological split into these three regimes. This work shows a strong correlation between the geometric and the early regime for the matter density, but no strong correlation between the growth regime and the others. All regimes are compatible in the posterior distribution, however the difference between the geometry and the early regimes, $\Delta\Omega_{\rm m}^{\rm geo,early}$, is 2$\sigma$ apart from 0.

astro-ph.CO↗

arXiv2026

Cosmology beyond standard sirens: cross-correlation of gravitational waves and neutral hydrogen intensity mapping

We explore the potential of cross-correlation between gravitational wave (GW) events and neutral hydrogen (HI) intensity mapping surveys to serve as an independent cosmological probe. Focusing on the ET and the SKAO, and assuming that binary black hole mergers and HI emission are biased tracers of the underlying dark matter distribution, we use their angular auto- and cross-correlation spectra to constrain cosmological parameters. We test three different GW detector networks: ET alone, both in its $\Delta$ and 2L configuration, and ET-2L together with Cosmic Explorer. We show that the cross-correlation method, by naturally mitigating tracer-specific systematics, yields robust cosmological bounds, allowing for a sub-percent ($\sim 0.5\%$) precision on the Hubble constant $H_\mathrm{0}$. Furthermore, this approach robustly constrains the cosmic expansion history throughout the post-reionization era of the Universe and, unlike conventional standard sirens, simultaneously probes the large scale distribution of matter perturbations, achieving relative uncertainties of approximately 1.3% on the total matter density $\Omega_\mathrm{m}$ and 1.6% on the late-time clustering amplitude $\sigma_8$.

astro-ph.CO↗

arXiv2026

cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era

cloelib is a Python library developed to compute cosmological observables within the Cosmology Likelihood for Observables in Euclid (CLOE) ecosystem (cloe-org). As cosmology enters a precision era driven by galaxy survey missions such as Euclid, there is a growing need for flexible, efficient, and differentiable software capable of supporting next-generation inference pipelines. cloelib addresses these demands through a modular architecture that interfaces seamlessly with established Boltzmann solvers whilst incorporating JAX-based automatic differentiation to enable gradient-based methods. The library defines consistent protocols for background evolution, perturbations, and non-linear structure formation, and supports a wide range of observables, including photometric and spectroscopic large-scale structure probes, as well as cross-correlations with the Cosmic Microwave Background and galaxy clusters. In its finalised form, cloelib is intended to serve as the reference theory computation infrastructure for Euclid's first cosmological release, bridging traditional numerical cosmology with modern optimisation techniques and emerging machine learning approaches to inference.

astro-ph.CO↗

arXiv2026

cloelike: A Python Library for Cosmological Likelihood Inference in the Euclid Era

cloelike is a Python package providing modular, composable Gaussian likelihood classes for the main cosmological large-scale structure observables targeted by the ESA Euclid space mission. It is a core component of the CLOE (Cosmology Likelihood for Observables in Euclid) ecosystem and interfaces directly with cloelib for theoretical predictions and euclidlib for reading official Euclid data products. The package implements Gaussian likelihoods covering harmonic angular power spectra and real-space two-point correlation functions for weak lensing (WL), photometric galaxy clustering (GCph), and Galaxy-Galaxy Lensing (GGL) in all joint probe combinations (3x2pt, 2x2pt), as well as spectroscopic full-shape power spectrum multipoles, and baryonic Acoustic oscillations (BAO). cloelike is actively used in internal Euclid Consortium analyses and is openly released to support community validation and reproducibility.

astro-ph.CO↗

arXiv2025

Probing the Distance Duality Relation with Machine Learning and Recent Data

The distance duality relation (DDR) relates two independent ways of measuring cosmological distances, namely the angular diameter distance and the luminosity distance. These can be measured with baryon acoustic oscillations (BAO) and Type Ia supernovae (SNe Ia), respectively. Here, we use recent DESI DR1, Pantheon+, SH0ES and DES-SN5YR data to test this fundamental relation. We employ a parametrised approach and also use model-independent Generic Algorithms (GA), which are a machine learning method where functions evolve loosely based on biological evolution. When we use DESI and Pantheon+ data without Cepheid calibration or big bang nucleosynthesis (BBN), there is a $2\sigma$ violation of the DDR in the parametrised approach. Then, we add high-redshift BBN data and the low-redshift SH0ES Cepheid calibration. This reflects the Hubble tension since both data sets are in tension in the standard cosmological model $\Lambda$CDM. In this case, we find a significant violation of the DDR in the parametrised case at $6\sigma$. Replacing the Pantheon+ SNe Ia data by DES-SN5YR, we find similar results. For the model-independent approach, we find no deviation in the uncalibrated case and a small deviation with BBN and Cepheids which remains at 1$\sigma$. This shows the importance of considering model-independent approaches for the DDR.

astro-ph.CO↗

arXiv2022

Classification of Fermi-LAT blazars with Bayesian neural networks

The use of Bayesian neural networks is a novel approach for the classification of gamma-ray sources. We focus on the classification of Fermi-LAT blazar candidates, which can be divided into BL Lacertae objects and Flat Spectrum Radio Quasars. In contrast to conventional dense networks, Bayesian neural networks provide a reliable estimate of the uncertainty of the network predictions. We explore the correspondence between conventional and Bayesian neural networks and the effect of data augmentation. We find that Bayesian neural networks provide a robust classifier with reliable uncertainty estimates and are particularly well suited for classification problems that are based on comparatively small and imbalanced data sets. The results of our blazar candidate classification are valuable input for population studies aimed at constraining the blazar luminosity function and to guide future observational campaigns.

astro-ph.HE↗