arXiv subjects

Jochen Weller

Publications and source records attributed to Jochen Weller.

At least 19 recordsLinked to original sources

arXiv2026

Cosmology with Intensity Mapping via Statistics Beyond the Power Spectrum in the SKAO Era

The cosmological distribution of neutral hydrogen (HI) during the post-reionization era is highly non-Gaussian due to the underlying non-linear structure formation, complex galaxy biasing, and potential primordial non-Gaussianity. One needs higher-order (beyond two-point) statistics to maximally extract the non-Gaussian information out of the 21-cm intensity maps. This chapter summarizes the potential of several higher-order statistics, including voxel intensity distribution, emission line stacking, probability density functions, $\ell_1$-norm, bispectrum, and various marked statistics. Additionally, image-based morphological descriptors, such as the Largest Cluster Statistic, local dimensions, and Minkowski functionals, etc., can potentially characterize the morphology and geometry of the cosmic web encoded in the 21-cm intensity maps. This chapter presents forecasts of the detectability of these higher-order statistics in the context of the future SKAO observations. These forecasts incorporate instrumental noise, observational effects, and, in some cases, foreground removal in their analyses. With its unprecedented sensitivity, the future SKAO 21-cm observations will enable us to measure these higher-order statistics more precisely, possibly helping to break degeneracies between astrophysical and cosmological parameters, and maximizing the science outcome from these surveys.

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arXiv2026

Overview of 21cm Experiments at high redshift with SKAO

We provide an overview of the eight SKAO Science Book chapters that motivate the Epoch of Reionisation and Cosmic Dawn experiments with SKA-Low. We describe the individual SKA-Low experiments and expected sensitivity - power spectrum, tomography, 21-cm forest, cross-correlations, building on the broad observational plan laid out in the 2015 SKA Science Book. Finally, we outline features of the telescope that will be critical for the success of EoR/CD science, e.g., beam apodization, substations, and multi-beaming.

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arXiv2026

Particle Physics and Gravitational Waves as complementary windows on the Universe

Particle physics and gravitational waves provide complementary probes of the deep structure of the Universe. Gravitational waves from the mergers of neutron stars and black holes are sensitive to the structure of dense quark matter and to different dark matter scenarios. Measurements of stochastic gravitational waves backgrounds can teach us about possible first order phase transitions in the early Universe, including providing sensitivity to the TeV scale which is of key interest to future particle collider experiments. Gravitational waves measurements will also give new probes of the evolution and expansion of the Universe, complementary to measurements with electromagnetic radiation. This Perspectives article explores the physics synergies between the science opportunities provided by next generation gravitational waves measurements and particle physics experiments. Gravitational waves can also probe deep into the early Universe reaching physics much above possible collider energies if the signals can be detected.

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arXiv2026

Brightest Cluster Galaxy ellipticity as proxy for halo shape: Orientation bias, assembly bias, and potential selection effects in SZ-selected clusters

The orientation of triaxial galaxy clusters with respect to the line-of-sight is expected to be one of the prime sources of scatter and potential bias in optical observables (e.g., richness and weak-lensing signal) of galaxy clusters. In this work, we use the observed shape of the central Brightest Cluster Galaxy (BCG) as proxy for the orientation along the line-of-sight for clusters selected via the Sunyaev-Zel'dovich (SZ) effect from the South Pole Telescope (SPT) and Atacama Cosmology Telescope (ACT) surveys, matched to optically selected clusters from the Dark Energy Survey Year 3 (DES). We construct two samples of clusters that are designed to be identical in SZ mass estimate and redshift but with the roundest vs. the most elliptical BCGs, which we expect to correspond to BCGs (and clusters) with major axes aligned along the line-of-sight vs. in the plane of the sky, respectively. We find that the optical richness of round-BCG clusters is $\sim 10$\% larger than that of elliptical-BCG clusters, in agreement with the expectation from projection effects and presenting the first such detection in data. The density profiles, however, are not in agreement with the expectation from projection effects: the 1-halo term (below $6~h^{-1}\rm{Mpc}$) of both the weak-lensing and galaxy density profiles are the same for the subsamples, contrary to previous studies based on X-ray selected clusters. In the 2-halo regime (above $6~h^{-1}\rm{Mpc}$), we find a significant excess of the elliptical-BCG cluster profiles compared to the round-BCG cluster profiles, which is the opposite of the expectation from numerical simulations. We hypothesize that the intrinsic shape of the BCG reflects not just the orientation angle, but also intrinsic properties of the cluster which can affect both the SZ signal and the amplitude of the 2-halo term.

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arXiv2026

Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity

As cosmology rapidly approaches the data-dominated phase of stage IV large scale structure surveys, the modelling of nonlinear scales has become a serious challenge that faces the community, particularly when analysing models beyond $w$CDM. In this work, we emulate the matter power spectrum in a phenomenological parameterisation of modified gravity in which a time-varying effective gravitational constant $\mu$ and a gravitational slip $\eta$ are binned in redshift. We are able to achieve accuracy $<1\%$ in the modified gravity boost relative to COLA (COmoving Lagrangian Acceleration) simulations. We forecast the constraining power for each bin using a simulated $3\times 2$pt LSST Y10-like data vector and a $6\times 2$pt LSST Y10 x Simons Observatory cosmic microwave background (CMB) lensing data vector. We recover the characteristic degeneracy between $\mu$ and $\eta$ previously identified in Fisher forecasts and demonstrate that the best-constrained direction corresponds to the combination $\Sigma=\mu(1+\eta)/2$ which governs the lensing potential. We show that while large scale structure is sensitive to growth of structure at low redshift, CMB lensing extends the sensitivity to a higher redshift range. These results demonstrate that fast emulation of nonlinear modified-gravity effects enables full Bayesian analyses of model-agnostic gravity parameterisations with realistic survey data vectors and astrophysical systematics.

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arXiv2026

C3NN-SBI: Learning Hierarchies of $N$-Point Statistics from Cosmological Fields with Physics-Informed Neural Networks

Cosmological analyses are moving past the well understood 2-point statistics to extract more information from cosmological fields. A natural step in extending inference pipelines to other summary statistics is to include higher order N-point correlation functions (NPCFs), which are computationally expensive and difficult to model. At the same time it is unclear how many NPCFs one would have to include to reasonably exhaust the cosmological information in the observable fields. An efficient alternative is given by learned and optimized summary statistics, largely driven by overparametrization through neural networks. This, however, largely abandons our physical intuition on the NPCF formalism and information extraction becomes opaque to the practitioner. We design a simulation-based inference pipeline, that not only benefits from the efficiency of machine learned summaries through optimization, but also holds on to the NPCF program. We employ the heavily constrained Cosmological Correlator Convolutional Neural Network (C3NN) which extracts summary statistics that can be directly linked to a given order NPCF. We present an application of our framework to simulated lensing convergence maps and study the information content of our learned summary at various orders in NPCFs for this idealized example. We view our approach as an exciting new avenue for physics-informed simulation-based inference.

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arXiv2025

Why Cosmic Voids Matter: Pristine Evolution

We utilize the Magneticum suite of hydrodynamical simulations to investigate the formation and evolution of cosmic voids from $z = 5.04$ to present day, using cold dark matter and (sub-) halo tracers in high-density samples. This includes the evolution of their global properties, such as size, shape, inner density, and average density, as well as their radial density profiles. Our results provide several key conclusions for void analyses in modern surveys. We demonstrate that a relative size framework is required, mitigating methodological selection effects and revealing the true physical evolution of densities around halo-defined voids. This necessity arises from our findings that a void's properties are more fundamentally tied to its rank within its contemporary population than to its absolute size. Using this framework, we show that the evolution of halo voids stabilizes at redshifts below $z \simeq 1$, driven primarily by cosmic expansion rather than ongoing halo formation. We further find that the matter evolution around these stable voids is remarkably well-described by linear growth theory, with deviations appearing as non-linear growth on small scales and suppressed growth in the largest voids, potentially driven by the influence of dark energy. This late-time stability and the predictable evolution confirm voids as pristine laboratories for probing the nature of dark energy with upcoming surveys.

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arXiv2025

Dimensional reduction for sampled priors and application to photometric redshift distributions

A typical Bayesian inference on the values of some parameters of interest $\bf q$ from some data $D$ involves running a Markov Chain (MC) to sample from the posterior $p({\bf q},{\bf n} | D) \propto \mathcal{L}(D | {\bf q},{\bf n}) p({\bf q}) p({\bf n}),$ where $\bf n$ are some nuisance parameters with separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior $p({\bf n})$ is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of $p({\bf n})$ at arbitrary values of ${\bf n},$ i.e.\ one needs to provide a density estimator over the full $\bf n$ space from the provided samples. But the high dimensionality of $\bf n$ hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the $\bf n$ space into a much lower-dimensional space $\bf u$ which projects away directions in $\bf n$ space that cannot appreciably alter $\mathcal{L}.$ The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on $p(\bf n)$ than other proposed solutions to this issue. We demonstrate this ``mode projection'' technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the \textit{Dark Energy Survey}, where $\bf n$ is a binned representation of the redshift distribution $n(z)$ of the galaxies.

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arXiv2024

Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI

How much cosmological information can we reliably extract from existing and upcoming large-scale structure observations? Many summary statistics fall short in describing the non-Gaussian nature of the late-time Universe in comparison to existing and upcoming measurements. In this article we demonstrate that we can identify optimal summary statistics and that we can link them with existing summary statistics. Using simulation based inference (SBI) with automatic data-compression, we learn summary statistics for galaxy catalogs in the context of cosmological parameter estimation. By construction these summary statistics do not require the ability to write down an explicit likelihood. We demonstrate that they can be used for efficient parameter inference. These summary statistics offer a new avenue for analyzing different simulation models for baryonic physics with respect to their relevance for the resulting cosmological features. The learned summary statistics are low-dimensional, feature the underlying simulation parameters, and are similar across different network architectures. To link our models, we identify the relevant scales associated to our summary statistics (e.g. in the range of modes between $k= 5 - 30 h/\mathrm{Mpc}$) and we are able to match the summary statistics to underlying simulation parameters across various simulation models.

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arXiv2024

PICZL: Image-based Photometric Redshifts for AGN

Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $\sigma_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $\eta$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.

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arXiv2024

On marginals and profiled posteriors for cosmological parameter estimation

With several examples and in an analysis of the Pantheon+ supernova sample we discuss the properties of the marginal posterior distribution versus the profiled posterior distribution -- the profile likelihood in a Bayesian disguise. We investigate whether maximisation, as used for the profiling, or integration, as used for the marginalisation, is more appropriate. To report results we recommend the marginal posterior distribution.

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arXiv2024

The Gravitational Lensing Imprints of DES Y3 Superstructures on the CMB: A Matched Filtering Approach

$ $Low density cosmic voids gravitationally lens the cosmic microwave background (CMB), leaving a negative imprint on the CMB convergence $\kappa$. This effect provides insight into the distribution of matter within voids, and can also be used to study the growth of structure. We measure this lensing imprint by cross-correlating the Planck CMB lensing convergence map with voids identified in the Dark Energy Survey Year 3 data set, covering approximately 4,200 deg$^2$ of the sky. We use two distinct void-finding algorithms: a 2D void-finder which operates on the projected galaxy density field in thin redshift shells, and a new code, Voxel, which operates on the full 3D map of galaxy positions. We employ an optimal matched filtering method for cross-correlation, using the MICE N-body simulation both to establish the template for the matched filter and to calibrate detection significances. Using the DES Y3 photometric luminous red galaxy sample, we measure $A_\kappa$, the amplitude of the observed lensing signal relative to the simulation template, obtaining $A_\kappa = 1.03 \pm 0.22$ ($4.6\sigma$ significance) for Voxel and $A_\kappa = 1.02 \pm 0.17$ ($5.9\sigma$ significance) for 2D voids, both consistent with $\Lambda$CDM expectations. We additionally invert the 2D void-finding process to identify superclusters in the projected density field, for which we measure $A_\kappa = 0.87 \pm 0.15$ ($5.9\sigma$ significance). The leading source of noise in our measurements is Planck noise, implying that future data from the Atacama Cosmology Telescope (ACT), South Pole Telescope (SPT) and CMB-S4 will increase sensitivity and allow for more precise measurements.

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arXiv2024

Galaxy dispersion measured by Fast Radio Bursts as a probe of baryonic feedback models

Fast Radio Bursts (FRBs) are a sensitive probe of the electron distribution in both the large-scale structure and their host galaxies through the dispersion measure (DM) of the radio pulse. Baryonic feedback models are crucial for modelling small scales for ongoing cosmological surveys that are expected to change the electron distribution in galaxies in a way that can be probed by FRB observations. In this paper, we explore the impact of baryonic feedback on FRB hosts using numerical simulations and make a detailed study of the host galaxy dispersion as a function of redshift, galaxy type, feedback model and how these properties vary in independent simulation codes. We find that the host galaxy dispersion varies dramatically between different implementations of baryonic feedback, allowing FRBs with host identification to be a valuable probe of feedback physics and thus provide necessary priors for upcoming analysis of the statistical properties of the large-scale structure. We further find that any dependency on the exact location of events within the halo is small. While there exists an evolution of the dispersion measure with redshift and halo mass, it is largely driven by varying star formation rates of the halo. Spectral information from FRB hosts can therefore be used to put priors on the host galaxy dispersion measure, and FRBs can be used to distinguish between competing models of baryonic feedback in future studies.

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arXiv2023

Why Cosmic Voids Matter: Mitigation of Baryonic Physics

We utilize the Magneticum suite of state-of-the-art hydrodynamical, as well as dark-matter-only simulations to investigate the effects of baryonic physics on cosmic voids in the highest-resolution study of its kind. This includes the size, shape and inner density distributions of voids, as well as their radial density and velocity profiles traced by halos, baryonic and cold dark matter particles. Our results reveal observationally insignificant effects that slightly increase with the inner densities of voids and are exclusively relevant on scales of only a few Mpc. Most notably, we identify deviations in the distributions of baryons and cold dark matter around halo-defined voids, relevant for weak lensing studies. In contrast, we find that voids identified in cold dark matter, as well as in halos of fixed tracer density exhibit nearly indistinguishable distributions and profiles between hydrodynamical and dark-matter-only simulations, consolidating the universality and robustness of the latter for comparisons of void statistics with observations in upcoming surveys. This corroborates that voids are the components of the cosmic web that are least affected by baryonic physics, further enhancing their use as cosmological probes.

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arXiv2023

The eROSITA Final Equatorial-Depth Survey (eFEDS): A Machine Learning Approach to Infer Galaxy Cluster Masses from eROSITA X-ray Images

We develop a neural network based pipeline to estimate masses of galaxy clusters with a known redshift directly from photon information in X-rays. Our neural networks are trained using supervised learning on simulations of eROSITA observations, focusing in this paper on the Final Equatorial Depth Survey (eFEDS). We use convolutional neural networks which are modified to include additional information of the cluster, in particular its redshift. In contrast to existing work, we utilize simulations including background and point sources to develop a tool which is usable directly on observational eROSITA data for an extended mass range from group size halos to massive clusters with masses in between $10^{13}M_\odot<M<10^{15}M_\odot.$ Using this method, we are able to provide for the first time neural network mass estimation for the observed eFEDS cluster sample from Spectrum-Roentgen-Gamma/eROSITA observations and we find consistent performance with weak lensing calibrated masses. In this measurement, we do not use weak lensing information and we only use previous cluster mass information which was used to calibrate the cluster properties in the simulations. When compared to simulated data, we observe a reduced scatter with respect to luminosity and count-rate based scaling relations. We comment on the application for other upcoming eROSITA All-Sky Survey observations.

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arXiv2022

Why Cosmic Voids Matter: Nonlinear Structure & Linear Dynamics

We use the Magneticum suite of state-of-the-art hydrodynamical simulations to identify cosmic voids based on the watershed technique and investigate their most fundamental properties across different resolutions in mass and scale. This encompasses the distributions of void sizes, shapes, and content, as well as their radial density and velocity profiles traced by the distribution of cold dark matter particles and halos. We also study the impact of various tracer properties, such as their sparsity and mass, and the influence of void merging on these summary statistics. Our results reveal that all of the analyzed void properties are physically related to each other and describe universal characteristics that are largely independent of tracer type and resolution. Most notably, we find that the motion of tracers around void centers is perfectly consistent with linear dynamics, both for individual, as well as stacked voids. Despite the large range of scales accessible in our simulations, we are unable to identify the occurrence of nonlinear dynamics even inside voids of only a few Mpc in size. This suggests voids to be among the most pristine probes of cosmology down to scales that are commonly referred to as highly nonlinear in the field of large-scale structure.

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arXiv2022

Lattice Simulations of Axion-U(1) Inflation

We present the first nonlinear lattice simulation of an axion field coupled to a U(1) gauge field during inflation. We use it to fully characterize the statistics of the primordial curvature perturbation {\zeta}. We find high-order statistics to be essential in describing non-Gaussianity of {\zeta} in the linear regime of the theory. On the contrary, non-Gaussianity is suppressed when the dynamics becomes nonlinear. This relaxes bounds from overproduction of primordial black holes, allowing for an observable gravitational waves signal at pulsar timing array and interferometers scales. Our work establishes lattice simulations as a crucial tool to study the inflationary epoch and its predictions.

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arXiv2021

Lattice simulations of Abelian gauge fields coupled to axions during Inflation

We use a lattice simulation to study a model of axion inflation where the inflaton is coupled to a U(1) gauge field through Chern-Simons interaction. These kinds of models have already been studied with a lattice simulation in the context of reheating. In this work, we focus on the deep inflationary phase and discuss the new aspects that need to be considered in order to simulate gauge fields in this regime. Our main result is reproducing with precision the growth of the gauge field on the lattice induced by the rolling of the axion on its potential, thus recovering the results of linear perturbation theory for this model. In order to do so, we study in detail how the spatial discretization, through the choice of the spatial derivatives on the lattice, influences the dynamics of the gauge field. We find that the evolution of the gauge field is highly sensitive to the choice of the spatial discretization scheme. Nevertheless, we are able to identify a discretization scheme for which the growth of the gauge field on the lattice reproduces the one of continuous space with good precision.

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