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Jens Jasche

Publications and source records attributed to Jens Jasche.

At least 19 recordsLinked to original sources
arXiv2026

Alignment of the Milky Way and M31 with their cosmic environment. New insights from constrained Local Group simulations

The large-scale environment is thought to play an important role in setting galaxy properties. The Milky Way (MW) and Andromeda (M31) reside in the Local Group, embedded in the Local Sheet (LS). To study the sheet's influence on the dark matter (DM) halo shapes, spins, and disks orientations of MW and M31 analogues, we use a new suite of constrained Local Group simulations that reproduce the observed configuration of the two main halos and the LS. We determine their shapes and alignments relative to the LS analogues, and the effect of infall and coalescence of massive mergers. We find that the DM halo shapes of our MW and M31 analogues are on average slightly rounder than literature reports for similar-mass galaxies in random environments. We find preferential alignment between the sheet normal and the halos' minor axes, but not with the halos' spins. The present-day disk angular momenta ($L_{\rm disk}$) closely align with the halos' minor axes (median $15^{+15}_{-8}$ degrees at $R_{\rm vir}$) and with the halos' spins. The direction of $L_{\rm disk}$ is often set by one of the two highest mass ratio mergers during the last 8-10 Gyr. While recent ($\leq 2$ Gyr) massive mergers can reorient the outer halo's minor axis leading to twisted shapes, $L_{\rm disk}$ retains the imprint of the earlier accretion event. These results can explain the peculiar alignment of the MW's disk and DM halo shape and their orientation relative to the LS. The prolate-like morphology and orientation of the MW's outer halo can be explained by the Magellanic Clouds (and perhaps Sagittarius) accreting from within the LS. As their orbital planes are nearly perpendicular to the Galactic disk, the disk orientation must be set earlier, possibly by the GES merger. This merger's estimated infall direction, highly inclined relative to the present-day LS, is broadly consistent with the LS's direction of maximum collapse.

astro-ph.GA↗
arXiv2026

Application of Bayesian Statistical Tools to SKA Telescopes Polarization Surveys to Study Magnetization of the Large-scale Structure of the Universe

Understanding cosmological magnetic fields requires a detailed knowledge of magnetism in the different environments of the large-scale structure of the Universe. Magnetic fields are well known to inhabit galaxy clusters, and recently their presence has been detected between galaxy clusters, along filaments extending up to 10-15 Mpc. Beyond that, there is limited information on the existence of magnetic fields in sheets and voids of the cosmic web. We propose a Bayesian statistical approach to study magnetic fields on large scales through observations of the Faraday rotation effect in large samples of polarized point-like background radio sources. We present the expectations to detect magnetization in environments of the large-scale structure with the SKA-Mid polarization survey planned by the SKAO Magnetism Science Working Group and with SKA-Low with AA4 telescopes, and discuss the required level of accuracy on the redshifts of the host galaxies for such a study. We find that about 50,000 mid-frequency Faraday rotation measurements complemented by high-precision redshifts are needed to constrain magnetization of dense environments as galaxy clusters. Investigation of magnetization in weakly-magnetized low-density enviroments, as filaments, will remain challenging, but low frequencies radio observations and spectroscopic redhifts for at least 17,000 will allow us to put first constraints.

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arXiv2026

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions

Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data. While Markov chain Monte Carlo methods provide asymptotic convergence guarantees, they are computationally demanding in high-dimensional settings. Neural network-based generative models for entire discretized 3D fields enable fast amortized inference but often lack convergence guarantees and principled accuracy assessment. Using Hamiltonian Monte Carlo to obtain reference posterior samples, we conduct a controlled field-level evaluation of an implicit generative model (Stochastic Interpolants) and an explicit likelihood-based model (GLOW normalizing flows). This comparison, unavailable in typical applications, enables the detection of posterior geometry failures that standard metrics cannot capture. As a case study, we consider the cosmological inverse problem of inferring cosmic initial conditions from present-day large-scale structure. To match the precision of modern cosmological data, this problem increasingly relies on complex, non-linear, and non-differentiable simulators, which are incompatible with gradient-based inference frameworks. Generative models offer a route to address these challenges, provided their inferred posteriors are reliable. In this work, we show that matching posterior means, marginal distributions, or achieving high cross-correlation does not imply correct uncertainty structure, as revealed by posterior variance fields and sample-based evaluations. Through this work, we aim to raise awareness of the challenges of uncertainty estimation in high-dimensional field-level settings, highlighting the importance of careful design and validation of neural generative approaches for scientific applications.

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arXiv2026

Learning the Universe: Constrained simulations of the Coma galaxy cluster -- I. Radial X-ray and Compton-y signatures

We present a suite of 50 high-fidelity simulations of Coma cluster analogues constructed from BORG/MANTICORE constrained initial conditions and evolved with the IllustrisTNG galaxy formation model. Regions predicted to form massive clusters comparable to Coma in mass and environment are selected and followed through cosmic time, producing realistic galaxy populations and intracluster medium properties. The ensemble captures both cosmic variance and uncertainties in the local initial conditions, providing a statistically robust framework for interpreting Coma in a cosmological context. We focus on direct comparisons with observed thermodynamical profiles of the intracluster medium. Specifically, we extract X-ray surface brightness profiles from the simulated clusters and confront them with measurements from eROSITA, as well as compute the thermal Sunyaev--Zel'dovich effect via integrated Compton-$y$ profiles for comparison with Planck satellite data. The simulations reproduce the broad shape and normalisation of both observables, while also highlighting the range of scatter expected from environmental and assembly history differences. This enables us to assess how feedback processes, merger activity, and large-scale environment shape observable cluster properties. Our results demonstrate that combining constrained cosmological initial conditions with state-of-the-art galaxy formation physics provides an effective strategy for generating targeted, observation-driven analogues of specific clusters. The resulting dataset offers a valuable resource for testing models of intracluster medium physics, calibrating scaling relations, and interpreting upcoming joint X-ray and Sunyaev--Zel'dovich observations of nearby massive clusters.

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arXiv2026

The Manticore Project II: Bayesian digital twins of cosmic structure across the SDSS and BOSS volumes

We present Manticore-Deep, a high-resolution Bayesian field-level reconstruction of cosmic large-scale structure over a comoving volume of $(4~h^{-1}\mathrm{Gpc})^{3}$ to $z\approx0.7$ at ${\sim}4$~Mpc/h resolution. Extending the companion Manticore-Local analysis (Paper~I), Manticore-Deep jointly constrains five galaxy redshift surveys within a single hierarchical Bayesian framework using the BORG algorithm. The inference reconstructs primordial initial conditions evolved under gravity, yielding a posterior ensemble of three-dimensional density and velocity fields that causally reproduce the observed large-scale structure. A novel tiled inference strategy extends the reconstructed volume by more than an order of magnitude beyond Paper~I. Posterior realisations are consistent with LCDM, reproducing Gaussian isotropic initial conditions and the expected $z=0$ matter power spectrum, bispectrum, and halo mass function over the resolved scales. We validate the reconstruction using two independent template-free posterior-predictive tests against observations excluded from the inference. Cross-correlation with the \textit{Planck} PR3 CMB lensing map yields a cumulative detection significance of 7.4 $\sigma$, while velocity-weighted stacking of $64{,}750$ galaxy clusters on the \textit{Planck} 217~GHz map detects the kinetic Sunyaev--Zel'dovich effect at $3.5\sigma$, with a model-independent approach--recession split confirming the inferred velocities. Together, these tests validate both the projected-density and three-dimensional velocity fields recovered by Manticore-Deep. The BOSS Great Wall is recovered as a ${\sim}3\sigma$ overdensity consistent with LCDM across the posterior ensemble. Manticore-Deep establishes a benchmark for survey-depth constrained cosmological digital twins and reproducible field-level validation of large-scale structure reconstructions.

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arXiv2026

Illuminating the Local Universe: Large-Scale Structure from ZTF Type Ia Supernovae

Within the volume-limited subsample at $z<0.06$ of the Zwicky Transient Facility (ZTF) DR2 sample, we confirm a statistically significant excess of Type Ia supernovae (SNe Ia) at $z \simeq 0.02$-$0.04$, previously reported but not explained by survey selection effects. Forward simulations assuming a uniform volumetric SN Ia rate and realistic ZTF detection efficiencies fail to reproduce the feature at the $5$-$7\sigma$ level. We also detect an excess in the rates compared to our survey simulations at $z \simeq 0.08$ and $0.14$, albeit at smaller significance. To investigate the origin of these inhomogeneities, we compare the observed SN distribution to constrained reconstructions of the local matter density field from the Manticore project, based on Bayesian forward modelling of the 2M++ galaxy catalogue. While SN overdensities are spatially associated with prominent nearby structures such as the Perseus, Coma, and Hercules superclusters, the amplitude of the SN excesses significantly exceeds that expected from matter overdensities alone. By reconstructing a redshift-dependent volumetric SN Ia rate, we find that local enhancements can reach factors of two to five within specific clusters, while the sample-averaged rate remains consistent with previous low-redshift measurements. These results indicate that the SN Ia rate is not a linear tracer of the underlying matter density and suggest a strong environmental dependence in dense structures. We discuss possible physical origins and highlight the implications for low-redshift SN cosmology, including correlated peculiar velocities and additional covariance beyond standard linear corrections.

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arXiv2026

Field-Level Inference of Primordial Non-Gaussianity with the Quijote Simulation Suite

Local primordial non-Gaussianity, parameterised as $f_{\rm NL}^{\rm local}$, will be stringently constrained using state-of-the-art methods applied to next-generation galaxy redshift survey data. In this paper, in preparation for the upcoming data sets, we demonstrate for the first time the joint field-level inference of $f_{\rm NL}^{\rm local}$, nuisance parameters, and the initial conditions in realistic halo catalogues, ones which are generated through full dark-matter-only $N$-body simulations. The field-level inference algorithm optimally constrains $f_{\rm NL}^{\rm local}$ through a Bayesian forward-modelling approach at the field level, which outperforms traditional methods by leveraging the full statistical power of the data at the scales considered. In addition, we assess its performance under various design choices in the forward model, including tests of the structure formation model and resolution. We demonstrate the robustness of our approach by applying it to a subset of the \textit{Quijote} simulation suite, performing the inference at scales down to $k_{\rm max} \approx 0.1 h \rm{Mpc}^{-1}$. Compared with a power spectrum and bispectrum estimator, we find a $\sim1.3$ improvement in $\sigma(f_{\rm NL}^{\rm local})$ when applying \borg{}, while marginalising over the initial conditions and bias parameters. From the small-scale information sensitivity tests, we show that the constraints on $f_{\rm NL}^{\rm local}$ improve as we increase the resolution of the inference. These findings underscore the transformative potential of field-level inference to leverage the information available in ongoing surveys such as \textit{Euclid}, providing accurate insights into the physics of cosmic inflation and the number of fields driving it.

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arXiv2026

Reconstructing the largest scales of the Universe with field-level inference applied to the Quaia Quasar Catalogue

The recently released Quaia quasar catalogue, with its broad redshift range and all-sky coverage, enables unprecedented three-dimensional reconstructions of matter across cosmic time. In this work, we apply the field-level inference algorithm BORG to the Quaia catalogues to reconstruct the initial conditions and present-day matter distribution of the Universe. We employ a physics-based forward model of large-scale structure using Lagrangian perturbation theory, incorporating light-cone effects, redshift-space distortions, quasar bias, and survey selection effects. This approach enables a detailed and physically motivated inference of the three-dimensional density field and initial conditions over the entire cosmic volume considered. We analyse both the G < 20.0 (Quaia Clean) and G < 20.5 (Quaia Deep) samples, where G denotes the Gaia broad optical-band magnitude, imposing conservative sky cuts to ensure robustness against foreground contamination. The resulting reconstructions span a comoving volume of (10h^{-1} Gpc)^3 with a maximum spatial resolution of 39.1 h^{-1}Mpc, making this the largest field-level reconstruction of the observable Universe in terms of comoving volume to date. We validate our reconstructions through a range of internal and external consistency checks, including the cross-correlation of the inferred density fields with Planck CMB lensing, where we detect a signal at ~4\sigma significance. Beyond delivering high-fidelity data products, including posterior maps of initial conditions, present-day dark matter, and velocity fields, this work establishes a framework for exploiting quasar surveys in field-level cosmology.

astro-ph.CO↗
arXiv2026

The mass distribution in and around the Local Group

Our Galaxy, Andromeda and their companion dwarf galaxies form the Local Group. Most of the mass in and around it is believed to be dark matter rather than gas or stars, so its distribution must be inferred from the effect of gravity on the motion of visible objects. Modelling efforts have long struggled to reproduce the quiet Hubble flow around the Local Group, as they require unrealistically little mass beyond the haloes of the two main galaxies. Here we revisit this using $\Lambda$CDM simulations of Local Group analogues with initial conditions constrained to match the observed dynamics of the two main haloes and the surrounding flow. The observations are reconcilable within $\Lambda$CDM, but only if mass is strongly concentrated in a plane out to 10 Mpc, with the surface density rising away from the Local Group and with deep voids above and below. This configuration, dynamically inferred, mirrors known structures in the nearby galaxy distribution. The resulting Hubble flow is quiet yet strongly anisotropic, a fact obscured by the paucity of tracers at high supergalactic latitude. This flattened geometry reconciles the dynamical mass estimates of the Local Group with the surrounding velocity field, thus demonstrating full consistency within the standard cosmological model.

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arXiv2026

Revisiting the Great Attractor: The Local Group's streamline trajectory, cosmic velocity and dynamical fate

We revisit the Great Attractor using the Manticore-Local suite of digital twins of the nearby Universe. The Great Attractor concept has been proposed as an answer to three distinct questions: what sources the Local Group velocity in the cosmic microwave background frame, where present-day velocity streamlines converge, and where the Local Group is moving to. Addressing the original motivation of the Great Attractor -- explaining the Local Group cosmic velocity -- we find that mass within $155~h^{-1}\mathrm{Mpc}$ accounts for only ${\sim}72\%$ of that velocity magnitude with ${\sim}38\,\deg$ directional offset. We show that even in the purely linear regime convergence within this volume is not guaranteed, particularly when also accounting for small-scale contributions to the observer velocity; no single structure, including the proposed Great Attractor, would be expected to dominate the velocity budget. Streamline convergence is smoothing-scale-dependent, transitioning from Virgo at small scales through the Hydra--Centaurus region at intermediate scales to Shapley at large scales; at intermediate smoothing the convergence point lies near Abell 3565 with an asymmetric basin of mass $\log( M / (h^{-1} \mathrm{M}_\odot)) = 16.4 \pm 0.1$ that excludes Norma. To address the third question, we evolve the Manticore-Local realisations to scale factor $a = 10$ in a new Beyond-Present-Time simulation suite and identify the asymptotic future location of the Local Group. We find that the dominant motion is towards Virgo, but even it contributes at most one third of the Local Group velocity. Our results demonstrate that the classical Great Attractor is not a dynamically dominant structure but an artefact of the instantaneous velocity field, and that no single attractor is likely to account for the Local Group motion in the cosmic rest frame.

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arXiv2025

Two per cent measurement of $H_0$ from Cepheids alone

One of the most pressing problems in current cosmology is the cause of the Hubble tension. We revisit a two-rung distance ladder, composed only of Cepheid periods and magnitudes, anchor distances in the Milky Way, Large Magellanic Cloud, NGC 4258, and host galaxy redshifts. We adopt the SH0ES data for the most up-to-date and carefully vetted measurements, where the Cepheid hosts were selected to harbour also Type Ia supernovae. We introduce two important improvements: a rigorous selection modelling and a state-of-the-art density and peculiar velocity model using Manticore-Local, based on the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm. We infer $H_0 = 71.1 \pm 1.4~\mathrm{km}\,\mathrm{s}^{-1}\,\mathrm{Mpc}^{-1}$, assuming the Cepheid host sample was selected by supernova magnitudes. However, the actual selection criteria are not clear, and other assumptions can increase $H_0$ by up to one statistical standard deviation. The posterior has a lower central value and a 41 per cent smaller uncertainty than a previous study using the same distance-ladder data. This result is lower than the supernova-based SH0ES inferred value of $H_0 = 73.2 \pm 0.9~\mathrm{km}\,\mathrm{s}^{-1}\,\mathrm{Mpc}^{-1}$ at about $1.3\sigma$, and is in $2.8\sigma$ tension with the latest cosmic microwave background results in the standard cosmological model. These results demonstrate that a measurement of $H_0$ of sufficient precision to weigh in on the Hubble tension is achievable using second-rung data alone, underscoring the importance of robust and accurate statistical and velocity-field modelling.

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arXiv2025

A Bayesian catalog of 100 high-significance voids in the Local Universe

While cosmic voids are now recognized as a valuable cosmological probe, identifying them in a galaxy catalog is challenging for multiple reasons: observational effects such as holes in the mask or magnitude selection hinder the detection process; galaxies are biased tracers of the underlying dark matter distribution; and it is non-trivial to estimate the detection significance and parameter uncertainties for individual voids. Our goal is to extract a catalog of voids from constrained simulations of the large-scale structure that are consistent with the observed galaxy positions, effectively representing statistically independent realizations of the probability distribution of the cosmic web. This allows us to carry out a full Bayesian analysis of the structures emerging in the Universe. We use 50 posterior realizations of the large-scale structure in the Manticore-Local suite, obtained from the 2M++ galaxies. Running the VIDE void finder on each realization, we extract 50 independent void catalogs. We perform a posterior clustering analysis to identify high-significance voids at the 5$\sigma$ level, and we assess the probability distribution of their properties. We produce a catalog of 100 voids with high statistical significance, available at https://voids.cosmictwin.org/, including the probability distributions of the centers and radii of the voids. We characterize the morphology of these regions, effectively producing a template for density environments that can be used in astrophysical applications such as galaxy evolution studies. While providing the community with a detailed catalog of voids in the nearby Universe, this work also constitutes an approach to identifying cosmic voids from galaxy surveys that allows us to account rigorously for the observational systematics intrinsic to direct detection, and provide a Bayesian characterization of their properties.

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arXiv2025

The Manticore Project I: a digital twin of our cosmic neighbourhood from Bayesian field-level analysis

We present the first results from the Manticore project, dubbed Manticore-Local, a suite of Bayesian constrained simulations of the nearby Universe, generated by fitting a physical structure formation model to the 2M++ galaxy catalogue using the BORG algorithm. This field-level inference yields physically consistent realizations of cosmic structure, leveraging a nonlinear gravitational solver, a refined galaxy bias model, and physics-informed priors. The Manticore-Local posterior realizations evolve within a parent cosmological volume statistically consistent with LCDM, demonstrated through extensive posterior predictive tests of power spectra, bispectra, initial condition Gaussianity, and the halo mass function. The inferred local supervolume shows no significant deviation from cosmological expectations; notably, we find no evidence for a large local underdensity. Our model identifies high-significance counterparts for fourteen prominent galaxy clusters each within one degree of its observed sky position. Across the posterior ensemble, these counterparts are consistently detected with 2-4 sigma significance, and their reconstructed masses and redshifts agree closely with observational estimates, confirming the inference's spatial and dynamical fidelity. The peculiar velocity field recovered by Manticore-Local achieves the highest Bayesian evidence across five datasets, surpassing state-of-the-art models. Unlike methods yielding only point estimates or using simplified dynamics, Manticore-Local provides a full Bayesian posterior over cosmic structure and evolution, enabling rigorous uncertainty quantification. These results establish Manticore-Local as the most advanced constrained realization suite of the Local Universe to date, offering a robust statistical foundation for future studies of galaxy formation, velocity flows, and environmental dependencies in our cosmic neighbourhood.

astro-ph.CO↗
arXiv2025

Preparing for Rubin-LSST -- Detecting Brightest Cluster Galaxies with Machine Learning in the LSST DP0.2 simulation

The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of galaxy clusters in the optical to the near-infrared, with unrivalled coverage, depth and uniformity. The study of galaxy clusters informs us on the effect of environmental processes on galactic formation, which directly translates onto the formation of the brightest cluster galaxy (BCG). These massive galaxies present traces of the whole merger history of their host clusters, which can be in the shape of intra-cluster light (ICL) that surrounds them, tidal streams, or simply by the accumulated stellar mass that has been acquired over the past 10 billion years as they have cannibalized other galaxies in their surroundings. In an era where new data is being generated faster than humans can deal with, new methods involving machine learning have been emerging more and more in the most recent years. In the aim of preparing for the future LSST data release which will allow the observations of more than 20000 clusters and BCGs, we present in this paper different methods based on machine learning to detect these BCGs on LSST-like optical images. This study is done by making use of the simulated LSST Data Preview images. We find that the use of machine learning allows to accurately identify the BCG in up to 95% of clusters in our sample. Compared to more conventional red sequence extraction methods, the use of machine learning appears to be faster, more efficient and consistent, and does not require much, if any, pre-processing.

astro-ph.GA↗
arXiv2025

Learning the Universe: Learning to Optimize Cosmic Initial Conditions with Non-Differentiable Structure Formation Models

Making the most of next-generation galaxy clustering surveys requires overcoming challenges in complex, non-linear modelling to access the significant amount of information at smaller cosmological scales. Field-level inference has provided a unique opportunity beyond summary statistics to use all of the information of the galaxy distribution. However, addressing current challenges often necessitates numerical modelling that incorporates non-differentiable components, hindering the use of efficient gradient-based inference methods. In this paper, we introduce Learning the Universe by Learning to Optimize (LULO), a gradient-free framework for reconstructing the 3D cosmic initial conditions. Our approach advances deep learning to train an optimization algorithm capable of fitting state-of-the-art non-differentiable simulators to data at the field level. Importantly, the neural optimizer solely acts as a search engine in an iterative scheme, always maintaining full physics simulations in the loop, ensuring scalability and reliability. We demonstrate the method by accurately reconstructing initial conditions from $M_{200\mathrm{c}}$ halos identified in a dark matter-only $N$-body simulation with a spherical overdensity algorithm. The derived dark matter and halo overdensity fields exhibit $\geq80\%$ cross-correlation with the ground truth into the non-linear regime $k \sim 1h$ Mpc$^{-1}$. Additional cosmological tests reveal accurate recovery of the power spectra, bispectra, halo mass function, and velocities. With this work, we demonstrate a promising path forward to non-linear field-level inference surpassing the requirement of a differentiable physics model.

astro-ph.CO↗
arXiv2025

The effect of environment on the mass assembly history of the Milky Way and M31

We study the mass growth histories of the halos of Milky Way and M31 analogues formed in constrained cosmological simulations of the Local Group. These simulations constitute a fair and representative set of $\Lambda$CDM realisations conditioned on properties of the main Local Group galaxies, such as their masses, relative separation, dynamics and environment. Comparing with isolated analogues extracted from the TNG dark-matter-only simulations, we find that while our M31 halos have a comparable mass growth history to their isolated counterparts, our Milky Ways typically form earlier and their growth is suppressed at late times. Mass growth associated to major and minor mergers is also biased early for the Milky Way in comparison to M31, with most accretion occurring 1 - 4 Gyr after the Big Bang, and a relatively quiescent history at later times. 32% of our Milky Ways experienced a Gaia-Enceladus/Sausage (GES)-like merger, while 13% host an LMC-like object at the present day, with 5% having both. In one case, an SMC- and a Sagittarius-analogue are also present, showing that the most important mergers of the Milky Way in its Local Group environment can be reproduced in $\Lambda$CDM. We find that the material that makes up the Milky Way and M31 halos at the present day first collapsed onto a plane roughly aligned with the Local Sheet and Supergalactic plane; after $z \sim 2$, accretion occurred mostly within this plane, with the tidal effects of the heavier companion, M31, significantly impacting the late growth history of the Milky Way.

astro-ph.GA↗
arXiv2024

$\texttt{PineTree}$: A generative, fast, and differentiable halo model for wide-field galaxy surveys

Mock halo catalogues are indispensable data products for developing and validating cosmological inference pipelines. A major challenge in generating mock catalogues is modelling the halo or galaxy bias, which is the mapping from matter density to dark matter halos or observable galaxies. To this end, N-body codes produce state-of-the-art catalogues. However, generating large numbers of these N-body simulations for big volumes, requires significant computational time. We introduce and benchmark a differentiable and physics-informed neural network that can generate mock halo catalogues of comparable quality to those obtained from full N-body codes. The model design is computationally efficient for the training procedure and the production of large mock suites. We present a neural network, relying only on 18 to 34 trainable parameters, that produces halo catalogues from dark matter overdensity fields. The reduction of network weights is realised through incorporating symmetries motivated by first principles into our model architecture. We train our model using dark matter only N-body simulations across different resolutions, redshifts, and mass bins. We validate the final mock catalogues by comparing them to N-body halo catalogues using different N-point correlation functions. Our model produces mock halo catalogues consistent with the reference simulations, showing that this novel network is a promising way to generate mock data for upcoming wide-field surveys due to its computational efficiency. Moreover, we find that the network can be trained on approximate overdensity fields to reduce the computational cost further. We also present how the trained network parameters can be interpreted to give insights into the physics of structure formation. Finally, we discuss the current limitations of our model as well as more general requirements and pitfalls for approximate halo mock generation.

astro-ph.CO↗
arXiv2024

Constrained cosmological simulations of the Local Group using Bayesian hierarchical field-level inference

We present a novel approach based on Bayesian field-level inference capable of resolving individual galaxies within the Local Group (LG), enabling detailed studies of its structure and formation via posterior simulations. We extend the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm with a multi-resolution approach, allowing us to reach smaller mass scales and apply observational constraints based on LG galaxies. Our updated data model simultaneously accounts for observations of mass tracers within the dark haloes of the Milky Way (MW) and M31, their observed separation and relative velocity, and the quiet surrounding Hubble flow represented through the positions and velocities of galaxies at distances from one to four Mpc. Our approach delivers representative posterior samples of $\Lambda$CDM realisations that are statistically and simultaneously consistent with all these observations, leading to significantly tighter mass constraints than found if the individual datasets are considered separately. In particular, we estimate the virial masses of the MW and M31 to be $\log_{10}(M_{200c}/M_\odot) = 12.07\pm0.08$ and $12.33\pm0.10$, respectively, their sum to be $\log_{10}(\Sigma M_{200c}/M_\odot)= 12.52\pm0.07$, and the enclosed mass within spheres of radius $R$ to be $\log_{10}(M(R)/M_\odot)= 12.71\pm0.06$ and $12.96\pm0.08$ for $R=1$ Mpc and 3 Mpc, respectively. The M31-MW orbit is nearly radial for most of our $\Lambda$CDM LG's, and most lie in a dark matter sheet that aligns approximately with the Supergalactic Plane, even though the surrounding density field was not used explicitly as a constraint. The approximate simulations employed in our inference are accurately reproduced by high-fidelity structure formation simulations, demonstrating the potential for future high-resolution, full-physics $\Lambda$CDM posterior simulations of LG look-alikes.

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