Searcharxiv⌕ Search

arXiv subjects

Jens Jasche

Publications and source records attributed to Jens Jasche.

At least 55 records · Page 3Linked to original sources

Constraints on Equivalence Principle Violation from Gamma Ray Bursts

Theories of gravity that obey the Weak Equivalence Principle have the same Parametrised Post-Newtonian parameter $γ$ for all particles at all energies. The large Shapiro time delays of extragalactic sources allow us to put tight constraints on differences in $γ$ between photons of different frequencies from spectral lag data, since a non-zero $Δγ$ would result in a frequency-dependent arrival time. The majority of previous constraints have assumed that the Shapiro time delay is dominated by a few local massive objects, although this is a poor approximation for distant sources. In this work we consider the cosmological context of these sources by developing a source-by-source, Monte Carlo-based forward model for the Shapiro time delays by combining constrained realisations of the local density field using the Bayesian origin reconstruction from galaxies algorithm with unconstrained large-scale modes. Propagating uncertainties in the density field reconstruction and marginalising over an empirical model describing other contributions to the time delay, we use spectral lag data of Gamma Ray Bursts from the BATSE satellite to constrain $Δγ< 2.1 \times 10^{-15}$ at $1 σ$ confidence between photon energies of $25 {\rm \, keV}$ and $325 {\rm \, keV}$.

gr-qc↗

Quantifying the Rarity of the Local Super-Volume

We investigate the extent to which the number of clusters of mass exceeding $10^{15}\,M_{\odot}\,h^{-1}$ within the local super-volume ($<135\mathrm{\,Mpc}h^{-1}$) is compatible with the standard $Λ$CDM cosmological model. Depending on the mass estimator used, we find that the observed number $N$ of such massive structures can vary between $0$ and $5$. Adopting $N=5$ yields $Λ$CDM likelihoods as low as $2.4\times 10^{-3}$ (with $σ_8=0.81$) or $3.8\times 10^{-5}$ (with $σ_8=0.74$). However, at the other extreme ($N=0$), the likelihood is of order unity. Thus, while potentially very powerful, this method is currently limited by systematic uncertainties in cluster mass estimates. This motivates efforts to reduce these systematics with additional observations and improved modelling.

astro-ph.CO↗

Bayesian cosmological inference through implicit cross-correlation statistics

Analyzes of next-generation galaxy data require accurate treatment of systematic effects such as the bias between observed galaxies and the underlying matter density field. However, proposed models of the phenomenon are either numerically expensive or too inaccurate to achieve unbiased inferences of cosmological parameters even at mildly-nonlinear scales of the data. As an alternative to constructing accurate galaxy bias models, requiring understanding galaxy formation, we propose to construct likelihood distributions for Bayesian forward modeling approaches that are insensitive to linear, scale-dependent bias and provide robustness against model misspecification. We use maximum entropy arguments to construct likelihood distributions designed to account only for correlations between data and inferred quantities. By design these correlations are insensitive to linear galaxy biasing relations, providing the desired robustness. The method is implemented and tested within a Markov Chain Monte Carlo approach. The method is assessed using a halo mock catalog based on standard full, cosmological, N-body simulations. We obtain unbiased and tight constraints on cosmological parameters exploiting only linear cross-correlation rates for $k\le 0.10$ Mpc/h. Tests for halos of masses ~10$^{12}$ M$_\odot$ to ~10$^{13}$ M$_\odot$ indicate that it is possible to ignore all details of the linear, scale dependent, bias function while obtaining robust constraints on cosmology. Our results provide a promising path forward to analyzes of galaxy surveys without the requirement of having to accurately model the details of galaxy biasing but by designing robust likelihoods for the inference.

astro-ph.CO↗

The SIBELIUS Project: E Pluribus Unum

We introduce "Simulations Beyond The Local Universe" (SIBELIUS) that connect the Local Group to its cosmic environment. We show that introducing hierarchical small-scale perturbations to a density field constrained on large scales by observations provides an efficient way to explore the sample space of Local Group analogues. From more than 60 000 simulations, we identify a hierarchy of Local Group characteristics emanating from different scales: the total mass, orientation, orbital energy and the angular momentum are largely determined by modes above $λ$ = 1.6 comoving Mpc (cMpc) in the primordial density field. Smaller scale variations are mostly manifest as perturbations to the MW-M31 orbit, and we find that the observables commonly used to describe the Local Group -- the MW-M31 separation and radial velocity -- are transient and depend on specifying scales down to 0.2 cMpc in the primordial density field. We further find that the presence of M33/LMC analogues significantly affects the MW-M31 orbit and its sensitivity to small-scale perturbations. We construct initial conditions that lead to the formation of a Local Group whose primary observables precisely match the current observations.

astro-ph.CO↗

Impacts of the physical data model on the forward inference of initial conditions from biased tracers

We investigate the impact of each ingredient in the employed physical data model on the Bayesian forward inference of initial conditions from biased tracers at the field level. Specifically, we use dark matter halos in a given cosmological simulation volume as tracers of the underlying matter density field. We study the effect of tracer density, grid resolution, gravity model, bias model and likelihood on the inferred initial conditions. We find that the cross-correlation coefficient between true and inferred phases reacts weakly to all ingredients above, and is well predicted by the theoretical expectation derived from a Gaussian model on a broad range of scales. The bias in the amplitude of the inferred initial conditions, on the other hand, depends strongly on the bias model and the likelihood. We conclude that the bias model and likelihood hold the key to an unbiased cosmological inference. Together they must keep the systematics -- which arise from the sub-grid physics that are marginalized over -- under control in order to obtain an unbiased inference.

astro-ph.CO↗

Velocity correction for Hubble constant measurements from standard sirens

Gravitational wave (GW) sources are an excellent probe of the luminosity distance and offer a novel measure of the Hubble constant, $H_0$. This estimation of $H_0$ from standard sirens requires an accurate estimation of the cosmological redshift of the host galaxy of the GW source, after correcting for its peculiar velocity. Absence of an accurate peculiar velocity correction affects both the precision and accuracy of the measurement of $H_0$, particularly for nearby sources. We propose a framework to incorporate such a peculiar velocity correction for GW sources. A first implementation of our method to the event GW170817 combined with the Very Large Baseline Interferometry (VLBI) observation leads to a revised value of $H_0= 68.3^{+ 4.6}_{-4.5}$ km/s/Mpc. While this revision is minor, it demonstrates that our method makes it possible for obtaining an unbiased and accurate measurements of $H_0$ at the precision required for the standard siren cosmology.

astro-ph.CO↗

Unbiased Cosmology Inference from Biased Tracers using the EFT Likelihood

We present updates on the cosmology inference using the effective field theory (EFT) likelihood presented previously in Schmidt et al., 2018, Elsner et al., 2019 [1,2]. Specifically, we add a cutoff to the initial conditions that serve as starting point for the matter forward model. We show that this cutoff, which was not employed in any previous related work, is important to regularize loop integrals that otherwise involve small-scale, non-perturbative modes. We then present results on the inferred value of the linear power spectrum normalization $σ_{8}$ from rest-frame halo catalogs using both second- and third-order bias expansions, imposing uniform priors on all bias parameters. Due to the perfect bias-$σ_{8}$ degeneracy at linear order, constraints on $σ_{8}$ rely entirely on nonlinear information. The results show the expected convergence behavior when lowering the cutoff in wavenumber, $Λ$. When including modes up to $k \leq Λ= 0.1\,h\,{\rm Mpc}^{-1}$ in the second-order case, $σ_{8}$ is recovered to within $\lesssim 6\,\%$ for a range of halo masses and redshifts. The systematic bias shrinks to $4\,\%$ or less for the third-order bias expansion on the same range of scales. Together with additional evidence we provide, this shows that the residual mismatch in $σ_{8}$ can be attributed to higher-order bias contributions. We conclude that the EFT likelihood is able to infer unbiased cosmological constraints, within expected theoretical systematic errors, from physical biased tracers on quasilinear scales

astro-ph.CO↗

Taking measurements of the kinematic Sunyaev-Zel'dovich effect forward: including uncertainties from velocity reconstruction with forward modeling

We measure the kinematic Sunyaev-Zel'dovich (kSZ) effect, imprinted by maxBCG clusters, on the Planck SMICA map of the Cosmic Microwave Background (CMB). Our measurement, for the first time, directly accounts for uncertainties in the velocity reconstruction step through the process of Bayesian forward modeling. We show that this often neglected uncertainty budget typically increases the final uncertainty on the measured kSZ signal amplitude by $\simeq15\%$ at cluster scale. We observe evidence for the kSZ effect, at a significance of $\simeq2σ$. Our analysis, when applied to future higher-resolution CMB data, together with minor improvements in map-filtering and signal-modeling methods, should yield both significant and unbiased measurements of the kSZ signal, which can then be used to probe and constrain baryonic content of galaxy clusters and galaxy groups.

astro-ph.CO↗

A hierarchical field-level inference approach to reconstruction from sparse Lyman-$α$ forest data

We address the problem of inferring the three-dimensional matter distribution from a sparse set of one-dimensional quasar absorption spectra of the Lyman-$α$ forest. Using a Bayesian forward modelling approach, we focus on extending the dynamical model to a fully self-consistent hierarchical field-level prediction of redshift-space quasar absorption sightlines. Our field-level approach rests on a recently developed semiclassical analogue to Lagrangian perturbation theory (LPT), which improves over noise problems and interpolation requirements of LPT. It furthermore allows for a manifestly conservative mapping of the optical depth to redshift space. In addition, this new dynamical model naturally introduces a coarse-graining scale, which we exploited to accelerate the Markov chain Monte-Carlo (MCMC) sampler using simulated annealing. By gradually reducing the effective temperature of the forward model, we were able to allow it to first converge on large spatial scales before the sampler became sensitive to the increasingly larger space of smaller scales. We demonstrate the advantages, in terms of speed and noise properties, of this field-level approach over using LPT as a forward model, and, using mock data, we validated its performance to reconstruct three-dimensional primordial perturbations and matter distribution from sparse quasar sightlines.

astro-ph.CO↗

Setting the Stage: Structures from Gaussian Random Fields

We study structure formation in a set of cosmological simulations to uncover the scales in the initial density field that gave rise to the formation of present-day structures. Our simulations share a common primordial power spectrum (here Lambda-CDM), but the introduction of hierarchical variations of the phase information allows us to systematically study the scales that determine the formation of structure at later times. We consider the variance in z=0 statistics such as the matter power spectrum and halo mass function. We also define a criterion for the existence of individual haloes across simulations, and determine what scales in the initial density field contain sufficient information for the non-linear formation of unique haloes. We study how the characteristics of individual haloes such as the mass and concentration, as well as the position and velocity, are affected by variations on different scales, and give scaling relations for haloes of different mass. Finally, we use the example of a cluster-mass halo to show how our hierarchical parametrisation of the initial density field can be used to create variants of particular objects. With properties such as mass, concentration, kinematics and substructure of haloes set on distinct and well-determined scales, and its unique ability to introduce variations localised in real space, our method is a powerful tool to study structure formation in cosmological simulations.

astro-ph.CO↗

Cosmology Inference from Biased Tracers using the EFT-based Likelihood

The effective-field-theory (EFT) approach to the clustering of galaxies and other biased tracers allows for an isolation of the cosmological information that is protected by symmetries, in particular the equivalence principle, and thus is robust to the complicated dynamics of dark matter, gas, and stars on small scales. All existing implementations proceed by making predictions for the lowest-order $n$-point functions of biased tracers, as well as their covariance, and comparing with measurements. Recently, we presented an EFT-based expression for the conditional probability of the density field of a biased tracer given the matter density field, which in principle combines information from arbitrarily high order $n$-point functions. Here, we report results based on this likelihood by applying it to halo catalogs in real space, specifically on the inference of the power spectrum normalization $σ_8$. We include bias terms up to second order as well as the leading higher-derivative term. For a cutoff value of $Λ= 0.1 h\,{\rm Mpc}^{-1}$, we recover the ground-truth value of $σ_8$ to within 95% CL for different halo samples and redshifts. We discuss possible sources for the remaining systematic bias in $σ_8$ as well as future developments.

astro-ph.CO↗

Targeting Earth: CRPropa learns to aim

Realistic predictions for the arrival directions of ultra-high-energy cosmic rays require extensive simulations of UHECR propagation through 3D space, potentially even including cosmological evolution and timing effects. Such 3D or 4D simulations of cosmic-ray propagation suffer from the fact that a relatively small target - the observer sphere - needs to be hit. If particles are ejected in any direction from the source according to the source emission geometry, such simulations are tremendously inefficient. We present here a targeting mechanism which finds an optimal emission geometry to maximize the number of hits while remaining unbiased in the arrival-direction distribution. This can lead to speedups by many of orders of magnitude, depending on the simulation setup. We present the basic mathematics to produce unbiased results from targeted simulations, demonstrate its effectiveness with the simulation package CRPropa 3 for various propagation scenarios, and discuss prospects to include this mechanism as a standard part of CRPropa in the future.

astro-ph.HE↗

The primordial magnetic field in our cosmic backyard

We reconstruct the 3D structure of magnetic fields, which were seeded by density perturbations during the radiation dominated epoch of the Universe and later on were evolved by structure formation. To achieve this goal, we rely on three dimensional initial density fields inferred from the 2M++ galaxy compilation via the Bayesian $\texttt{BORG}$ algorithm. Using those, we estimate the magnetogenesis by the so called Harrison mechanism. This effect produced magnetic fields exploiting the different photon drag on electrons and ions in vortical motions, which are exited due to second order perturbation effects in the Early Universe. Subsequently we study the evolution of these seed fields through the non-linear cosmic structure formation by virtue of a MHD simulation to obtain a 3D estimate for the structure of this primordial magnetic field component today. At recombination we obtain a reliable lower limit on the large scale magnetic field strength around $10^{-23} \mathrm{G}$, with a power spectrum peaking at about $ 2\, \mathrm{Mpc}^{-1}h$ in comoving scales. At present we expect this evolved primordial field to have strengthts above $\approx 10^{-27}\, \mathrm{G}$ and $\approx 10^{-29}\, \mathrm{G}$ in clusters of galaxies and voids, respectively. We also calculate the corresponding Faraday rotation measure map and show the magnetic field morphology and strength for specific objects of the Local Universe.

astro-ph.CO↗

Primordial power spectrum and cosmology from black-box galaxy surveys

We propose a new, likelihood-free approach to inferring the primordial matter power spectrum and cosmological parameters from arbitrarily complex forward models of galaxy surveys where all relevant statistics can be determined from numerical simulations, i.e. black-boxes. Our approach, which we call simulator expansion for likelihood-free inference (SELFI), builds upon approximate Bayesian computation using a novel effective likelihood, and upon the linearisation of black-box models around an expansion point. Consequently, we obtain simple "filter equations" for an effective posterior of the primordial power spectrum, and a straightforward scheme for cosmological parameter inference. We demonstrate that the workload is computationally tractable, fixed a priori, and perfectly parallel. As a proof of concept, we apply our framework to a realistic synthetic galaxy survey, with a data model accounting for physical structure formation and incomplete and noisy galaxy observations. In doing so, we show that the use of non-linear numerical models allows the galaxy power spectrum to be safely fitted up to at least $k_\mathrm{max} = 0.5$ $h$/Mpc, outperforming state-of-the-art backward-modelling techniques by a factor of $\sim 5$ in the number of modes used. The result is an unbiased inference of the primordial matter power spectrum across the entire range of scales considered, including a high-fidelity reconstruction of baryon acoustic oscillations. It translates into an unbiased and robust inference of cosmological parameters. Our results pave the path towards easy applications of likelihood-free simulation-based inference in cosmology. We have made our code pySELFI and our data products publicly available at http://pyselfi.florent-leclercq.eu.

astro-ph.CO↗

Inferring high redshift large-scale structure dynamics from the Lyman-alpha forest

One of the major science goals over the coming decade is to test fundamental physics with probes of the cosmic large-scale structure out to high redshift. Here we present a fully Bayesian approach to infer the three-dimensional cosmic matter distribution and its dynamics at $z>2$ from observations of the Lyman-$α$ forest. We demonstrate that the method recovers the unbiased mass distribution and the correct matter power spectrum at all scales. Our method infers the three-dimensional density field from a set of one-dimensional spectra, interpolating the information between the lines of sight. We show that our algorithm provides unbiased mass profiles of clusters, becoming an alternative for estimating cluster masses complementary to weak lensing or X-ray observations. The algorithm employs a Hamiltonian Monte Carlo method to generate realizations of initial and evolved density fields and the three-dimensional large-scale flow, revealing the cosmic dynamics at high redshift. The method correctly handles multi-modal parameter distributions, which allow constraining the physics of the intergalactic medium (IGM) with high accuracy. We performed several tests using realistic simulated quasar spectra to test and validate our method. Our results show that detailed and physically plausible inference of three-dimensional large-scale structures at high redshift has become feasible.

astro-ph.CO↗

Systematic-free inference of the cosmic matter density field from SDSS3-BOSS data

We perform an analysis of the three-dimensional cosmic matter density field traced by galaxies of the SDSS-III/BOSS galaxy sample. The systematic-free nature of this analysis is confirmed by two elements: the successful cross-correlation with the gravitational lensing observations derived from Planck 2018 data and the absence of bias at scales $k \simeq 10^{-3}-10^{-2}h$ Mpc$^{-1}$ in the a posteriori power spectrum of recovered initial conditions. Our analysis builds upon our algorithm for Bayesian Origin Reconstruction from Galaxies (BORG) and uses a physical model of cosmic structure formation to infer physically meaningful cosmic structures and their corresponding dynamics from deep galaxy observations. Our approach accounts for redshift-space distortions and light-cone effects inherent to deep observations. We also apply detailed corrections to account for known and unknown foreground contaminations, selection effects and galaxy biases. We obtain maps of residual, so far unexplained, systematic effects in the spectroscopic data of SDSS-III/BOSS. Our results show that unbiased and physically plausible models of the cosmic large scale structure can be obtained from present and next-generation galaxy surveys.

astro-ph.CO↗

Neural physical engines for inferring the halo mass distribution function

An ambitious goal in cosmology is to forward-model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ~Mpc scales, and the distribution of dark matter halos (used as a proxy for galaxies) in the same region. Here we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model. The method is based on the Bayesian analysis of simple, physically motivated, neural network-like architectures, which we denote as neural physical engines, and neural density estimation. As a result, we are able to sample the initial phases of the dark matter density field whilst inferring the parameters describing the halo mass distribution function, providing a fully Bayesian interpretation of both the initial dark matter density distribution and the neural bias model. We successfully run an upgraded BORG inference using our new likelihood and neural bias model with halo catalogues derived from full N-body simulations. We notice orders of magnitude improvement in modelling compared to classical biasing techniques.

astro-ph.CO↗

Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys

The treatment of unknown foreground contaminations will be one of the major challenges for galaxy clustering analyses of coming decadal surveys. These data contaminations introduce erroneous large-scale effects in recovered power spectra and inferred dark matter density fields. In this work, we present an effective solution to this problem in the form of a robust likelihood designed to account for effects due to unknown foreground and target contaminations. Conceptually, this robust likelihood marginalizes over the unknown large-scale contamination amplitudes. We showcase the effectiveness of this novel likelihood via an application to a mock SDSS-III data set subject to dust extinction contamination. In order to illustrate the performance of our proposed likelihood, we infer the underlying dark-matter density field and reconstruct the matter power spectrum, being maximally agnostic about the foregrounds. The results are compared to those of an analysis with a standard Poissonian likelihood, as typically used in modern large-scale structure analyses. While the standard Poissonian analysis yields excessive power for large-scale modes and introduces an overall bias in the power spectrum, our likelihood provides unbiased estimates of the matter power spectrum over the entire range of Fourier modes considered in this work. Further, we demonstrate that our approach accurately accounts for and corrects the effects of unknown foreground contaminations when inferring three-dimensional density fields. Robust likelihood approaches, as presented in this work, will be crucial to control unknown systematic error and maximize the outcome of the decadal surveys.

astro-ph.CO↗