The phase-space structure of nearby dark matter as constrained by the SDSS

Leclercq, Florent; Jasche, Jens; Lavaux, Guilhem; Wandelt, B. D.; Percival, Will J.

doi:10.1088/1475-7516/2017/06/049

Cited by 37 publications

(47 citation statements)

References 88 publications

(220 reference statements)

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“…Some examples of secondary projects derived from these results aimed at studying dark matter voids in the galaxy distribution (Leclercq et al 2015a), the phase-space distribution of matter in the SDSS survey (Leclercq et al 2017), properties of the population of Active Galactic Nuclei (AGN) (Porqueres et al 2018) as well as gravitational screening mechanisms (Desmond et al 2018a,b) and cosmic magnetic fields (Hutschenreuter et al 2018).…”

Section: The Borg Algorithmmentioning

confidence: 99%

Physical Bayesian modelling of the non-linear matter distribution: New insights into the nearby universe

Jasche

Lavaux

2019

A&A

124

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Accurate analyses of present and next-generation cosmological galaxy surveys require new ways to handle effects of non-linear gravitational structure formation processes in data. To address these needs we present an extension of our previously developed algorithm for Bayesian Origin Reconstruction from Galaxies to analyse matter clustering at non-linear scales in observations. This is achieved by incorporating a numerical particle mesh model of gravitational structure formation into our Bayesian inference framework. The algorithm simultaneously infers the three-dimensional primordial matter fluctuations from which present non-linear observations formed and provides reconstructions of velocity fields and structure formation histories. The physical forward modelling approach automatically accounts for the non-Gaussian features in gravitationally evolved matter density fields and addresses the redshift space distortion problem associated with peculiar motions of observed galaxies. Our algorithm employs a hierarchical Bayes approach to jointly account for various observational effects, such as unknown galaxy biases, selection effects, and observational noise. Corresponding parameters of the data model are marginalized out via a sophisticated Markov Chain Monte Carlo approach relying on a combination of a multiple block sampling framework and an efficient implementation of a Hamiltonian Monte Carlo sampler. We demonstrate the performance of the method by applying it to the 2M++ galaxy compilation, tracing the matter distribution of the Nearby Universe. We show accurate and detailed inferences of the three-dimensional non-linear dark matter distribution of the Nearby Universe. As exemplified in the case of the Coma cluster, our method provides complementary mass estimates that are compatible with those obtained from weak lensing and X-ray observations. For the first time, we also present a reconstruction of the vorticity of the non-linear velocity field from observations. In summary, our method provides plausible and very detailed inferences of the dark matter and velocity fields of our cosmic neighbourhood.

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Section: The Borg Algorithmmentioning

confidence: 99%

Physical Bayesian modelling of the non-linear matter distribution: New insights into the nearby universe

Jasche

Lavaux

2019

A&A

124

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“…Such model model of large scale structures as the one we have derived have many applications for the study and observation of the Universe through different instruments. Some of the applications that were considered in the past are cosmic-web identifications and characterization (Leclercq et al 2015b(Leclercq et al , 2017, cosmic voids properties…”

Section: Resultsmentioning

confidence: 99%

Protein–Lipid Interactions Stabilize the Oligomeric State of BOR1p from Saccharomyces cerevisiae

et al. 2019

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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 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.

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“…It also allows for novel analyses of cosmological data from fully non-linear simulator-based models, as required e.g. for the cosmic web (see the discussions in Leclercq, Jasche & Wandelt, 2015;Leclercq et al ., 2016;Leclercq et al ., 2017). Other applications may include the cosmic microwave background, weak gravitational lensing or intensity mapping experiments.…”

Section: Discussionmentioning

confidence: 99%

Bayesian optimization for likelihood-free cosmological inference

Leclercq

2018

Phys. Rev. D

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Many cosmological models have only a finite number of parameters of interest, but a very expensive datagenerating process and an intractable likelihood function. We address the problem of performing likelihoodfree Bayesian inference from such black-box simulation-based models, under the constraint of a very limited simulation budget (typically a few thousand). To do so, we adopt an approach based on the likelihood of an alternative parametric model. Conventional approaches to approximate Bayesian computation such as likelihood-free rejection sampling are impractical for the considered problem, due to the lack of knowledge about how the parameters affect the discrepancy between observed and simulated data. As a response, we make use of a strategy previously developed in the machine learning literature (Bayesian optimisation for likelihood-free inference, bolfi), which combines Gaussian process regression of the discrepancy to build a surrogate surface with Bayesian optimisation to actively acquire training data. We extend the method by deriving an acquisition function tailored for the purpose of minimising the expected uncertainty in the approximate posterior density, in the parametric approach. The resulting algorithm is applied to the problems of summarising Gaussian signals and inferring cosmological parameters from the Joint Lightcurve Analysis supernovae data. We show that the number of required simulations is reduced by several orders of magnitude, and that the proposed acquisition function produces more accurate posterior approximations, as compared to common strategies.

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The phase-space structure of nearby dark matter as constrained by the SDSS

Cited by 37 publications

References 88 publications

Physical Bayesian modelling of the non-linear matter distribution: New insights into the nearby universe

Physical Bayesian modelling of the non-linear matter distribution: New insights into the nearby universe

Protein–Lipid Interactions Stabilize the Oligomeric State of BOR1p from Saccharomyces cerevisiae

Bayesian optimization for likelihood-free cosmological inference

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