scholarly journals Mapping dark matter and finding filaments: calibration of lensing analysis techniques on simulated data

2020 ◽  
Vol 496 (3) ◽  
pp. 3973-3990
Author(s):  
Sut-Ieng Tam ◽  
Richard Massey ◽  
Mathilde Jauzac ◽  
Andrew Robertson
Keyword(s):  
Gravitational Lensing ◽  
Simulated Data ◽  
Line Of Sight ◽  
Radial Density ◽  
Density Profiles ◽  
Analysis Techniques ◽  
Scientific Goal ◽  
Mass Mapping ◽  
Mapping Techniques ◽  
Suppress Noise

ABSTRACT We quantify the performance of mass mapping techniques on mock imaging and gravitational lensing data of galaxy clusters. The optimum method depends upon the scientific goal. We assess measurements of clusters’ radial density profiles, departures from sphericity, and their filamentary attachment to the cosmic web. We find that mass maps produced by direct (KS93) inversion of shear measurements are unbiased, and that their noise can be suppressed via filtering with mrlens. Forward-fitting techniques, such as lenstool, suppress noise further, but at a cost of biased ellipticity in the cluster core and overestimation of mass at large radii. Interestingly, current searches for filaments are noise-limited by the intrinsic shapes of weakly lensed galaxies, rather than by the projection of line-of-sight structures. Therefore, space-based or balloon-based imaging surveys that resolve a high density of lensed galaxies could soon detect one or two filaments around most clusters.

scholarly journals Testing Gravity using Void Profiles

2014 ◽  
Vol 11 (S308) ◽  
pp. 555-560 ◽  
Author(s):  
Yan-Chuan Cai ◽  
Nelson Padilla ◽  
Baojiu Li
Keyword(s):  
Dark Matter ◽  
Gravitational Lensing ◽  
Modified Gravity ◽  
Large Scale ◽  
Line Of Sight ◽  
Number Density ◽  
Density Profiles ◽  
Photometric Redshift ◽  
Redshift Survey ◽  
Near Future

AbstractWe investigate void properties inf(R)models using N-body simulations, focusing on their differences from General Relativity (GR) and their detectability. In the Hu-Sawickif(R)modified gravity (MG) models, the halo number density profiles of voids are not distinguishable from GR. In contrast, the samef(R)voids are more empty of dark matter, and their profiles are steeper. This can in principle be observed by weak gravitational lensing of voids, for which the combination of a spectroscopic redshift and a lensing photometric redshift survey over the same sky is required. Neglecting the lensing shape noise, thef(R)model parameter amplitudesfR0=10-5and 10-4may be distinguished from GR using the lensing tangential shear signal around voids by 4 and 8 σ for a volume of 1 (Gpc/h)3. The line-of-sight projection of large-scale structure is the main systematics that limits the significance of this signal for the near future wide angle and deep lensing surveys. For this reason, it is challenging to distinguishfR0=10-6from GR. We expect that this can be overcome with larger volume. The halo void abundance being smaller and the steepening of dark matter void profiles inf(R)models are unique features that can be combined to break the degeneracy betweenfR0and σ8.

scholarly journals Sparse Bayesian mass mapping with uncertainties: local credible intervals

2019 ◽  
Vol 492 (1) ◽  
pp. 394-404 ◽  
Author(s):  
M A Price ◽  
X Cai ◽  
J D McEwen ◽  
M Pereyra ◽  
T D Kitching ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Gravitational Lensing ◽  
Large Scale ◽  
Approximation Error ◽  
Stage Iv ◽  
Statistical Framework ◽  
Credible Intervals ◽  
Mass Mapping ◽  
Mapping Techniques ◽  
Highly Correlated

ABSTRACT Until recently, mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical framework upon which to quantify reconstruction uncertainties, without making strong assumptions of Gaussianity. In previous work, we presented a sparse hierarchical Bayesian formalism for convergence reconstruction that addresses this shortcoming. Here, we draw on the concept of local credible intervals (cf. Bayesian error bars) as an extension of the uncertainty quantification techniques previously detailed. These uncertainty quantification techniques are benchmarked against those recovered via Px-MALA – a state-of-the-art proximal Markov chain Monte Carlo (MCMC) algorithm. We find that, typically, our recovered uncertainties are everywhere conservative (never underestimate the uncertainty, yet the approximation error is bounded above), of similar magnitude and highly correlated with those recovered via Px-MALA. Moreover, we demonstrate an increase in computational efficiency of $\mathcal {O}(10^6)$ when using our sparse Bayesian approach over MCMC techniques. This computational saving is critical for the application of Bayesian uncertainty quantification to large-scale stage IV surveys such as LSST and Euclid.

scholarly journals Sparse Bayesian mass-mapping with uncertainties: Full sky observations on the celestial sphere

2020 ◽  
Vol 500 (4) ◽  
pp. 5436-5452
Author(s):  
M A Price ◽  
J D McEwen ◽  
L Pratley ◽  
T D Kitching
Keyword(s):  
Gravitational Lensing ◽  
Stage Iv ◽  
Small Scale ◽  
Data Sets ◽  
Statistical Interpretation ◽  
Dark Matter Mass ◽  
Spherical Mass ◽  
Mass Mapping ◽  
Mapping Techniques ◽  
Celestial Sphere

ABSTRACT To date weak gravitational lensing surveys have typically been restricted to small fields of view, such that the flat-sky approximation has been sufficiently satisfied. However, with Stage IV surveys (e.g. LSST and Euclid) imminent, extending mass-mapping techniques to the sphere is a fundamental necessity. As such, we extend the sparse hierarchical Bayesian mass-mapping formalism presented in previous work to the spherical sky. For the first time, this allows us to construct maximum a posteriori spherical weak lensing dark-matter mass-maps, with principled Bayesian uncertainties, without imposing or assuming Gaussianty. We solve the spherical mass-mapping inverse problem in the analysis setting adopting a sparsity promoting Laplace-type wavelet prior, though this theoretical framework supports all log-concave posteriors. Our spherical mass-mapping formalism facilitates principled statistical interpretation of reconstructions. We apply our framework to convergence reconstruction on high resolution N-body simulations with pseudo-Euclid masking, polluted with a variety of realistic noise levels, and show a significant increase in reconstruction fidelity compared to standard approaches. Furthermore, we perform the largest joint reconstruction to date of the majority of publicly available shear observational data sets (combining DESY1, KiDS450, and CFHTLens) and find that our formalism recovers a convergence map with significantly enhanced small-scale detail. Within our Bayesian framework we validate, in a statistically rigorous manner, the community’s intuition regarding the need to smooth spherical Kaiser-Squires estimates to provide physically meaningful convergence maps. Such approaches cannot reveal the small-scale physical structures that we recover within our framework.

scholarly journals Radial density profiles of PNe halos from numerical models of mass-loss history

2011 ◽  
Vol 7 (S283) ◽  
pp. 518-519
Author(s):  
Juan-Luis Verbena ◽  
Klaus-Peter Schröder ◽  
Astrid Wachter
Keyword(s):  
Mass Loss ◽  
Observational Data ◽  
Numerical Models ◽  
Stellar Mass ◽  
Line Of Sight ◽  
Red Giants ◽  
Agb Stars ◽  
Radial Density ◽  
Density Profiles ◽  
Outflow Velocity

AbstractWe review the stellar mass loss of red giants and tip-AGB objects analizing the variation in the outflow velocity for different mass models (Wachter et al. 2002). We approach the superwind problem and see the evolution of tip-AGB stars via previously made mass-loss histories that are consistent with the Weidemann initial-final mass relationship (for carbon-rich stars). Finally density profiles are produced from these mass-loss histories, and the corresponding line-of-sight integration is compared with observational data (Phillips et al. 2009). We note the resemblance between the results obtained with our models and the observational data. We are thus able to reproduce the general trends of the emission from simple models (see Verbena et al. 2011).

scholarly journals MACS J0416.1–2403: Impact of line-of-sight structures on strong gravitational lensing modelling of galaxy clusters

2018 ◽  
Vol 614 ◽  
pp. A8 ◽  
Author(s):  
G. Chirivì ◽  
S. H. Suyu ◽  
C. Grillo ◽  
A. Halkola ◽  
I. Balestra ◽  
...  
Keyword(s):  
Galaxy Clusters ◽  
Gravitational Lensing ◽  
Line Of Sight ◽  
Mass Distributions ◽  
Strong Gravitational Lensing ◽  
Cluster Mass ◽  
Critical Curves ◽  
Single Lens ◽  
Best Fitting ◽  
Modelling Techniques

Exploiting the powerful tool of strong gravitational lensing by galaxy clusters to study the highest-redshift Universe and cluster mass distributions relies on precise lens mass modelling. In this work, we aim to present the first attempt at modelling line-of-sight (LOS) mass distribution in addition to that of the cluster, extending previous modelling techniques that assume mass distributions to be on a single lens plane. We have focussed on the Hubble Frontier Field cluster MACS J0416.1–2403, and our multi-plane model reproduces the observed image positions with a rms offset of ~0.′′53. Starting from this best-fitting model, we simulated a mock cluster that resembles MACS J0416.1–2403 in order to explore the effects of LOS structures on cluster mass modelling. By systematically analysing the mock cluster under different model assumptions, we find that neglecting the lensing environment has a significant impact on the reconstruction of image positions (rms ~0.′′3); accounting for LOS galaxies as if they were at the cluster redshift can partially reduce this offset. Moreover, foreground galaxies are more important to include into the model than the background ones. While the magnification factor of the lensed multiple images are recovered within ~10% for ~95% of them, those ~5% that lie near critical curves can be significantly affected by the exclusion of the lensing environment in the models. In addition, LOS galaxies cannot explain the apparent discrepancy in the properties of massive sub-halos between MACS J0416.1–2403 and N-body simulated clusters. Since our model of MACS J0416.1–2403 with LOS galaxies only reduced modestly the rms offset in the image positions, we conclude that additional complexities would be needed in future models of MACS J0416.1–2403.

scholarly journals Deep learning dark matter map reconstructions from DES SV weak lensing data

2020 ◽  
Vol 492 (4) ◽  
pp. 5023-5029 ◽  
Author(s):  
Niall Jeffrey ◽  
François Lanusse ◽  
Ofer Lahav ◽  
Jean-Luc Starck
Keyword(s):  
Dark Matter ◽  
Deep Learning ◽  
Power Spectrum ◽  
Cosmological Parameters ◽  
Simulated Data ◽  
Wiener Filtering ◽  
Weak Lensing ◽  
Gaussian Shape ◽  
Non Linear ◽  
Mass Mapping

ABSTRACT We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network with a U-Net-based architecture on over 3.6 × 105 simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created dark energy survey science verification (DES SV) map as an approximation of the posterior mean P(κ|γ) of the convergence given observed shear. Our DeepMass1 method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realizations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean square error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering, with the optimal known power spectrum, still gives a worse MSE than our generalized method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.

Sign in / Sign up

Export Citation Format

Share Document