arXiv subjects

Adam Amara

Publications and source records attributed to Adam Amara.

At least 55 records · Page 3Linked to original sources

arXiv2018

Wavelet based speckle suppression for exoplanet imaging - Application of a de-noising technique in the time domain

Context. High-contrast exoplanet imaging is a rapidly growing field as can be seen through the significant resources invested. In fact, the detection and characterization of exoplanets through direct imaging is featured at all major ground-based observatories. Aims. We aim to improve the signal-to-noise ratio (SNR) achievable for ground-based, adaptive-optics assisted exoplanet imaging by applying sophisticated post-processing algorithms. In particular, we investigate the benefits of including time domain information. Methods. We introduce a new speckle-suppression technique in data post-processing based on wavelet transformation. This technique explicitly considers the time domain in a given data set (specifically the frequencies of speckle variations and their time dependence) and allows us to filter-out speckle noise. We combine our wavelet-based algorithm with state-of-the-art principal component analysis (PCA) based PSF subtraction routines and apply it to archival data sets of known directly imaged exoplanets. The data sets were obtained in the L filter where the short integration times allow for a sufficiently high temporal sampling of the speckle variations. Results. We demonstrate that improvements in the peak SNR of up to forty to sixty percent can be achieved. We also show that, when combined with wavelet-denoising, the PCA PSF model requires systematically smaller numbers of components for the fit to achieve the highest SNR. The improvement potential is, however, data set dependent or, more specifically, closely linked to the field rotation available in a given data set: larger amounts of rotation allow for a better suppression of the speckle noise. Conclusions. We have demonstrated that by applying advanced data post-processing techniques, the contrast performance in archival high-contrast imaging data sets can be improved.

astro-ph.IM↗

arXiv2018

Gauge-Invariant Formalism of Cosmological Weak Lensing

We present the gauge-invariant formalism of cosmological weak lensing, accounting for all the relativistic effects due to the scalar, vector, and tensor perturbations at the linear order. While the light propagation is fully described by the geodesic equation, the relation of the photon wavevector to the physical quantities requires the specification of the frames, where they are defined. By constructing the local tetrad bases at the observer and the source positions, we clarify the relation of the weak lensing observables such as the convergence, the shear, and the rotation to the physical size and shape defined in the source rest-frame and the observed angle and redshift measured in the observer rest-frame. Compared to the standard lensing formalism, additional relativistic effects contribute to all the lensing observables. We explicitly verify the gauge-invariance of the lensing observables and compare our results to previous work. In particular, we demonstrate that even in the presence of the vector and tensor perturbations, the physical rotation of the lensing observables vanishes at the linear order, while the tetrad basis rotates along the light propagation compared to a FRW coordinate. Though the latter is often used as a probe of primordial gravitational waves, the rotation of the tetrad basis is indeed not a physical observable. We further clarify its relation to the E-B decomposition in weak lensing. Our formalism provides a transparent and comprehensive perspective of cosmological weak lensing.

astro-ph.CO↗

arXiv2018

Dark Matter Distribution Induced by a Cosmic String Wake in the Nonlinear Regime

We study the distribution of dark matter in the nonlinear regime in a model in which the primordial fluctuations include, in addition to the dominant primordial Gaussian fluctuations generated by the standard $ΛCDM$ cosmological model, the effects of a cosmic string wake set up at the time of equal matter and radiation, making use of cosmological $N$-body simulations. At early times the string wake leads to a planar overdensity of dark matter. We study how this non-Gaussian pattern of a cosmic string wake evolves in the presence of the Gaussian perturbations, making use of wavelet and ridgelet-like statistics specifically designed to extract string wake signals. At late times the Gaussian fluctuations disrupt the string wake. We find that for a string tension of $G μ= 10^{-7}$, a value just below the current observational limit, the effects of a string wake can be identified in the dark matter distribution, using the current level of the statistical analysis, down to a redshift of $z = 10$.

astro-ph.CO↗

arXiv2018

Fast Generation of Covariance Matrices for Weak Lensing

Upcoming weak lensing surveys will probe large fractions of the sky with unprecedented accuracy. To infer cosmological constraints, a large ensemble of survey simulations are required to accurately model cosmological observables and their covariances. We develop a parallelized multi-lens-plane pipeline called UFalcon, designed to generate full-sky weak lensing maps from lightcones within a minimal runtime. It makes use of L-PICOLA, an approximate numerical code, which provides a fast and accurate alternative to cosmological $N$-Body simulations. The UFalcon maps are constructed by nesting 2 simulations covering a redshift-range from $z=0.1$ to $1.5$ without replicating the simulation volume. We compute the convergence and projected overdensity maps for L-PICOLA in the lightcone or snapshot mode. The generation of such a map, including the L-PICOLA simulation, takes about 3 hours walltime on 220 cores. We use the maps to calculate the spherical harmonic power spectra, which we compare to theoretical predictions and to UFalcon results generated using the full $N$-Body code GADGET-2. We then compute the covariance matrix of the full-sky spherical harmonic power spectra using 150 UFalcon maps based on L-PICOLA in lightcone mode. We consider the PDF, the higher-order moments and the variance of the smoothed field variance to quantify the accuracy of the covariance matrix, which we find to be a few percent for scales $\ell \sim 10^2$ to $10^3$. We test the impact of this level of accuracy on cosmological constraints using an optimistic survey configuration, and find that the final results are robust to this level of uncertainty. The speed and accuracy of our developed pipeline provides a basis to also include further important features such as masking, varying noise and will allow us to compute covariance matrices for models beyond $Λ$CDM. [abridged]

astro-ph.CO↗

arXiv2017

Cosmic Shear with Einstein Rings

We explore a new technique to measure cosmic shear using Einstein rings. In Birrer et al. (2017), we showed that the detailed modelling of Einstein rings can be used to measure external shear to high precision. In this letter, we explore how a collection of Einstein rings can be used as a statistical probe of cosmic shear. We present a forecast of the cosmic shear information available in Einstein rings for different strong lensing survey configurations. We find that, assuming that the number density of Einstein rings in the COSMOS survey is representative, future strong lensing surveys should have a cosmological precision comparable to the current ground based weak lensing surveys. We discuss how this technique is complementary to the standard cosmic shear analyses since it is sensitive to different systematic and can be used for cross-calibration.

astro-ph.CO↗

arXiv2017

A PCA-based approach for subtracting thermal background emission in high-contrast imaging data

Ground-based observations at thermal infrared wavelengths suffer from large background radiation due to the sky, telescope and warm surfaces in the instrument. This significantly limits the sensitivity of ground-based observations at wavelengths longer than 3 microns. We analyzed this background emission in infrared high contrast imaging data, show how it can be modelled and subtracted and demonstrate that it can improve the detection of faint sources, such as exoplanets. We applied principal component analysis to model and subtract the thermal background emission in three archival high contrast angular differential imaging datasets in the M and L filter. We describe how the algorithm works and explain how it can be applied. The results of the background subtraction are compared to the results from a conventional mean background subtraction scheme. Finally, both methods for background subtraction are also compared by performing complete data reductions. We analyze the results from the M dataset of HD100546 qualitatively. For the M band dataset of beta Pic and the L band dataset of HD169142, which was obtained with an annular groove phase mask vortex vector coronagraph, we also calculate and analyze the achieved signal to noise (S/N). We show that applying PCA is an effective way to remove spatially and temporarily varying thermal background emission down to close to the background limit. The procedure also proves to be very successful at reconstructing the background that is hidden behind the PSF. In the complete data reductions, we find at least qualitative improvements for HD100546 and HD169142, however, we fail to find a significant increase in S/N of beta Pic b. We discuss these findings and argue that in particular datasets with strongly varying observing conditions or infrequently sampled sky background will benefit from the new approach.

astro-ph.IM↗

arXiv2017

Integrated cosmological probes: Concordance quantified

Assessing the consistency of parameter constraints derived from different cosmological probes is an important way to test the validity of the underlying cosmological model. In an earlier work [Nicola et al., 2017], we computed constraints on cosmological parameters for $Λ$CDM from an integrated analysis of CMB temperature anisotropies and CMB lensing from Planck, galaxy clustering and weak lensing from SDSS, weak lensing from DES SV as well as Type Ia supernovae and Hubble parameter measurements. In this work, we extend this analysis and quantify the concordance between the derived constraints and those derived by the Planck Collaboration as well as WMAP9, SPT and ACT. As a measure for consistency, we use the Surprise statistic [Seehars et al., 2014], which is based on the relative entropy. In the framework of a flat $Λ$CDM cosmological model, we find all data sets to be consistent with one another at a level of less than 1$σ$. We highlight that the relative entropy is sensitive to inconsistencies in the models that are used in different parts of the analysis. In particular, inconsistent assumptions for the neutrino mass break its invariance on the parameter choice. When consistent model assumptions are used, the data sets considered in this work all agree with each other and $Λ$CDM, without evidence for tensions.

astro-ph.CO↗

arXiv2017

PyCosmo: An Integrated Cosmological Boltzmann Solver

As wide-field surveys yield ever more precise measurements, cosmology has entered a phase of high precision requiring highly accurate and fast theoretical predictions. At the heart of most cosmological model predictions is a numerical solution of the Einstein-Boltzmann equations governing the evolution of linear perturbations in the Universe. We present PyCosmo, a new Python-based framework to solve this set of equations using a special pur- pose solver based on symbolic manipulations, automatic generation of C++ code and sparsity optimisation. The code uses a consistency relation of the field equations to adapt the time step and does not rely on physical approximations for speed-up. After reviewing the system of first-order linear homogeneous differential equations to be solved, we describe the numerical scheme implemented in PyCosmo. We then compare the predictions and performance of the code for the computation of the transfer functions of cosmological perturbations and compare it to existing cosmological Boltzmann codes. We find that we achieve comparable execution times for comparable accuracies. While PyCosmo does not yet have all the features of other codes, our approach is complementary to existing cosmological Boltzmann solvers and can be used as an independent test of their numerical solutions. The symbolic representation of the Einstein-Boltzmann equation system in PyCosmo provides a convenient interface for implementing extended cosmological models. We also discuss how the PyCosmo framework can also be used as a general framework to compute cosmological quantities as well as observables for both interactive and high-performance batch jobs applications. Information about the PyCosmo package and future code releases are available at http://www.cosmology.ethz.ch/research/software-lab.html.

astro-ph.CO↗

arXiv2017

The redshift distribution of cosmological samples: a forward modeling approach

Determining the redshift distribution $n(z)$ of galaxy samples is essential for several cosmological probes including weak lensing. For imaging surveys, this is usually done using photometric redshifts estimated on an object-by-object basis. We present a new approach for directly measuring the global $n(z)$ of cosmological galaxy samples, including uncertainties, using forward modeling. Our method relies on image simulations produced using UFig (Ultra Fast Image Generator) and on ABC (Approximate Bayesian Computation) within the $MCCL$ (Monte-Carlo Control Loops) framework. The galaxy population is modeled using parametric forms for the luminosity functions, spectral energy distributions, sizes and radial profiles of both blue and red galaxies. We apply exactly the same analysis to the real data and to the simulated images, which also include instrumental and observational effects. By adjusting the parameters of the simulations, we derive a set of acceptable models that are statistically consistent with the data. We then apply the same cuts to the simulations that were used to construct the target galaxy sample in the real data. The redshifts of the galaxies in the resulting simulated samples yield a set of $n(z)$ distributions for the acceptable models. We demonstrate the method by determining $n(z)$ for a cosmic shear like galaxy sample from the 4-band Subaru Suprime-Cam data in the COSMOS field. We also complement this imaging data with a spectroscopic calibration sample from the VVDS survey. We compare our resulting posterior $n(z)$ distributions to the one derived from photometric redshifts estimated using 36 photometric bands in COSMOS and find good agreement. This offers good prospects for applying our approach to current and future large imaging surveys.

astro-ph.CO↗

arXiv2017

Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions

Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis tool in cosmology and astrophysics. Its drawback, however, is a slow convergence rate. We propose a novel method, which we call qABC, to accelerate ABC with Quantile Regression. In this method, we create a model of quantiles of distance measure as a function of input parameters. This model is trained on a small number of simulations and estimates which regions of the prior space are likely to be accepted into the posterior. Other regions are then immediately rejected. This procedure is then repeated as more simulations are available. We apply it to the practical problem of estimation of redshift distribution of cosmological samples, using forward modelling developed in previous work. The qABC method converges to nearly same posterior as the basic ABC. It uses, however, only 20\% of the number of simulations compared to basic ABC, achieving a fivefold gain in execution time for our problem. For other problems the acceleration rate may vary; it depends on how close the prior is to the final posterior. We discuss possible improvements and extensions to this method.

astro-ph.CO↗

arXiv2017

Cosmic shear calibration with forward modeling

Weak Gravitational Lensing is a powerful probe of the dark sector of the Universe. One of the main challenges for this technique is the treatment of systematics in the measurement of cosmic shear from galaxy shapes. In an earlier work, Refregier & Amara (2014) have proposed the Monte Carlo Control Loops (MCCL) to overcome these effects using a forward modeling approach. We focus here on one of the control loops in this method, the task of which is the calibration of the shear measurement. For this purpose, we first consider the requirements on the shear systematics for a given survey and propagate them to different systematics terms. We use two one-point statistics to calibrate the shear measurement and six further one-point statistics as diagnostics. We also propagate the systematics levels that we estimate from the one-point functions to the two-point functions for the different systematic error sources. This allows us to assess the consistency between the systematics levels measured in different ways. To test the method, we construct synthetic sky surveys with an area of 1,700 deg$^2$. With some simplifying assumptions, we are able to meet the requirements on the shear calibration for this survey configuration. Furthermore, we account for the total residual shear systematics in terms of the contributing sources. We discuss how this MCCL framework can be applied to current and future weak lensing surveys.

astro-ph.CO↗

arXiv2017

Cosmological model discrimination with Deep Learning

We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our method to be able to distinguish between five models, which were chosen to lie along the $σ_8$ - $Ω_m$ degeneracy, and have nearly the same two-point statistics. We design and implement a Deep Convolutional Neural Network (DCNN) which learns the relation between five cosmological models and the mass maps they generate. We develop a new training strategy which ensures the good performance of the network for high levels of noise. We compare the performance of this approach to commonly used non-Gaussian statistics, namely the skewness and kurtosis of the convergence maps. We find that our implementation of DCNN outperforms the skewness and kurtosis statistics, especially for high noise levels. The network maintains the mean discrimination efficiency greater than $85\%$ even for noise levels corresponding to ground based lensing observations, while the other statistics perform worse in this setting, achieving efficiency less than $70\%$. This demonstrates the ability of CNN-based methods to efficiently break the $σ_8$ - $Ω_m$ degeneracy with weak lensing mass maps alone. We discuss the potential of this method to be applied to the analysis of real weak lensing data and other datasets.

astro-ph.CO↗

arXiv2017

M Dwarf Exoplanet Surface Density Distribution: A Log-Normal Fit from 0.07-400 AU

We fit a log-normal function to the M dwarf orbital surface density distribution of gas giant planets, over the mass range 1-10 times that of Jupiter, from 0.07-400 AU. We use a Markov Chain Monte Carlo approach to explore the likelihoods of various parameter values consistent with point estimates of the data given our assumed functional form. This fit is consistent with radial velocity, microlensing, and direct imaging observations, is well-motivated from theoretical and phenomenological viewpoints, and makes predictions of future surveys. We present probability distributions for each parameter as well as a Maximum Likelihood Estimate solution. We suggest this function makes more physical sense than other widely used functions, and explore the implications of our results on the design of future exoplanet surveys.

astro-ph.EP↗

arXiv2017

Core or cusps: The central dark matter profile of a redshift one strong lensing cluster with a bright central image

We report on SPT-CLJ2011-5228, a giant system of arcs created by a cluster at $z=1.06$. The arc system is notable for the presence of a bright central image. The source is a Lyman Break galaxy at $z_s=2.39$ and the mass enclosed within the 14 arc second radius Einstein ring is $10^{14.2}$ solar masses. We perform a full light profile reconstruction of the lensed images to precisely infer the parameters of the mass distribution. The brightness of the central image demands that the central total density profile of the lens be shallow. By fitting the dark matter as a generalized Navarro-Frenk-White profile---with a free parameter for the inner density slope---we find that the break radius is $270^{+48}_{-76}$ kpc, and that the inner density falls with radius to the power $-0.38\pm0.04$ at 68 percent confidence. Such a shallow profile is in strong tension with our understanding of relaxed cold dark matter halos; dark matter only simulations predict the inner density should fall as $r^{-1}$. The tension can be alleviated if this cluster is in fact a merger; a two halo model can also reconstruct the data, with both clumps (density going as $r^{-0.8}$ and $r^{-1.0}$) much more consistent with predictions from dark matter only simulations. At the resolution of our Dark Energy Survey imaging, we are unable to choose between these two models, but we make predictions for forthcoming Hubble Space Telescope imaging that will decisively distinguish between them.

astro-ph.CO↗

arXiv2017

Lensing substructure quantification in RXJ1131-1231: A 2 keV lower bound on dark matter thermal relic mass

We study the substructure content of the strong gravitational lens RXJ1131-1231 through a forward modelling approach that relies on generating an extensive suite of realistic simulations. We use a semi-analytic merger tree prescription that allows us to stochastically generate substructure populations whose properties depend on the dark matter particle mass. These synthetic halos are then used as lenses to produce realistic mock images that have the same features, e.g. luminous arcs, quasar positions, instrumental noise and PSF, as the data. We then analyse the data and the simulations in the same way with summary statistics that are sensitive to the signal being targeted and are able to constrain models of dark matter statistically using Approximate Bayesian Computing (ABC) techniques. In this work, we focus on the thermal relic mass estimate and fix the semi-analytic descriptions of the substructure evolution based on recent literature. We are able, based on the HST data for RXJ1131-1231, to rule out a warm dark matter thermal relic mass below 2 keV at the 2$σ$ confidence level.

astro-ph.CO↗

arXiv2017

Line-of-sight effects in strong lensing: Putting theory into practice

We present a simple method to accurately infer line of sight (LOS) integrated lensing effects for galaxy scale strong lens systems through image reconstruction. Our approach enables us to separate weak lensing LOS effects from the main strong lens deflector. We test our method using mock data and show that strong lens systems can be accurate probes of cosmic shear with a precision on the shear terms of $\pm 0.003$ (statistical error) for an HST-like dataset. We apply our formalism to reconstruct the lens COSMOS 0038+4133 and its LOS. In addition, we estimate the LOS properties with a halo-rendering estimate based on the COSMOS field galaxies and a galaxy-halo connection. The two approaches are independent and complementary in their information content. We find that when estimating the convergence at the strong lens system, performing a joint analysis improves the measure by a factor of two compared to a halo model only analysis. Furthermore the constraints of the strong lens reconstruction lead to tighter constraints on the halo masses of the LOS galaxies. Joint constraints of multiple strong lens systems may add valuable information to the galaxy-halo connection and may allow independent weak lensing shear measurement calibrations.

astro-ph.CO↗

arXiv2017

A halo model for cosmological neutral hydrogen: abundances and clustering

We extend the results of previous analyses towards constraining the abundance and clustering of post-reionization ($z \sim 0-5$) neutral hydrogen (HI) systems using a halo model framework. We work with a comprehensive HI dataset including the small-scale clustering, column density and mass function of HI galaxies at low redshifts, intensity mapping measurements at intermediate redshifts and the UV/optical observations of Damped Lyman Alpha (DLA) systems at higher redshifts. We use a Markov Chain Monte Carlo (MCMC) approach to constrain the parameters of the best-fitting models, both for the HI-halo mass relation and the HI radial density profile. We find that a radial exponential profile results in a good fit to the low-redshift HI observations, including the clustering and the column density distribution. The form of the profile is also found to match the high-redshift DLA observations, when used in combination with a three-parameter HI-halo mass relation and a redshift evolution in the HI concentration. The halo model predictions are in good agreement with the observed HI surface density profiles of low-redshift galaxies, and the general trends in the the impact parameter and covering fraction observations of high-redshift DLAs. We provide convenient tables summarizing the best-fit halo model predictions.

astro-ph.CO↗

arXiv2017

Integrated Cosmological Probes: Extended Analysis

Recent progress in cosmology has relied on combining different cosmological probes. In earlier work, we implemented an integrated approach to cosmology where the probes are combined into a common framework at the map level. This has the advantage of taking full account of the correlations between the different probes, to provide a stringent test of systematics and of the validity of the cosmological model. We extend this analysis to include not only CMB temperature, galaxy clustering, weak lensing from SDSS but also CMB lensing, weak lensing from the DES SV survey, Type Ia SNe and $H_{0}$ measurements. This yields 12 auto- and cross-power spectra as well as background probes. Furthermore, we extend the treatment of systematic uncertainties. For $Λ$CDM, we find results that are consistent with our earlier work. Given our enlarged data set and systematics treatment, this confirms the robustness of our analysis and results. Furthermore, we find that our best-fit cosmological model gives a good fit to the data we consider with no signs of tensions within our analysis. We also find our constraints to be consistent with those found by WMAP9, SPT and ACT and the KiDS weak lensing survey. Comparing with the Planck Collaboration results, we see a broad agreement, but there are indications of a tension from the marginalized constraints in most pairs of cosmological parameters. Since our analysis includes CMB temperature Planck data at $10 < \ell < 610$, the tension appears to arise between the Planck high$-\ell$ and the other measurements. Furthermore, we find the constraints on the probe calibration parameters to be in agreement with expectations, showing that the data sets are mutually consistent. In particular, this yields a confirmation of the amplitude calibration of the weak lensing measurements from SDSS, DES SV and Planck CMB lensing from our integrated analysis. [abridged]

astro-ph.CO↗