7,568 research outputs found
Deep learning dark matter map reconstructions from DES SV weak lensing data
No full textInternational audienceWe 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 × 10^5 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 DeepMass^1 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
Redshift distributions of galaxies in the Dark Energy Survey science verification shear catalogue and implications for weak lensing
No full textWe present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model- or machine learning-based photometric redshift methods—annz2, bpz calibrated against BCC-Ufig simulations, skynet, and tpz—are analyzed. For training, calibration, and testing of these methods, we construct a catalogue of spectroscopically confirmed galaxies matched against DES SV data. The performance of the methods is evaluated against the matched spectroscopic catalogue, focusing on metrics relevant for weak lensing analyses, with additional validation against COSMOS photo-z ’s. From the galaxies in the DES SV shear catalogue, which have mean redshift 0.72±0.01 over the range 0.
Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing
No full textWe present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model- or machine learning-based photometric redshift methods—annz2, bpz calibrated against BCC-Ufig simulations, skynet, and tpz—are analyzed. For training, calibration, and testing of these methods, we construct a catalogue of spectroscopically confirmed galaxies matched against DES SV data. The performance of the methods is evaluated against the matched spectroscopic catalogue, focusing on metrics relevant for weak lensing analyses, with additional validation against COSMOS photo-z’s. From the galaxies in the DES SV shear catalogue, which have mean redshift 0.72±0.01 over the range 0.
The DES Science Verification weak lensing shear catalogues
No full textWe present weak lensing shear catalogues for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey (DES). We describe our object selection, point spread function estimation and shear measurement procedures using two independent shear pipelines, IM3SHAPE and NGMIX, which produce catalogues of 2.12 million and 3.44 million galaxies respectively. We detail a set of null tests for the shear measurements and find that they pass the requirements for systematic errors at the level necessary for weak lensing science applications using the SV data. We also discuss some of the planned algorithmic improvements that will be necessary to produce sufficiently accurate shear catalogues for the full 5-year DES, which is expected to cover 5000 square degrees
The DES Science Verification weak lensing shear catalogues
No full textWe present weak lensing shear catalogues for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey (DES). We describe our object selection, point spread function estimation and shear measurement procedures using two independent shear pipelines, im3shape and ngmix, which produce catalogues of 2.12 million and 3.44 million galaxies, respectively. We detail a set of null tests for the shear measurements and find that they pass the requirements for systematic errors at the level necessary for weak lensing science applications using the SV data. We also discuss some of the planned algorithmic improvements that will be necessary to produce sufficiently accurate shear catalogues for the full 5-yr DES, which is expected to cover 5000 square degrees.</p
Joint measurement of lensing–galaxy correlations using SPT and DES SV data
No full textWe measure the correlation of galaxy lensing and cosmic microwave background lensing with a set of galaxies expected to trace the matter density field. The measurements are performed using pre-survey Dark Energy Survey (DES) Science Verification optical imaging data and millimetre-wave data from the 2500 sq. deg. South Pole Telescope Sunyaev–Zel'dovich (SPT-SZ) survey. The two lensing–galaxy correlations are jointly fit to extract constraints on cosmological parameters, constraints on the redshift distribution of the lens galaxies, and constraints on the absolute shear calibration of DES galaxy-lensing measurements. We show that an attractive feature of these fits is that they are fairly insensitive to the clustering bias of the galaxies used as matter tracers. The measurement presented in this work confirms that DES and SPT data are consistent with each other and with the currently favoured ? cold dark matter cosmological model. It also demonstrates that joint lensing–galaxy correlation measurement considered here contains a wealth of information that can be extracted using current and future surveys
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Joint measurement of lensing-galaxy correlations using SPT and DES SV data
No full textWe measure the correlation of galaxy lensing and cosmic microwave background lensing with a set of galaxies expected to trace the matter density field. The measurements are performed using pre-survey Dark Energy Survey (DES) Science Verification optical imaging data and millimetre-wave data from the 2500 sq. deg. South Pole Telescope Sunyaev-Zel'dovich (SPT-SZ) survey. The two lensing-galaxy correlations are jointly fit to extract constraints on cosmological parameters, constraints on the redshift distribution of the lens galaxies, and constraints on the absolute shear calibration of DES galaxy-lensing measurements. We show that an attractive feature of these fits is that they are fairly insensitive to the clustering bias of the galaxies used as matter tracers. The measurement presented in this work confirms that DES and SPT data are consistent with each other and with the currently favoured ∩ cold dark matter cosmological model. It also demonstrates that joint lensing-galaxy correlation measurement considered here contains a wealth of information that can be extracted using current and future surveys.</p
Consistent cosmic shear in the face of systematics:a B-mode analysis of KiDS-450, DES-SV and CFHTLenS
No full textWe analyse three public cosmic shear surveys; the Kilo-Degree Survey (KiDS-450), the Dark Energy Survey (DES-SV) and the Canada France Hawaii Telescope Lensing Survey (CFHTLenS). Adopting the COSEBIs statistic to cleanly and completely separate the lensing E-modes from the non-lensing B-modes, we detect B-modes in KiDS-450 and CFHTLenS at the level of about 2.7 . For DES- SV we detect B-modes at the level of 2.8 in a non-tomographic analysis, increasing to a 5.5 B-mode detection in a tomographic analysis. In order to understand the origin of these detected B-modes we measure the B-mode signature of a range of different simulated systematics including PSF leakage, random but correlated PSF modelling errors, camera-based additive shear bias and photometric redshift selection bias. We show that any correlation between photometric-noise and the relative orientation of the galaxy to the point-spread-function leads to an ellipticity selection bias in tomographic analyses. This work therefore introduces a new systematic for future lensing surveys to consider. We find that the B-modes in DES-SV appear similar to a superposition of the B-mode signatures from all of the systematics simulated. The KiDS-450 and CFHTLenS B-mode measurements show features that are consistent with a repeating additive shear bias
Bounded model checking of multi-threaded c programs via lazy sequentialization
No full textBounded model checking (BMC) has successfully been used for many practical program verification problems, but concurrency still poses a challenge. Here we describe a new approach to BMC of sequentially consistent C programs using POSIX threads. Our approach first translates a multi-threaded C program into a nondeterministic sequential C program that preserves reachability for all round-robin schedules with a given bound on the number of rounds. It then re-uses existing high-performance BMC tools as backends for the sequential verification problem. Our translation is carefully designed to introduce very small memory overheads and very few sources of nondeterminism, so that it produces tight SAT/SMT formulae, and is thus very effective in practice: our prototype won the concurrency category of SV-COMP14. It solved all verification tasks successfully and was 30x faster than the best tool with native concurrency handling.<br/
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
No full textInternational audienceIn many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect inference of cosmological parameters, including the nature of dark matter and dark energy, or create artificial model tensions. Likelihood-free inference covers a novel family of methods to rigorously estimate posterior distributions of parameters using forward modelling of mock data. We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) Science Verification data, using neural data compression of weak lensing map summary statistics. We explore combinations of the power spectra, peak counts, and neural compressed summaries of the lensing mass map using deep convolution neural networks. We demonstrate methods to validate the inference process, for both the data modelling and the probability density estimation steps. Likelihood-free inference provides a robust and scalable alternative for rigorous large-scale cosmological inference with galaxy survey data (for DES, Euclid, and LSST). We have made our simulated lensing maps publicly available
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