Search

The nature of dark matter, dark energy and large-scale gravity pose some of the most pressing questions in cosmology today. These fundamental questions require highly precise measurements, and a number of wide-field spectroscopic survey instruments are being designed to meet this requirement. A key component in these experiments is the development of a simulation tool to forecast science performance, define requirement flow-downs, optimize implementation, demonstrate feasibility, and prepare for exploitation. We present SPOKES (SPectrOscopic KEn Simulation), an end-to-end simulation facility for spectroscopic cosmological surveys designed to address this challenge. SPOKES is based on an integrated infrastructure, modular function organization, coherent data handling and fast data access. These key features allow reproducibility of pipeline runs, enable ease of use and provide flexibility to update functions within the pipeline. The cyclic nature of the pipeline offers the possibility to make the science output an efficient measure for design optimization and feasibility testing. We present the architecture, first science, and computational performance results of the simulation pipeline. The framework is general, but for the benchmark tests, we use the Dark Energy Spectrometer (DESpec), one of the early concepts for the upcoming project, the Dark Energy Spectroscopic Instrument (DESI). We discuss how the SPOKES framework enables a rigorous process to optimize and exploit spectroscopic survey experiments in order to derive high-precision cosmological measurements optimally.

The Python programming language is becoming increasingly popular for scientific applications due to its simplicity, versatility, and the broad range of its libraries. A drawback of this dynamic language, however, is its low runtime performance which limits its applicability for large simulations and for the analysis of large data sets, as is common in astrophysics and cosmology. While various frameworks have been developed to address this limitation, most focus on covering the complete language set, and either force the user to alter the code or are not able to reach the full speed of an optimised native compiled language. In order to combine the ease of Python and the speed of C++, we developed HOPE, a specialised Python just-in-time (JIT) compiler designed for numerical astrophysical applications. HOPE focuses on a subset of the language and is able to translate Python code into C++ while performing numerical optimisation on mathematical expressions at runtime. To enable the JIT compilation, the user only needs to add a decorator to the function definition. We assess the performance of HOPE by performing a series of benchmarks and compare its execution speed with that of plain Python, C++ and the other existing frameworks. We find that HOPE improves the performance compared to plain Python by a factor of 2 to 120, achieves speeds comparable to that of C++, and often exceeds the speed of the existing solutions. We discuss the differences between HOPE and the other frameworks, as well as future extensions of its capabilities. The fully documented HOPE package is available at http://hope.phys.ethz.ch and is published under the GPLv3 license on PyPI and GitHub., Astronomy and Computing, 10, ISSN:2213-1337

We study the benefits and limits of parallelised Markov chain Monte Carlo (MCMC) sampling in cosmology. MCMC methods are widely used for the estimation of cosmological parameters from a given set of observations and are typically based on the Metropolis–Hastings algorithm. Some of the required calculations can however be computationally intensive, meaning that a single long chain can take several hours or days to calculate. In practice, this can be limiting, since the MCMC process needs to be performed many times to test the impact of possible systematics and to understand the robustness of the measurements being made. To achieve greater speed through parallelisation, MCMC algorithms need to have short autocorrelation times and minimal overheads caused by tuning and burn-in. The resulting scalability is hence influenced by two factors, the MCMC overheads and the parallelisation costs. In order to efficiently distribute the MCMC sampling over thousands of cores on modern cloud computing infrastructure, we developed a Python framework called CosmoHammer which embeds emcee, an implementation by Foreman-Mackey et al. (2012) of the affine invariant ensemble sampler by Goodman and Weare (2010). We test the performance of CosmoHammer for cosmological parameter estimation from cosmic microwave background data. While Metropolis–Hastings is dominated by overheads, CosmoHammer is able to accelerate the sampling process from a wall time of 30 h on a dual core notebook to 16 min by scaling out to 2048 cores. Such short wall times for complex datasets open possibilities for extensive model testing and control of systematics., Astronomy and Computing, 2, ISSN:2213-1337

Simulated wide-field images are becoming an important part of observational astronomy, either to prepare for new surveys or to test measurement methods. In order to efficiently explore vast parameter spaces, the computational speed of simulation codes is a central requirement to their implementation. We introduce the Ultra Fast Image Generator (UFig) which aims to bring wide-field imaging simulations to the current limits of computational capabilities. We achieve this goal through: (1) models of galaxies, stars and observational conditions, which, while simple, capture the key features necessary for realistic simulations, and (2) state-of-the-art computational and implementation optimizations. We present the performances of UFig and show that it is faster than existing public simulation codes by several orders of magnitude. It allows us to produce images more quickly than SExtractor needs to analyze them. For instance, it can simulate a typical 0.25 deg^2 Subaru SuprimeCam image (10k x 8k pixels) with a 5-sigma limiting magnitude of R=26 in 30 seconds on a laptop, yielding an average simulation time for a galaxy of 30 microseconds. This code is complementary to end-to-end simulation codes and can be used as a fast, central component of observational methods relying on simulations., Comment: Submitted to Astronomy and Computing. 13 pages, 9 figures

Weak lensing by large scale structure or ‘cosmic shear’ is a potentially powerful cosmological probe to shed new light on Dark Matter, Dark Energy and Modified Gravity. It is based on the weak distortions induced by large-scale structures on the observed shapes of distant galaxies through gravitational lensing. While the potentials of this purely gravitational effect are great, results from this technique have been hampered because the measurement of this weak effect is difficult and limited by systematics effects. In particular, a demanding step is the measurement of the weak lensing shear from wide field CCD images of galaxies. We describe the origin of the problem and propose a way forward for cosmic shear. Our proposed approach is based on Monte-Carlo Control Loops and draws upon methods widely used in particle physics and engineering. We describe the control loop scheme and show how it provides a calibration method based on fast image simulations tuned to reproduce the statistical properties of a specific cosmic shear data set. Through a series of iterative loops and diagnostic tests, the Monte Carlo image simulations are made robust to perturbations on modeling input parameters and thus to systematic effects. We discuss how this approach can make the problem tractable and unleash to full potential of cosmic shear for cosmology.