Astronomy and Computing

Journal: Astronomy and Computing

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Elsevier

ISSN

2213-1337

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Publications 1 - 10 of 18

Karabo: A versatile SKA observation simulation framework
Sharma , Rohit; Felix , Simon; Machado Poletti Valle, Luis; et al. (2026)
Astronomy and Computing
Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometre Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo relevant to some of the SKA science cases from HI intensity mapping, simulation of the radio surveys, radio source detection, the epoch of re-ionization and heliophysics. We discuss the capabilities, scalabilities and challenges of simulating large radio datasets in the context of SKA.
sympy2c: From symbolic expressions to fast C/C++ functions and ODE solvers in Python
Schmitt, Uwe; Moser, Beatrice; Lorenz, Christiane S.; et al. (2023)
Astronomy and Computing
Computer algebra systems play an important role in science as they facilitate the development of new theoretical models. The resulting symbolic equations are often implemented in a compiled programming language in order to provide fast and portable codes for practical applications. We describe sympy2c, a new Python package designed to bridge the gap between the symbolic development and the numerical implementation of a theoretical model. sympy2c translates symbolic equations implemented in the SymPy Python package to C/C++ code that is optimized using symbolic transformations. The resulting functions can be conveniently used as an extension module in Python. sympy2c is used within the PyCosmo Python package to solve the Einstein–Boltzmann equations, a large system of ODEs describing the evolution of linear perturbations in the Universe. After reviewing the functionalities and usage of sympy2c, we describe its implementation and optimization strategies. This includes, in particular, a novel approach to generate optimized ODE solvers making use of the sparsity of the symbolic Jacobian matrix. We demonstrate its performance using the Einstein–Boltzmann equations as a test case. sympy2c is general and potentially useful for various areas of computational physics. sympy2c is publicly available at https://cosmology.ethz.ch/research/software-lab/sympy2c.html.
Optical follow-up of gravitational wave triggers with DECam during the first two LIGO/VIRGO observing runs
Herner, Kenneth; Hartley, William G.; et al. (2020)
Astronomy and Computing
© 2020 Elsevier B.V. Gravitational wave (GW) events detectable by LIGO and Virgo have several possible progenitors, including black hole mergers, neutron star mergers, black hole–neutron star mergers, supernovae, and cosmic string cusps. A subset of GW events is expected to produce electromagnetic (EM) emission that, once detected, will provide complementary information about their astrophysical context. To that end, the LIGO–Virgo Collaboration (LVC) sends GW candidate alerts to the astronomical community so that searches for their EM counterparts can be pursued. The DESGW group, consisting of members of the Dark Energy Survey (DES), the LVC, and other members of the astronomical community, uses the Dark Energy Camera (DECam) to perform a search and discovery program for optical signatures of LVC GW events. DESGW aims to use a sample of GW events as standard sirens for cosmology. Due to the short decay timescale of the expected EM counterparts and the need to quickly eliminate survey areas with no counterpart candidates, it is critical to complete the initial analysis of each night's images as quickly as possible. We discuss our search area determination, imaging pipeline, and candidate selection processes. We review results from the DESGW program during the first two LIGO–Virgo observing campaigns and introduce other science applications that our pipeline enables.
The PAU Survey: Operation and orchestration of multi-band survey data
Tonello, Nadia; Tallada, Pau; Serrano, Santiago; et al. (2019)
Astronomy and Computing
Predicting cosmological observables with PyCosmo
Tarsitano, Federica; Schmitt, U.; Refregier, Alexandre; et al. (2021)
Astronomy and Computing
Current and upcoming cosmological experiments open a new era of precision cosmology, thus demanding accurate theoretical predictions for cosmological observables. Because of the complexity of the codes delivering such predictions, reaching a high level of numerical accuracy is challenging. Among the codes already fulfilling this task, PyCosmo is a Python-based framework providing solutions to the Einstein–Boltzmann equations and accurate predictions for cosmological observables. We present the first public release of the code, which is valid in ΛCDM cosmology. The novel aspect of this version is that the user can work within a Python framework, either locally or through an online platform, called PyCosmo Hub. In this work we first describe how the observables are implemented. Then, we check the accuracy of the theoretical predictions for background quantities, power spectra and Limber and beyond-Limber angular power spectra by comparison with other codes: the Core Cosmology Library (CCL), CLASS, HMCode and iCosmo. In our analysis we quantify the agreement of PyCosmo with the other codes, for a range of cosmological models, monitored through a series of unit tests. PyCosmo, conceived as a multi-purpose cosmology calculation tool in Python, is designed to be interactive and user-friendly. The PyCosmo Hub is accessible from this link: https://cosmology.ethz.ch/research/software-lab/PyCosmo.html. On this platform the users can perform their own computations using Jupyter Notebooks without the need of installing any software, access to the results presented in this work and benefit from tutorial notebooks illustrating the usage of the code. The link above also redirects to the code release and documentation.
PynPoint Code for Exoplanet Imaging
Amara, A.; Quanz, Sascha Patrick; Akeret, J. (2015)
Astronomy and Computing
Symbolic implementation of extensions of the PyCosmo Boltzmann solver
Moser, Beatrice; Lorenz, Cristiane S.; Schmitt, Uwe; et al. (2022)
Astronomy and Computing
PyCosmo is a Python-based framework for the fast computation of cosmological model predictions. One of its core features is the symbolic representation of the Einstein-Boltzmann system of equations. Efficient C/C++ code is generated from the SymPy symbolic expressions making use of the sympy2c package. This enables easy extensions of the equation system for the implementation of new cosmological models. We illustrate this with three extensions of the PyCosmo Boltzmann solver to include a dark energy component with a constant equation of state, massive neutrinos and a radiation streaming approximation. We describe the PyCosmo framework, highlighting new features, and the symbolic implementation of the new models. We compare the PyCosmo predictions for the ?CDM model extensions with CLASS, both in terms of accuracy and computational speed. We find a good agreement, to better than 0.1% when using high-precision settings and a comparable computational speed. Links to the Python Package Index (PyPI) page of the code release and to the PyCosmo Hub, an online platform where the package is installed, are available at: https://cosmology.ethz.ch/research/softwarelab/PyCosmo.html. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
GPU accelerated particle visualization with Splotch
Rivi, M.; Gheller, C.; Dykes, T.; et al. (2014)
Astronomy and Computing
CosmoHammer: Cosmological parameter estimation with the MCMC Hammer
Akeret, Joël; Seehars, Sebastian; Amara, Adam; et al. (2013)
Astronomy and Computing
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.
An Ultra Fast Image Generator (UFIG) for wide-field astronomy
Bergé, Joel; Gamper, Lukas; Refregier, Alexandre; et al. (2013)
Astronomy and Computing

Publications 1 - 10 of 18

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