Computing challenges of the Cosmic Microwave Background

The Cosmic Microwave Background (CMB) encodes information on the origin and evolution of the universe, buried in a fractional anisotropy of one part in 100,000 on angular scales from arcminutes to tens of degrees. We await the coming onslaught of data from experiments measuring the microwave sky from the ground, from balloons and from space. However, we are faced with the harsh reality that current algorithms for extracting cosmological information cannot handle data sets of the size and complexity expected even in the next few years. Here we review the challenges involved in understanding this data: making maps from time-ordered data, removing the foreground contaminants, and finally estimating the power spectrum and cosmological parameters from the CMB map. If handled naively, the global nature of the analysis problem renders these tasks effectively impossible given the volume of the data. We discuss possible techniques for overcoming these issues and outline the many other challenges that wait to be addressed

Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions

We present a general method for accelerating by more than an order of magnitude the convolution of pixelated functions on the sphere with a radially-symmetric kernel. Our method splits the kernel into a compact real-space component and a compact spherical harmonic space component. These components can then be convolved in parallel using an inexpensive commodity GPU and a CPU. We provide models for the computational cost of both real-space and Fourier space convolutions and an estimate for the approximation error. Using these models we can determine the optimum split that minimizes the wall clock time for the convolution while satisfying the desired error bounds. We apply this technique to the problem of simulating a cosmic microwave background (CMB) anisotropy sky map at the resolution typical of the high resolution maps produced by the Planck mission. For the main Planck CMB science channels we achieve a speedup of over a factor of ten, assuming an acceptable fractional rms error of order 1.e-5 in the power spectrum of the output map.Comment: 9 pages, 11 figures, 1 table, accepted by Astronomy & Computing w/ minor revisions. arXiv admin note: substantial text overlap with arXiv:1211.355

CMB-S4 Science Book, First Edition

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales

Status of CMB observations in 2015

The 2.725 K cosmic microwave background has played a key role in the development of modern cosmology by providing a solid observational foundation for constraining possible theories of what happened at very large redshifts and theoretical speculation reaching back almost to the would-be big bang initial singularity. After recounting some of the lesser known history of this area, I summarize the current observational situation and also discuss some exciting challenges that lie ahead: the search for B modes, the precision mapping of the CMB gravitational lensing potential, and the ultra-precise characterization of the CMB frequency spectrum, which would allow the exploitation of spectral distortions to probe new physics.Comment: 17 pages, 3 figures, Latex, conference proceeding based on talk at CosPA 2015 in Daejeon, South Korea in October 2015, minor typos correcte

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio

Mapping Cosmic Dawn and Reionization: Challenges and Synergies

Cosmic dawn and the Epoch of Reionization (EoR) are among the least explored observational eras in cosmology: a time at which the first galaxies and supermassive black holes formed and reionized the cold, neutral Universe of the post-recombination era. With current instruments, only a handful of the brightest galaxies and quasars from that time are detectable as individual objects, due to their extreme distances. Fortunately, a multitude of multi-wavelength intensity mapping measurements, ranging from the redshifted 21 cm background in the radio to the unresolved X-ray background, contain a plethora of synergistic information about this elusive era. The coming decade will likely see direct detections of inhomogenous reionization with CMB and 21 cm observations, and a slew of other probes covering overlapping areas and complementary physical processes will provide crucial additional information and cross-validation. To maximize scientific discovery and return on investment, coordinated survey planning and joint data analysis should be a high priority, closely coupled to computational models and theoretical predictions.Comment: 5 pages, 1 figure, submitted to the Astro2020 Decadal Survey Science White Paper cal

Decaying dark energy in light of the latest cosmological dataset

Decaying Dark Energy models modify the background evolution of the most common observables, such as the Hubble function, the luminosity distance and the Cosmic Microwave Background temperature-redshift scaling relation. We use the most recent observationally-determined datasets, including Supernovae Type Ia and Gamma Ray Bursts data, along with $H(z)$ and Cosmic Microwave Background temperature versus $z$ data and the reduced Cosmic Microwave Background parameters, to improve the previous constraints on these models. We perform a Monte Carlo Markov Chain analysis to constrain the parameter space, on the basis of two distinct methods. In view of the first method, the Hubble constant and the matter density are left to vary freely. In this case, our results are compatible with previous analyses associated with decaying Dark Energy models, as well as with the most recent description of the cosmological background. In view of the second method, we set the Hubble constant and the matter density to their best fit values obtained by the {\it Planck} satellite, reducing the parameter space to two dimensions, and improving the existent constraints on the model's parameters. Our results suggest that the accelerated expansion of the Universe is well described by the cosmological constant, and we argue that forthcoming observations will play a determinant role to constrain/rule out decaying Dark Energy.Comment: 15 pages, 3 figure, 2 table. Accepted in the Special Issue "Cosmological Inflation, Dark Matter and Dark Energy" on Symmetry Journa

Destriping Cosmic Microwave Background Polarimeter data

Destriping is a well-established technique for removing low-frequency correlated noise from Cosmic Microwave Background (CMB) survey data. In this paper we present a destriping algorithm tailored to data from a polarimeter, i.e. an instrument where each channel independently measures the polarization of the input signal. We also describe a fully parallel implementation in Python released as Free Software and analyze its results and performance on simulated datasets, both the design case of signal and correlated noise, and with additional systematic effects. Finally we apply the algorithm to 30 days of 37.5 GHz polarized microwave data gathered from the B-Machine experiment, developed at UCSB. The B-Machine data and destriped maps are made publicly available. The purpose is the development of a scalable software tool to be applied to the upcoming 12 months of temperature and polarization data from LATTE (Low frequency All sky TemperaTure Experiment) at 8 GHz and to even larger datasets.Comment: Submitted to Astronomy and Computing on 15th August 2013, published 7th November 201

Modeling and replicating statistical topology, and evidence for CMB non-homogeneity

Under the banner of `Big Data', the detection and classification of structure in extremely large, high dimensional, data sets, is, one of the central statistical challenges of our times. Among the most intriguing approaches to this challenge is `TDA', or `Topological Data Analysis', one of the primary aims of which is providing non-metric, but topologically informative, pre-analyses of data sets which make later, more quantitative analyses feasible. While TDA rests on strong mathematical foundations from Topology, in applications it has faced challenges due to an inability to handle issues of statistical reliability and robustness and, most importantly, in an inability to make scientific claims with verifiable levels of statistical confidence. We propose a methodology for the parametric representation, estimation, and replication of persistence diagrams, the main diagnostic tool of TDA. The power of the methodology lies in the fact that even if only one persistence diagram is available for analysis -- the typical case for big data applications -- replications can be generated to allow for conventional statistical hypothesis testing. The methodology is conceptually simple and computationally practical, and provides a broadly effective statistical procedure for persistence diagram TDA analysis. We demonstrate the basic ideas on a toy example, and the power of the approach in a novel and revealing analysis of CMB non-homogeneity