Accelerating Cosmic Microwave Background map-making procedure through preconditioning

Estimation of the sky signal from sequences of time ordered data is one of the key steps in Cosmic Microwave Background (CMB) data analysis, commonly referred to as the map-making problem. Some of the most popular and general methods proposed for this problem involve solving generalised least squares (GLS) equations with non-diagonal noise weights given by a block-diagonal matrix with Toeplitz blocks. In this work we study new map-making solvers potentially suitable for applications to the largest anticipated data sets. They are based on iterative conjugate gradient (CG) approaches enhanced with novel, parallel, two-level preconditioners. We apply the proposed solvers to examples of simulated non-polarised and polarised CMB observations, and a set of idealised scanning strategies with sky coverage ranging from nearly a full sky down to small sky patches. We discuss in detail their implementation for massively parallel computational platforms and their performance for a broad range of parameters characterising the simulated data sets. We find that our best new solver can outperform carefully-optimised standard solvers used today by a factor of as much as 5 in terms of the convergence rate and a factor of up to $4$ in terms of the time to solution, and to do so without significantly increasing the memory consumption and the volume of inter-processor communication. The performance of the new algorithms is also found to be more stable and robust, and less dependent on specific characteristics of the analysed data set. We therefore conclude that the proposed approaches are well suited to address successfully challenges posed by new and forthcoming CMB data sets.Comment: 19 pages // Final version submitted to A&

Accelerating Cosmic Microwave Background map-making procedure through preconditioning

Estimation of the sky signal from sequences of time ordered data is one of the key steps in Cosmic Microwave Background (CMB) data analysis, commonly referred to as the map-making problem. Some of the most popular and general methods proposed for this problem involve solving generalised least squares (GLS) equations with non-diagonal noise weights given by a block-diagonal matrix with Toeplitz blocks. In this work we study new map-making solvers potentially suitable for applications to the largest anticipated data sets. They are based on iterative conjugate gradient (CG) approaches enhanced with novel, parallel, two-level preconditioners. We apply the proposed solvers to examples of simulated non-polarised and polarised CMB observations, and a set of idealised scanning strategies with sky coverage ranging from nearly a full sky down to small sky patches. We discuss in detail their implementation for massively parallel computational platforms and their performance for a broad range of parameters characterising the simulated data sets. We find that our best new solver can outperform carefully-optimised standard solvers used today by a factor of as much as 5 in terms of the convergence rate and a factor of up to $4$ in terms of the time to solution, and to do so without significantly increasing the memory consumption and the volume of inter-processor communication. The performance of the new algorithms is also found to be more stable and robust, and less dependent on specific characteristics of the analysed data set. We therefore conclude that the proposed approaches are well suited to address successfully challenges posed by new and forthcoming CMB data sets.Comment: 19 pages // Final version submitted to A&

Cosmic cartography

The cosmic origin and evolution is encoded in the large-scale matter distribution observed in astronomical surveys. Galaxy redshift surveys have become in the recent years one of the best probes for cosmic large-scale structures. They are complementary to other information sources like the cosmic microwave background, since they trace a different epoch of the Universe, the time after reionization at which the Universe became transparent, covering about the last twelve billion years. Regarding that the Universe is about thirteen billion years old, galaxy surveys cover a huge range of time, even if the sensitivity limitations of the detectors do not permit to reach the furthermost sources in the transparent Universe. This makes galaxy surveys extremely interesting for cosmological evolution studies. The observables, galaxy position in the sky, galaxy ma gnitude and redshift, however, give an incomplete representation of the real structures in the Universe, not only due to the limitations and uncertainties in the measurements, but also due to their biased nature. They trace the underlying continuous dark matter field only partially being a discrete sample of the luminous baryonic distribution. In addition, galaxy catalogues are plagued by many complications. Some have a physical foundation, as mentioned before, others are due to the observation process. The problem of reconstructing the underlying density field, which permits to make cosmological studies, thus requires a statistical approach. This thesis describes a cosmic cartography project. The necessary concepts, mathematical frame-work, and numerical algorithms are thoroughly analyzed. On that basis a Bayesian software tool is implemented. The resulting Argo-code allows to investigate the characteristics of the large-scale cosmological structure with unprecedented accuracy and flexibility. This is achieved by jointly estimating the large-scale density along with a variety of other parameters ---such as the cosmic flow, the small-scale peculiar velocity field, and the power-spectrum--- from the information provided by galaxy redshift surveys. Furthermore, Argo is capable of dealing with many observational issues like mask-effects, galaxy selection criteria, blurring and noise in a very efficient implementation of an operator based formalism which was carefully derived for this purpose. Thanks to the achieved high efficiency of Argo the application of iterative sampling algorithms based on Markov Chain Monte Carlo is now possible. This will ultimately lead to a full description of the matter distribution with all its relevant parameters like velocities, power spectra, galaxy bias, etc., including the associated uncertainties. Some applications are shown, in which such techniques are used. A rejection sampling scheme is successfully applied to correct for the observational redshift-distortions effect which is especially severe in regimes of non-linear structure formation, causing the so-called finger-of-god effect. Also a Gibbs-sampling algorithm for power-spectrum determination is presented and some preliminary results are shown in which the correct level and shape of the power-spectrum is recovered solely from the data. We present in an additional appendix the gravitational collapse and subsequent neutrino-driven explosion of the low-mass end of stars that undergo core-collapse Supernovae. We obtain results which are for the first time compatible with the Crab Nebula

The C-Band All Sky Survey commissioning and data analysis.

Doctor of Philosophy in Mathematics, University of KwaZulu-Natal, Westville, 2017.The C-Band All Sky Survey (C-BASS) is a ground based radio survey that scans the entire sky in Stokes I, Q and U at a central frequency of 5 GHz with a 1 GHz bandwidth and an angular resolution of 0.73 . The experiment consists of two telescopes, one at Owens Valley Radio Observatory in California and the other at the SKA support base in Klerefontein, South Africa. The primary aim of this experiment is to produce high fidelity maps of the entire sky in Stokes I, Q and U. These maps will be used by CMB experiments for the removal of Galactic foreground radiation, via component separation, and will provide vital aid in the search for the primordial CMB B-mode polarized signal. C-BASS also aims to probe the Galactic magnetic field using synchrotron radiation and will search for new areas of anomalous microwave emission. In this thesis, I present the contribution that I have made to the C-BASS experiment. I contributed to C-BASS instrumentation development by working extensively on the commissioning of the southern telescope; in particular, I developed an optical pointing system and refined the automated analysis process. I contributed to the development of the C-BASS data analysis pipeline for both the northern and southern telescopes, with the development of a new RFI flagging method, work on map making techniques and convergence, and self-consistency tests. The northern survey is complete and data analysis is at an advanced stage. The southern instrument is undergoing commissioning on site and will soon begin survey operations. My contributions to the project have improved the processed data quality in both surveys and will aid in the successful completion of the southern survey

Effects of stellar outflows on interstellar sulfur oxide chemistry

Interferometer Maps with 2" to 6" resolution of a number of regions with active star formation (Orion A, W49, W51, SGRB2) show that the distribution of the molecule SO is very compact around stellar outflow sources. Both SO and SO2 were studied near three outflows, OrionA/IRc2 and two sources in W49. The two molecules have similar distributions and abundances. More than 95% of the emission comes from regions whose extents are only .05 to .2 pc., being larger around the more energetic sources. Their spectra are broad, 30 km/sec or more, suggesting that the oxide production is associated with the flows. The outflows are identified by water masers and by extended bipolar flows in SiO. Maps in other molecules, such as HCO+ and CS, which have similar collisional excitation requirements, have much greater spatial extent. Thus it appears that the SO and SO2 abundances are truly compact and are closely associated with the outflows