English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Nowadays, analyzing and reducing the ever larger astronomical datasets isbecoming a crucial challenge, especially for long cumulated observation times.The INTEGRAL/SPI X-gamma-ray spectrometer is an instrument for which it isessential to process many exposures at the same time in order to increase thelow signal-to-noise ratio of the weakest sources. In this context, theconventional methods for data reduction are inefficient and sometimes notfeasible at all. Processing several years of data simultaneously requirescomputing not only the solution of a large system of equations, but also theassociated uncertainties. We aim at reducing the computation time and thememory usage. Since the SPI transfer function is sparse, we have used somepopular methods for the solution of large sparse linear systems; we brieflyreview these methods. We use the Multifrontal Massively Parallel Solver (MUMPS)to compute the solution of the system of equations. We also need to compute thevariance of the solution, which amounts to computing selected entries of theinverse of the sparse matrix corresponding to our linear system. This can beachieved through one of the latest features of the MUMPS software that has beenpartly motivated by this work. In this paper we provide a brief presentation ofthis feature and evaluate its effectiveness on astrophysical problems requiringthe processing of large datasets simultaneously, such as the study of theentire emission of the Galaxy. We used these algorithms to solve the largesparse systems arising from SPI data processing and to obtain both theirsolutions and the associated variances. In conclusion, thanks to these newlydeveloped tools, processing large datasets arising from SPI is now feasiblewith both a reasonable execution time and a low memory usage.

Simultaneous analysis of large INTEGRAL/SPI datasets: optimizing the computation of the solution and its variance using sparse matrix algorithms