High performance Stingray
Abstract
X-ray astrophysical objects show variability on a wide range of timescales, i.e. from fractions of second to years. The open-source Python library stingray is able to perform time series analyses with a focus on high-energy astrophysics. Comprising the most commonly used Fourier analyses techniques, it also supports a range of additional extensions able to analyse pulsar data, simulate data sets and perform statistical modelling. With the latest release of the code, new Fourier methods and support for additional missions have been implemented, making Stingray more easily adaptable and extendable to other use cases. In this paper we focus on testing the performance and robustness of the latest version of stingray . We consider the possible different behaviour of the code when dealing with data sets smaller or larger than the RAM. We address the problem of dealing with large data sets and implemented parallel versions of the slowest methods in this regime. For small data sets, we investigate the possibility of a porting in GPU of key functions of the code.
Versions
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BibTeX
@misc{lai2026high,
title = {{High performance Stingray}},
author = {Eleonora Veronica Lai and Matteo Bachetti and Maura Pilia and Daniela Huppenkothen and Matteo Lucchini and Guglielmo Mastroserio and Hamza El Byad},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.02857},
eprint = {2609.02857},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.02857},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - High performance Stingray
AU - Eleonora Veronica Lai
AU - Matteo Bachetti
AU - Maura Pilia
AU - Daniela Huppenkothen
AU - Matteo Lucchini
AU - Guglielmo Mastroserio
AU - Hamza El Byad
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.02857
UR - https://arxiv.org/abs/2609.02857
AB - X-ray astrophysical objects show variability on a wide range of timescales, i.e. from fractions of second to years. The open-source Python library stingray is able to perform time series analyses with a focus on high-energy astrophysics. Comprising the most commonly used Fourier analyses techniques, it also supports a range of additional extensions able to analyse pulsar data, simulate data sets and perform statistical modelling. With the latest release of the code, new Fourier methods and support for additional missions have been implemented, making Stingray more easily adaptable and extendable to other use cases. In this paper we focus on testing the performance and robustness of the latest version of stingray . We consider the possible different behaviour of the code when dealing with data sets smaller or larger than the RAM. We address the problem of dealing with large data sets and implemented parallel versions of the slowest methods in this regime. For small data sets, we investigate the possibility of a porting in GPU of key functions of the code.
ER -