On the Acceleration of Pulsar Timing computations using Normalising Flows and Parallelisation
Abstract
Single-Pulsar Noise Analysis (SPNA) and Gravitational Wave (GW) searches done on Pulsar Timing Array (PTA) datasets have everlastingly suffered from the computational bottleneck arising due to high dimensionality and multi-modality of the PTA likelihood landscape, along with strong correlations amongst various single-pulsar noises and ensemble-level common noise processes. We addressed this outstanding issue by employing a Normalising Flow-based Preconditioned Monte-Carlo sampling technique implemented in the POCOMC package, for the first time on PTA-specific computations, and comparing the achieved acceleration with the widely used PTMCMCSAMPLER and DYNESTY packages. We further investigated the acceleration achieved via parallelisation over an increasing array of communicating nodes on a high-performance computing (HPC) resource, by employing the PARALLEL_BILBY architecture with DYNESTY. We tested the acceleration on realistic long baseline simulated datasets with SPNA and Common Red Noise (CRN) analysis. We found PARALLEL_BILBY to be the most efficient in parallelisation, achieving a runtime of ~10min and ~100min with 16 nodes for spatially uncorrelated and Hellings and Downs-correlated CRN searches, respectively. POCOMC outperforms in single node performance requiring only ~10h for correlated search. PTMCMCSAMPLER was found to be the least efficient. We envisage POCOMC to be of great importance for PTA analyses, without requiring any GPU or HPC support, while also performing ensemble-level GW searches within a manageable time span. These results have everlasting implications with increasing data volumes and need to incorporate more complicated models, which were otherwise beyond reach due to the associated computational costs.
Related
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BibTeX
@misc{dwivedi2026acceleration,
title = {{On the Acceleration of Pulsar Timing computations using Normalising Flows and Parallelisation}},
author = {Churchil Dwivedi and Hiya Shah and Hemanga Tahbildar},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.13991},
eprint = {2608.13991},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.13991},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - On the Acceleration of Pulsar Timing computations using Normalising Flows and Parallelisation
AU - Churchil Dwivedi
AU - Hiya Shah
AU - Hemanga Tahbildar
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2608.13991
UR - https://arxiv.org/abs/2608.13991
AB - Single-Pulsar Noise Analysis (SPNA) and Gravitational Wave (GW) searches done on Pulsar Timing Array (PTA) datasets have everlastingly suffered from the computational bottleneck arising due to high dimensionality and multi-modality of the PTA likelihood landscape, along with strong correlations amongst various single-pulsar noises and ensemble-level common noise processes. We addressed this outstanding issue by employing a Normalising Flow-based Preconditioned Monte-Carlo sampling technique implemented in the POCOMC package, for the first time on PTA-specific computations, and comparing the achieved acceleration with the widely used PTMCMCSAMPLER and DYNESTY packages. We further investigated the acceleration achieved via parallelisation over an increasing array of communicating nodes on a high-performance computing (HPC) resource, by employing the PARALLEL_BILBY architecture with DYNESTY. We tested the acceleration on realistic long baseline simulated datasets with SPNA and Common Red Noise (CRN) analysis. We found PARALLEL_BILBY to be the most efficient in parallelisation, achieving a runtime of ~10min and ~100min with 16 nodes for spatially uncorrelated and Hellings and Downs-correlated CRN searches, respectively. POCOMC outperforms in single node performance requiring only ~10h for correlated search. PTMCMCSAMPLER was found to be the least efficient. We envisage POCOMC to be of great importance for PTA analyses, without requiring any GPU or HPC support, while also performing ensemble-level GW searches within a manageable time span. These results have everlasting implications with increasing data volumes and need to incorporate more complicated models, which were otherwise beyond reach due to the associated computational costs.
ER -