Search arXiv⌕ Search

arXiv · 2610.11749

A Coherent Harmonic Summing Pulsar Search Code

Abstract

Pulsars have narrow pulses, so their signals spread over many harmonics in a Fourier transform. Standard pulsar searches add the powers of those harmonics and discard their phases, which throws away the pulse shape and some of the signal. Folding the time series at each trial period keeps both, but is expensive even with the Fast Folding Algorithm (FFA). We describe CoherentSearch.jl, an open-source Julia code that reconstructs the pulse profile at every trial spin frequency directly from the Fourier transform. It interpolates the complex amplitudes of typically 60 harmonics, inverse transforms them into a profile, and tests that profile against boxcar templates of many widths. Because the input spectrum is normalized, the noise in every profile is known in closed form, so one threshold gives one false-alarm rate across the whole search. In an injection study of over 860,000 simulated pulsars at a matched false-alarm rate, the new search recovers 76% of the pulsars in white noise, against 64% for riptide's FFA in its recommended configuration and 42% for PRESTO's accelsearch, and its threshold does not move under red noise. On one CPU core it is 1.4-2.2 times faster than riptide at matched frequency coverage, it scales to 27 times faster on 48 cores, and modern GPUs run it up to nine times faster than a 20-core workstation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Scott M. Ransom. 2026-10-08. A Coherent Harmonic Summing Pulsar Search Code. https://arxiv.org/abs/2610.11749

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

radio-astro-tools: linking radio astronomical data to the astronomical Python ecosystem

We present the radio-astro-tools code suite, which consists of several Python packages that enable analysis of radio data, especially interferometric spectral cubes, in the context of the Astropy software ecosystem. While these tools were designed with radio data in mind, they are built to be general and have applications on data sets at other wavelengths. The core package, spectral-cube, handles reading, writing and analysis of cube data, and it enables straightforward parallelization via dask and joblib backends. Support packages include casa-formats-io and radio-beam, which handle reading of CASA tables & images and reading and manipulation of point spread functions, respectively. The pvextractor package facilitates creation of position-velocity diagrams. The uvcombine package implements the "feather" algorithm for combining single-dish and interferometric data. The development of radio-astro-tools included several contributions to other repositories, including matplotlib, astropy, and regions to support interaction between CASA and other parts of the astronomy software ecosystem. We also present a detailed set of tutorials written in Jupyter notebooks that can be run interactively in a browser, providing more accessible access to data analysis for radio spectral-line data cubes.

astro-ph.IM↗

Pseudo Closed-Loop Bootstrapping for Reinforcement Learning Based AO Control

High contrast imaging with ground-based telescopes requires extremely precise wavefront control. The performance of the controller ultimately depends on the quality and efficiency of the wavefront sensing technique, which has led to the development of ever more sensitive wavefront sensors (WFS). However, the increase in sensitivity comes at the cost of greater nonlinearity, which poses challenges for conventional linear reconstructors and controllers. Reinforcement Learning (RL) is a branch of machine learning in which control policies are learned by interacting with the environment. RL has recently attracted interest in the field of XAO, and previous studies have demonstrated that a model-based RL variant, the Policy Optimization for Adaptive Optics (PO4AO), can effectively compensate for temporal delays, misregistration errors, and moderate WFS nonlinearities. PO4AO learns an initial control strategy by collecting closed-loop data from the integrator controller with a linear reconstructor. However, in cases of highly non-linear WFS and challenging conditions, closing the loop with the integrator is difficult, and high-fidelity data cannot be collected. Consequently, learning robust control with PO4AO is challenging. We propose a robust learning strategy in which PO4AO is pre-calibrated using an internal light source and a DM, enabling direct on-sky closed-loop operation without a stable integrator controller for bootstrapping the initial models.

astro-ph.IM↗

Opening the Black Box: What Neural Networks Learn from Pulsar Timing Array Data

In recent years, simulation-based inference (SBI) methods have been proposed to address several data-analysis challenges faced by existing and planned gravitational-wave experiments. For example, SBI classification has recently been shown to significantly improve the prospects for detecting anisotropies in pulsar timing array (PTA) data. In this work, we use a simple toy model of a PTA to provide a more pedagogical explanation of how, and under which circumstances, SBI-based detection can improve on classical detection statistics for gravitational-wave background anisotropies.

astro-ph.IM↗