Tied-array beam flatfielding

TL;DR

This paper introduces a simple, computationally inexpensive method called beam flatfielding that uses multi-beam spatial information to stabilize bandpasses, reduce false positives, and improve the detection of genuine astrophysical signals in phased-array radio telescopes.

Contribution

It presents a novel post-beamforming technique that significantly reduces false triggers in pulsar and transient detection pipelines by leveraging multi-beam data for bandpass stabilization.

Findings

Reduces false positives by a factor of ~200 in LOTAAS data.

Improves pulse signal-to-noise ratios with beam flatfielding.

Produces flatter dynamic spectra and more Gaussian noise statistics.

Abstract

Context. Multi-element phased-array radio telescopes use digital beamforming to widen their field-of-view with numerous tied-array beams (TABs). These beams share bandpass variations and radio frequency interference (RFI). Yet, most pulsar and transient pipelines process each beam independently, ignoring shared spatial information. This leads to many RFI-dominated false positives that require extensive later sifting. Aims. We exploit multi-beam spatial information to stabilize bandpasses, suppress red noise and broad-band RFI, and drastically reduce false positives without degrading genuine astrophysical signals. Methods. We derive tied-array gain against residual phase dispersion, showing off-beam sources converge to the incoherent limit. Using chi-squared statistics, we analyze dividing a TAB by a beam-averaged reference and quantify the necessary smoothing. We test these predictions using LOFAR high-band antenna voltages (PSR B0329+54), simulations, and LOTAAS survey data (PSR J0250+5854). Results. Off-beam sources contribute nearly uniform power across beams once primary-beam effects are handled. Dividing by a smoothed multi-beam reference yields flatter dynamic spectra and equal or higher pulse signal-to-noise ratios compared to incoherent subtraction. Applied to LOTAAS data, this "beam flatfielding" cuts single-pulse false triggers by a factor of ~200 while preserving profile morphology and peak S/N. Conclusions. Beam flatfielding is a computationally cheap, simple post-beamforming step. For current and future multi-beam facilities, it provides stable bandpasses, closer-to-Gaussian noise statistics, and drastically fewer false positives, easing downstream classification without sacrificing sensitivity.