arXiv subjects

Dirk Kuiper

Publications and source records attributed to Dirk Kuiper.

2 recordsLinked to original sources

arXiv2026

Tied-array beam flatfielding

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.

astro-ph.IM↗

arXiv2024

Representation learning for fast radio burst dynamic spectra

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, with diverse time-frequency patterns and emission properties that require explanation. With one possible exception, FRBs are detected only in the radio, so analyzing their dynamic spectra is therefore crucial to disentangling the physical processes governing their generation and propagation. Furthermore, comparing FRB morphologies provides insights into possible differences among their progenitors and environments. This study applies unsupervised learning and deep learning techniques to investigate FRB dynamic spectra, focusing on two approaches: Principal Component Analysis (PCA) and a Convolutional Autoencoder (CAE) enhanced by an Information-Ordered Bottleneck (IOB) layer. PCA served as a computationally efficient baseline, capturing broad trends, identifying outliers, and providing valuable insights into large datasets. However, its linear nature limited its ability to reconstruct complex FRB structures. In contrast, the IOB-augmented CAE excelled at capturing intricate features, with high reconstruction accuracy and effective denoising at modest signal-to-noise ratios. The IOB layer's ability to prioritize relevant features enabled efficient data compression, preserving key morphological characteristics with minimal latent variables. When applied to real FRBs from CHIME, the IOB-CAE generalized effectively, revealing a latent space that highlighted the continuum of FRB morphologies and the potential for distinguishing intrinsic differences between burst types. This framework demonstrates that while FRBs may not naturally cluster into discrete groups, advanced representation learning techniques can uncover meaningful structures, offering new insights into the diversity and origins of these bursts.

astro-ph.HE↗