Ruby Shrestha | Search arXiv

arXiv2026

Single-pulse reanalysis of the 2024 Vela glitch and new observations of PSR~J0437$-$4715 and PSR~J1644$-$4559

The Pulsar Monitoring in Argentina (PuMA) collaboration systematically monitors southern glitching pulsars, maintaining high-cadence single-pulse records of the Vela pulsar. We present a pulse-per-pulse reanalysis of the 2024 major glitch of Vela (PSR~J0835$-$4510) with the 400~MHz-bandwidth ROACH backend of the Argentine Institute of Radioastronomy, and extend our machine-learning single-pulse pipeline---Isolation Forest outlier rejection, $\beta$-Variational-AutoEncoder denoising, and Self-Organizing-Map clustering---to new observations of PSR~J1644$-$4559 and the millisecond pulsar PSR~J0437$-$4715. For Vela, the 4- and 6-cluster decompositions of the eight days bracketing the glitch reproduce, with seven times the previous bandwidth, the behavior found with the narrow-band ETTUS receivers: higher-amplitude clusters peak earlier in phase, are narrower, more skewed, and less populated. With the glitch jump and its two exponential recovery terms included in the timing solution, the mean profile is stable to 1\% across all eight days (width change $-0.5\pm0.9$\% from pre- to post-glitch); omitting the recovery terms would mimic a post-glitch broadening of up to 55\% through a folding-frequency error at the $10^{-7}$ level. The clusters of PSR~J1644$-$4559 differ almost exclusively in amplitude, as expected for a scattering-dominated profile. For PSR~J0437$-$4715, retaining the 10\% of pulses with the highest peak-dominance score doubles the signal-to-noise ratio, and a five-cluster decomposition yields narrow, phase-ordered groups a factor $3.6\pm0.4$ narrower than the average profile, suggesting a $\sim$3.5-fold improvement in cluster-based timing precision for this pulsar-timing-array target, to be confirmed in a follow-up paper.

astro-ph.HE↗

arXiv2023

Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations

Spurious correlation caused by subgroup underrepresentation has received increasing attention as a source of bias that can be perpetuated by deep neural networks (DNNs). Distributionally robust optimization has shown success in addressing this bias, although the underlying working mechanism mostly relies on upweighting under-performing samples as surrogates for those underrepresented in data. At the same time, while invariant representation learning has been a powerful choice for removing nuisance-sensitive features, it has been little considered in settings where spurious correlations are caused by significant underrepresentation of subgroups. In this paper, we take the first step to better understand and improve the mechanisms for debiasing spurious correlation due to subgroup underrepresentation in medical image classification. Through a comprehensive evaluation study, we first show that 1) generalized reweighting of under-performing samples can be problematic when bias is not the only cause for poor performance, while 2) naive invariant representation learning suffers from spurious correlations itself. We then present a novel approach that leverages robust optimization to facilitate the learning of invariant representations at the presence of spurious correlations. Finetuned classifiers utilizing such representation demonstrated improved abilities to reduce subgroup performance disparity, while maintaining high average and worst-group performance.

cs.CV↗