Chi-Wai Chan | Searcharxiv

arXiv2026

GW070605: An Undisclosed Binary Neutron Star Hardware Injection in LIGO's Fifth Science Run

The authors wished to document the sensitivity improvement that has been contributed to the GW detection rate by detection algorithm research and development efforts, and set about re-analyzing S5 and S6 to determine the sensitive time-volumes of a modern pipeline and compare them to that of analysis algorithms of the day. To our surprise, this effort led to the discovery of GW070605, what at first appeared to be a previously unreported high significance binary neutron star merger at a time when only the Livingston detector (L1) was operating -- data that could not have been analyzed and a signal that could not have been discovered previously because the algorithms of the day required coincidence between two or more detectors. GW070605's end time occurs in LIGO's L1 detector at 2007-06-05 18:37:02 UTC, and is estimated to be a merger with component masses of 1.82$M_\odot$ and 1.24$M_\odot$. The GstLAL detection algorithm estimates that noise processes produce false positives at least as significant as GW070605 at a rate of $8.6\times10^{-10}$ per year. Disappointingly, subsequent investigations revealed the presence of a previously undocumented hardware injection in the L1 detector's Y arm end test mass' excitation channel, whose time and properties match that of GW070605. The injection does not appear in the Gravitational Wave Open Science Center list of hardware injections. We determined that while there is no sensitivity improvement between GstLAL and previous algorithms at the null-result threshold, there is marked improvement at above null-result thresholds; specifically, an approximately 55-times detection rate increase from initial-era algorithms at a FAR threshold of 1 per 7000 years.

gr-qc↗

arXiv2025

High-Throughput Unsupervised Profiling of the Morphology of 316L Powder Particles for Use in Additive Manufacturing

Selective Laser Melting (SLM) is a powder-bed additive manufacturing technique whose part quality depends critically on feedstock morphology. However, conventional powder characterization methods are low-throughput and qualitative, failing to capture the heterogeneity of industrial-scale batches. We present an automated, machine learning framework that couples high-throughput imaging with shape extraction and clustering to profile metallic powder morphology at scale. We develop and evaluate three clustering pipelines: an autoencoder pipeline, a shape-descriptor pipeline, and a functional-data pipeline. Across a dataset of approximately 126,000 powder images (0.5-102 micrometer diameter), internal validity metrics identify the Fourier-descriptor + k-means pipeline as the most effective, achieving the lowest Davies-Bouldin index and highest Calinski-Harabasz score while maintaining sub-millisecond runtime per particle on a standard desktop workstation. Although the present work focuses on establishing the morphological-clustering framework, the resulting shape groups form a basis for future studies examining their relationship to flowability, packing density, and SLM part quality. Overall, this unsupervised learning framework enables rapid, automated assessment of powder morphology and supports tracking of shape evolution across reuse cycles, offering a path toward real-time feedstock monitoring in SLM workflows.

cs.CV↗