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Dave Buckley

Publications and source records attributed to Dave Buckley.

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Multiwavelength Analysis of the Einstein Probe X-ray Transient EP240305a

We report multiwavelength observations of EP240305a, an uncatalogued X-ray transient detected by the Einstein Probe on March 5, 2024. The source exhibits distinct characteristics across the X-ray, optical, near-infrared, and radio bands. The soft X-ray observations show two significant flares lasting ~100-250 s, accompanied by rapid flux decay in a few days, and the optical and near-infrared data reveal a faint, candidate counterpart. In contrast, the radio observations expose a long-term spectral evolution from a self-absorbed to an optically thin state within two months, implying discrete jet ejection. We compare EP240305a with known classes of X-ray transients and find that it is unlikely to be associated with long-timescale transients such as jetted tidal disruption events or X-ray binaries. Its properties also disfavor a short-timescale stellar flare origin. Although the absence of optical spectroscopy prevents a redshift determination, the source exhibits properties similar to those of gamma-ray-dark gamma-ray burst-like transients, which may be associated with relativistic jets viewed off-axis or with choked jets. The discovery of EP240305a, along with other uncataloged transients detected by the Einstein Probe, underscores the scientific potential of highly sensitive X-ray survey telescopes and rapid-response multiwavelength follow-up observations in exploring the nature of atypical astronomical transients.

astro-ph.HE

Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets

The modern AI data economy centralizes power, limits innovation, and misallocates value by extracting data without control, privacy, or fair compensation. We introduce Private Map-Secure Reduce (PMSR), a network-native paradigm that transforms data economics from extractive to participatory through cryptographically enforced markets. Extending MapReduce to decentralized settings, PMSR enables computation to move to the data, ensuring verifiable privacy, efficient price discovery, and incentive alignment. Demonstrations include large-scale recommender audits, privacy-preserving LLM ensembling (87.5\% MMLU accuracy across six models), and distributed analytics over hundreds of nodes. PMSR establishes a scalable, equitable, and privacy-guaranteed foundation for the next generation of AI data markets.

cs.CR