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Juan D. Perez-Navarro

Publications and source records attributed to Juan D. Perez-Navarro.

2 recordsLinked to original sources

Morphological Fingerprints of Forbush Decreases and Their Relation to Geomagnetic Storm Severity

Forbush decreases (FDs) are transient depressions in the galactic cosmic-ray flux observed by global neutron-monitor networks and are commonly associated with interplanetary disturbances driven by coronal mass ejections and related shocks. Despite extensive observational work, quantitatively comparing FD morphology across events and linking it to storm severity remains challenging due to heterogeneous station responses, coverage gaps, and the multivariate nature of the network. This work introduces a graph-based event representation in which each FD is mapped to an event network constructed from pairwise dissimilarities between station response time series. A controlled sparse backbone is obtained via the minimum spanning tree, enabling comparable event graphs across cases. From each graph, a compact set of geometric/topological fingerprints is computed, including global integration measures, spectral summaries, mesoscopic structure, centrality aggregates, and complexity descriptors. Predictive skill is assessed using strict leave-one-event-out validation over a pre-defined grid of distance metrics and distance-domain transformations, with selection criteria fixed \emph{a priori}. The proposed fingerprints exhibit measurable signal for three tasks: (i) multi-class classification of geomagnetic storm intensity (G3/G4/G5) with moderate but consistent performance and errors dominated by adjacent categories; (ii) stronger binary severity screening ($\ge$G4 vs. G3) with high sensitivity to severe events; and (iii) drop regression with partial least squares achieving positive explained variance relative to a fold-wise mean baseline.

astro-ph.IM↗

Derivative-Aligned Anticipation of Forbush Decreases from Entropy and Fractal Markers

We develop a feature-based framework to anticipate Forbush decreases in one-minute neutron-monitor records by tracking sliding-window invariants from information theory, scaling, and geometry. For each station we compute marker time series, including Shannon, spectral, approximate and sample entropy; Lempel-Ziv complexity; correlation dimension; and Higuchi and Katz fractal dimensions. Markers are smoothed with an exponentially weighted moving average and analyzed through within-station standardized first differences. Timing is referenced to an operational alignment time defined as the minimum of the smoothed count first difference, and marker leads are reported in minutes (negative values indicate anticipation). Station-level detectability is evaluated on a pre-alignment window using a robust z-score detector with bilateral threshold and persistence, without cross-correlation or hypothesis testing. We apply the pipeline to two FD episodes with broad station coverage (2023-04-23 and 2024-05-10; 28 stations each). Across events, a compact CORE panel shows consistently high detection rates and predominantly anticipatory lead distributions, with typical median leads on the order of several hours depending on the invariant and event. Lead dispersion across stations is substantial, with interquartile ranges commonly spanning a few hours, highlighting the need for station-wise criteria and distributional summaries rather than single-station inference. Representative marker trajectories confirm that early flagging corresponds to sustained pre-alignment excursions in marker differences, not tabulation artifacts. The approach is reproducible from open code, operates on native station units without cross-station homogenization, and remains qualitatively stable under sensitivity sweeps of windowing, smoothing, and detector parameters.

astro-ph.IM↗