Morris Cohen | Searcharxiv

arXiv2026

Real-time detection of solar flares from ground-based VLF data

A method for real-time solar flare detection and characterization using ground-based Very Low Frequency (VLF, 15-45 kHz) data is presented. The D-region, the ionosphere's lowest region, is monitored by VLF waves propagating in the Earth-Ionosphere waveguide. The D-region electron density increases during sudden surges in X-ray radiation from solar flares. This subsequently enhances HF absorption. By seeking trend changes in VLF phase data, an incremental algorithm finds solar flares. 82.7% of M and X solar flares are detected within one fourth of their rise time. In addition, several VLF transmitters are monitored simultaneously. Combining information from their phase variations leads to an estimation of the Sun's X-ray flux. Last, propagation models such as LMP or LWPC are combined with the VLF measurements to compute D-region electron density profiles. This method and its implementation in a new Python package are a step towards building a more resilient system for flare detection and alerts. Its reliance on ground-based data alone ensures an easy maintenance and a backup in case a satellite failure. It also provides alerts comparable to or faster than those obtained through satellite data, due to shortened data latency.

astro-ph.IM↗

arXiv2020

Simultaneously forecasting global geomagnetic activity using Recurrent Networks

Many systems used by society are extremely vulnerable to space weather events such as solar flares and geomagnetic storms which could potentially cause catastrophic damage. In recent years, many works have emerged to provide early warning to such systems by forecasting these events through some proxy, but these approaches have largely focused on a specific phenomenon. We present a sequence-to-sequence learning approach to the problem of forecasting global space weather conditions at an hourly resolution. This approach improves upon other work in this field by simultaneously forecasting several key proxies for geomagnetic activity up to 6 hours in advance. We demonstrate an improvement over the best currently known predictor of geomagnetic storms, and an improvement over a persistence baseline several hours in advance.

cs.LG↗