We present a comparison of the measured cosmic ray (CR) muon fluxes from two identical detectors at different geolocations and their sensitivity to space weather events in real time. The first detector is installed at Mount Wilson Observatory, CA, USA (geomagnetic cutoff rigidity Rc $\sim$ 4.88 GV), and the second detector is running on the downtown campus of Georgia State University in Atlanta, GA, USA (Rc $\sim$3.65 GV). The variation of the detected muon fluxes is compared to the changes of the interplanetary solar wind parameters at L1 Lagrange point and geomagnetic indexes. We have also investigated the muon flux behavior during major interplanetary shock events and geomagnetic disturbances. To validate the interpretation of the measured muon signals, the muon fluxes are compared to the neutron flux measurement from the Oulu neutron monitor (NM) in northern Finland (Rc $\sim$0.8 GV). The results of this analysis show that the cosmic ray flux percentage changes from all stations are significantly correlated with each other and with solar wind parameters at L1, and the decreases of the muon fluxes can sometimes be observed several hours ahead of the onsets of the interplanetary shock arrivals at L1 and geomagnetic disturbances. Although this is yet an initial effort of building a global network of cosmic ray muon detectors for monitoring the space and earth weather in real time, the study provides evidence that muon network detection efficiency can be a diagnostic and forecasting tool for geomagnetic storms hours before they hit the Earth.

Solar Energy Particles (SEPs) can be associated with solar flares and coronal mass ejections (CMEs) and offer energy spectra ranging from few KeVs to many GeVs. These events can occur without any notable indication and alter the radiation environment of the inner solar systems, which can potentially lead to precarious conditions for humans in space, affect the interior of spacecraft’s sensitive electronics, and trigger radio blackouts. Identifying the most critical physical parameters of the Solar Dynamic Observatory (SDO) to detect SEPs can allow for a swift response against its adverse effects. With the profusion of high-quality time series data from the SDO, which accounts for the modulating background of magnetic activity and the inherently dynamic phenomenon of pre-flares and post-flare phases; antithetical to non-representative data with the point-in-time measurements employed earlier, selection of vital parameters for solar flare classification using machine learning algorithms appears to be a well-fitted problem in this realm. The primary issue of dealing with multivariate time series data (mvts) is the large number of physical parameters operating at a rapid frequency, making the data dimensionality very high and thus causing the learning process to curb. Moreover, manually selecting vital parameters is a tedious and costly task on which experts may not always agree on the results. In response, we examined feature subset selection using multiple algorithms on both mvts data and the statistical features derived from mvts segments (vectorized data). We used the SWAN-SF (Space Weather Analytics for Solar Flares) benchmark dataset collected from May 2010 - September 2018 to conduct our experiments. The comprehensive study gives a stable scheme to recognize the critical physical parameters, which boosts the learning process and can be used as a blueprint to foretell future solar flare episodes.