Modelling and Prediction of Global Magnetic Disturbance in Near-Earth Space: a Case Study for Kp Index using NARX Models

Severe geomagnetic disturbances can be hazardous for mod-ern technological systems. The reliable forecast of parameters related to thestate of the magnetosphere can facilitate the mitigation of adverse effects ofspace weather. This study is devoted to the modeling and forecasting of theevolution of the Kp index related to global geomagnetic disturbances. Through-out this work the Nonlinear AutoRegressive with eXogenous inputs (NARX)methodology is applied. Two approaches are presented: i) a recursive slid-ing window approach, and ii) a direct approach. These two approaches arestudied separately and are then compared to evaluate their performances.It is shown that the direct approach outperforms the recursive approach, butboth tend to produce predictions slightly biased from the true values for lowand high disturbances

A novel logistic-NARX model as a classifier for dynamic binary classification

System identification and data-driven modeling techniques have seen ubiquitous applications in the past decades. In particular, parametric modeling methodologies such as linear and nonlinear autoregressive with exogenous input models (ARX and NARX) and other similar and related model types have been preferably applied to handle diverse data-driven modeling problems due to their easy-to-compute linear-in-the-parameter structure, which allows the resultant models to be easily interpreted. In recent years, several variations of the NARX methodology have been proposed that improve the performance of the original algorithm. Nevertheless, in most cases, NARX models are applied to regression problems where all output variables involve continuous or discrete-time sequences sampled from a continuous process, and little attention has been paid to classification problems where the output signal is a binary sequence. Therefore, we developed a novel classification algorithm that combines the NARX methodology with logistic regression and the proposed method is referred to as logistic-NARX model. Such a combination is advantageous since the NARX methodology helps to deal with the multicollinearity problem while the logistic regression produces a model that predicts categorical outcomes. Furthermore, the NARX approach allows for the inclusion of lagged terms and interactions between them in a straight forward manner resulting in interpretable models where users can identify which input variables play an important role individually and/or interactively in the classification process, something that is not achievable using other classification techniques like random forests, support vector machines, and k-nearest neighbors. The efficiency of the proposed method is tested with five case studies

Search for decaying dark matter in the Virgo cluster of galaxies with HAWC

The decay or annihilation of dark matter particles may produce a steady flux of very-high-energy gamma rays detectable above the diffuse background. Nearby clusters of galaxies provide excellent targets to search for the signatures of particle dark matter interactions. In particular, the Virgo cluster spans several degrees across the sky and can be efficiently probed with a wide field-of-view instrument. The High Altitude Water Cherenkov (HAWC) observatory, due to its wide field of view and sensitivity to gamma rays at an energy scale of 300 GeV-100 TeV is well-suited for this search. Using 2141 days of data, we search for γ-ray emission from the Virgo cluster, assuming well-motivated dark matter substructure models. Our results provide some of the strongest constraints on the decay lifetime of dark matter for masses above 10 TeV

Study of the Very High Energy Emission of M87 through its Broadband Spectral Energy Distribution

The radio galaxy M87 is the central dominant galaxy of the Virgo Cluster. Very high-energy (VHE, ≳0.1 TeV) emission from M87 has been detected by imaging air Cherenkov telescopes. Recently, marginal evidence for VHE long-term emission has also been observed by the High Altitude Water Cherenkov Observatory, a gamma-ray and cosmic-ray detector array located in Puebla, Mexico. The mechanism that produces VHE emission in M87 remains unclear. This emission originates in its prominent jet, which has been spatially resolved from radio to X-rays. In this paper, we construct a spectral energy distribution from radio to gamma rays that is representative of the nonflaring activity of the source, and in order to explain the observed emission, we fit it with a lepto-hadronic emission model. We found that this model is able to explain nonflaring VHE emission of M87 as well as an orphan flare reported in 2005

Constraining the pˉ/p\bar{p}/p Ratio in TeV Cosmic Rays with Observations of the Moon Shadow by HAWC

An indirect measurement of the antiproton flux in cosmic rays is possible as the particles undergo deflection by the geomagnetic field. This effect can be measured by studying the deficit in the flux, or shadow, created by the Moon as it absorbs cosmic rays that are headed towards the Earth. The shadow is displaced from the actual position of the Moon due to geomagnetic deflection, which is a function of the energy and charge of the cosmic rays. The displacement provides a natural tool for momentum/charge discrimination that can be used to study the composition of cosmic rays. Using 33 months of data comprising more than 80 billion cosmic rays measured by the High Altitude Water Cherenkov (HAWC) observatory, we have analyzed the Moon shadow to search for TeV antiprotons in cosmic rays. We present our first upper limits on the $\bar{p}/p$ fraction, which in the absence of any direct measurements, provide the tightest available constraints of $\sim1\%$ on the antiproton fraction for energies between 1 and 10 TeV.Comment: 10 pages, 5 figures. Accepted by Physical Review