The proton spectrum in the kinetic energy range 0.1 to 200 GeV was measured by the Alpha Magnetic Spectrometer (AMS) during space shuttle flight STS-91 at an altitude of 380 km. Above the geomagnetic cutoff the observed spectrum is parameterized by a power law. Below the geomagnetic cutoff a substantial second spectrum was observed concentrated at equatorial latitudes with a flux ~ 70 m^-2 sec^-1 sr^-1. Most of these second spectrum protons follow a complicated trajectory and originate from a restricted geographic region.Comment: 19 pages, Latex, 7 .eps figure
The Alpha Magnetic Spectrometer (AMS) was flown on the space shuttle Discovery during flight STS-91 in a 51.7• orbit at altitudes between 320 and 390 km. A total of 2.86 × 10 6 helium nuclei were observed in the rigidity range 1 to 140 GV. No antihelium nuclei were detected at any rigidity. An upper limit on the flux ratio of antihelium to helium of < 1.1 × 10 −6 is obtained.Submitted to Phys. Lett. B
We present theoretical and computational analyses of energy conversions in a magnetized collisionless plasma. We first revisit the theoretical approach to energy conversion analysis and discuss the expected correlations between the different conversion terms. We then present results from a Hybrid‐Vlasov simulation of a turbulent plasma, focusing on the immediate vicinity of a reconnection site. Energy transfers are examined locally and correlations between them are discussed in detail. We show a good anticorrelation between pressure‐driven and electromagnetic acceleration terms. A similar but weaker anticorrelation is found between the heat flux and thermodynamic work acting on internal energies. It is the departure from these anticorrelations that drives the effective changes in the species’ kinetic and internal energies. We also show that overall energy gain or loss is statistically related to the local scale of the system, with higher conversion rates occurring mostly at the smallest local plasma scales. To summarize, we can say that the energization and de‐energization of a plasma is the result of the complex interplay between multiple electromagnetic and thermodynamic effects, which are best taken into account via such a point‐by‐point analysis of the system.
Magnetic reconnection is a fundamental process that quickly releases magnetic energy stored in a plasma. Identifying from simulation outputs where reconnection is taking place is nontrivial and, in general, has to be performed by human experts. Hence, it would be valuable if such an identification process could be automated. Here, we demonstrate that a machine-learning algorithm can help to identify reconnection in 2D simulations of collisionless plasma turbulence. Using a Hybrid Vlasov Maxwell model, a data set containing over 2000 potential reconnection events was generated and subsequently labeled by human experts. We test and compare two machine-learning approaches with different configurations on this data set. The best results are obtained with a convolutional neural network combined with an “image cropping” step that zooms in on potential reconnection sites. With this method, more than 70% of reconnection events can be identified correctly. The importance of different physical variables is evaluated by studying how they affect the accuracy of predictions. Finally, we also discuss various possible causes for wrong predictions from the proposed model.
Kinetic turbulence in magnetized space plasmas has been extensively studied via in situ observations, numerical simulations, and theoretical models. In this context, a key point concerns the formation of coherent current structures and their disruption through magnetic reconnection. We present automatic techniques aimed at detecting reconnection events in a large data set of numerical simulations. We make use of clustering techniques known as K-means and DBscan (usually referred to in literature as unsupervised machine-learning approaches), and other methods based on thresholds of standard reconnection proxies. All our techniques also use a threshold on the aspect ratio of the regions selected. We test the performance of our algorithms. We propose an optimal aspect ratio to be used in the automated machine-learning algorithm: AR = 18. The performance of the unsupervised approach results in it being strongly competitive with respect to those of other methods based on thresholds of standard reconnection proxies.
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