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Time-dependent energy spectra of galactic cosmic rays (GCRs) carry crucial information regarding their origin and propagation throughout the interstellar environment. When observed at the Earth, after traversing the interplanetary medium, such spectra are heavily affected by the solar wind and the embedded solar magnetic field permeating the inner sectors of the heliosphere. The activity of the Sun changes significantly over an 11 yr solar cycle—and so does the effect on cosmic particles; this translates into a phenomenon called solar modulation. Moreover, GCR spectra during different epochs of solar activity provide invaluable information for a complete understanding of the plethora of mechanisms taking place in various layers of the Sun’s atmosphere and how they evolve over time. The High-Energy Particle Detector (HEPD-01) has been continuously collecting data since 2018 August, during the quiet phase between solar cycles 24 and 25; the activity of the Sun is slowly but steadily rising and is expected to peak around 2025/2026. In this paper, we present the first spectra for ∼50–250 MeV galactic protons measured by the HEPD-01 instrument—placed on board the CSES-01 satellite—from 2018 August to 2022 March over a one-Carrington-rotation time basis. Such data are compared to the ones from other spaceborne experiments, present (e.g., EPHIN, Parker Solar Probe) and past (PAMELA), and to a state-of-the-art three-dimensional model describing the GCRs propagation through the heliosphere.

High-energy, long gamma-ray bursts (GRBs) can be generated by the core collapse of massive stars at the end of their lives. When they happen in the close-by universe they can be exceptionally bright, as seen from the Earth in the case of the recent, giant, long-lasting GRB221009A. GRB221009A was produced by a collapsing star with a redshift of 0.152: this event was observed by many gamma-ray space experiments, which also detected an extraordinary long gamma-ray afterglow. The exceptionally large fluence of the prompt emission of about 0.013 erg cm ^−2 illuminated a large geographical region centered on India and including Europe and Asia. We report in this paper the observation of sudden electron flux changes correlated with GRB221009A and measured by the HEPP-L charged particle detector on board the China Seismo-Electromagnetic Satellite, which was orbiting over Europe at the time of the GRB event. The time structure of the observed electron flux closely matches the very distinctive time dependence of the photon flux associated with the main part of the emission at around 13:20 UTC on 2022 October 9. To test the origin of these signals, we set up a simplified simulation of one HEPP-L subdetector: the results of this analysis suggest that the signals observed are mostly due to electrons created within the aluminum collimator surrounding the silicon detector, providing real-time monitoring of the very intense photon fluxes. We discuss the implications of this observation for existing and forthcoming particle detectors on low Earth orbits.

In this paper we report the detection of five strong gamma-ray bursts (GRBs) by the High-Energy Particle Detector (HEPD-01) mounted on board the China Seismo-Electromagnetic Satellite, operational since 2018 on a Sun-synchronous polar orbit at a ∼507 km altitude and 97° inclination. HEPD-01 was designed to detect high-energy electrons in the energy range 3–100 MeV, protons in the range 30–300 MeV, and light nuclei in the range 30–300 MeV n ^−1 . Nonetheless, Monte Carlo simulations have shown HEPD-01 is sensitive to gamma-ray photons in the energy range 300 keV–50 MeV, even if with a moderate effective area above ∼5 MeV. A dedicated time correlation analysis between GRBs reported in literature and signals from a set of HEPD-01 trigger configuration masks has confirmed the anticipated detector sensitivity to high-energy photons. A comparison between the simultaneous time profiles of HEPD-01 electron fluxes and photons from GRB190114C, GRB190305A, GRB190928A, GRB200826B, and GRB211211A has shown a remarkable similarity, in spite of the different energy ranges. The high-energy response, with peak sensitivity at about 2 MeV, and moderate effective area of the detector in the actual flight configuration explain why these five GRBs, characterized by a fluence above ∼3 × 10 ^−5 erg cm ^−2 in the energy interval 300 keV–50 MeV, have been detected.

We investigate the magnetic response of transmission lines with embedded Josephson junctions and thus generating a 1D underdamped array. The measured multi-junction interference patterns are compared with the theoretical predictions for Josephson supercurrent modulations when an external magnetic field couples both to the inter-junction loops and to the junctions themselves. The results provide a striking example of the analogy between Josephson phase modulation and 1D optical diffraction grid. The Fiske resonances in the current-voltage characteristics with voltage spacing , where L is the total physical length of the array, the magnetic flux quantum and the speed of light in the transmission line, demonstrate that the discrete line supports stable dynamic patterns generated by the ac Josephson effect interacting with the cavity modes of the line. [ABSTRACT FROM AUTHOR]

Earth's atmosphere, whose ionization stability plays a fundamental role for the evolution and endurance of life, is exposed to the effect of cosmic explosions producing high energy Gamma-ray-bursts. Being able to abruptly increase the atmospheric ionization, they might deplete stratospheric ozone on a global scale. During the last decades, an average of more than one Gamma-ray-burst per day were recorded. Nevertheless, measurable effects on the ionosphere were rarely observed, in any case on its bottom-side (from about 60 km up to about 350 km of altitude). Here, we report evidence of an intense top-side (about 500 km) ionospheric perturbation induced by significant sudden ionospheric disturbance, and a large variation of the ionospheric electric field at 500 km, which are both correlated with the October 9, 2022 Gamma-ray-burst (GRB221009A)., (© 2023. The Author(s).)

We report on microwave emission from linear parallel arrays of underdamped Josephson junctions, which are described by the Frenkel-Kontorova (FK) model. Electromagnetic radiation is detected from the arrays when biased on current singularities (steps) appearing at voltages V(n)=Φ(0)(nc̅/L), where Φ(0)=2.07×10(-15)  Wb is the magnetic flux quantum, and c̅, L, and n are, respectively, the speed of light in the transmission line embedding the array, L its physical length, and n an integer. The radiation, detected at fundamental frequency c̅/2L when biased on different singularities, indicates shuttling of bunched 2π kinks (magnetic flux quanta). Resonance of flux-quanta motion with the small-amplitude oscillations induced in the arrays gives rise to fine structures in the radiation spectrum, which are interpreted on the basis of the FK model describing the resonance. The impact of our results on design and performances of new digital circuit families is discussed.

We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and external connectivity with address-event representation compliant devices. We demonstrate a marked improvement in the efficiency of the network in classifying correlated patterns, owing to the self-regulating mechanism.

Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in the electronic network.

We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short timescales and preserves the memory of two stable states (efficacies) on long timescales. The transitions (LTP/LTD) are stochastic because both the number and the distribution of neural spikes in any finite (stimulation) interval fluctuate, even at fixed pre- and postsynaptic spike rates. The dynamics of the single synapse is studied analytically by extending the solution to a classic problem in queuing theory (Takacs process). The model of the synapse is implemented in aVLSI and consists of only 18 transistors. It is also directly simulated. The simulations indicate that LTP/LTD probabilities versus rates are robust to fluctuations of the electronic parameters in a wide range of rates. The solutions for these probabilities are in very good agreement with both the simulations and measurements. Moreover, the probabilities are readily manipulable by variations of the chip's parameters, even in ranges where they are very small. The tests of the electronic device cover the range from spontaneous activity (3-4 Hz) to stimulus-driven rates (50 Hz). Low transition probabilities can be maintained in all ranges, even though the intrinsic time constants of the device are short (approximately 100 ms). Synaptic transitions are triggered by elevated presynaptic rates: for low presynaptic rates, there are essentially no transitions. The synaptic device can preserve its memory for years in the absence of stimulation. Stochasticity of learning is a result of the variability of interspike intervals; noise is a feature of the distributed dynamics of the network. The fact that the synapse is binary on long timescales solves the stability problem of synaptic efficacies in the absence of stimulation. Yet stochastic learning theory ensures that it does not affect the collective behavior of the network, if the transition probabilities are low and LTP is balanced against LTD.

LANN27 is an electronic device implementing in discrete electronics a fully connected (full feedback) network of 27 neurons and 351 plastic synapses with stochastic Hebbian learning. Both neurons and synapses are dynamic elements, with two time constants--fast for neurons and slow for synapses. Learning, synaptic dynamics, is analogue and is driven in a Hebbian way by neural activities. Long-term memorization takes place on a discrete set of synaptic efficacies and is effected in a stochastic manner. The intense feedback between the nonlinear neural elements, via the learned synaptic structure, creates in an organic way a set of attractors for the collective retrieval dynamics of the neural system, akin to Hebbian learned reverberations. The resulting structure of the attractors is a record of the large-scale statistics in the uncontrolled, incoming flow of stimuli. As the statistics in the stimulus flow changes significantly, the attractors slowly follow it and the network behaves as a palimpsest--old is gradually replaced by new. Moreover, the slow learning creates attractors which render the network a prototype extractor: entire clouds of stimuli, noisy versions of a prototype, used in training, all retrieve the attractor corresponding to the prototype upon retrieval. Here we describe the process of studying the collective dynamics of the network, before, during and following learning, which is rendered complex by the richness of the possible stimulus streams and the large dimensionality of the space of states of the network. We propose sampling techniques and modes of representation for the outcome.