The origin of the optical flashes: The case study of GRB 080319B and GRB 130427A

Correlations between optical flashes and gamma-ray emissions in gamma-ray bursts have been searched in order to clarify the question whether these emissions occur at internal and/or external shocks. Among the most powerful gamma-ray bursts ever recorded are GRB 080319B and GRB 130427A which at early phase presented bright optical flashes possible correlated with $\gamma$-ray components. Additionally, both bursts were fortuitously located within the field of view of the TeV $\gamma$-ray Milagro and HAWC observatories, and although no statistically significant excess of counts were collected, upper limits were placed on the GeV - TeV emission. Considering the synchrotron self-Compton emission from internal shocks and requiring the GeV-TeV upper limits we found that the optical flashes and the $\gamma$-ray components are produced by different electron populations. Analyzing the optical flashes together the multiwavelength afterglow observation, we found that these flashes can be interpreted in the framework of the synchrotron reverse-shock model when outflows have arbitrary magnetizations.Comment: 9 pages, 5 figures and 4 tables. Accepted for publication in Ap

Gamma-Ray Bursts: Temporal Scales and the Bulk Lorentz Factor

For a sample of Swift and Fermi GRBs, we show that the minimum variability timescale and the spectral lag of the prompt emission is related to the bulk Lorentz factor in a complex manner: For small $\Gamma$'s, the variability timescale exhibits a shallow (plateau) region. For large $\Gamma$'s, the variability timescale declines steeply as a function of $\Gamma$ ($\delta T\propto\Gamma^{-4.05\pm0.64}$). Evidence is also presented for an intriguing correlation between the peak times, t$_p$, of the afterglow emission and the prompt emission variability timescale.Comment: Accepted for publication in Ap

Laser thermoelastic generation in metals above the melt threshold

An approach is presented for calculating thermoelastic generation of ultrasound in a metal plate exposed to nanosecond pulsed laser heating, sufficient to cause melting but not ablation. Detailed consideration is given to the spatial and temporal profiles of the laser pulse, penetration of the laser beam into the sample, the appearance and subsequent growth and then contraction of the melt pool, and the time dependent thermal conduction in the melt and surrounding solid throughout. The excitation of the ultrasound takes place during and shortly after the laser pulse and occurs predominantly within the thermal diffusion length of a micron or so beneath the surface. It is shown how, because of this, the output of the thermal simulations can be expressed as axially symmetric transient radial and normal surface force distributions. The epicentral displacement response to these force distributions is obtained by two methods, the one based on the elastodynamic Green’s functions for plate geometry determined by the Cagniard generalized ray method and the other using a finite element numerical method. The two approaches are in very close agreement. Numerical simulations are reported on the epicentral displacement response of a 3.12mm thick tungsten plate irradiated with a 4 ns pulsed laser beam with Gaussian spatial profile, at intensities below and above the melt threshol

Learning of Signaling Networks: Molecular Mechanisms

Molecular processes of neuronal learning have been well described. However, learning mechanisms of non-neuronal cells are not yet fully understood at the molecular level. Here, we discuss molecular mechanisms of cellular learning, including conformational memory of intrinsically disordered proteins (IDPs) and prions, signaling cascades, protein translocation, RNAs [miRNA and long noncoding RNA (lncRNA)], and chromatin memory. We hypothesize that these processes constitute the learning of signaling networks and correspond to a generalized Hebbian learning process of single, non-neuronal cells, and we discuss how cellular learning may open novel directions in drug design and inspire new artificial intelligence methods. © 2020 The Author