3,677 research outputs found
Early Afterglows of Gamma-Ray Bursts in a Stratified Medium with a Power-Law Density Distribution
A long-duration gamma-ray burst (GRB) has been widely thought to arise from
the collapse of a massive star, and it has been suggested that its ambient
medium is a homogenous interstellar medium (ISM) or a stellar wind. There are
two shocks when an ultra-relativistic fireball that has been ejected during the
prompt gamma-ray emission phase sweeps up the circumburst medium: a reverse
shock that propagates into the fireball, and a forward shock that propagates
into the ambient medium. In this paper, we investigate the temporal evolution
of the dynamics and emission of these two shocks in an environment with a
general density distribution of (where is the radius) by
considering thick-shell and thin-shell cases. A GRB afterglow with one smooth
onset peak at early times is understood to result from such external shocks.
Thus, we can determine the medium density distribution by fitting the onset
peak appearing in the light curve of an early optical afterglow. We apply our
model to 19 GRBs, and find that their values are in the range of 0.4 - 1.4,
with a typical value of , implying that this environment is neither a
homogenous interstellar medium with nor a typical stellar wind with
. This shows that the progenitors of these GRBs might have undergone a new
mass-loss evolution.Comment: 32 pages, 5 figures, 1 table, published in Ap
Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications
This work concerns receiver design for light emitting diode (LED)
communications where the LED nonlinearity can severely degrade the performance
of communications. We propose extreme learning machine (ELM) based
non-iterative receivers and iterative receivers to effectively handle the LED
nonlinearity and memory effects. For the iterative receiver design, we also
develop a data-aided receiver, where data is used as virtual training sequence
in ELM training. It is shown that the ELM based receivers significantly
outperform conventional polynomial based receivers; iterative receivers can
achieve huge performance gain compared to non-iterative receivers; and the
data-aided receiver can reduce training overhead considerably. This work can
also be extended to radio frequency communications, e.g., to deal with the
nonlinearity of power amplifiers
- …