2,389 research outputs found
The February 5, 1965 solar proton event 2 - Low energy proton observations and their relation to the magnetosphere
Temporal and spatial behavior of low energy solar protons in magnetospher
Proton energy into the magnetosphere on 26 May 1967
Proton entry into magnetosphere over polar cap on 26 May 196
Solar Protons and Magnetic Storms in July 1961
Injun i satellite observations of solar protons and magnetic storm
Relativistic Disk Reflection in the Neutron Star X-ray Binary XTE J1709-267 with NuSTAR
We perform the first reflection study of the soft X-ray transient and Type 1
burst source XTE J1709-267 using NuSTAR observations during its 2016 June
outburst. There was an increase in flux near the end of the observations, which
corresponds to an increase from 0.04 L to 0.06
L assuming a distance of 8.5 kpc. We have separately examined
spectra from the low and high flux intervals, which were soft and show evidence
of a broad Fe K line. Fits to these intervals with relativistic disk reflection
models have revealed an inner disk radius of (where
) for the low flux spectrum and
for the high flux spectrum at the 90\% confidence level. The disk is likely
truncated by a boundary layer surrounding the neutron star or the
magnetosphere. Based on the measured luminosity and using the accretion
efficiency for a disk around a neutron star, we estimate that the theoretically
expected size for the boundary layer would be from the
neutron star's surface, which can be increased by spin or viscosity effects.
Another plausible scenario is that the disk could be truncated by the
magnetosphere. We place a conservative upper limit on the strength of the
magnetic field at the poles, assuming and , of
G, though X-ray pulsations have not been detected
from this source.Comment: Accepted for publication in ApJ, 5 pages, 4 figures, 1 table. arXiv
admin note: text overlap with arXiv:1701.0177
Prediction and explanation in the multiverse
Probabilities in the multiverse can be calculated by assuming that we are
typical representatives in a given reference class. But is this class well
defined? What should be included in the ensemble in which we are supposed to be
typical? There is a widespread belief that this question is inherently vague,
and that there are various possible choices for the types of reference objects
which should be counted in. Here we argue that the ``ideal'' reference class
(for the purpose of making predictions) can be defined unambiguously in a
rather precise way, as the set of all observers with identical information
content. When the observers in a given class perform an experiment, the class
branches into subclasses who learn different information from the outcome of
that experiment. The probabilities for the different outcomes are defined as
the relative numbers of observers in each subclass. For practical purposes,
wider reference classes can be used, where we trace over all information which
is uncorrelated to the outcome of the experiment, or whose correlation with it
is beyond our current understanding. We argue that, once we have gathered all
practically available evidence, the optimal strategy for making predictions is
to consider ourselves typical in any reference class we belong to, unless we
have evidence to the contrary. In the latter case, the class must be
correspondingly narrowed.Comment: Minor clarifications adde
An Infrared Divergence Problem in the cosmological measure theory and the anthropic reasoning
An anthropic principle has made it possible to answer the difficult question
of why the observable value of cosmological constant (
GeV) is so disconcertingly tiny compared to predicted value of vacuum
energy density GeV. Unfortunately, there is a
darker side to this argument, as it consequently leads to another absurd
prediction: that the probability to observe the value for randomly
selected observer exactly equals to 1. We'll call this controversy an infrared
divergence problem. It is shown that the IRD prediction can be avoided with the
help of a Linde-Vanchurin {\em singular runaway measure} coupled with the
calculation of relative Bayesian probabilities by the means of the {\em
doomsday argument}. Moreover, it is shown that while the IRD problem occurs for
the {\em prediction stage} of value of , it disappears at the {\em
explanatory stage} when has already been measured by the observer.Comment: 9 pages, RevTe
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