21,828 research outputs found
Statistical Tests for Scaling in the Inter-Event Times of Earthquakes in California
We explore in depth the validity of a recently proposed scaling law for
earthquake interevent time distributions in the case of the Southern
California, using the waveform cross-correlation catalog of Shearer et al. Two
statistical tests are used: on the one hand, the standard two-sample
Kolmogorov-Smirnov test is in agreement with the scaling of the distributions.
On the other hand, the one-sample Kolmogorov-Smirnov statistic complemented
with Monte Carlo simulation of the inter-event times, as done by Clauset et
al., supports the validity of the gamma distribution as a simple model of the
scaling function appearing on the scaling law, for rescaled inter-event times
above 0.01, except for the largest data set (magnitude greater than 2). A
discussion of these results is provided.Comment: proceedings of Erice conference, 200
Scaling in the Timing of Extreme Events
Extreme events can come either from point processes, when the size or energy
of the events is above a certain threshold, or from time series, when the
intensity of a signal surpasses a threshold value. We are particularly
concerned by the time between these extreme events, called respectively waiting
time and quiet time. If the thresholds are high enough it is possible to
justify the existence of scaling laws for the probability distribution of the
times as a function of the threshold value, although the scaling functions are
different in each case. For point processes, in addition to the trivial Poisson
process, one can obtain double-power-law distributions with no finite mean
value. This is justified in the context of renormalization-group
transformations, where such distributions arise as limiting distributions after
iterations of the transformation. Clear connections with the generalized
central limit theorem are established from here. The non-existence of finite
moments leads to a semi-parametric scaling law in terms of the sample mean
waiting time, in which the (usually unkown) scale parameter is eliminated but
not the exponents. In the case of time series, scaling can arise by considering
random-walk-like signals with absorbing boundaries, resulting in distributions
with a power-law "bulk" and a faster decay for long times. For large thresholds
the moments of the quiet-time distribution show a power-law dependence with the
scale parameter, and isolation of the latter and of the exponents leads to a
non-parametric scaling law in terms only of the moments of the distribution.
Conclusions about the projections of changes in the occurrence of natural
hazards lead to the necessity of distinguishing the behavior of the mean of the
distribution with the behavior of the extreme events.Comment: Submitted to a Chaos, Solitons and Fractals special issue on Extreme
Event
Relativistic heavy-ion physics
The study of relativistic heavy-ion collisions is an important part of the
LHC research programme at CERN. This emerging field of research focuses on the
study of matter under extreme conditions of temperature, density, and pressure.
Here we present an introduction to the general aspects of relativistic
heavy-ion physics. Afterwards we give an overview of the accelerator facility
at CERN and then a quick look at the ALICE project as a dedicated experiment
for heavy-ion collisions.Comment: 23 pages, Lectures given at the 5th CERN-Latin-American School of
High-Energy Physics, Recinto Quirama, Colombia, 15 - 28 Mar 200
Fitting and goodness-of-fit test of non-truncated and truncated power-law distributions
Power-law distributions contain precious information about a large variety of
processes in geoscience and elsewhere. Although there are sound theoretical
grounds for these distributions, the empirical evidence in favor of power laws
has been traditionally weak. Recently, Clauset et al. have proposed a
systematic method to find over which range (if any) a certain distribution
behaves as a power law. However, their method has been found to fail, in the
sense that true (simulated) power-law tails are not recognized as such in some
instances, and then the power-law hypothesis is rejected. Moreover, the method
does not work well when extended to power-law distributions with an upper
truncation. We explain in detail a similar but alternative procedure, valid for
truncated as well as for non-truncated power-law distributions, based in
maximum likelihood estimation, the Kolmogorov-Smirnov goodness-of-fit test, and
Monte Carlo simulations. An overview of the main concepts as well as a recipe
for their practical implementation is provided. The performance of our method
is put to test on several empirical data which were previously analyzed with
less systematic approaches. The databases presented here include the half-lives
of the radionuclides, the seismic moment of earthquakes in the whole world and
in Southern California, a proxy for the energy dissipated by tropical cyclones
elsewhere, the area burned by forest fires in Italy, and the waiting times
calculated over different spatial subdivisions of Southern California. We find
the functioning of the method very satisfactory.Comment: 26 pages, 9 figure
Variability of North Atlantic hurricanes: seasonal versus individual-event features
Tropical cyclones are affected by a large number of climatic factors, which
translates into complex patterns of occurrence. The variability of annual
metrics of tropical-cyclone activity has been intensively studied, in
particular since the sudden activation of the N Atl in the mid 1990's. We
provide first a swift overview on previous work by diverse authors about these
annual metrics for the NAtl basin, where the natural variability of the
phenomenon, the existence of trends, the drawbacks of the records, and the
influence of global warming have been the subject of interesting debates. Next,
we present an alternative approach that does not focus on seasonal features but
on the characteristics of single events [Corral et al Nature Phys 6, 693,
2010]. It is argued that the individual-storm power dissipation index (PDI)
constitutes a natural way to describe each event, and further, that the PDI
statistics yields a robust law for the occurrence of tropical cyclones in terms
of a power law. In this context, methods of fitting these distributions are
discussed. As an important extension to this work we introduce a distribution
function that models the whole range of the PDI density (excluding
incompleteness effects at the smallest values), the gamma distribution,
consisting in a power-law with an exponential decay at the tail. The
characteristic scale of this decay, represented by the cutoff parameter,
provides very valuable information on the finiteness size of the basin, via the
largest values of the PDIs that the basin can sustain. We use the gamma fit to
evaluate the influence of sea surface temperature (SST) on the occurrence of
extreme PDI values, for which we find an increase around 50 % in the values of
these basin-wide events for a 0.49 degC SST average difference. ...Comment: final version available soon in the 1st author's web,
http://www.crm.cat/Researchers/acorral/Pages/PersonalInformation.asp
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