Using Extreme Value Theory for Determining the Probability of Carrington-Like Solar Flares

Space weather events can negatively affect satellites, the electricity grid, satellite navigation systems and human health. As a consequence, extreme space weather has been added to the UK and other national risk registers. By their very nature, extreme space weather events occur rarely and, therefore, statistical methods are required to determine the probability of their occurrence. Space weather events can be characterised by a number of natural phenomena such as X-ray (solar) flares, solar energetic particle (SEP) fluxes, coronal mass ejections and various geophysical indices (Dst, Kp, F10.7). In this paper extreme value theory (EVT) is used to investigate the probability of extreme solar flares. Previous work has assumed that the distribution of solar flares follows a power law. However such an approach can lead to a poor estimation of the return times of such events due to uncertainties in the tails of the probability distribution function. Using EVT and GOES X-ray flux data it is shown that the expected 150-year return level is approximately an X60 flare whilst a Carrington-like flare is a one in a 100-year event. It is also shown that the EVT results are consistent with flare data from the Kepler space telescope mission.Comment: 13 pages, 4 figures; updated content following reviewer feedbac

Pulling Out All the Stops: Searching for RPV SUSY with Stop-Jets

If the lighter stop eigenstate decays directly to two jets via baryonic R-parity violation, it could have escaped existing LHC and Tevatron searches in four-jet events, even for masses as small as 100 GeV. In order to recover sensitivity in the face of increasingly harsh trigger requirements at the LHC, we propose a search for stop pairs in the highly-boosted regime, using the approaches of jet substructure. We demonstrate that the four-jet triggers can be completely bypassed by using inclusive jet-H_T triggers, and that the resulting QCD continuum background can be processed by substructure methods into a featureless spectrum suitable for a data-driven bump-hunt down to 100 GeV. We estimate that the LHC 8 TeV run is sensitive to 100 GeV stops with decays of any flavor at better than 5-sigma level, and could place exclusions up to 300 GeV or higher. Assuming Minimal Flavor Violation and running a b-tagged analysis, exclusion reach may extend up to nearly 400 GeV. Longer-term, the 14 TeV LHC at 300/fb could extend these mass limits by a factor of two, while continuing to improve sensitivity in the 100 GeV region.Comment: 28 pages, 10 figure

Analysis of estimation methods for the extremal index

Many datasets present time-dependent variation and short-term clustering within extreme values. The extremal index is a primary measure to evaluate clustering of high values in a stationary sequence. Estimation procedures are based on the choice of a threshold and/or a declustering parameter or a block size. Here we revise several different methods and compare them through simulation. In particular, we will see that a recent declustering methodology may be useful for the popular runs estimator and for a new estimator that works under the validation of a local dependence condition. An application to real data is also presented.Fundação para a Ciência e Tecnologia (FCT)info:eu-repo/semantics/publishedVersio

Parallelizing Windowed Stream Joins in a Shared-Nothing Cluster

The availability of large number of processing nodes in a parallel and distributed computing environment enables sophisticated real time processing over high speed data streams, as required by many emerging applications. Sliding window stream joins are among the most important operators in a stream processing system. In this paper, we consider the issue of parallelizing a sliding window stream join operator over a shared nothing cluster. We propose a framework, based on fixed or predefined communication pattern, to distribute the join processing loads over the shared-nothing cluster. We consider various overheads while scaling over a large number of nodes, and propose solution methodologies to cope with the issues. We implement the algorithm over a cluster using a message passing system, and present the experimental results showing the effectiveness of the join processing algorithm.Comment: 11 page

Return period curves for extreme 5-min rainfall amounts at the Barcelona urban network

Heavy rainfall episodes are relatively common in the conurbation of Barcelona and neighbouring cities (NE Spain), usually due to storms generated by convective phenomena in summer and eastern and south-eastern advections in autumn. Prevention of local flood episodes and right design of urban drainage have to take into account the rainfall intensity spread instead of a simple evaluation of daily rainfall amounts. The database comes from 5-min rain amounts recorded by tipping buckets in the Barcelona urban network along the years 1994–2009. From these data, extreme 5-min rain amounts are selected applying the peaks-over-threshold method for thresholds derived from both 95% percentile and the mean excess plot. The return period curves are derived from their statistical distribution for every gauge, describing with detail expected extreme 5-min rain amounts across the urban network. These curves are compared with those derived from annual extreme time series. In this way, areas in Barcelona submitted to different levels of flood risk from the point of view of rainfall intensity are detected. Additionally, global time trends on extreme 5-min rain amounts are quantified for the whole network and found as not statistically significant.Peer ReviewedPostprint (author's final draft

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks constructed from real-world data sets (a location-based social media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis --- such as community structure and motif analysis --- of the reconstructed networks

Impact of Returns Time Dependency on the Estimation of Extreme Market Risk

The estimation of Value-at-Risk generally used models assuming independence. However, financial returns tend to occur in clusters with time dependency. In this paper we study the impact of negligence of returns dependency in market risk assessment. The main methods which take into account returns dependency to assess market risk are: Declustering, Extremal index and Time series-Extreme Value The- ory combination. Results shows an important reduction of the estimation error under dependency assumption. For real data, methods which take into account returns dependency have generally the best performances.Value-at-Risk, Market risk, Dependency, Declustering, Extremal index, Time Series-EVT Combination.