dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses of the HEN sample show no sign of associated GW bursts. This paper is organized as follows. In Section 2 we discuss sources of coincident HEN and GW emission and expected prospects for their detection. In Section 3 we describe the ANTARES, LIGO, and Virgo detectors and the joint data taking period. Section 4 describes how the HEN sample was selected. Section 5 describes the search for GWs coincident in time and direction with the HEN events. We present the results of the search in Section 6. We discuss the astrophysical implications of the results in Section 7 and conclude with considerations of the potential for future joint GW-HEN searches.
(2004)  (but see  Hümmer et al. 2012  for refined results). Assuming a jet with Γ = 300, E jet = 2 × 10 50 erg, one finds a distance d 10 ∼ 10 Mpc at which the probability of HEN detection by the 5-line ANTARES detector is 10%. Hence, only the closest and/or the most powerful SGRBs may be detectable. The GW emission from NS-NS and BH-NS mergers is well studied (see  Shibata & Taniguchi 2011; Faber & Rasio 2012, for reviews). Most of the emission comes from the late inspiral and merger phase during which the binary sweeps through the 50 Hz to 1000 Hz band of highest sensitivity of LIGO/Virgo. The total emitted E GW is of order 10 -2 M c 2 to 10 -1 M c 2. At the time of this analysis the LIGO/Virgo network had maximum sensitive distances of ∼30 Mpc for equal-mass NS-NS binaries and ∼50 Mpc for a BH-NS binary with a mass ratio of 4 : 1, and a dedicated merger search on this data did not find any evidence for GW candidates  (Abadie et al.
The sudden release of energy and magnetic field rearrangement lead to the creation and acceleration of pair plasma that may have some baryon loading, thus leading to the emission of HENs in pγ reactions  (Halzen et al. 2005). Ioka et al. (2005)  estimated the detectability of the 2004 giant flare of the Galactic SGR 1806-20 by HEN detectors. They found that detectors such as IceCube and ANTARES should detect multiple HEN events from similar Galactic SGR eruptions, provided the baryon loading is sufficiently high. The AMANDA-II detector, which was operating during the giant flare of SGR 1806-20, did not detect HENs  (Achterberg et al. 2006). A search of IceCube data for HENs from regular (non-giant) Galactic SGR flares also found no significant coincident events  (Abbasi et al. 2012c ). Estimates based on  Ioka et al.
2006). While not designed specifically for UHENs, HEN detectors like ANTARES and IceCube have some sensitivity to UHENs in the 10 11 GeV energy range. Alvarez-Muñiz & Halzen (2001) predict, depending on details of the underlying emission model, up to a few events per year for a km 3 -scale detector. Since Earth is opaque to UHENs, downgoing events must be selected. Since we are only considering ANTARES data for neutrinos that have passed through Earth (see §3. 1), the present data set does not contain any potential UHEN events. Abbasi et al. (2011a) , who searched a year of IceCube-40 data for very energetic HENs, did not find neutrinos in the 10 6 GeV to 10 9 GeV energy range, but did not report limits on UHENs. A number of dedicated UHEN experiments exist, including ANITA  (Gorham et al.
Joint data taking periods The fifth LIGO science run, S5  (Abbott et al. 2008a) , was held from 2005 November 4 to 2007 October 1. Over one year of science-quality data were collected with all three LIGO interferometers in simultaneous operation at their design sensitivity, with duty factors of 75%, 76%, and 65% for H1, H2, and L1. The Virgo detector started its first science run, VSR1  (Acernese et al. 2008) , on 2007 May 18. The Virgo duty factor over VSR1 was 78%. During this period, ANTARES was operating in its 5 line configuration. The concomitant set of ANTARES 5-line (5L), VSR1 and S5 data covers the period between January 27 and September 30, 2007; these data are the subject of the analysis presented here.
HEN data sample The ANTARES data sample used in the analysis is composed of runs from 2007 selected according to various quality criteria, based mainly on environmental parameters (e. g. sea current, counting rates), configuration and behaviour of the detector during the given run (e. g. duration of the run, alignment of the detector). Two basic quantities are used to characterise the counting rate of a given OM: the baseline rate ( 40 K activity and bioluminescence) and the burst fraction (flashes of light emitted by marine organisms). The baseline rate represents the most probable counting rate of a given OM computed from the rate distributions in each PMT over the whole run (typically a few hours). The burst fraction corresponds to the fraction of time during which the OM counting rates exceed by more than 20% the estimated baseline.
The data selected for this search are required to have a baseline rate below 120 kHz and a burst fraction lower than 40%, with 80% of all OMs being active. With these quality criteria, the active time is 103. 4 days out of the 244. 8 days of the 5-line period. Finally, when restricting the data to the concomitant period with LIGO/Virgo, the remaining equivalent time of observation is T obs = 91 days.
149 Details of each of the HEN candidate events are given at https://dcc. ligo. org/cgi-bin/DocDB/ShowDocument?docid= p1200006. Finally, we note that IceCube operated in its 22-string configuration for part of 2007  (Abbasi et al. 2012c ). However, this data was only used for time-dependent searches applied to source directions with observed Xray or gamma-ray emission, such as GRBs; there were no untriggered, time-dependent searches over the sky. Furthermore, a comparison of ANTARES and IceCube sensitivities in 2007 indicates that the bulk of our HEN neutrino triggers come from declinations (the southern sky) such that it is unlikely that IceCube could have detected the source independently.
Per-HEN GW candidates We analysed GW data in coincidence with 158 neutrino candidates for the low frequency search, and 14 neutrino events for the high frequency search. In the low frequency analysis, only one neutrino trigger had a corresponding GW event with false alarm probability below the threshold of p = 0. 01 to become a candidate event. We found no candidates in the high frequency search. For the low-frequency candidate, additional time shifts totaling 18064 background trials yielded a refined false alarm probability of p = 0. 004, which is not significant given a trials factor of 158 (this statement is quantified below). This event came from analysis of the H1, H2, and V1 data; follow-up checks were performed, including checks of detector performance at the time as indicated by monitoring programs and operator logs, and scans of data from detector and environmental monitoring equipment to look for anomalous behaviour.
GW upper limits The sensitivity of the GW search is determined by a Monte-Carlo analysis. For each neutrino trigger, we add simulated GW signals to the on-source data and repeat the analysis described in Section 5. 2. We consider a simulated signal detected if it produces an event louder than the loudest on-source event after all event tests have been applied. We define a 90% confidence level lower limit on the distance to the source as the maximum distance D 90% such that for any distance D ≤ D 90% the probability of detection is 0. 9 or greater.
ASTROPHYSICAL IMPLICATIONS Observational constraints on joint sources of GW and HEN signals have been derived in  Bartos et al. (2011). However, they are based on the interpretation and the combination of previously published and independent GW and HEN observational results. The results presented in this section are the first derived from a joint GW-HEN analysis, using concomitant data obtained with LIGO/Virgo and ANTARES.
CONCLUSIONS This first joint GW-HEN search using 2007 data, obtained with the ANTARES HEN telescope and the Virgo/LIGO GW interferometers, opens the way to a novel multi-messenger astronomy. Limits on the rate density ρ GW-HEN of joint GW-HEN emitting systems were extracted for the first time using the analysis of coincident GW-HEN data. We note that these limits are consistent with the ones obtained in  Bartos et al. (2011)  derived from independent GW-HEN observations. More stringent limits will be available by performing similar coincidence analyses using other data sets provided by the same instruments. For instance, the sixth LIGO science run S6 and second and third Virgo science runs VSR2,3 covered the period from 7 July 2009 to 21 October 2010. Meanwhile, the ANTARES telescope has taken data with first 10 then 12 active lines since the end of December 2007.
Their enhanced sensitivities should permit a combined analysis to gain the factor required to obtain ρ LGRB GW-HEN ≤ ρ SNII/SNIbc and begin to constrain the fraction of stellar collapse events accompanied by the coincident emission of relativistic jets beamed towards Earth. The analysis of these data is underway, and a similar analysis using data from the LIGO/Virgo S5-VSR1 periods and the IceCube HEN telescope in its 22 string configuration is being finalized. Future observing runs involving IceCube, KM3NeT (Hernandez-Rey 2009), and the advanced LIGO and advanced Virgo projects  (Harry et al. 2010) , are likely to coincide as well. They will give other opportunities to look for potential coincident GW-HEN emissions.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Educación y Ciencia , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the  Polish Ministry of Science and Higher Education , the  FOCUS Programme of Foundation for Polish Science , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , The  National Aeronautics and Space Administration , the  Carnegie Trust , the  Leverhulme Trust , the The  ANTARES Collaboration , the  LIGO Scientific Collaboration  and the  Virgo Collaboration David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
This paper focuses on the assessment of the environmental extreme conditions in term of wind and waves at the SEMREV wave energy test site for application in the design of Marine Renewable Energy (MRE) devices and components. The paper will first present the existing in situ wind and wave measurements. A prediction chain from global to regional scales, and based on a regional wave model calibrated at the SEMREV location, is then described. It enables to build a specific 22 year hindcast dataset for the test site. Long term extrapolation is finally achieved at the SEMREV location using existing methodologies for deep water conditions. Long term extrapolations methods are usually very sensitive to the parameterization and configuration of the prediction chain and we demonstrate in this study that the best overall performances are reached by a POT method at this stage of development in the prediction chain.
INTRODUCTION The wave energy test site SEM-REV is running through a development schedule since October 2007. Consents have been granted in 2010/2011 and installation works finished in October 2012. The test facility will be fully operational by mid-2013. Operations on site are supervised from the ECN land station of Pen Avel in Le Croisic -west coast of France -by a dedicated team with all the required equipment to ensure operability, security and safety of data acquisition, energy converter, control and survey. The SEMREV restricted area (1km 2 , Figure  1 ) is located 12 nautical miles offshore in about 35 meters L. A. T of water depth and opened to Atlantic Ocean's conditions. The SEMREV test site is bordered on his east side Figure  1  Onshore base and station (green dot), electrical cable (purple line), offshore test zone location (4 red dots for the special marks), and DWR Belle-île (purple dot).
by two shoals (Figure  2 , "Banc de Guérande" and "Plateau du Four"). A 8MVA cable connects the test site to an electrical substation nearby the onshore base, which grants access to the local grid. Environmental and system operation control data are transmitted in real time from the offshore test site to the land base through optical fibers. The cable will be associated to a subsea connector by mid-2013 for a first phase of exploitation, and a central subsea hub is planned to enable the connection of several independent test slots by mid-2014. The SEM-REV test site has been equipped with a set of sensors monitoring the environmental conditions since 2009. The instrumentation comprises an array of directional Waverider buoys (DWR). East and West buoys are located inside SEM-REV and DWR Belle-île is located some 40km westward from the site, offshore of the south coast of "Belle-Ile-en-Mer".
We will rather focus here on the evaluation of wind and waves extreme conditions. The assessment of those extreme conditions at sea for a given return period (typical order of tenth of years for dimensioning purposes) generally results from the use of extrapolation methods. However, because of a too high uncertainty level when applied to short duration in-situ reference data, an extrapolation method cannot be directly applied to the 3 years SEMREV measurements. It is however commonly admitted to apply an extrapolation method to hindcast data from a wave model calibrated with real measurements  [1]. This indirect use of in-situ data is crucial for the extrapolation as it enables a qualification of the overall accuracy of a wave model. As a case study, existing methodologies for the assessment of environmental condition in deep water have been applied to the SEMREV test site.
WAVE MODEL A 22-year hindcast simulation (from 01/01/1989 to 28/02/2011) was performed with a combination of the spectral wave models WAVEWATCH III TM  [2]  (hereafter WWATCH) for the global grid and SWAN  [3]  for the regional grid (Simulating Wave Near Shore version 40. 85). WWATCH is parameterized by  Dodet et al. (2010) ,  [4] , following recommendations from  Ardhuin et al. (2009)   [5]. ERA-Interim data are used as wind input instead of the original NCEP/NCAR data due to their better performances in this configuration  [6]. The ERA-Interim data set provided by the European Centre for Medium-Range Weather Forecasting (ECMWF) enables to force the model with 6-h wind fields. The global WWATCH wave hindcast data set provides 6-h input 2D spectra over 32 frequency and 24 directions to the regional grid at its west and south borders (west border following the longitude 3.
5°W from 46. 5°N to 47. 5°N, and south border following the latitude 46. 5°N from 3. 5°W to 2. 0°W). From then, the SWAN model was used to compute the wave evolution in the coastal region and complete the prediction chain up to the SEM-REV test site. Once again the ERA-Interim 6-h wind fields force the model with a spatial resolution of 0. 75°. A coarse computation grid (2km resolution over a 115x112 km area) provides boundary conditions to a refined nested grid (500m resolution over a 60x44 km area, including SEMREV and "Belle-Ile-en-Mer" island) (Figure  4 ). The bathymetric grids for both domains are based on raw high resolution data issued by SHOM (Service Hydrographique et Océanographique de la Marine). The SWAN model is run in a 3 rd generation mode, taking into account a linear wind growth, the exponential wind growth and with-capping term by Janssen (1991), a the bottom friction related to Madsen (1988)'s  [3]  formulation, as well as depth induced breaking and quadruplet and triad non-linear source terms.
LONG TERM EXTRAPOLATION METHODS Several long term extrapolation methods are applied to the ERA Interim wind hindcast data set and to the wave hindcast data previously described. As a first step, the Initial Distribution Approach (IDA)  [10] , combined with a 'censoring' process enabling all values above a given threshold to be used for the extrapolation, is applied to the 33 years wind data set. Figure  8  presents the results of this method in the case of a threshold set to 15m/s. The probability function of occurrence, ordinate of the graph, is expressed by λ * X 1 1 F - = , where X corresponds to the return period in years and λ to a correction factor for the time scales and related to the censoring and data resolutions by.
× --= , with N the total number of elements, t start and t end the initial and final date in years, and f the yearly number of data. Lastly, fitting a Weibull statistical distribution to the measured data leads to a satisfactory agreement (Figure  8 ). If the statistical solution slightly overestimates the extreme values for short return periods (20. 5m/s for a return period of about 3 years), the modeled 33-years maximum wind speed 23. 7m/s lies conservatively within the confidence interval of the statistical law. The IDA method is then applied to the 22-year wave hindcast data set computed with the SWAN regional model. For a too low threshold, the Weibull distribution does not fit properly to the modeled data and the statistical solution systematically overestimates the highest empirical values (not shown here).
After some trial and error, the best fit of the statistical distribution seems to be provided by a threshold set to 5. 5m (Figure  9 ). Still, the Weibull fitting is not able to provide an appropriate extrapolation method as some of the long term return values remain underestimated (e. g. second highest value has a higher return period than statistical estimation). Moreover, if the IDA leads to a relatively good correlation with the modeled data and is a commonly used approach, it remains an empirical method that does not respect the extreme values theory. The dataset selected for the extrapolation needs to be independent and identically distributed  [10] , which isn't the case for the 6-hourly Hs output from the wave model data set. In that sense, the wave conditions might be more persistent than the wind conditions for the same sampling frequency.
Both methods are based on the adjustment of a standard statistical distribution (respectively, Pareto and Poisson) over the extreme part of the empirical distribution. The lowest threshold estimated from both methods shall provide a value high enough to keep accuracy and low enough to provide a proper sampling size. The selected data for a POT extrapolation with threshold set to 4. 3m are presented Figure  10. The correlation of the Pareto distribution to the wave modeled data is pictured Figure  11. The statistical fitted distribution presents a proper overall tendency compared to the empirical model data. It predicts for instance a significant height of 9. 17m for a 25 years return period, when the empirical maximum wave height reached 9. 13m in December 1989 for this 22-year dataset. Moreover, the latest measurements from the end date of the dataset to December 2012 remain in agreement with this fit.
During the Joachim storm, occurring mid December 2011, the maximum Hs reached 8. 2m. According to the POT statistical fit, such an extreme sea state can be seen as a 5-year return period event. For longer return periods, results have to be considered cautiously as the uncertainty tied up to these values will increase significantly. In order to verify the choice of this threshold a sensitivity study based on the evolution of the 25years return period is carried out. Figure  12  shows that for a threshold high enough, a certain convergence in term of return value seem to occur. The choice of the 4. 3m threshold is consistent as it compares well with the results observed for higher thresholds. At this stage, such a combination of a Pareto distribution with a POT method provides the best fit to our hindcast dataset.
CONCLUSIONS The specific application of long-term extrapolation methods has been presented here for wind and wave extreme values on the SEMREV coastal test site. Wind data have been studied in term of intensity through the ERA-Interim data set, and a censored-IDA extrapolation method combined to a Weibull fit employed here. This method has been demonstrated to provide a satisfactory conservative estimation of the extreme wind return values. A different methodology has been applied to the long-term extrapolation of extreme sea states. A wave model chain has been built from a global WWATCH domain to a regional SWAN set up, so as to account for the proper physical evolution of wave in the SEMREV coastal region. The model results have then been qualified and calibrated at the regional scale to specifically provide the best conservative estimations for extreme wave conditions.
The calibration has been achieved on several parameters related to usual source term in the SWAN wave model. The standard tuning coefficient have finally provided the best overall results in term of relative error between the significant height from the model chain compared to in situ measurements for several extreme events. A 22-year wave hindcast data set has been built accordingly, and various extrapolation methods have been tested. An IDA method combined with a proper threshold and Weibull distribution has provided a first fit which, despite its apparent accuracy, does not grant a conservative estimation. It emphasizes the need for a proper independent and identically distributed dataset for the good performances of this method. Due to the small size of the data sample, an ABM method is not suitable either here.
A POT method combined with an appropriately estimated threshold, a Pareto distribution and a given time interval finally provides the best overall conservative fit to the hindcast wave dataset. An analysis of mean direction of origin for the most extreme events finally emphasizes the predominant southwest influence. Atmospheric models are not always properly calibrated for extreme conditions. ERA-Interim data were selected due to their 33-year duration, reducing therefore the uncertainty of a long term extrapolation. However the use of the 1-h wind fields from NCEP/CFSR  [12]  could provide a refinement of the representation of the storm peaks. Despite its higher time consumption, the direct simulation approach (DSA) used for the creation of the wave hindcast data set seems physically more coherent and, is preferred to the propagation of an already extrapolated synthetic extreme event.
The macro-tidal environment in this area has already been proved to be able to influence significantly the propagation of sea states through the refraction by currents  [13] , and in specific configurations through the varying shadow with tide created by a chaotic bathymetry up-wave. -the refinement of the time resolution for the wind forcing fields. The current 6-hourly ERA-Interim dataset is inherently interpolated in time in the WWATCH global domain simulation. An hourly data set would enable a better description of the wind forcing conditions.