dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental uncertainties Jet energy scale and resolution: The jet energy scale (JES) and its uncertainty have been derived by combining information from test-beam data, LHC collision data and simulation  [43]. The jet energy scale uncertainty is split into 22 uncorrelated components which can have different jet p T and η dependencies and are treated independently in this analysis. The jet energy resolution (JER) has been determined as a function of jet p T and rapidity using dijet events from data and simulation. The JER in data and in simulation are found to agree within 10%, and the corresponding uncertainty is assessed by smearing the jet p T in the simulation. The JES and JER uncertainties represent the leading sources of uncertainty associated with reconstructed objects in this analysis.
Others Monte Carlo sample size: To assess the effect on the measurement of the limited number of Monte Carlo events, an ensemble of 1 000 response matrices, each of them fluctuated according to the raw number of simulated events, is produced. Unfolding is repeated with the same pseudo-dataset for each fluctuated response matrix. The uncertainty is estimated as the standard deviation of the ensemble of the 1 000 A C values obtained. The estimated systematic uncertainty associated with limited number of Monte Carlo events is about ten times smaller than the data statistical uncertainty; this is consistent with the size of the available Monte Carlo sample.
The impact of uncertainties related to PDFs are found to be negligible in t t modelling. Unfolding response: The response of the unfolding procedure, i. e. any non-linearity or bias, is determined using a set of six pseudo-datasets, each of them being composed of the default t t signal reweighted to simulate an asymmetry and the default MC simulation predictions. The injected A C value ranges between -0. 2 and 0. 2 depending on the differential variable and bin. The six reweighted pseudo-datasets are unfolded using the default response matrix and the uncertainty associated with the unfolding response is calculated as:.
After the calibration, the background components are scaled to the measured values for the nuisance parameters, and the prediction for tt events in each bin is estimated by folding the measured parton-level parameters through the response matrix. The bottom part of each plot shows the ratio of the data to the predicted value together with combined statistical and systematic uncertainties. Table  1 : Observed number of data events compared to the expected number of signal events and different background contributions for different b-tagging multiplicities in the combined µ+jets and e+jets channels. These yields are shown after marginalisation of the nuisance parameters and the in situ calibration of the W+jets background, and the marginalized uncertainties are shown. The marginalized uncertainties for each background and signal component are correlated, and the correlation is taken into account in their combination.
24740158. Note added. -Recently,we became aware of a Letter from the BESIII Collaboration [23] that also reports on the Zð3900Þ AE at the same time. Acknowledgments We are grateful to  David Kaplan  and the  INT  for financial and logistics support. The authors thank the following agencies for financial support: the  U. S. Department of Energy (Cohen) ; the  Institute of Modern Physics and Chinese Academy of Sciences  under contract  Y104160YQ0  and agreement No. 2015-BH-02  (Coito); the  U. S. Department of Energy , for grant  DE-AC05-06OR23177 , under which  Jefferson Science Associates, LLC , manages and operates  Jefferson Laboratory  and  de-sc0006765 ,  Early Career  award (Dudek);  Fermilab , operated by the  Fermi Research Alliance  under contract number  DEAC02-07CH11359  with the  U.
S. Department of Energy (Eichten) ;  BMBF , under contract No. 06GI7121 , and the  DAAD  under contract No. 56889822  and by the  Helmholtz International Center for FAIR  within the  LOEWE program  of the  State of Hesse (Fischer) ; the  German Research Foundation DFG  under contract number  Collaborative Research Centre CRC-1044  (Gradl); the  Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq , Grant No. 305894/2009-9  and  Fundação de Amparo à Pesquisa do Estado de São Paulo -FAPESP , Grant No. 2013/01907-0  (Krein);  U. S. National Science Foundation , under grants  PHY-1068286  and  PHY-1403891  (Lebed); the  Brazilian National Council for Scientific and Technological Development  under grant  CNPq/CAPES-208188/2014-2  (Machado);  U. S. Department of Energy  under grant  DE-FG02-05ER41374  (Mitchell);  U.
S. National Science Foundation  under grant  PHY-1306805  (Morningstar);  U. S. Department of Energy , supported by  Jefferson Science Associates, LLC  under contract No. DE-AC05-06OR23177  (Pennington); the  National Natural Science Foundation of China (NSFC)  under contract No. 11575017 , the  Ministry of Science and Technology of China  under Contract No. 2015CB856701  ( Shen) ;  U. S. Department of Energy , under grant  DE-FG02-05ER41374  (Shepherd);  U. S. National Science Foundation  under grant  PHY-1507572  (Skwarnicki);  U. S. Department of Energy , under contract  DE-AC05-06OR23177  and grant  DE-FG0287ER40365  (Szczepaniak); the  National Natural Science Foundation of China (NSFC)  under contract numbers  11235011  and  11475187  (Yuan).
We present the results of a search for small to intermediate scale anisotropies in the distribution of arrival directions of ultra-high energy cosmic rays recorded at the Pierre Auger Observatory. The data set, gathered in ten years of operation, includes arrival directions with zenith angles up to 80 • , and is about three times larger than that used in earlier studies. We update the test based on correlations with active galactic nuclei (AGNs) from the Véron-Cetty and Véron catalog, which does not yield a significant indication of anisotropy with the present data set. We perform a blind search for localized excess fluxes and for self-clustering of arrival directions at angular scales up to 30 • and for different energy thresholds between 40 EeV and 80 EeV. We also examine the correlation of arrival directions with relatively nearby galaxies in the 2MRS catalog, AGNs detected by Swift-BAT,and a sample of radio galaxies with jets and with the Centaurus A galaxy.
The present paper describes the latest analysis of the distribution of arrival directions of cosmic rays with energies above 40 EeV detected at the Pierre Auger Observatory in 10 years of operation, with a total exposure of about 66,000 km 2 sr yr. After a brief description of the data set, we first present an update of the correlation analysis performed with the AGNs from the Véron-Cetty and Véron catalog  [4]. The result of this test does not confirm the initial evidence of anisotropy that was observed  [5]  for cosmic rays with energies above 57 EeV. Consequently, we searched for the presence of a possible anisotropy in the distribution of arrival directions for events with energies above 40 EeV, applying different types of tests. In section 2 we first analyze the distribution of arrival directions in the data set without using any external information such as a catalog of candidate sources.
Data set The data set used in the present analysis consists of 602 events with energy above 40 EeV measured by the Surface Detector (SD) of the Pierre Auger Observatory between the 1 st January 2004 and the 31 st March 2014. The Pierre Auger Observatory is a 3000 km 2 array of water-Cherenkov detectors with 1. 5 km spacing, overlooked by 24 fluorescence telescopes located on its periphery. A recent and detailed description of the detector can be found in  [7]. The total data set is the combination of 454 events with zenith angle θ < 60 • (vertical events) and 148 events with 60 • < θ • < 80 • (inclined events). The extension of the zenith angle range, compared to previous anisotropy searches that were limited to vertical events, has two important advantages: a 30% increase of statistics and a higher fraction of the sky covered, ranging from -90 • to +45 • in declination.
Acknowledgements The authors are indebted to  M. H. Urin ,  V. O. Nesterenko  and  S. P. Kamerdzhiev  for useful discussions. N. N. A. , A. P. S. , and V. V. V. thank the hospitality of  INP-Orsay  where a part of this work was done. This work was partly supported by the  IN2P3-JINR agreement, the Heisenberg-Landau program  and  JINR   Fellowship grant for Young Scientists  (No. 15-302-01 ). NSRT15.
ACKNOWLEDGMENTS The authors would like to thank the  ASDEX Upgrade Team  and especially  W. Zeidner ,  T. Vierle , and  W. Popken  for their help with the technical aspects of this project, also  E. Freire  for his advice regarding the multibody model numerical solution. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 under Grant Agreement No. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
6 < z < 1. The densities of each field are reported in Table  1 , and the errors on the photometric redshift are σz < 0. 05 (1+z) for i < 22. 5 and z < 1. The ugri ELG selection is brighter than i < 22. 5. We match the photometric targets to the available spectroscopic surveys -BOSS DR12, DEEP2, VIPERS  (Bolton et al. 2012; Alam et al. 2015; Newman et al. 2013; Guzzo et al. 2014 ) -within 1" radius; see Table  2. Based on KStests, the VIPERS, BOSS and DEEP2 spectroscopic selections constitute fair sub-samples of the complete selection: the hypothesis that they are drawn from the same distribution cannot be rejected at the 90% confidence level. For these samples, we create random catalogs with the same redshift distribution of the data and 30 times denser. Figure  2  displays the ELG spectroscopic redshift distribution per unit volume for the three Wide fields (dashed, dotted, dot-dashed histograms), and their mean (solid line).
MultiDark simulations The We use the MDPL halo catalogs to build a mock light-cone that matches the mean ELG redshift distribution shown in Figure  2. Given the high density of the ELG tracers and their expected low-mass host halos, the MDPL box is an excellent compromise between numerical resolution and volume. We apply the SUrvey GenerAtoR code (SUGAR;  Rodriguez-Torres et al. (2015) , in prep. ) to the 11 snapshots available from MDPL to construct a light-cone with a volume ten times the observations that covers the redshift range 0. 6< z <1 (∼ 1h -1 Gpc depth). The procedure used is analogous to the method presented by  Blaizot et al. (2005)  and  Kitzbichler & White (2007) , and can be summarized as follows: (i) Set the properties of the light-cone: angular mask, radial selection function (number density) and number of snapshots within the redshift range considered.
Including also the off-diagonal elements of the covariance matrices would result in large fluctuations of the error bars. Of course, excluding covariances we are adopting a simplified approach, but it provides a good sense of how the SDSS BOSS ELG clustering behaves. On the other hand, the ELG sample considered here is too sparse to derive tight constraints from our clustering analysis. Newgeneration large-volume spectroscopic surveys as eBOSS, DESI and 4MOST, will provide new data with unprecedented statistics, sky coverage and imaging quality. Using those data, a fully covariant approach will return reliable and accurate error estimates. We compare the combined ξ s measurement from BOSS and VIPERS to previous measurements by  Marulli et al. (2013)  to provide a first interpretation. Our result matches both the clustering signal of galaxies selected in the stellar mass range 9.
RESULTS AND DISCUSSION 5. 1 ELG clustering trends as a function of magnitude, flux, luminosity and stellar mass We employ the complete VIPERS data sample at z ∼ 0. 8, which has about 30, 000 reliable redshifts in the range 0. 6 < z < 1, to investigate trends of the clustering amplitude (bias) with observed or rest frame broad band magnitude or emission line flux. To this purpose, we measure the emission line properties in the VIPERS spectra and find a significant [Oii] flux in about two thirds of them; the rest does not show emission lines  (Comparat et al. , in prep. ). We bin the data according to apparent and absolute magnitude, [Oii] flux and luminosy, and measure the clustering in each sample (the binning scheme was set to contain between 9000 and 10, 000 data points). Figure  7  shows our ELG results in the observed (bottom row) and rest frame (top row).
ACKNOWLEDGMENTS GF is supported by the  Ministerio de Educación y Ciencia of the Spanish Government through FPI  grant  AYA2010-2131-C02-01. JC acknowledges financial support from  MINECO (Spain)  under project number  AYA2012 -31101. GY acknowledges financial support from  MINECO (Spain)  under project number  AYA2012-31101  and grant  FPA2012-34694. EJ acknowledges the support of  CNRS , and the  Labex OCEVU. SEN acknowledges support by the  Deutsche Forschungsgemeinschaft  under the grant  NU 332/2-1. GF, FP, SART, AK, SN and CC acknowledge financial support from the  Spanish MICINN  Consolider- Ingenio 2010 Programme  under grant MultiDark  CSD2009 -00064 ,  MINECO Centro de Excelencia Severo Ochoa Programme  under grant  SEV-2012-0249 , and  MINECO  grant  AYA2014-60641-C2-1-P. GF, JC and FP wish to thank the  Lawrence Berkeley National Laboratory  for the hospitality during the creation of this work.
LIGO EVENT GW150914 On 2015 September 14 at 09:50:45 UT the Advanced LIGO interferometers at Hanford and Livingston recorded burst candidate event GW150914 during Engineering Run 8. This event was triggered by the cWB (coherent WaveBurst) unmodeled burst analysis during real-time data processing. On 2015 September 16, the LIGO Virgo Collaboration (LVC) provided two all-sky localization probability maps for the event, generated from the cWB and LALInferenceBurst (LIB) analyses (LIGO Virgo Collaboration 2015a). The cWB online trigger analysis makes minimal assumptions about signal shape by searching for coherent power across the LIGO network  (Klimenko et al. 2008). The LIB analysis is a version of the the LALInference analysis  (Veitch et al. 2015)  Bayesian forward-modeling-based follow up tool that uses a Sine-Gaussian signal morphology instead of models of compact binary mergers; for information on both algorithms see  Essick et al.
München, Germany Subsequently, on 2015 October 3, the LVC revised its analysis: the data were most consistent with a binary black hole merger (LIGO Virgo Collaboration 2015b). On 2016 January 13, the LVC provided new skymaps, the most accurate and authoritative of which was the LALInference analysis (LIGO Virgo Collaboration 2016). The new contour enclosing 50% of the total probability shifted southward of the LMC, although the LMC is still inside the 90% contour.
Centro de Excelencia Severo Ochoa SEV-2012-0234. Research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. R. J. F. gratefully acknowledges support from NSF grant AST-1518052 and the Alfred P. Sloan Foundation. FS acknowledges financial support provided by So Paulo Research Foundation (FAPESP) under grants 2015/12338-1.
The fake-factor method is validated independently in each of the four analysis channels. In each case a comparison between the data and the background prediction is made with a loosened signal selection, which provides a test of the method with a large number of events in a dataset that is dominated by the background from misidentified jets. In addition, a series of orthogonal regions are formed to validate the method for each of the analysis channels. The definition of the loosened signal selection and validation regions are given for each channel in Table  4. Example distributions of the p T of τ had candidates for the loosened signal selection and validation regions are shown in Figure  1  for the W → µν/eν, H → τ lep τ had channel. MC simulation studies show that this Z → ττ validation region is dominated by Z → ττ events where an additional jet in the event is misidentified as a τ had candidate.
Experimental systematic uncertainties arise from uncertainties on trigger efficiencies, particle reconstruction and identification, uncertainties on the energy scale and resolution of jets, leptons, and τ had candidates. The efficiency-related uncertainties are estimated in data using tag-and-probe techniques. The MC samples used are corrected for differences in these efficiencies between data and simulation and the associated uncertainties are propagated through the analysis. The lepton energy scale uncertainties are measured in data. For τ had candidates, where the uncertainty is dominated by calorimeter response, this is done by fitting the visible Z → ττ mass  [27]. The systematic uncertainties due to energy resolution have a negligible impact on the result. Systematic effects from electron-and muon-related uncertainties are smaller in general than those from jets and τ had candidates.
[52]. The signal samples, generated in QCD LO with Pythia8, are normalized using cross sections computed in NNLO in QCD and NLO in electroweak, but kinematic distributions, such as the Higgs boson p T , are not re-weighted. The HAWK MC program  [53] , which calculates NLO QCD and NLO electroweak corrections for all the V H processes, is used to evaluate the resulting systematic uncertainties due to kinematic differences. The impact of the QCD scale choice on the signal acceptance is evaluated in MC simulation before the ATLAS detector simulation is performed, separately for the four analysis channels, by varying the QCD scales in Powheg + Pythia8. Table  6 : Impact of systematic uncertainties on the expected yields of the signal and/or relevant background(s) as well as the impact on the signal strength µ.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  RCN, Norway ;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
Galaxy classification is based on location in the UVJ colour-colour diagram, in a similar way to GEEC2, but without the intermediate classification or rest-NUV information. The stellar mass completeness limit is calculated separately for each cluster, and is derived from the 80 percent completeness depth of the K-band imaging, and the highest M/L ratio measured for galaxies near that limiting magnitude. The exposure times were adjusted to achieve an approximately uniform mass completeness across the redshift range of the sample. The stellar mass limits range from 9. 92 ă log M {Md ă 10. 53. We use their mass functions as published 1  for the analysis in the present paper. When computing stellar mass functions, only the clusters with sufficiently deep data are included in a given mass bin. There are six clusters with stellar mass limits log M {Md ď 10.
As expected from the previous Figure, the limits are consistent with the GCLASS measurements. At lower masses they remain consistent with the corresponding z " 0 measurements of similar-mass groups from SDSS. Even in this conservative case the data support a modest increase in the derived t delay with decreasing stellar mass; they indicate a lower limit on t delay of " 2 Gyr at Mstar ă 10 10. 3 Md. Thus we consider the main result, or an increasing t delay with decreasing stellar mass, to be quite robust, although quantitatively it is sensitive to having the correct probability distribution function for the photometric redshifts. We showed in Balogh et al. (  2014   1 ) is shown as a function of stellar mass, here including conservative upper limits on the GEEC2 measurements (inverted triangles).
ACKNOWLEDGMENTS MLB would like to acknowledge generous support from  NOVA  and  NWO  visitor grants, as well as the excellent hospitality extended by the  Sterrewacht of Leiden University , where this paper was mostly written during sabbatical leave from Waterloo. We are grateful to the  SDSS, COSMOS  and zCOSMOS teams for making their excellent data products publicly available. This research is supported by  NSERC   Discovery grants  to MLB and LCP. RFJvdB acknowledges support from the  Netherlands Organisation for Scientific Research  grant number  639. 042. 814 , and the  European Research Council   FP7  grant number  340519. DJW acknowledges the support of the  Deutsche Forschungsgemeinschaft via  Project  ID 3871/1-1. AF wishes to acknowledge  Finnish Academy  award, decision 266918.
Cross-section measurement The cross-section, σ i , for W and Z boson production in association with one or more jets in the i th phase space bin is given by σ i = U i A i • ρ i • N i ε muon i • ε jet i • ε sel i • L , (1) where U i is an unfolding correction which accounts for resolution effects causing migrations between different bins of phase space. The number of candidates selected in bin i is given by N i while ρ i represents the signal purity. The acceptance factor, A i , accounts for differences between the fiducial region of the measurement and the kinematic requirements placed on the muons and jets. The efficiencies for reconstructing the muons and the jet are given by ε muon i and ε jet i , respectively, while the efficiency of any additional event selection is given by ε sel i. The instantaneous luminosity is measured continuously during the acquisition of physics data by recording the rates of several selected standard processes.
Systematic uncertainties Several sources of systematic uncertainty have been evaluated. The uncertainty on the estimated purity of the W j sample is evaluated by repeating the fit using alternative templates. The fit is performed for a number of different scenarios: • the data-driven corrections are not applied to the simulated W j shape, • the simulated W j shape is replaced by the "pseudo-W j" data sample, • the subtraction of signal events from the background template is performed by obtaining the normalisation from simulation instead of the data-driven method outlined in Sec. 4. 1. The uncertainty on the contributions from electroweak templates is taken to be the statistical precision on the Zj and W j samples used to perform the data-driven normalisation. For the Zj sample, the uncertainty on the misidentification background is given by the sum in quadrature of the statistical precision and the accuracy of the method, obtained by comparing the two approaches described in Sec.