dataesr/scientific-paragraphs-categorization huggingface dataset:
08 °C compared with 0. 5 °C without enclosure. One can observe a temperature change period of about 60 min, but the correlation between variations of temperature and mass is much less clear because of other small disturbances of almost the same amplitude (gentle movement of air, vibrations, instability of servo-control…). Note that the maximum mass oscillation amplitude is about 0. 5 mg and that the oscillations between temperature and mass measurements are of opposite phase. In addition, we observe also a double oscillation (period of about 6. 5 min and amplitude of about 0. 3 mg) on the mass change curve after each maximum temperature corresponding to a maximum of the second time derivative of temperature d 2 T/dt 2 possibly coming from the servo-control system of the mass comparator. A correction of air temperature based on the linearization of temperature change over t max has been applied on mass change measurements using a Matlab ® program.
The resonant gg → H → WW signal contribution is simulated with the POWHEG-BOX v2 event generator  [30]  and normalised using the inclusive next-to-next-to-next-to-leading order (N 3 LO) predicted cross section  [31]. The non-resonant gg → WW signal contribution is modelled with SHERPA v2. 1. 1 at leading order (LO) using OpenLoops with up to one additional parton in the final state  [32]  and normalised using the inclusive NLO predicted cross section  [33]. The Z(→ ee/µµ/ττ)+jets production processes are simulated with the Madgraph5_aMC@NLO v2. 2. 2  [34]  event generator interfaced to Pythia v8. 186. The matrix elements for Z production with up to four associated partons are calculated at LO and the NNPDF2. 3 LO PDF set  [35]  is used. The PHOTOS++ program version 3. 52  [36]  is used for QED emissions from electroweak vertices and charged leptons.
428 with extra jet radiation emitted in the matrix element and in the parton shower. In addition, the modelling of the overlap at NLO between Wt and t t diagrams  [43]  is studied. The effect is assessed by generating Wt events with different schemes for overlap removal using the POWHEG-BOX v2 event generator interfaced to PYTHIA v6. 428 for the simulation of parton showering and non-perturbative effects. These samples are simulated following the recommendations documented in Ref. [44]. The t t samples are normalised using the NNLO+NNLL soft-gluon resummation prediction  [45] , while the Wt samples are normalised using the NLO+NNLL prediction  [46]. The EvtGen v1. 2. 0  [47]  program is used for the properties of the bottom and charm hadron decays in all samples generated using the POWHEG-BOX v2 and Madgraph5_aMC@NLO v2.
The yield from W+jets production is estimated using data event samples that are selected with different lepton selection criteria: a loose lepton identification criterion is defined, leptons are selected using either the loose or the default (as used in the signal region) lepton identification criteria, and events are classified according to whether the leptons, that all satisfy the loose criteria, satisfy or not the default identification criteria. With the introduction of the efficiencies of the default lepton identification relative to the loose lepton identification for both real and misidentified leptons, a system of four equations can be solved to estimate the number of events meeting the default lepton identification criteria. This follows the same procedure as that described in Ref.
[6]. For electrons, the loose identification corresponds to the medium criterion defined in Ref. [50]  without isolation requirements. For muons, the loose identification is the same as the default one, except that the isolation requirement is omitted. The efficiencies for jet misidentification are determined for electrons and muons separately as a function of the lepton p T and are cross-checked with a two-dimensional parameterisation in the lepton p T and η. These efficiencies are measured using data in a control region with one lepton, at least one jet and requirements on the lepton-E miss T transverse mass and E miss T to suppress the prompt-lepton contribution from W+jet production. The remaining W+jets contribution in the selected control sample is subtracted using the MC prediction.
Conclusions The cross section for production of W + W -pairs in pp collisions at √ s = 13 TeV is measured in a fiducial phase space of the eµ final state in which events with reconstructed jets are excluded. The data used in the analysis correspond to an integrated luminosity of 3. 16 fb -1 collected by the ATLAS detector at the LHC in 2015. The measurement is made in a relatively pure signal region with the contamination from the dominant background processes estimated using data in dedicated control regions. The measured cross section is 529 ± 20 (stat. ) ± 50 (syst. ) ± 11 (lumi. ) fb and is found to be consistent with the most up-to-date SM predictions that include high-order QCD effects. Furthermore, the ratio of the measured fiducial cross sections at 13 and 8 TeV centre-of-mass energies is compared to the theory predictions with reduced uncertainties, thanks to their cancellation in the ratio.
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. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom.
Acknowledgements 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  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG , Germany; GSRT, Greece; RGC, Hong.
e. a thermal accommodation coefficient g < 1 (g = 1 corresponds to the wall temperature). These estimates are based on Monte Carlo calculations from the code TRIM  [44]. Average reflection probability is also taken from the same database. Furthermore, we assume that atoms which are not backscattered will recombine. The interaction of + H 3 and + H 2 ions with the walls and the corresponding coefficients are not well known. The coefficients used in the simulations are reported in table  3. For + H 2 we use coefficients that are consistent with the measurements of  [45]. For + H 3 we assume guessed values (the + H 3 flux to the walls is relatively small with respect to the + H 2 and + H , and the results are not very sensitive to these coefficients).
Acknowledgments 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. Support from  CEA  and from the  French Fédération de Recherche sur la Fusion Magnétique  is acknowledged. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation  2013-P1125. The authors would like to thank  A Simonin  for his continued support.
We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i. e. , pre-RA  (2004-2008) and post-RA (2009 -2014). The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure <∼ 50 hPa in the high latitudes. It demonstrates clear improvement over the a priori down to the lower troposphere in the tropics and down to an average of ∼ 550 (300) hPa at middle (high) latitudes. In the tropics and midlatitudes, the profile mean biases (MBs) are less than 6 %, and the standard deviations (SDs) range from 5 to 10 % for pressure <∼ 50 hPa to less than 18 % (27 %) in the tropics (midlatitudes) for pressure >∼ 50 hPa after applying OMI averaging kernels to ozonesonde data.
The SAO ozone profile retrieval algorithm was initially developed for Global Ozone Monitoring Experiment (GOME) data and was adapted to OMI data  (Liu et al. , 2010b). Total ozone column (OC), stratospheric ozone column (SOC), and tropospheric ozone column (TOC) can be directly derived from the retrieved ozone profile with retrieval errors in the range of a few Dobson units (DU)  (Liu et al. , 2006b (Liu et al. , , 2010a)). This algorithm has been put into production in the OMI Science Investigator-led Processing System (SIPS), processing the entire OMI data record with an approximately 1-month delay. The ozone profile product titled PROFOZ is publicly available at the Aura Validation Data Center (AVDC) (https://avdc. gsfc. nasa. gov/index. php?site= 1389025893&id=74). This long-term ozone profile product, with high spatial resolution and daily global coverage, constitutes a useful dataset to study the spatial and temporal distribution of ozone.
To effectively use the retrieval dataset, it is necessary to evaluate and understand its retrieval quality and long-term performance. Although validation of the ozone profile product (mostly earlier versions) has been partially performed against aircraft, ozonesonde, and microwave limb sounder (MLS) data, these evaluations are limited to certain time periods and/or spatial region and/or to only portion of the product (e. g. , OC or TOC only)  (Bak et al. , 2013a; Hayashida et al. , 2015; Lal et al. , 2013; Liu et al. , 2010a, b; Pittman et al. , 2009; Sellitto et al. , 2011; Wang et al. , 2011; Yang et al. , 2007; Ziemke et al. , 2014). Additionally, the quality of ozone profile retrievals is very sensitive to the signal-to-noise ratio (SNR) of the radiance measurements as well as their radiometric calibration, which may degrade over time as shown in GOME and GOME-2 retrievals  (Cai et al.
OMI and ozonesonde datasets 2. 1 OMI and OMI ozone profile retrievals OMI is a Dutch-Finnish nadir-viewing push-broom UVvisible instrument aboard the NASA Earth Observing System (EOS) Aura satellite that was launched into a sunsynchronous orbit in July 2004. It measures backscattered radiances in three channels covering the 270-500 nm wavelength range (UV1: 270-310 nm; UV2: 310-365 nm; visible: 350-500 nm) at spectral resolutions of 0. 42-0. 63 nm  (Levelt et al. , 2006). Measurements across the track are binned to 60 positions for UV2 and visible channels and 30 positions for the UV1 channels due to the weaker signals. This results in daily global coverage with a nadir spatial resolution of 13 km × 24 km (along × across track) for UV2 and visible channels and 13 km × 48 km for the UV1 channel.
OMI radiances are precalibrated based on 2 days of average radiance differences in the tropics between OMI observations and simulations with zonal mean MLS data for pressure less than 215 hPa and climatological ozone profile for pressure greater than 215 hPa. This "soft calibration" varies with wavelength and cross-track positions but does not depend on space and time. The current algorithm of our SAO OMI ozone product that is used in this paper was briefly described in  Kim et al. (2013). The radiative transfer calculations have been improved through the convolution of simulated radiance spectra at high resolutions rather than effective cross sections, which is done by interpolation from calculation at selected wavelengths assisted by weighting function. In addition, four spatial pixels along the track are co-added to speed up production processes at a nadir spatial resolution of 52 km × 48 km.
Errors caused by the forward model and forward model parameter assumptions are generally much smaller than the smoothing error  (Liu et al. , 2005). The main sources of these errors include systematic errors in temperature and cloud-top pressure. Systematic measurement errors are the most difficult to estimate, mostly due to lack of full understanding of the OMI instrument calibration. Certain cross-track positions in OMI data have been affected by RA since June 2007  (Kroon et al. , 2011). Loose thermal insulating material in front of the instrument's entrance slit is believed to block and scatter light, causing measurement error. The anomaly affects radiance measurements at all wavelengths for specific cross-track viewing directions that are imaged to CCD rows. Initially, the anomaly only affected a few rows.
cessing technique, sensor solution, and instrument type and other factors. Consequently, station-to-station biases may occur in ozonesonde measurements and could be as great as 10 %  (Thompson et al. , 2007c; Worden et al. , 2007). A decade  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  of global ozonesonde data with locations shown in Fig. 2 , are utilized in this study to validate our OMI ozone profile product. Most of our ozonesonde data were obtained from the AVDC archive. It contains routine launches from ozonesonde stations, mostly weekly and occasionally two to three times a week at some stations. It also collects launches from field campaigns, for instance IONS 06  (INTEX-B Ozone Network Study 2006)  and ARCIONS (Arctic Intensive Ozonesonde Network Study) (http://croc.
gsfc. nasa. gov/arcions/) (Tarasick et al. , 2010; Thompson et al. , 2008). Data not available at AVDC are obtained from other archives such as the World Ozone and Ultraviolet Radiation Data Center (WOUDC) (http://woudc. org/) and the Southern Hemisphere Additional Ozonesondes (SHADOZ)  (Thompson et al. , 2007a, b) , as well as archives of recent field campaigns including DISCOVER-AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality, http: //discover-aq. larc. nasa. gov/) (Thompson et al. , 2015)  and SEACR 4 S (Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys, https://espo. nasa. gov/home/seac4rs) (Toon et al. , 2016). Almost all of the ozonesonde data in this study were obtained from electrochemical concentration cell (ECC) ozonesondes, which is based on the oxidation reaction of ozone with potassium iodide (KI) in solution.
The exceptions are Hohenpeissenberg station in Germany that uses Brewer-Mast (BM) ozonesondes, the New Delhi, Poona, and Trivandrum stations that use Indian ozonesondes, and four Japanese stations (i. e. , Sapporo, Tsukuba, Naha, and Syowa) that switched from KC ozonesondes to ECC ozonesondes during late 2008 and early 2010. These types of ozonesondes have been reported to have larger uncertainties than ECC ozonesondes  (Hassler et al. , 2014; Liu et al. , 2013; WMO, 1998). To avoid using anomalous profiles, we screen out ozonesondes that burst at pressure exceeding 200 hPa, ozone profiles with gaps greater than 3 km, more than 80 DU TOC, or less than 100 DU SOC. In the SOC comparison, we also filter measurements that do not reach 12 hPa. Some ozonesonde data used in this paper (e. g. WOUDC data) are provided with a correction factor (CF) derived by normalizing the integrated ozone column (appended with ozone climatology above burst altitude) to the coincident total ozone column measured by a Dobson or Brewer instrument to account for uncertainties mainly from the pump efficiency especially near the top of the profiles.
Comparison methodology Previous studies on the validation of satellite observations used a range of coincidence criteria. Wang et al. (2011)  set a 100 km radius and 3 h time difference as coincidence criteria. Kroon et al. (2011)  applied coincidence criteria of ±0. 5 • for both latitude and longitude and 12 h. In this paper, we determine our coincident criteria based on the balance between finding most coincident OMI-ozonesonde pairs to minimize differences due to spatiotemporal samplings and finding a sufficient number of pairs for statistical analysis. For each screened ozonesonde profile, we first select all filtered OMI data within ±1 • latitude, ±3 • longitude, and ±6 h and then find the nearest OMI retrieval within 100 km from the ozonesonde station to perform the validation on the individual profile basis.
To investigate the RA impacts on OMI retrievals, we contrasted the comparison before  (2004-2008, i. e. , pre-RA) and after  (2009 ( -2014, i. e. , post-RA), i. e. , post-RA). Although we filter OMI data based on cloud fraction, cross-track position, and SZA in the final evaluation of our retrievals against ozonesonde observations as shown in Sect. 4. 1. 1. , we conduct the comparison as a function of these parameters using coincidences at all latitude bands to show how these parameters affect the retrieval quality as shown in the Sect. 4. 1. 2-4. 1. 4. In these evaluations, the filtering of OMI data based on cloud fraction, cross-track position, and SZA, respectively, are switched off. Approximately 15 000 additional ozonesonde profiles are used in this extended evaluation. To evaluate the long-term performance of our ozone profile retrievals, we analyze the monthly mean biases (MBs) of the OMI-ozonesonde differences as a function of time using coincidences in the 60 • S-60 • N region and then derive a linear trends over the entire period as well as the pre-RA and post-RA periods.
Cross-track position dependence The OMI swath is divided into 30 cross-track pixels at the UV1 spatial resolution of our product. Each cross-track position is measured by a different part of the CCD detector, i. e. , essentially a different instrument. Radiometric calibration coefficients of the instrument are characterized during pre-launch only at selected CCD column pixels and then interpolated to other columns, causing variation in the radiometric calibration performance across the CCD detector. This in turn causes cross-track-dependent biases in the calibrated radiance  (Liu et al. , 2010b) , which therefore causes stripping in almost all the OMI data products if no de-striping procedure is applied. Our retrieval algorithm has included a first-order empirical correction independent of space and time to remove the cross-track variability  (Liu et al.
Comparison of SOCs The left column of Fig. 8  shows the MBs and SDs of the comparisons of OMI and ozonesonde SOCs for each of the five latitude bands during 2004-2014. In all regions, the OMI SOCs have excellent agreement with ozonesonde SOCs regardless of whether ozonesonde data are convolved with OMI AKs. The application of OMI AKs to ozonesonde SOCs only slightly improves the comparison statistics. The MBs with OMI AKs are within 1. 8 % except for a negative bias of 3 % at northern high latitudes, while the SDs are within 5. 1 % except for 5. 7 % at high latitudes. The correlation coefficient is greater than 0. 95 except for 0. 90 in the tropics due to the smaller SOC range. The SDs are typically larger than the comparisons with MLS data  (Liu et al. , 2010a)  due to worse coincidence criteria, relatively larger uncertainty in the ozonesonde stratospheric ozone columns compared to MLS data, and different altitude ranges of integration.
Acknowledgements. This study was supported by the  NASA Atmospheric Composition: Aura Science Team  ( NNX14AF16G ) and the  Smithsonian Institution. The Dutch-Finnish OMI instrument is part of the  NASA EOS Aura satellite payload. The  OMI  Project is managed by  NIVR  and  KNMI  in the Netherlands. We acknowledge the  OMI International Science Team  for producing OMI data. We also acknowledge the ozonesonde providers and their funding agencies for making ozonesonde measurements, and the  Aura Validation Data Center (AVDC) ,  WOUDC, SHADOZ, DISCOVER-AQ , and SEACR 4 S for archiving the ozonesonde data. Edited by: Piet Stammes Reviewed by: two anonymous referees.
ACKNOWLEDGMENTS This work has been funded by the  Ministerio de Economía y Competitividad (MINECO/FEDER, UE) ,  Spain  under project  TEC2015-65212-C3-1-P. A. Quirce acknowledges  FWO for her Post Doc fellowship  and  H. Thienpont  and  K. Panajotov  are grateful to the  Methusalem foundation  for financial support. M. Sciamanna  acknowledges the support of  Préfecture de Lorraine and SGAR  through the  PHOTON  project (FEDER) and the  APOLLO (FEDER/FNADT)  project. Y. Hong  acknowledges the support of the  Ser Cymru National Research Network in Advanced Engineering and Materials.
Similar limits were set by CMS using the 2015 data set. Both the resonant and non-resonant models considered as the benchmark for this search are further discussed in Section 2. The presented search utilises the invariant mass spectra of the observed dilepton final states as discriminating variables. The analysis and interpretation of these spectra rely primarily on simulated samples of signal and background processes. The interpretation is performed taking into account the expected shape of different signals in the dilepton mass distribution. The use of the shape of the full dilepton invariant mass distribution reduces the uncertainties in the background modelling, thereby increasing the sensitivity of this search at high masses. This article is structured as follows: Section 2 covers the theoretical motivation of the models considered in this search, followed by a description of the ATLAS detector in Section 3, and a summary in Section 4 of the data and Monte Carlo (MC) samples used.
Data and Monte Carlo samples This analysis uses data collected at the LHC during 2015 and 2016 pp collisions at √ s = 13 TeV. The total integrated luminosity corresponds to 36. 1 fb -1 , considering the periods of data-taking with all subdetectors functioning nominally. The event quality is also checked to remove events which contain noise bursts or coherent noise in the calorimeters. Modelling of the various background sources primarily relies on MC simulation. The dominant background contribution arises from the DY process, which was simulated using the next-to-leading-order (NLO) Powheg Box  [23]  event generator, implementing the CT10  [24]  parton distribution function (PDF), in conjunction with Pythia 8. 186  [25]  for event showering, and the ATLAS AZNLO set of tuned parameters  [26].
2. 1  [34]  with the CT10 PDF. The t t and single-top-quark MC samples were simulated using the Powheg Box generator with the CT10 PDF, and are normalised to a cross-section as calculated with the Top++ 2. 0 program  [35] , which is accurate to NNLO in perturbative QCD, including resummation of next-to-next-to-leading logarithmic soft gluon terms. Background processes involving W and Z bosons decaying into τ lepton(s) were found to have a negligible contribution, and are not included. In the case of the dielectron channel, multi-jet and W+jets processes (which contribute due to the misidentification of jets as electrons) are estimated using a data-driven method, described in Section 6. Signal processes were produced at leading-order (LO) using Pythia 8. 186 with the NNPDF23LO PDF set  [36]  and the ATLAS A14 set of tuned parameters  [37]  for event generation, parton showering and hadronisation.