dataesr/scientific-paragraphs-categorization huggingface dataset:
An additional 1% systematic uncertainty is also applied to account for differences in efficiencies observed between inclusive Z events and Zj events. The uncertainty on the jet reconstruction efficiency is evaluated by comparing the differences in efficiency between Zj data and simulation where the quality requirements are varied about their nominal values. This results in an uncertainty of 1. 9%. The uncertainty on the selection efficiency, 1%, includes the statistical uncertainty due to the limited size of the "pseudo-W j" data sample and the uncertainty on the corrections evaluated from simulation for differences between W j and "pseudo-W j" events. The uncertainty on the GEC efficiency is taken to be the sum in quadrature of the accuracy of the method, 0. 3%  [7, 8] , and the difference observed between W + j, W -j and Zj events in simulation, typically smaller than 0.
8  to fixed-order calculations performed with Fewz separately for the NNPDF3. 0, CT14 and MMHT14 PDF sets. The fixed-order predictions are expected to give a good description of the ratios and asymmetries as the effects of higher-order terms and hadronisation largely cancel between the positively and negatively charged W j predictions. In general, good agreement is seen between the data and the predictions, although the data presents a slightly larger ratio and asymmetry, particularly in the first bin of η µ. However, when the spread of predictions obtained using different PDF sets is considered, the deviations are not significant. W + j σ W -j σ Zj R W Z R W ± Statistical 0. 4 0. 5 1. Theory/Data 0. 8 ) + W ( σ ) j - W ( σ ) Zj ( σ WZ R Z + W R Z - W R ± W R LHCb = 1 1. 2 j.
Doppler Tomography of Stars (DoTS) is a Doppler imaging code that utilises a two-temperature model to recover surface brightness distributions of active stars  (Collier Cameron 2001). DoTS uses maximum entropy regularisation to obtain image solutions that minimise artefacts in the presence of noise; it has been used extensively to recover spot distributions on single stars and binaries. A brief overview has also been given recently in  Barnes et al. (2015) , with application in particular to late M dwarfs. For an image with i pixels, spot filling factors fi are obtained with DoTS, assuming a two-temperature model representing the spots and photosphere. For stars that are rotating slowly and for which insufficient Doppler resolution elements can be obtained across the stellar line profile, the derived images are not particularly informative.
ACKNOWLEDGMENTS We thank the anonymous referee for taking the time to review this manuscript. J. R. B. and C. A. H. were supported by the  STFC  under the grant  ST/L000776/1. S. V. J. acknowledges research funding by the  Deutsche Forschungsgemeinschaft (DFG)  under grant  SFB 963/1 , project  A16. JSJ acknowledges funding by  Fondecyt  through grants  1161218  and  3110004 , and partial support from  CATA-Basal  ( PB06 , Conicyt), the  GEMINI-CONICYT FUND  and from the  Comité Mixto ESO-GOBIERNO DE CHILE.
The effects of pile-up were also studied with data recorded from randomly selected bunch crossings (zero-bias data) as discussed in Section 5. The baseline t t full simulation sample was produced using the next-to-leading-order (NLO) QCD matrixelement generator Powheg-Box v1. 0  [20] [21] [22]  using the CT10 PDFs  [23]  and interfaced to Pythia6 (version 6. 426)  [24]  with the CTEQ6L1 PDF set  [25]  and the Perugia 2011C (P2011C) tune  [26]  for the parton shower, fragmentation and underlying event modelling. The renormalisation and factorisation scales were set to the generator default value of m 2 t + p 2 T , the sum in quadrature of the top quark mass m t and transverse momentum p T , the latter evaluated for the underlying Born configuration before radiation. The Powheg parameter h damp , used in the damping function that limits the resummation of higher-order effects incorporated into the Sudakov form factor, was set to infinity, corresponding to no damping.
The total jet energy scale uncertainty varies in the range 1-6 % with a dependence on both jet p T and |η|. Jet energy resolution/efficiency: The jet energy resolution (JER) was found to be well-modelled in simulation  [61] , and residual uncertainties were assessed by applying additional smearing to the simulated jet energies. The calorimeter jet reconstruction efficiency was measured in data using trackbased jets, and found to be generally well-described by the simulation. Residual uncertainties were assessed by discarding 2 % of jets with p T < 30 GeV; the uncertainties for higher-momentum jets are negligible. Both these uncertainties were symmetrised about the nominal value. The uncertainty due to the veto on events failing jet quality requirements is negligible. Unmatched jets modelling: The modelling of the component of unmatched jets from pile-up collisions was checked by comparing the predictions from simulated t t events combined with either Powheg+Pythia8 pile-up simulation or 'zero-bias' data.
The latter were recorded from randomly triggered bunch crossings throughout the data-taking period, and reweighted to match the instantaneous luminosity distribution in the simulated t t sample. The estimated number of additional jets per event from pile-up is 0. 017 ± 0. 002 in the central region used by the gap fraction measurements (|y| < 2. 1) and 0. 038 ± 0. 005 over the full region used by the differential cross-section measurements (|η| < 4. 5). The uncertainties represent the full difference between the rate in zero-bias data and simulation. The rate of unmatched jets in simulation was varied by these uncertainties in order to determine the effect on the results. In the differential cross-section measurements, the full rate of particle-level jets that were split in two at reconstruction level in the baseline simulation was taken as an additional uncertainty on the rate of unmatched jets.
ATLAS Simulation A Bayesian iterative unfolding method  [64]  implemented in the RooUnfold  [65]  software package was used. The response matrix M is not unitary because in mapping from particle to reconstruction level, some events and objects are lost due to inefficiencies and some are gained due to misreconstruction or migration of objects from outside the fiducial acceptance into the reconstructed distribution. This results in the response matrix being almost singular, and it is therefore not possible to obtain stable unfolded results by inverting the response matrix and applying it to the measured data. Instead, an assumed particle-level distribution (the 'prior') was chosen, the response matrix applied and the resulting trial reconstruction set was compared to the observed reconstruction set.
In all cases, the renormalisation and factorisation scales are set to the defaults provided by the code authors. For leadingorder generators, the predicted cross-section can depend strongly on the choice of QCD scales and parton shower parameters; Figures 6-9 also show the effects of the variations discussed in Section 3 for samples generated with AcerMC + Pythia6, Alpgen + Pythia6 and MadGraph + Pythia6. The measurement gives an uncertainty in the differential cross-sections that is smaller than the range spanned by these variations in the leading-order generators. [GeV]      The level of agreement between the generator predictions and the data was assessed quantitatively using a χ 2 test taking into account all bins of the measured jet p T distributions with rank one to five. Since the systematic uncertainties and unfolding corrections induce large correlations between bins, the χ 2 was calculated from the full covariance matrix.
1, for subsets of this y region, and as a function of the invariant mass of the eµb b system. Taken together, these measurements can help to characterise the production of additional jets in t t events, an important test of QCD and a significant source of systematic uncertainty in many measurements and searches for new physics at the LHC. The results will be made available in the HepData repository and through the Rivet analysis framework. The measurements are generally well-described by the predictions of the next-to-leading-order generators used in ATLAS physics analyses. Both Powheg (interfaced to Pythia6, Pythia8 or Herwig) and MC@NLO + Herwig give good descriptions of the p T spectrum of the first additional jet, although MC@NLO + Herwig does not describe higher jet multiplicities, or the gap fraction as a function of a threshold on the sum of the p T of all additional jets.
Acknowledgements H. T. thanks  JSPS  for the financial support under the  Grant-in-Aid for Scientific Research (C) 24540168. He is also grateful to  Aix-Marseille  and  Toulon Universities  for their hospitality. V. A. Z. acknowledges the  Institute of Science and Engineering as well as Graduate School of the Natural Science and Technology of Kanazawa University  for hospitality and for financial support during the visit allowed to complete this paper.
The function describing the diphoton continuum background is a sum of Landau and an exponential distribution over the full m γγ low-mass range. The bias on the signal yield induced by the choice of the functional form is required to be lower than 20% of the statistical uncertainty on the fitted signal yield for the background-only spectrum. This was studied on an Asimov dataset  [11]. Figure  1  shows background-only fits to the data in the low-mass region. background and the SM Higgs boson background. Relative cuts E γ 1 T /m γγ > 0. 4 and E γ 2 T /m γγ > 0. 3 are added to increase the signal sensitivity. The continuum background is fitted with an exponential of a secondorder polynomial inside a sliding window of width 80. (m X -110 GeV)/110 + 20 GeV. The function and the fit window were determined by requiring the signal yield bias criteria introduced above.
Surface morphology and Compositional analysis. After substrate landing, nanocomposite-loaded Si (100) substrates were load-lock transferred to an inert gas (N 2 ) glove-box and surface morphology was characterized by atomic force microscopy (AFM) using a Multimode 8 (Bruker, Santa Barbara, CA) instrument operating in tapping mode. The AFM system height "Z" resolution and noise floor are less than 0. 030 nm. The scanning probe processor (SPIP) (Image Metrology, Hørsholm, DK) software was employed for the root-mean-square (RMS) roughness analysis. Nanocomposite structures were characterized using both grazing incidence x-ray diffraction (GIXRD, Bruker D8 Discover XRD 2 system with Cu K α x-ray source) at grazing angle of 0. 25 o and Cs-corrected transmission electron microscopy (TEM, FEI Titan G2 TM 80-300 kV) operating at 300 kV.
The H + 0-jet cross section, σ 0 (p thresh T ), can be calculated from the product of the total cross section, σ tot , and the jet-veto efficiency for H + 0-jet events, ε 0 (p thresh T ), which is defined as the fraction of events with the leading jet below a given threshold, p thresh (1) In addition to the measurement of the N jet distribution, a measurement of the jet-veto efficiency for H + 0-jet events, ε 0 , is presented for three different values of p thresh T. All results are compared to a set of predictions from fixed-order calculations and Monte Carlo (MC) generators. Differential cross-section measurements are performed for the first time in the H→ WW * → eνµν final state. This analysis is an extension of the ggF coupling measurement performed using the Run-1 dataset  [19] , and uses the same object definitions, background-estimation techniques, and strategies to evaluate the systematic uncertainties.
The Powheg ggF model takes into account finite quark masses and a running-width Breit-Wigner distribution that includes electroweak corrections at NLO  [29]. To improve the modelling of the Higgs boson p T distribution, a reweighting scheme is applied to reproduce the prediction of the next-to-next-to-leading-order (NNLO) and next-to-next-to-leading-logarithm (NNLL) dynamic-scale calculation given by the HRes 2. 1 program  [30]. Events with ≥ 2 jets are further reweighted to reproduce the p H T spectrum predicted by the NLO Powheg simulation of Higgs boson production in association with two jets (H + 2 jets)  [31]. Interference with continuum WW production  [32, 33]  has a negligible impact on this analysis due to the transverse-mass selection criteria described in Section 4 and is not included in the signal model.
The W + jets and multijet background models are derived from data as described in Section 5. For the dominant WW and top-quark backgrounds, the MC generator is Powheg +Pythia6  [35]  (version 6. 426), also with CT10 for the input PDFs. The Perugia 2011 parameter set is used for Pythia6  [36]. For the WW background with N jet ≥ 2, to better model the additional partons, the Sherpa  [37]  program (version 1. 4. 3) with the CT10 PDF set is used. The Drell-Yan background, including Z/γ * → ττ, is simulated with the Alpgen  [38]  program (version 2. 14). It is interfaced with Herwig  [39]  (version 6. 520) with parameters set to those of the ATLAS Underlying Event Tune 2  [40]  and uses the CTEQ6L1  [41]  PDF set. The same configuration is applied for Wγ events. Events in the Z/γ * sample are reweighted to the MRSTmcal PDF set  [42].
For the Wγ * and Z/γ backgrounds, the Sherpa program is used, with the same version number and PDF set as the WW background with ≥ 2 jets. Additional diboson backgrounds, from WZ and ZZ, are modelled using Powheg +Pythia8. For all MC samples, the ATLAS detector response is simulated  [43]  using either Geant4  [44]  or Geant4 combined with a parameterised Geant4-based calorimeter simulation  [45]. Multiple proton-proton (pileup) interactions are modelled by overlaying minimum-bias interactions generated using Pythia8.
The efficiency of the N jet = 0 signal region selection is modelled using MC simulation. To reduce the uncertainty on the efficiency of the jet veto, the fraction of b-tagged events which have no additional jets is measured in a data sample with at least one b-tagged jet and compared to the fraction predicted by simulation. The efficiency of the jet veto is corrected by the square of the ratio of the measured fraction over the predicted one to account for the presence of two jets in t t production. In the N jet = 1 category, the normalisation of the top-quark background is determined from a control region distinguished from the signal region by requiring that the jet is b-tagged. To reduce the effect of b-tagging systematic uncertainties, the extrapolation factor from the CR to the SR is corrected using an effective b-jet tagging scale factor derived from a control region with two jets, at least one of which is b-tagged.
In the N jet ≥ 2 category, the number of top-quark events is sufficiently large that a CR with a b-jet veto can be defined using m > 80 GeV. in the N jet ≥ 2 top-quark CR are shown in Figure  3. The W + jets background contribution is estimated using a control sample of events in which one of the two lepton candidates satisfies the identification and isolation criteria used to define the signal sample (these lepton candidates are denoted "fully identified"), and the other ("anti-identified") lepton fails to meet the nominal selection criteria but satisfies a less restrictive one. Events in this sample are otherwise required to satisfy all of the signal-region selection criteria. The W + jets contamination in the SR is determined by scaling the number of events in the control sample by an extrapolation factor measured in a Z + jets data sample.
The extrapolation factor is the ratio of the number of fully identified leptons to the number of anti-identified leptons, measured in bins of anti-identified lepton p T and η. To account for differences between the composition of jets associated with W-and Z-boson production, the extrapolation factors are measured in simulated W + jets and Z + jets events. The ratio of the two extrapolation factors is applied as a multiplicative correction to the extrapolation factor measured in the Z + jets data. The background due to multijet events is determined similarly to the W + jets background, using a control sample that has two anti-identified lepton candidates, but otherwise satisfies the SR selection criteria. The extrapolation factor is constructed from data events dominated by QCD-produced jet activity, and is applied to both anti-identified leptons.
ACKNOWLEDGMENTS We thank  T. S. Hahm , and the other attendants and the organizers of the  Festival de Théorie, Aix en Provence , for useful discussions. This work is supported by the  Direction des Relations Extérieures (DRE)  of  Ecole Polytechnique  and part of it has been done within the  LABEX Plas@Par project , and received financial state aid managed by the  "Agence Nationale de la Recherche ", as part of the " Programme d'Investissements d'Avenir"  under the Grant No. ANR-11-IDEX-0004-02. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY11-25915.
XRS was used to study e. g. chemical reactions  [22] [23] [24]  or to investigate low-Z materials under extreme pressure and temperature conditions. 25, 26 ] [29] [30]  The Ca L 2,3 -edges were studied so far using only electron energy loss spectroscopy or soft X-ray absorption spectroscopy e. g. to characterize materials of geological and biological relevance  31, 32  with the exception of a high pressure XRS study of the Ca L-edge in CaSiO 3 glass. 33 Recently, in situ XRS spectroscopy was used to investigate hydrogen desorption in LiBH 4 , LiBH 4 nanocomposites, and Mg(BH 4 ) 2. 23, 34, 35 he high resolution powder XRD data were collected at ID22 of the ESRF (Grenoble, France) using a multi-channel detector. We measured the different patterns in two different runs (using incident wavelengths of l = 0.
40 Å and l = 0. 32 Å, respectively). The capillaries were mounted on a rotating support and were spun at approximately 2 Hz to improve the powder average. We collected data to cover a momentum transfer range of 0. 0 o |q| o 9. 0 Å À1 and binned the data onto a constant step size of 0. 002 Å À1. We performed the measurements of the B K-edge at beamline ID20 of the ESRF using the multi-analyzer-crystal XRS spectrometer. The analyzer energy was fixed at 9. 7 keV using the Si(660) Bragg reflection and the incident energy was scanned to obtain energy losses in the vicinity of the B K-edge from 180 eV to 225 eV. The spectrometer is equipped with 6 Maxipix Timepix area detectors, one for each set of 12 spherically bent Si analyzer crystals. We utilized three of these analyzer arrays and set the mean scattering angles of these arrays to 501 for an average momentum transfer of 4.
2 AE 0. 2 Å À1. With this setup, we achieved an overall energy resolution of 0. 6 eV. We measured the Ca L 2,3 -and B K-edges at beamline P01 of PetraIII (Hamburg, Germany) using 11 Si(660) analyzers focused on 4 avalanche photodiodes. The scattering angle was 321 resulting in an average momentum transfer of 2. 9 AE 0. 4 Å À1 and an overall energy resolution of 0. 9 eV. The incident energy was scanned to measure the Ca L 2,3 -edges (B K-edge) for the energy loss between 340 eV (173 eV) and 375 eV (223 eV). For the final spectra shown in this report, we averaged over the scattering signals from the different analyzer crystals. Before further processing, we corrected for the valence electrons' response as discussed in ref. 36 and 37. Details of the experimental data processing can be found in ref.
X-ray diffraction The high-resolution powder XRD patterns are shown in Fig. 1  together with the Rietveld refinements (black dashed lines). All refinements were achieved using the TOPAS program package. 38 he overall agreement between the refinement and the measured powder diffraction patterns is satisfactory (5. 76% o R wp o 8. 55%). The quantitative analysis yields the values summarized in Fig. 4a  (and in the ESI † in a tabular form). The diffraction patterns indicate significant contributions from oxides (violet) and hydroxides (gray) in the samples (Fig. 4a ). Ca(BH 4 ) 2 is highly sensitive to contamination (see e. g. ref. 5, 17 and 39) and the formation of oxides and hydroxides could not be completely avoided. We assume that the formation of the oxides occured during initial sample treatment, and that their contribution does not affect the decomposition process due to their high thermal stability.
Acknowledgements We thank the  European Synchrotron Radiation Facility  for providing synchrotron radiation and the technical and scientific staff of ESRF beamlines ID20 and ID22 for support and valuable discussions. We are further grateful for synchrotron radiation provided by  PetraIII. We thank  H. Yavas  and the P01 beamline staff for support during the measurements. This work was supported by  RESOLV  ( EXC 1069 ) and  BMBF  (Project No. 05K13PE2 ) within  FSP-302. Y. Yan  and  A. Remhof  acknowledge financial support granted by  Switzerland  through the Swiss contribution to the enlarged  European Union.
We describe in detail the methods used to obtain the lower bound on the lifetime of neutrinoless double-beta (0νββ) decay in 130 Te and the associated limit on the effective Majorana mass of the neutrino using the CUORE-0 detector. CUORE-0 is a bolometric detector array located at the Laboratori Nazionali del Gran Sasso that was designed to validate the background reduction techniques developed for CUORE, a next-generation experiment scheduled to come online in 2016. CUORE-0 is also a competitive 0νββ decay search in its own right and functions as a platform to further develop the analysis tools and procedures to be used in CUORE. These include data collection, event selection and processing, as well as an evaluation of signal efficiency. In particular, we describe the amplitude evaluation, thermal gain stabilization, energy calibration methods, and the analysis event selection used to create our final 0νββ search spectrum.
In Section III, we describe the production of the CUORE-0 energy spectrum; this process closely follows the one used for Cuoricino found in  [16]  (hereafter referred to as CINO2011), so here we focus on the parts of the analysis that have been further developed for CUORE-0, including the new data blinding procedure. In Section IV, we outline the data selection criteria and the signal efficiency evaluation. Section V summarizes our unblinding procedure. In Section VI, we present our technique for searching for a 0νββ decay signal and derive the limit on the half-life of 0νββ decay of 130 Te previously presented in  [11]. In Section VII, we detail the performance of the CUORE-0 detector, particularly in comparison to the Cuoricino detector. In Section VIII we present the technique for combining the results of CUORE-0 and Cuoricino to obtain the limit on the 0νββ decay half-life of 130 Te presented in  [11].
5 V dynamic range and 18 bit resolution. We trigger each data stream in software and store events in 5 second windows: the one second of data preceding the trigger and the four seconds after. Particle pulses -pulses coming from energy deposits in the crystals -have typical rise times of ∼0. 05 s and two decay time components, a fast decay time of ∼0. 2 s and a slower decay time of ∼1. 5 s. The former decay time is determined by the heat capacity of the crystal and the thermal conductivity to the thermal bath, and the latter decay time by the heat capacity of the of the auxiliary components (i. e. the PTFE spacers and nearby copper frame). The rise time is determined primarily by the roll-off of the Bessel filter. Typical trigger thresholds range from 30 keV to 120 keV. Every 200 s, we collect 5 second waveforms simultaneously on all channels with no signal trigger and use these to study the noise behavior of the detector.