dataesr/scientific-paragraphs-categorization huggingface dataset:
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. This analysis uses the data from pp collisions collected by the LHCb experiment in 2011 and 2012 corresponding to integrated luminosities of, respectively, 1 fb -1 and 2 fb -1 at centre-of-mass energies of 7 TeV and 8 TeV. The polarity of the dipole magnet is reversed periodically throughout data-taking. The configuration with the magnetic field pointing upwards (downwards), bends positively (negatively) charged particles in the horizontal plane towards the centre of the LHC.
Similar amounts of data were recorded with each polarity, which reduces the effect of charge-dependent detection and reconstruction efficiencies on results obtained from the full data sample. In the simulation, pp collisions are generated using Pythia 8  [11]  with a specific LHCb configuration  [12]. Decays of hadronic particles are described by EvtGen  [13]. The interaction of the generated particles with the detector and its response are implemented using the Geant4 toolkit  [14]  as described in Ref. [15]. The online event selection is performed by a trigger, which consists of a hardware stage, based on high-p T signatures from the calorimeter and muon systems, followed by a two-level software stage. Events are required to pass both hardware and software trigger levels. The software trigger at its first level applies partial event reconstruction.
Sensitivity studies To interpret the results, a study of the sensitivity of the present data sample to different types of CP violation is required. The sensitivity is examined based on simplified simulation samples generated according to a preliminary version of the model in Ref. [25]  based on CLEO-c measurements. The generation is performed using MINT, a software package for amplitude analysis of multibody decays that has also been used by the CLEO collaboration  [26]. Given the high purity obtained in the selection, backgrounds are neglected in these sensitivity studies. The variation of the selection efficiency across phase space is taken into account in these studies. This efficiency is measured using a sample of events based on the full LHCb detector simulation. The efficiency varies mainly as a function of the momentum of the lowest momentum pion in the event in the D 0 rest frame, and this efficiency variation is parameterised.
Experimental setup The measurement was performed in 2010 at the n TOF facility at CERN, which is a high instantaneous intensity spallation neutron source driven by the CERN PS synchrotron. We used the Total Absorption Calorimeter (TAC)  [2]  to detect the capture reactions as a function of the time-of-flight. This detector is made with 40 BaF 2 crystals of 15 cm length covering ∼ 95% of the solid angle, and is used to detect in coincidence (nearly) all the γ -rays coming from the (n,γ ) reactions. The neutron beam was monitored during the entire measurement with a 6 Li foil based detector  [6]. The detector signals were recorded by a digital data acquisition system  [7]  operating at 250 and 500 MSamples/s with 8 bits resolution, recording continuously a time of flight of 32 or 16 ms for each pulse (above 0.
Above 1-2 keV there are pile-up effects in the capture signals induced by the high background counting rate. This effect has been corrected with a method which is quite similar to the ones described in  [15]  and that will be published in a future work. Below 1-2 keV this effect is very small. The obtained capture yield has been analyzed with SAMMY, using a resonant description of the cross section up to 700 eV. The URR was analyzed up to 10 keV with the modified version of the FITACS code available in SAMMY.
The fitted function is the sum of a fourth-order polynomial background and two double-Gaussian functions. The double-Gaussian functions for ψ(2S ) and X(3872) contain about 470 k and 30 k candidates, respectively. Monte Carlo (MC) simulation is used to study the selection and reconstruction efficiencies. The MC samples with b-hadron production and decays are generated with Pythia 6. 4  [17] , complemented, where necessary, with a dedicated extension for B c production based on calculations from Refs. [18] [19] [20] [21]. The decays of b-hadrons are then simulated with EvtGen  [22]. The generated events are passed through a full simulation of the detector using the ATLAS simulation framework  [23]  based on Geant4  [24, 25]  and processed with the same software as that used for the data. Figure  1 : (a) The invariant mass distribution of the J/ψ candidates satisfying all selection criteria except the ±120MeV J/ψ mass window requirement indicated here by the dotted vertical lines.
Data set and detector selection The data was recorded over a period of 280 calendar days, from July 2014 to April 2015. During that period, 160 days were devoted to WIMP searches. All twenty-four detectors were used to define coincidences between detectors. The average heat trigger threshold and baseline resolutions were monitored hour by hour. For this analysis, only hours when this trigger threshold is below 2 keV were selected. The hourly fiducial baseline resolution on the fiducial ionization measurement was also required to be less than 400 eV. Two detectors with a failing ionization channel, preventing an efficient application of the fiducial cut, were excluded from this exposure. Three detectors had less than one day of running time with an online threshold of below 2 keV and were also excluded from further analysis.
Activity measurements The fit of the entire 2 keV threshold data set results in an observed tritium activity of 0. 94 ± 0. 06 (stat. ) ± 0. 10 (syst. ) events per day and per detector, extrapolated down to 0 keV using Eq. (  2 ). However, the rate can be expected to vary from detector to detector. The fit is therefore performed for each detector individually. The tritium rate is obtained from the fit with the flat Compton background. The systematic uncertainty associated with the Compton shape is taken as the quadratic sum of the common 11% contribution discussed in the previous section and the difference between the fitted value with the flat and linear Compton backgrounds in individual detectors. The same systematic tests applied to the peak intensities showed that they are not significantly affected by uncertainties in the shape of the Compton background.
We carried out the calculations of production rates by replacing the original neutron spectrum with that from Ref. [18]. These calculations are performed using a cosmic ray neutron spectrum in the northern hemisphere parametrized as follows  [18] : φ(E) = 1. 006 × 10 -6 e -0. 35 ln 2 E+2. 1451 ln E +1. 011 × 10 -3 e -0. 4106 ln 2 E-0. 667 ln E ( 7 ) where E is the neutron kinetic energy in MeV and φ is in units of cm -2 s -1 MeV -1. We have also used semi-empirical cross sections from Refs. [19, 20, 21, 22, 23]  and the cross sections from the MENDL-2P libraries  [24] , shown, respectively, as dashed and dash-dotted light-blue lines in Figs. 5 to 9. The results of the calculations are listed in the third column of Table  6. They are labeled (a) or (b) if they use the semi-empirical cross sections  [19, 20, 21, 22, 23]  or the MENDL-2P  [24]  database, respectively.
Conclusion The cosmogenic activation of various isotopes in the germanium detectors of the EDELWEISS-III experiment has been measured. The data for five isotopes and thirteen detectors with different exposure times lead to a consistent set of measurements. The first measurement of the 3 H decay rate in germanium detectors is presented. It has been interpreted in terms of production rate of 82 ± 21 nuclei/kg/day with statistical and systematic uncertainties included. The tritium production due to cosmic-ray neutrons is thus important and the present measurement provides valuable information needed to evaluate the reduction of the exposure to cosmic rays necessary for germanium detector arrays used for dark matter searches. The measured production rates on 49 V, 55 Fe and 65 Zn of 2. 8 ± 0. 6 nuclei/kg/day, 4.
Listed as ∆t is the uncertainty considered in our history model due to the absence of some records. 3. 17 V f s σ(f s ) f s σ(f s ) f s σ(f s ) f s σ(f s ) f s σ(f s ) FID823 0. Table  6 : Rates of production (expressed in kg -1 • day -1 ) of isotopes induced in natural germanium at sea level as measured in EDELWEISS III germanium detectors, compared with previous estimates and measurements in Ref. [26] ,  [5] ,  [17] ,  [27]  and  [4]. Errors on the production rate include statistical and for 3 H and 65 Zn systematic uncertainties, too. Systematic uncertainty is based on the minimization of the reduced χ 2. Estimate in this work refers to ACTIVIA calculation, considering semi-empirical  [19, 20, 21, 22, 23]  (a) and MENDL-2P database (b)  [24]  cross sections. For 49 V, both calculations give the same result.
Acknowledgments The help of the technical staff of the  Laboratoire Souterrain de Modane  and the participant laboratories is gratefully acknowledged. The EDEL- WEISS  project is supported in part by the  German ministry of science and education (BMBF Verbundforschung ATP Proj. -Nr. 05A14VKA ), by the  Helmholtz Alliance for Astroparticle Physics (HAP) , by the  French Agence Nationale pour la Recherche (ANR)  and the  LabEx Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-00007 ), by the  P2IO LabEx  ( ANR-10-LABX-0038 ) in the framework " Investissements d'Avenir " ( ANR11IDEX-000301 ) managed by the  ANR (France) , by  Science and Technology Facilities Council (UK) , and the  Russian Foundation for Basic Research  (grant No.
Acknowledgements This work was supported by the  Institutional Program for Scholarships Abroad  (Grant number  OAICE-02-CAB-045-2014 ) for University Officials of the  Office of International Affairs and External Cooperation (OAICE) of the University of Costa Rica  and the  Institut d′Amérique centrale (IFAC) of the Ministère français des affaires étrangères et du développement international (MAEDI)  (Grant number  789478C ).
Evaluation Deformable imaging registrations were performed between the 2 CT image sets by using commercial deformable registration software (Velocity Medical, Varian Medical Systems). The accuracy of the DIR algorithms has previously been evaluated by Kirby et al  [15]  and Schreibmann et al  [16]. The authors' systems were also additionally validated according to Mohamed et al  [17]. The treatment targets had 3 CTVs (CTV1, CTV2, and CTV3) corresponding to intended dose levels of 70, 63, and 57 Gy (RBE), respectively. Major OARs were parotids, oral cavity, spinal cord, and brainstem. Both CTVs and OARs were deformed and transferred from the original CT images to the verification CT image data set. All deformed contours were reviewed by a staff radiation oncologist before the adaptive plan would be generated.
Statistical Considerations All dosimetric data were extracted by using the Eclipse Application Programming Interface (API) tool, which was developed inhouse, to access the Eclipse dosimetric database. This API tool facilitated the efficient and accurate extraction of multiple dosimetric data from the Eclipse treatment planning database, compared with the manual methods. All CTVs and OAR volumes were compared between the original and verification CT scans with paired sample analyses; these analyzed OARs included brainstem, parotid glands, and oral cavity, with their own calculated dose-volume histograms along with the CTVs. Wilcoxon matched pairs nonparametric tests  [18]  were used to evaluate the effect of adaptive versus verification planning, based on volume and dosimetric changes. A probability value of P.
The dosimetry outcomes reported in this study are consistent with those findings  [21, 22] , but they did not use DIR, which has played an important role in generating adaptive treatment planning in this modern series. Deformable imaging registration can reduce the workload of physicians in recontouring all structures in the new CT image data set, which can be time-consuming during a busy clinical practice in both academic and community settings; however, it certainly represents a very important step for ensuring the basis and accuracy of the adaptive treatment plan design and evaluation. There were no significant volume changes for the brainstem, spinal cord, or oral cavity, which are expected. The radiation doses to these organs are typically lower. It is also worth noting that many commercial and in-house DIR systems have been developed in recent years.
These systems use a variety of approaches, and the accuracy of contour deformation needs to be further evaluated and used with caution  [15]. As a result, the new contours review or modification process on the new CT data set cannot be completely eliminated and replaced with DIR  [17] ; this is a subject of ongoing research at the authors' facility. The generated DVHs were used for clinical target volume comparisons (all shown in 33 fractions), which were derived from the original (square lines) and adaptive (triangles) plans. (B) These DVHs were generated for CTV and organs-at-risk comparisons based on the verification (square lines) and adaptive (triangle lines) plans. In this case, there were no significant differences for CTV coverage. However, a significant difference between the verification and adaptive plans was seen.
E were used in the data analysis. Four silicon detectors were placed symmetrically at θ lab =16 • around the beam direction, in the sliding seal reaction chamber. These detectors were used to monitor the beam and normalize to the Mott scattering cross section. The XTU Tandem accelerator at LNL provided 30 Si beams in the energy range of 47-90 MeV, with intensities of 15-30 pnA. The targets consisted of 50 µg/cm 2 metallic 30 Si evaporated on 30 µg/cm 2 carbon backings facing the beam. The isotopic enrichment of 30 Si was 99. 64 %, where the small amounts of 29 Si and 28 Si did not affect the fusion cross section because of their higher Coulomb barriers. The carbon backing and the silicon target itself introduced an average beam energy loss of around 750-850 keV, which was taken into account in the analysis.
High-frequency oscillations (HFO) have been suggested as biomarkers of epileptic tissues. While visual marking of these short and small oscillations is tedious and time-consuming, automatic HFO detectors have not yet met a large consensus. Even though detectors have been shown to perform well when validated against visual marking, the large number of false detections due to their lack of robustness hinder their clinical application. In this study, we developed a validation framework based on realistic and controlled simulations to quantify precisely the assets and weaknesses of current detectors. We constructed a dictionary of synthesized elements-HFOs and epileptic spikes-from different patients and brain areas by extracting these elements from the original data using discrete wavelet transform coefficients.
The validation process typically consists in three steps  [4, 5, 7, 9] : firstly, expert reviewers mark events of interest, i. e. ripple (R, 80-250Hz) and fast-ripple (FR, 250-500Hz), on a recording; then the detector is run over the same data and finally the detections are compared to the reviewers' marking and its performance is measured. This strategy however suffers from several issues. The main drawback is that there is no gold standard: is an HFO marked by the detector but not by the reviewers necessarily wrong? It could be that the detector extracts a feature that is not evident for the reviewers. Without gold standard, this situation will always be considered as an erroneous detection. Furthermore, the performance highly depends on the content of the tested signal. On the one hand, the performance is measured as a global agreement over two classes (R and FR) between the detections and the reviewers' marking.
Clinical database Simulated data were created by using recordings of non-REM slow wave sleep of drug resistant epileptic patients undergoing pre-surgical examination with stereoelectroencephalography (SEEG) using macro-electrodes of diameter 0. 8mm. The same type of electrodes was used in all patients. The frequency sampling was 2048Hz with an anti-aliasing filter set at one third of the sampling frequency (688Hz). We only selected contacts which were inside the epileptogenic zone defined by expert neurophysiologists (FP and FB). Selected channels had to exhibit interictal spikes, ripples and fast-ripples on bipolar montage. Explored brain areas included the mesiotemporal, lateral temporal, frontomesial and frontolateral regions. We did not simulate posterior regions because they are under-explored and little is known about HFOs in these areas.
Simulation Extraction of events. To build the dictionary of representative events, we only used visual marking and no automatic detection to avoid favoring one detector. We visually marked the events of interest on a bipolar montage according to  [14]. We extracted them from the signal using the following method. We developed a Graphical User Interface (GUI) which displays in three different panels simultaneously the raw signal with the extracted signal, the normalized time-frequency (TF) obtained using Continuous Wavelet Transform (CWT) and, finally the TF relative to the discrete wavelet transform (DWT) (Wavelet Toolbox of MATLAB). By selecting the relevant coefficients of the DWT TF, one can reconstruct parts of the signal and thus extract elements without the surrounding BKG. If elements were superimposed, i.
This also allows us to use frequency and amplitude modulations found in real HFOs which makes the simulation more realistic. For the same reasons, we used this technique to extract spikes. Background simulation. The ongoing background activity (BKG) of the areas where the HFOs and spikes were extracted were simulated. This step is useful to verify the robustness of the algorithm to variability in BKG activity across brain structures. We marked several baseline sections of the recording, i. e. containing only BKG activity. Since errors may occur, reviewers were asked to mark several pieces of baseline of several second for each channel of interest. The coefficients of an autoregressive (AR) model were estimated for each section (e. g. Matlab's LPC function) and averaged over the pieces of the same channel to have an estimate of the BKG for each channel.
HFO detectors Five automatic detectors were tested; four of them come from the RIPPLELAB Toolbox  [16]  which we integrated in our open source software AnyWave  [20]. The fifth detector is our detector called Delphos (Detector of ElectroPhysiological Oscillations and Spikes) described in  [17]. The next paragraph will briefly summarize the different detection methods. The Short Time Energy detector (STE)  [4]  is based on the moving average of the root mean square amplitude of the filtered signal. The segments above five times the standard deviation (SD) plus the mean energy lasting more than 6ms are considered as putative HFOs. These events are kept if containing more than 6 peaks greater than 3 times the SD above the mean value of the rectified band-pass signal. The Short Line Length detector (SLL)  [5]  calculates the line length energy of a sliding window applied to a first-order backward differencing and band-pass filtered signal.
This benchmark will be uploaded in open-access on the AnyWave (http://meg. univ-amu. fr/wiki/AnyWave:Plugin_Simul) website as well as the simulated SEEG data and the detection markers of the detectors. We believe that, by building such a broad and cross-modality simulation set, we will answer some unsolved questions concerning HFOs. Appendix A Let x, x and Γ be the signal, the Fourier transform of x and the auto-correlation function of x, respectively. The power spectrum density (PSD) of x is defined as S ¼ x j j 2 : ð3Þ The first stage of the SLL detector is a first-order backward differencing filter. We showed in  [17]  that this process, which corresponds to a pre-whitening stage aiming at flattening the 1/f spectrum, does not completely flatten the spectrum but does not over-express the high frequency.
Acknowledgments This work has been carried out within the  FHU EPINEXT  with the support of the A Ã  MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program  managed by the  French National Research Agency (ANR). This study was also supported by the  ANR  project  FORCE  ( ANR-13-TECS-0013 ).
This work has been carried out within the  FHU EPINEXT  with the support of the A Ã  MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program  managed by the  French National Research Agency (ANR). This study was also supported by the  ANR  project  FORCE  ( ANR-13-TECS-0013 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All data are available on our website (http://meg. univ-amu. fr/) or directly here: http://meg. univ-amu. fr/data_papers/ roehri2017/simulation_rate_3. zip and here: https:// figshare. com/articles/Simulation_Dataset_of_ SEEG_signal_for_HFO_detector_validation/ 4729645 (DOI: 10. 6084/m9. figshare. 4729645).
A search is presented for massive long-lived particles decaying into a muon and two quarks. The dataset consists of proton-proton interactions at centre-of-mass energies of 7 and 8 TeV, corresponding to integrated luminosities of 1 and 2 fb -1 , respectively. The analysis is performed assuming a set of production mechanisms with simple topologies, including the production of a Higgs-like particle decaying into two long-lived particles. The mass range from 20 to 80 GeV/c 2 and lifetimes from 5 to 100 ps are explored. Results are also interpreted in terms of neutralino production in different R-Parity violating supersymmetric models, with masses in the 23-198 GeV/c 2 range. No excess above the background expectation is observed and upper limits are set on the production cross-section for various points in the parameter space of theoretical models.
In this paper a search for massive long-lived particles is presented, using proton-proton collision data collected by the LHCb detector at √ s = 7 and 8 TeV, corresponding to integrated luminosities of 1 and 2 fb -1 , respectively. The event topology considered in this study is a displaced vertex with several tracks including a high p T muon. This topology is found in the context of the minimal super-gravity (mSUGRA) realisation of the MSSM, with R-parity violation  [13] , in which the neutralino can decay into a muon and two jets. Neutralinos can be produced by a variety of processes. In this paper four simple production mechanisms with representative topologies and kinematics are considered, with the assumed LLP mass in the range 20-80 GeV/c 2. The LLP lifetime range considered is 5-100 ps, i.
To preserve enough background events in the signal-free region for the signal determination algorithm described in Sect. 5, no isolation requirement is applied at this stage. Secondary vertices are selected by requiring R xy > 0. 55 mm, at least four tracks in the forward direction (i. e. in the direction of the spectrometer) including the muon and no tracks in the backward direction. The total invariant mass of the tracks coming from a selected vertex must be larger than 4. 5 GeV/c 2. Particles interacting with the detector material are an important source of background. A geometric veto is used to reject events with vertices in regions occupied by detector material  [25]. The number of data events selected is 18 925 (53 331) in the 7 TeV (8 TeV) datasets. Less than 1% of the events have more than one candidate vertex, in which case the candidate with the highest-p T muon is chosen.