dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction During 2011 the LHCb experiment  [1]  at CERN collected 1. 0 fb - 1  of √ s = 7 TeV pp collisions. Due to the large production cross-section, σ(pp → b bX) = (89. 6 ± 6. 4 ± 15. 5) µb in the LHCb acceptance  [2] , with the comparable number for charm production about 20 times larger  [3, 4] , these data provide unprecedented samples of heavy flavoured hadrons. The first results from LHCb have made a significant impact on the flavour physics landscape and have definitively proved the concept of a flavour physics experiment in the forward region at a hadron collider. The physics objectives of the first phase of LHCb were set out prior to the commencement of data taking in the "roadmap document"  [5]. They centred on six main areas, in all of which LHCb has by now published its first results: (i) the tree-level determination of γ  [6, 7] , (ii) charmless two-body B decays  [8, 9] , (iii) the measurement of mixing-induced CP violation in B 0 s → J/ψ φ  [10] , (iv) analysis of the decay B 0 s → µ + µ -  [11] [12] [13] [14] , (v) analysis of the decay B 0 → K * 0 µ + µ -  [15] , (vi) analysis of B 0 s → φγ and other radiative B decays  [16, 17].
On the other hand, limitations imposed by the hardware trigger lead to a maximum instantaneous luminosity at which data can most effectively be collected (higher luminosity requires tighter trigger thresholds, so that there is no gain in yields, at least for channels that do not involve muons). To overcome this limitation, an upgrade of the LHCb experiment has been proposed to be installed during the long shutdown of the LHC planned for 2018. The upgraded detector will be read out at the maximum LHC bunch-crossing frequency of 40 MHz so that the trigger can be fully implemented in software. With such a flexible trigger strategy, the upgraded LHCb experiment can be considered as a general purpose detector in the forward region. The Letter of Intent for the LHCb upgrade  [24] , containing a detailed physics case, was submitted to the LHCC in March 2011 and was subsequently endorsed.
44 At CKM 2012, LHCb presented results of a model-independent GGSZ analysis of B -→ DK -with D → K 0 S π + π -and D → K 0 S K + K -[7] , preliminary results from a ADS-type analysis of B -→ DK - with D → K3π  [334] , a preliminary determination of γ from combined results using B -→ DK - and B -→ Dπ -  [335] , and preliminary results on the time-dependent CP violation parameters in B 0 s → D ∓ s K ± [328]. In the GLW/ADS analysis  [6]  of 1. 0 fb -1 of √ s = 7 TeV data collected in 2011, the CP eigenstates D → K + K -, π + π -, and the quasi-flavour-specific D → π -K + decay are used. The CP asymmetries defined in Eq. (  58 ), and the ratios R ± defined in Eq. (  61 ), are measured for both the B → DK signal and the abundant B → Dπ control channel. The latter has limited sensitivity to γ but provides a large control sample from which probability density functions are shaped, and can be used to help reduce certain systematic uncertainties.
• Time-dependent Dalitz plot fits to self-conjugate final states (e. g. D 0 → K 0 S π -π + ). These combine features of the two methods above, along with simultaneous extraction of the strong phases relative to CP eigenstate final states. Consequently they yield measurements of x D and y D directly. Likewise, the indirect CP violation parameters |q/p| and φ may be extracted, along with the asymmetry in phase and magnitude of each contributing amplitude (in a model-dependent analysis). • Measurements of the ratio of time-integrated rates of wrong-sign to right-sign semileptonic decays (e. g. D 0 → D 0 → K + l -νl vs. D 0 → K -l + ν l ). These yield r M and A m. Within LHCb, analyses are planned or in progress for each of these methods. A measurement of y CP and A Γ from the 2010 data sample has been published  [19].
In the published LHCb analysis, the impact of these backgrounds on the asymmetry was estimated by measuring their size and asymmetry in the h + h -mass sidebands and computing the effect of such a background on the signal with a toy Monte Carlo study. The alternative approach would be to use a full 2D fit to m(h + h -) and δm, which would distinguish this class of peaking background from the signal by its m(h + h -) distribution. The three issues discussed above -terms entering at second order in the asymmetries, non-cancellation due to kinematic correlations, and peaking backgrounds -are particular to this analysis and will require some changes to the procedure as larger data samples become available. In addition, there are more generic systematic uncertainties associated with the fit procedure and with the handling of events with more than one candidate.
Prospects of future LHCb measurements Numbers of events in various channels are projected directly from the numbers reconstructed in the 2011 data set, in most cases. This involves assumptions that the prompt charm cross-section will increase by a factor of 1. 8 when doubling the centre-of-mass energy from √ s = 7 TeV to √ s = 14 TeV, that the integrated luminosity will increase from 1 fb -1 to 50 fb -1 , and that the trigger efficiency for charm will increase by a factor of 2 as the current hardware trigger requirement is effectively removed (or substantially relaxed). Additionally, a factor of 3. 5 times greater efficiency in channels with K 0 S → π -π + daughters is predicted based on progress made in the trigger software between 2011 and 2012. This primarily results from reconstructing candidates which decay downstream of the vertex detector.
Acknowledgements The LHCb collaboration expresses its gratitude to its colleagues in the CERN accelerator departments for the excellent performance of the LHC. LHCb thanks the technical and administrative staff at the LHCb institutes, and acknowledges support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  and  Region Auvergne (France) ;  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  ANCS/IFA (Romania) ;  MinES, Rosatom ,  RFBR  and  NRC "Kurchatov Institute" (Russia) ;  MinECo, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). LHCb also acknowledges the support received from the  ERC  under  FP7.
The Tier1 computing centres are supported by  IN2P3 (France) ,  KIT  and  BMBF (Germany) ,  INFN (Italy) ,  NWO  and SURF (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ). LHCb is thankful for the computing resources put at its disposal by  Yandex LLC (Russia) , as well as to the communities behind the multiple open source software packages that are depended upon. The work of A. Datta was supported by the  National Science Foundation  under Grant No. NSF   PHY-1068052. D. M. Straub  was supported by the  EU ITN "Unification  in the LHC Era", contract  PITN-GA-2009-237920  (UNILHC). We thank  D. Gorbunov  for useful comments.
Two data processing centres (DPCs) The Planck data used throughout this paper are an internal data set known as DX7, whose properties are described in appendices to the LFI and HFI data processing papers  (Planck Collaboration II 2013; Planck Collaboration VI 2013). However, we have tested the analysis to the extent that the results will not change if carried out on the maps which has been released to the public in March 2013. The specifications of the Planck maps are reported in Table  1. The dataset used for the analysis consists of full resolution frequency maps and the corresponding noise information. We will indicate whenever the CMB-removed version of this dataset has been used for display purposes. When analysing the results we apply a point source mask based on blind detection of sources above 5 σ in each Planck map, as described in  Zacchei et al.
AME frequency spectrum We modelled the mixing matrix to account for five components: CMB; synchrotron emission; thermal dust emission; free-free emission; and AME. We neglected the presence of the CO component by excluding from the analysis the 100 and 217 GHz Planck channels, which are significantly contaminated by the CO lines J = 1 → 0 and J = 2 → 1 respectively (Planck HFI Core Team 2011b). CO is also present at 353 GHz, where it can contaminate the dust emission by up to 3% in the region of interest, and at 545 and 857 GHz, where the contamination is negligible. For the estimation of the mixing matrix we used the following dataset: We verified that the inclusion of the WMAP Ka-W bands in this analysis did not produce appreciable changes in the results. The explored frequency range is now covered by Planck data with higher angular resolution and sensitivity.
Reconstruction of the amplitudes The reconstruction of the amplitude of the components has been performed on the 1 • resolution version of the dataset. We used the same frequencies exploited for the estimation of the mixing matrix, except for the free-free template, which has been excluded to avoid possible biases in the reconstruction. The results are shown in Fig. 4. The first and second rows show the components reconstructed at 30 GHz (from left to right: synchrotron emission, free-free emission, AME, and thermal dust emission) and the corresponding noise RMS maps. Thanks to the linearity of the problem, the noise variance maps can be obtained by combining the noise variance maps of the channels at 1 • degree resolution with the squared reconstruction matrix W. The noise on and free-free emission.
Acknowledgements Based on observations obtained with Planck (http://www. esa. int/Planck), an ESA science mission with instruments Planck Collaboration et al. : Galactic diffuse components in the Gould Belt system bins. The term λc T V Cc V is a quadratic stabilizer for the source power cross-spectra: the matrix C is in our case the identity matrix, and the parameter λ must be tuned to balance the effects of data fit and regularization in the final solution. The functional in Eq. (A. 13) can be considered as a negative joint log-posterior for p and c V , where the first quadratic form represents the loglikelihood, and the regularization term can be viewed as a logprior density for the source power cross-spectra.
Planck Collaboration et al. : Galactic diffuse components in the Gould Belt system and contributions directly funded by  ESA Member States ,  NASA , and  Canada. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN  and  JA (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU).
Acknowledgements We would like to thank  Matteo Bertolini ,  Anatoly Dymarsky ,  Liam McAllister ,  Paul McGuirk  and  Timm Wrase  for discussions, and specially  Thomas Van Riet  for stimulating exchanges and Dutch grammar corrections. This work was supported in part by the  ANR  grant  08-JCJC-0001-0  and the  ERC   Starting Grants 240210 -String-QCD-BH and 259133 -ObservableString. SM is supported in part by a  Contrat de Formation.
IAXO requirements highly motivate the development of low background x-ray detection strategies. The goal background level of IAXO is 10 -7 -10 -8 keV -1 cm -2 s -1 , while in CAST-2013, Micromegas detectors have already demostrated levels as low as 6×10 -7 keV -1 cm -2 s -1. The implementation of these strategies in CAST over the years, many of them imported from deep underground experiments (e. g. shielding design, radiopurity), have provided a background reduction of two orders of magnitude (see Fig. 11 ). The applied techniques that led to such reductions are explained in detail in a recent paper  [13]. While the reader is referred to that document for a complete description of the CAST-Micromegas, a background model of the current detectors and an explanation of low background strategies, this document focuses on the very latest upgrades, tests and results of the CAST 2013 data taking campaign.
When the readout electronics is triggered by the mesh pulse, the analog data from all the channels are passed to an ADC converter. A pure digital electronics card, the FEMINOS board  [18] , gathers the data and performs pedestal subtraction. Finally, those samples whose value is above a user-defined number of standard deviations from the pedestals mean for each channel are sent to the DAQ system by means of a standard network connection, performing therefore an important data reduction. The 3D reconstruction of an event is thus possible using the charge collected by every pad (strip) and the detector decoding. The spatial coordinates, x and y, are determined by the integrated charge collected by each strip, while the relative z position is determined from the temporal position of the strip pulses.
Acknowledgements We want to thank our colleagues of  CAST  for many years of collaborative work in the experiment, and many helpful discussions and encouragement. We thank  R. de Oliveira  and his team at  CERN  for the manufacturing of the microbulk readouts. We also thank the  LSC  staff for their help in the support of the Micromegas setup at the LSC. Authors would like to acknowledge the use of  Servicio General de Apoyo a la Investigación-SAI, Universidad de Zaragoza. F. I. acknowledges the support of the  Eurotalents program. We acknowledge support from the  European Commission  under the  European Research Council T-REX  Starting Grant ref. ERC-2009-StG-240054 of the  IDEAS program  of the  7th EU Framework Program. We also acknowledge support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under contracts ref.
By contrast, the bone below, to the right of, and above this region (pb) has all been deposited by an external periosteum. (J) 3D model of fibres showing three distinct fibre orientations indicated by the white arrows in side and plan views (orientation of anterior and dorsal indicated with arrows). (K) Interpretative representation of muscle attachments, blue arrows showing approximate orientations of muscles. doi:10. 1371/journal. pone. 0056992. g007 Australia  [36]  (Figure  2A ). Additional data were obtained from the extant frog Xenopus. A dried humerus of Desmognathus (private collection) with some remaining muscles was used as a test case for imaging muscle attachments in an extant vertebrate with known muscular anatomy. The identification of bone microstructures in a scan data set was carefully checked by comparison with a thin section made at the exact same place after that this bone was scanned  [12].
24 mm scans). Virtual thin sections were made using the protocol established by Tafforeau and Smith  [41]  for virtual histology of teeth. The segmentation of the scan data sets was done using the software VGStudio MAX version 2. 1 (Volume Graphics Inc. , Germany). Compagopiscis, modelled from scan with 0. 678 mm voxel size. The bone is oriented obliquely. Top, dorsolateral view; middle, anterior view; bottom, anteroventral view. The dorsal surface shows a dimpled texture identical to that on the arrested growth surfaces in the muscle attachment, whereas the ventral surface is smoother. Scale bar: 1 mm. On the right, close-ups of surfaces showing transition from dimpled (top) to non-dimpled (bottom) surface. The dimples are the size of single cells, and each appears to form the entry point for an attachment fibre that is cemented into the bone.
Acknowledgments We gratefully acknowledge  Z. Johanson  at the  Natural History Museum, London ,  M. Siversson  at the  Western Australian Museum, Perth  and  T. Mo  ¨rs at  Naturhistoriska Riksmuseet, Stockholm , for lending us specimens in their care. We also thank  C. Berg  and  M. Sa ¨fholm  ( Dpt of Environmental Toxicology ) at  Uppsala University  for help in providing the Xenopus forelimb,  J. Castanet  at  UniversitePierre et Marie Curie, Paris  for providing the Desmognathus forelimb and  H. Lamrous  at  Universite Ṕierre et Marie Curie, Paris , for making the petrographic thin sections. We thank  P. Dong  at  Inserm U1044, Lyon  for his participation in the development of the software for osteocyte-lacuna quantification. We are very grateful to the editorial team of the journal and two anonymous reviewers whose remarks greatly improved the final manuscript.
All scans were performed at the  European Synchrotron Radiation Facility (ESRF) in Grenoble, France , as parts of projects  EC203  and  EC519  which were funded by  ESRF  ,www. esrf. eu/. SS, VD and PEA are supported by  ERC  Advanced Investigator Grant  233111 ,erc. europa. eu/. , and PEA by a  Wallenberg Scholarship  from  Knut och Alice Wallenbergs Stiftelse  ,www. wallenberg. com/kaw/. BR is supported by a  KoF grant  awarded by  Uppsala University  ,www. uu. se. KT is supported by an  ARC   QEII Fellowship  and  DP110101127 ,www. arc. gov. au/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Consistency of Different Data in the Peak ρ Region The direct comparison of the cross sections for BABAR and the other experiments has been shown in detail in Ref. [9]. There is indeed a discrepancy with KLOE, but agreement with CMD-2 and SND within the quoted respective systematic uncertainties. One can quantify the observations by considering the ratio of the cross sections measured by each experiment to the BABAR result. Since the energy corresponding to the experimental data points do not match the BABAR bins, we perform the quantitative comparison to Table  1 : Ratios of the measured e + e -→ π + π -cross sections from the quoted experiments relative to the BABAR form factor fit in the mass range 0. 7-0. 85 GeV, excluding the ρω interference region. The quoted uncertainties are statistical ('stat'), systematic ('syst'), and total ('tot').
The fitted values for the ratios are given in Table  3. Of course the BABAR value in the first row is very close to one by construction, but we show it to demonstrate that the form factor fit, performed in the full mass range from theshold to 3 GeV, does represent well the BABAR data between 0. 7 and 0. 85 GeV. Only statistical uncertainties are used in the ratio fits, while systematic uncertainties are quoted separately. All the values are displayed in Fig. 2. From Table  3  and Fig 2 we see that, unlike the qualitative claim in Ref. [1] , the BABAR data in the ρ peak region is fully compatible with CMD-2 and SND, and disagrees with KLOE. The CMD-2 data also disagrees with KLOE, while SND with a larger uncertainty is consistent with both BABAR and KLOE. Since in Ref. [1]  CMD-2 and SND are treated together, it is interesting to quote the ratio for the combined Novosibisrk data (taking into account correlated uncertainties).
Using a sample of 24264 optically selected AGNs from the SDSS DR7 database, we characterise how the profile of the [O III]λ5007 emission line relates to bolometric luminosity (L AGN ), Eddington ratio, radio loudness, radio luminosity (L 1. 4 GHz ) and optical class (i. e. , broad/narrow line Seyfert 1, Type 2) to determine what drives the kinematics of this kpcscale line emitting gas. Firstly, we use spectral stacking to characterise how the average [O III]λ5007 profile changes as function of these five variables. After accounting for the known correlation between L AGN and L 1. 4 GHz , we report that L 1. 4 GHz has the strongest influence on the [O III]λ5007 profile, with AGNs of moderate radio luminosity (L 1. 4 GHz = 10 23 -10 25 W Hz -1 ) having the broadest [O III]λ5007 profiles. Conversely, we find only a modest change in the [O III]λ5007 profile with increasing radio loudness and find no significant difference between the [O III]λ5007 profiles of broad and narrow-line Seyfert 1s.
2006 ; although see also  Zhang et al. 2011). However, while some recent studies have explored, in detail, the links between gas kinematics and the radio properties for small samples of AGNs (e. g. ,  Wu 2009; Husemann et al. 2013) , analysis of much larger samples are needed to break the degeneracy between radio and [O III]λ5007 luminosities to determine how gas kinematics relate to the radio properties for the AGN population in general. In this study, we use the SDSS spectral database to derive the average [O III]λ5007 line profiles of a large sample (i. e. , numbering 24,267) of optically-selected AGNs at z < 0. 4 and explore how these average profiles change as a function of key AGN parameters (specifically, [O III]λ5007 luminosity, Eddington ratio, radio loudness, radio luminosity and spectral classification [i.
Selection of AGNs The vast majority of extragalactic emission line sources in the SDSS database are dominated by star formation rather than AGN activity and need to be removed from the sample prior to further analysis. We used a combination of emission line flux ratios ("BPT" diagnostics;  Baldwin et al. 1981 ) and line widths (as measured by the SDSS spectral fitting routine) to remove the ∼90 per cent of the initial sample likely dominated by star formation. We classified all narrow line objects (defined here as having FWHM Hα < 600 km s -1 ) as being probable Type 2 AGNs if their [O III]λ5007/Hβ and [N II]λ6584/Hα flux ratios satisfy the diagnostics outlined in  Kauffmann et al. (2003;  see our Fig. 1 ) and identify all sources with FWHM Hα > 600 km s -1 as potential Type 1 AGNs. 2 Our choice of FWHM Hα cut is a compromise between obtaining as complete a sample of Type 1 AGNs as possible and the exclusion of HII galaxies and LINERS, whose numbers increase significantly at FWHM Hα < 600 km s -1.
Multi-Component Fitting Routine In the previous subsection we described how we used the results from the SDSS emission line fitting routine to crudely select samples of Type 1 and Type 2 AGNs in the SDSS database. However, the single gaussian (i. e. , one per emission line) fits performed by the SDSS pipeline cannot account for the complex emission line profiles that are common among AGN spectra. Instead, we developed our own multi-component fitting routine to accurately measure these line profiles. We used this routine to re-measure the profiles of the Hα, Hβ.
The spectral stacks were built by coadding the continuumsubtracted spectra provided by the SDSS database. Each spectrum was shifted into the rest frame using the [O III]λ5007 redshift derived from our fits (assuming that the narrowest [O III]λ5007 component lies at the systematic redshift). We note that the redshift derived from the [O III]λ5007 line is in very good agreement with the redshift provided by the SDSS database -a gaussian fit to the ∆z = z SDSS -z [O III] distribution reveals an average offset of ∆z = (0. 9 ± 2. 1) × 10 -4 (i. e. , 26 ± 63 km s -1 ); indeed, our tests show there is no significant impact in our results if we adopt the SDSS-reported redshifts instead. We take a bootstrapping approach to calculate the error on the average emission line profiles. We select, at random, a third of the sample in each of our bins and stack their spectra.