dataesr/scientific-paragraphs-categorization huggingface dataset:
We supplement this 'low-' temperature likelihood with the pixel-based polarization likelihood at large scales ( < 23) from the WMAP 9-year data release  (Bennett et al. 2012). The WMAP9 data must be corrected for the dust contamination, for which we use the WMAP procedure. However, we have checked that switching to a correction based  At smaller scales, 50 < < 2500, we compute the power spectra of the multi-frequency Planck temperature maps and their associated covariance matrices using the 100, 143, and 217 GHz channels, and cross-spectra between these channels  12. Given the limited frequency range used in this part of the analysis, the Galaxy is conservatively masked to avoid contamination Fig. 17. Angular spectra for the SMICA CMB products, evaluated over the confidence mask, and after removing the beam window function: spectrum of the CMB map (dark blue), spectrum of the noise in that map from the half-rings (magenta), their difference (grey), and a binned version of it (red).
Note that the lensing reconstruction, while highly statistically significant, is still noise dominated for every individual mode, and is at best SNR ≈ 0. 7 around L = 30. following similar analyses in previous CMB experiments (e. g. ,  Sievers et al. 2013; Dunkley et al. 2011; Reichardt et al. 2012; Dunkley et al. 2013). φ WF ( n) Galactic North φ WF ( n) Galactic South In this 'high-' Planck likelihood, we model the total theoretical power as the sum of the lensed CMB and a set of parameterized foreground emission spectra. Parameters are in-troduced describing the beam uncertainties. We simultaneously marginalize over all the "nuisance" parameters when estimating cosmological model parameters. This approach is designed to allow easy combination with data from the ACT and SPT experiments, which measure the millimetre-wave spectra from scales of ∼ 200 to < 10000.
VII Many low-frequency extragalactic sources are variable; however, considering typical variability timescales and amplitudes, the temporal observing pattern of Planck, and the sensitivity of the PCCS, a clear detection of variability is likely only for the brightest sources. Recent efforts in this direction can be found in Chen et al. (  2013 );  Kurinsky et al. (2013). Similar work along these lines has not yet been attempted with the PCCS, but will be pursued with the full-mission dataset in the future.
The information derived from the validation of the Planck SZ detections and included in the released catalogue, in particular the SZ-based mass estimate, provides high value to the catalogue, and will make it a reference for studies of cluster physics. Considering that only a small fraction of the new Planck cluster candidates have been followed up with other observatories to date, it will also motivate multi-wavelength follow-up efforts. Using an extended sub-sample of the Planck SZ clusters with high-quality XMM-Newton data, the scaling relation between SZ and X-ray properties has been reassessed and updated  (Planck Collaboration XXIX 2014). With better quality data and thus higher precision, we show excellent agreement between SZ and X-ray measurements of the intra-cluster gas properties.
Diffuse emission Eight types of diffuse foreground have been identified that must be removed or controlled for CMB analysis: dust thermal emission; dust anomalous emission (from rotating small grains); three CO rotational lines; free-free emission; synchrotron emission; the CIB not fully correlated between frequencies; Sunyaev-Zeldovich secondary CMB distortions; and the background of unresolved radio sources. Some of these have been independently extracted from the Planck maps, in some cases with the help of external information (e. g. , ancillary maps tracing specific astrophysical components, or prior knowledge of the spectral energy distribution of the power spectrum), to yield astrophysically meaningful foregrounds. We describe first the astrophysical foregrounds that result from CMB-directed component separation, which are combinations of physically distinct components, and then describe several physical foregrounds extracted with the help of additional data or specialized techniques.
Foregrounds from CMB component separation Planck's wide frequency range allows us to use component separation techniques based on Planck data alone to derive tight constraints on several astrophysical components in addition to the primary CMB fluctuations. In Planck Collaboration XII (2014), we present individual maps of: 1) a combined high-frequency component accounting for Galactic thermal dust emission and fluctuations in the CIB; 2) Galactic carbon monoxide (CO Type 3, see 8. 2. 4); and 3) a combined low-frequency component accounting for synchrotron, free-free, and anomalous microwave emission (AME, almost certainly emission from microscopic spinning dust grains; Fig. 24 ). Only Planck frequencies between 30 and 353 GHz are used, as the systematics of 545 and 857 GHz are less well understood than those of the lower ones, the dust signal is already strongly dominant at 353 GHz, and higher frequencies may include emission from higher-temperature dust.
(2012)  covering the redshift range 0. 1 to 0. 7. The Planck data, in combination with polarization measured by WMAP, high-anisotropies from ACT and SPT and other, lower redshift data sets, provide strong constraints on deviations from the minimal model. The low redshift measurements provided by BAO allow us to break some degeneracies still present in the Planck data and significantly tighten constraints on cosmological parameters in these model extensions. The ACT and SPT data help to fix our foreground model at high. The combination of these experiments provides our best constraints on the standard 6-parameter model; values of some key parameters in this model are summarized in Table  10. From an analysis of an extensive grid of models, we find no strong evidence to favour any extension to the base ΛCDM cosmology, either from the CMB temperature power spectrum alone or in combination with the Planck lensing power spectrum and other astrophysical datasets.
The BDT correction, identical to that used in Ref. [17] , improves the resolution on the mass of the bb system by ≈15%, resulting in an increase in the sensitivity of the analysis of 10-20%, depending on the specific channel. The Z → bb invariant mass resolution after this correction is ≈10%. Simulated samples of events are produced using several event generators, and the response of the CMS detector is modeled using the GEANT4 program  [22]. The MADGRAPH 5. 1  [23]  generator is used to generate the diboson signals, as well as the background from W+jets, Z+jets, and tt events. The single-top-quark samples are generated with POWHEG  [24] [25] [26] [27] , and generic multijet samples using PYTHIA 6. 4  [28]. VH event samples with a SM H boson mass of m H = 125 GeV are also produced using the POWHEG  [29]  event generator interfaced to HERWIG ++  [30]  for parton showering and hadronization.
Each systematic uncertainty is represented by a nuisance parameter and profiled in the combined fit. To evaluate the impact of individual uncertainties a fit to a simulated pseudo-dataset is performed removing individual nuisance parameters. Theoretical uncertainties in the acceptances are evaluated using the MCFM  [1]  generator by changing the QCD factorization and renormalization scales up and down by a factor of two relative to the default scales of µ R = µ F = m Z. The impact of uncertainties in PDF and α s on the cross section and acceptance of the VZ signal are evaluated following the PDF4LHC prescription  [35, 36] , using CT10  [37] , MSTW08  [31] , and NNPDF2. 0 [38]  sets of PDF, and the combined uncertainty is found to be 5% for both WZ and ZZ production. Because of the large p V T values required in this analysis, the results are sensitive to electroweak (EW) and NNLO QCD corrections, both of which can be significant.
The estimated uncertainty is 3%. The jet energy scale is also varied within its uncertainty as a function of jet p T and η, and the efficiency of the selections is then recomputed to assess the dependence on these variables. The effect of this uncertainty on the jet energy resolution is evaluated by smearing the jet energies according to their measured uncertainties, a process that affects both the normalization and distribution of events. An uncertainty of 3% is assigned to the yields of all processes in the 0-lepton and 1-lepton channels due to uncertainties related to E miss T , such as its scale and resolution. Scaling factors to normalize b-tagging in simulation to that in data (measured in b enhanced samples of jets that contain muons) are applied consistently to jets in simulated signal and background events.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  SF0690030s09  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  NSC (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Weather regimes To evaluate the effects of wind on larval dispersal and lagoonscale connectivity, we identified a number of typical 30-day realistic wind sequences for the simulations using a long-term meteorological reanalysis. Meteorological dataset from ERA-Interim analysis (http://www. ecmwf. int/research/era/do/get/ era-interim) were extracted daily at the closest grid point to Ahe from 1st January 1979 to 31th December 2011. The 30-day sequences were selected with an overlap of 20 days between each, giving 1203 sequences for the entire 32-year period. Principal component analysis (PCA) and clustering techniques are widely used to identify climatological regimes in observations and model results  [32]. PCA was performed on the wind stress components since it is linearly related to the transport within Ekman layer.
It could be expressed as: t x ~ra : Cd : W s : u t x ~ra : Cd : W s : v where r a is the air density, Cd the drag coefficient, W s the wind speed and (u, v) its zonal and meridian components. To a certain extent (i. e. , under moderate wind conditions and in a medium where sea roughness remains moderate, like in an enclosed area such as a lagoon), Cd can be considered constant (e. g. , 0. 0016 following Deacon and Webb 1962  [33] ). Under this hypothesis, our classification could be performed using: t x r a : Cd ~Ws : u t x r a : Cd ~Ws : v which scale homothetically with the wind stress itself and thus does not influence the classification. W s , u, and v come directly from the ERA interim re-analysis data set. In order to optimize the PCA calculation and still preserve the intra-sequence variability (e.
g. , distinguish a permanent null sequence from ''loop shaped'' sequence), each sequence was summarized into 15 sub-sequences of 2-day averages. This was found to be the best trade off. PCA was performed on nonstandardized dataset, in order to maintain the absolute variability. A k-means clustering was performed on the PCA scores using the pam function of the cluster package in R software  [34]. No significant gain in cluster dissimilarity was found beyond 12 clusters, and 12 was kept as the optimal number of clusters, corresponding to a 10% threshold in dissimilarity. The real wind sequence the closest to the barycenter of each cluster was selected as reference for the cluster. This finally yielded the 12 wind regimes used for the different connectivity scenarios. Finally, the occurrence probability was calculated for each cluster as the ratio between the number of sequences included in each cluster and the total number of sequences (i.
In the second variation, the samples were prepared as in the first, but with the process repeated after cooling. The total time for the two polishings was recorded. This set of data was taken to look at whether any advantage could be gained from repeated polishing of the stock. fibers were also pulled with no initial polish, shown in figure  4  as circular points at zero seconds polishing time. Finally a set of fibers were pulled by turning the laser power down as low as possible while still being able to draw the silica, and these fibers are shown as blue crosses at zero seconds polishing time. Figure  4  shows a clear correlation between polishing time and strength. For comparison, the fibers that are to be used in Advanced LIGO will be under a static load of 10 kg, and a factor of three in strength, 30 kg, is required to ensure safety of the suspensions.
Acknowledgments We would like to thank our colleagues in the  LIGO Scientific Collaboration , the  GEO600  project, and the  Scottish Universities Physics Alliance (SUPA)  for their interest in this work. We also wish to thank the  ILIAS Strega project and Leverhulme Trust  for support. We are grateful for the financial support provided by the  NSF  in the  USA  and the  STFC  and the  University of Glasgow  in the UK. The LIGO Observatories were constructed by the  California Institute of Technology and Massachusetts Institute of Technology  with funding from the  NSF  under cooperative agreement  PHY-9210038. The  LIGO Laboratory operates  under cooperative agreement  PHY-0107417. STFC funding is provided under grant  ST/J000361/1.
Since 1995 several ice shelves in the Northern Antarctic Peninsula have collapsed and triggered ice-mass unloading, invoking a solid Earth response that has been recorded at continuous GPS (cGPS) stations. A previous attempt to model the observation of rapid uplift following the 2002 breakup of Larsen B Ice Shelf was limited by incomplete knowledge of the pattern of ice unloading and possibly the assumption of an elastic-only mechanism. We make use of a new high resolution dataset of ice elevation change that captures ice-mass loss north of 66 • S to first show that non-linear uplift of the Palmer cGPS station since 2002 cannot be explained by elastic deformation alone. We apply a viscoelastic model with linear Maxwell rheology to predict uplift since 1995 and test the fit to the Palmer cGPS time series, finding a well constrained upper mantle viscosity but less sensitivity to lithospheric thickness.
In this study we use cGPS data from the NAP to constrain a local model of solid Earth response to a high resolution present-day mass loss field  (Scambos et al. , in review). By comparing the modelled elastic uplift time series with the observed cGPS uplift time series from Palmer Station we show that elastic uplift alone cannot reproduce the cGPS observations. A viscoelastic model is then used to predict uplift based on a range of Earth models, and results are compared with the Palmer record to obtain the range of best fitting models. Finally, the Earth model is further refined using six shorter cGPS time series from the NAP (see Table  1 ).
GPS Fig. 1  shows the locations of available cGPS sites in the NAP. Of these, we used the seven sites closest to the region of ice-mass change (see Table  1 ). Palmer is a long term station with an excess of 15 years of data, and the remaining six sites were installed in 2009-2010 as part of the LARISSA project (LARsen Ice Shelf System, Antarctica, 2013) (http://www. hamilton. edu/expeditions/ larissa). We did not use the record from O'Higgins (a compilation of three records from two adjacent stations, OHIG, OHI2, OHI3; labelled OHI2 in Fig. 1 ) as a constraint as it lies far from the region of largest mass loss and as such may be affected by potential lateral heterogeneity in Earth structure. Spring Point  (Bevis et al. , 2009)  (SPPT in Fig. 1 ) was also excluded due to the lack of data at this site; however we compare our results with both of these records in the supplementary material.
2 ocean tide model  (Egbert et al. , 2009) , which has been shown to perform very well in this region  (King et al. , 2011) , using the SPOTL software  (Agnew, 1996 (Agnew, , 1997) )  in a centre of mass of the whole Earth system (CM) reference frame  (Fu et al. , 2012). We fixed carrier phase ambiguities to integers where possible  (Bertiger et al. , 2010). Observations were weighted according to their elevationdependent scatter from a preliminary, but otherwise identical, solution. Fiducial-free daily site coordinates were transformed into the ITRF2008  (Altamimi et al. , 2011)  using a 6-parameter transformation and then corrected for changes in atmospheric pressure loading using global 2. 5 × 2. 5 degree grids of loading, in a centre of figure of the Earth (CF) reference frame, derived from the National Center for Environmental Protection (NCEP) reanalysis surface pressure dataset  (van Dam, 2010).
Ice-mass loss The input ice load model is based on a dataset of elevation change derived from Digital Elevation Model (DEM) differencing and ICESat data covering the NAP region north of 66 • S. The time span of the data varies for different sub-regions. For the Larsen B tributary glaciers data are available for two time  periods, 2001 -2006 (Shuman et al. , 2011 ) and 2006 -2011 (Berthier et al. , 2012). Comparing the two time-periods reveals differences in spatial patterns of elevation change (Figs. 1b, c ) but the overall estimated mass loss during these two periods differs by less than 10%  (Berthier et al. , 2012). For the areas north and west of Larsen B, the data span the period 2003-2009  (Scambos et al. , in review). In all cases we take the rate of ice-mass change as being constant throughout the respective data periods; we discuss extrapolations to other time periods later.
The data were converted to a set of 17 846 loading discs with areas between 0. 9 and 1. 1 km 2 for input to the model, where the height of each disc represents a mass loss or gain, using an ice density of 900 kg/m 3 to convert to equivalent water height following  Berthier et al. (2012). Data gaps over large glaciers were infilled using an inverse distance weighting algorithm (inpaint_nans within Matlab). Discs with very small mass change in the range ±0. 5 m weq /yr have a negligible effect on deformation at sites tens to hundreds of km distance from the source of loading and were discounted from the ice load model to speed computation time. This was tested using the best fitting Earth model and resulted in no more than ±0. 2 mm/yr differences in uplift rates at the cGPS sites. The resulting ice-mass change model is shown in Fig.
1a  with the two periods of mass change for  Larsen B glaciers, 2001 Larsen B glaciers, -2006 Larsen B glaciers, and 2006 Larsen B glaciers, -2011, ,  shown separately in Figs. 1b  and 1c , respectively. The uncertainty on the elevation change dataset is ±1 m/yr (2-sigma)  (Scambos et al. , in review) , and this error bound was used to create upper and lower limits on our input ice load model. The pattern of mass loss for glaciers feeding the former Prince Gustav and Larsen A ice shelves prior to 2001 is not known. However, there is evidence that these glaciers reached their current velocities within a few years of the breakup in 1995  (Rott et al. , 2008) , and this velocity has been maintained 15 years after ice shelf collapse  (Rott et al. , 2011). Observations of the glaciers feeding the more southern former Wordie Ice Shelf  (Wendt et al.
Elastic modelling The elastic uplift was computed with the elastic component of the software VE-HresV2 output (Visco-Elastic High Resolution technique for Earth deformations)  (Barletta et al. , 2006; Barletta and Bordoni, in preparation) , which is based on a compressible, spherically symmetric, self-gravitating Earth. Green's functions were spatially convolved with the ice loading discs according to the methods presented in  Barletta et al. (2006). Load Love numbers, based on the PREM Earth structure  (Dziewonski and Anderson, 1981) , were computed in the centre of mass reference frame up to a maximum spherical harmonic degree of 3700 using VE-CL0V3RS v1. 4 (Visco-Elastic Compressible LOVe numbER Solver)  (Barletta and Bordoni, in preparation)  and the degree-1 Love number was computed as described by  Spada et al.
Viscoelastic modelling The viscous uplift of the Earth in response to the ice-mass loss was computed using the compressible viscous component of the software VE-HresV2 output, which uses VE-CL0V3RS v1. 4 to compute the elementary viscoelastic time-dependent Green's functions (convolved with Heaviside function) up to degree 1195, and assumes that at higher degrees they do not change with time so the combined Green's function is negligible. This was verified when choosing the maximum degree so that the results do not suffer from effects of truncation and, as for the elastic modelling, we were able to capture the response from glaciers a few km wide. We limit our study to a Maxwell rheological model. It is worth noting that models with alternative and more complex rheologies may also sufficiently explain the observations, however at present the dataset is too sparse to resolve or require them; we return to this point in the discussion.
Elastic modelling The Palmer cGPS record displays significant non-linearity after 2002; however, the results of the elastic modelling show that even within the uncertainty bounds of the ice-mass change data (±1 m/yr), these changes in rate cannot be explained by elastic  -0. 1 uplift only. In fact, more than five times the amount of observed mass loss (i. e. five times the mass loss shown in Fig. 1 , applied to each disc) would be required to reproduce the magnitude of the observed uplift (modelled uplift shown by the green line in Fig. 2 ). This is not plausible and so we reject the possibility of missing ice unloading in our model, as the missing mass would not only need to be large, or be very close to Palmer, but also sustained from 2002 to present. Such a large signal would require a major ice shelf collapse or substantial glacier mass-loss adjacent to Palmer and neither scenario exists.
Earth model Using the ice-mass change dataset and observations from seven cGPS stations we have been able to constrain a range of Earth models for the NAP. We first used Palmer station only to constrain the Earth model, as the background uplift rate due to long-term glacial isostatic adjustment is well constrained by the pre-2002 data, available only at this site. The addition of the six LARISSA cGPS sites narrows the overall range of best fitting Earth models to a lithospheric thickness between 100 km and 140 km and upper mantle viscosity in the range 6 × 10 17 -2 × 10 18 Pa s. Using a uniform background rate for the whole region may introduce some error in these results if the signal is in fact spatially variable, as suggested by  Nield et al. (2012). Nonetheless, using the LARISSA cGPS data provides some verification for the inferences from the Palmer dataset.