dataesr/scientific-paragraphs-categorization huggingface dataset:
We generate a mock cluster catalogue by applying the DES mask to the full-sky cluster catalogue from F16. We further apply the same point-source mask as applied to the SPT-SZ data. For choosing the appropriate mass range to match the redMaPPer catalogue, we use the mass-richness relation that  Saro et al. (2015)  infer from clusters found in both DES-SV and SPT: We compute P(M i |λ i , z i ) for every cluster i in the redMaPPer catalogue. Based on the average mass distribution P(M i |λ i , z i ) i , we then choose a mass range of 0. 9 < M 500c /10 14 M < 4; this results in a mock cluster catalogue with 6015 clusters in the redshift range 0. 1 < z < 0. 8. The latter is comparable to the number found in the DES data with the corresponding richness range (6693). The number of clusters in the simulation and data catalogues are not expected to match exactly both because of potential differences between the true and simulated cosmology and Poisson noise, as well as some idealities we assume in our simulated cluster catalogue: in our main analysis we apply a cut in mass in the simulation catalogue rather than explicitly modelling the purity and completeness of the optically selected cluster catalogue, and we ignore the scatter in the massrichness relation.
Contamination by the tSZ effect On individual clusters the kSZ signal is only a subdominant contribution to the millimetre-wave signal, which is instead dominated by primary CMB, noise residuals, and the thermal SZ component. The primary CMB and noise are uncorrelated with cluster positions, and they would therefore already average out in a stacking analysis, whereas the thermal SZ component is only removed by the pairwise estimator (equation 19). This cancellation might however be insufficient for the most massive clusters, as these are rare objects and have the highest tSZ temperature decrement. For this reason, we have excluded the most massive clusters ( λ > 60) from the sample and hence we do not expect a high degree of tSZ contamination. None the less, we test for this possible systematic by processing a mock CMB data set with all components except for the tSZ component through the same pipeline as before, in order to estimate the impact of the tSZ to our measurement.
Centring uncertainties Offsets between the cluster centre derived from optical data and the centre of the SZ emission have the potential of diluting the pairwise kSZ signal. This miscentring can happen for clusters in which the brightest cluster galaxy (BCG) is not at the location of the potential minimum, such as in unrelaxed or merging clusters. Furthermore, there are cases where the galaxy labelled as the central galaxy by the redMaPPer algorithm is not truly the BCG. Here, we test the impact of miscentring on the measurement of τe by introducing random offsets into the mock cluster catalogue. Following F16, we consider here two different miscentring models. Our first miscentring model follows  Johnston et al. (2007) , where we assume that a fraction f J = 0. 75 of clusters in the sample have the correct centre, and a fraction 1f J of clusters are miscentred, following a 2D-Gaussian distribution with width σ J = 300 h -1 kpc (see F16 for a discussion of the choice of the parameters f J and σ J ).
Mass scatter In order to obtain a good match between the optical cluster catalogue (selected in richness) and the mock catalogue (selected in mass), an accurate knowledge of the mass range and average mass of the cluster sample is required. Furthermore, the calculation of the theory template also requires a mass range. If incompleteness and impurities in the catalogue or a large scatter in the mass-richness relation substantially affect the typical cluster mass in the optical catalogue, the inferred optical depth could be biased, both with respect to the true value and the simulations. In this work, we have selected our sample based on the mass-richness relation calibrated by  Saro et al. (2015) , who also accounted for scatter in the relation, finding good agreement between the optical depth from the real data and simulations.
1 < z < 0. 8, in combination with CMB temperature maps from the SPT that have been match-filtered to extract an estimate for the SZ signal at the position of the clusters. In parallel to the measurement on these data, we have repeated the analysis on a set of mock cluster catalogues and CMB maps derived from a new suite of high-resolution kSZ simulations, introduced by F16. In both data and simulations, the temperature estimates at the positions of individual clusters are dominated by thermal SZ and primary CMB, foregrounds and noise residuals. We have removed these contaminants statistically with a differential measurement that isolates the pairwise kSZ signal. We have then fit the recovered signal with an analytic template that models the mean pairwise velocity on linear and mildly non-linear scales and incorporates the effect of photo-z uncertainties.
We have explicitly tested for the effect of extinction by Galactic dust and for a range of spatially varying DES observing conditions. Based on the simulated catalogues, we have also investigated the effect of centring errors, thermal SZ contamination and mass uncertainties, finding that they do not cause any significant bias for our main result. As an astrophysical application of our measurement, we have studied the optical depth as a function of the scale of the matched filter and found a consistent behaviour between the data and the simulations, indicating that the data are in agreement with the astrophysical model used in the simulations. At our fiducial filtering scale of θ c = 0. 5 arcmin, we have translated the central optical depth into a gas fraction within R 500 of f 500 gas = 0.
2014 ) and J-PAS  (Benitez et al. 2014 ) and low-resolution spectroscopic surveys such as SPHEREX  (Doré et al. 2014 ). However, kSZ analyses from future photometric or low-resolution spectroscopic data might require a more sophisticated modelling of the effect of photo-z uncertainties to ensure that the results stay unbiased within their significantly reduced uncertainties. Next-generation CMB experiments like SPT-3G (covering the same region as SPT-SZ, but to much lower noise levels; see  Benson et al. 2014)  and AdvACTPol (covering ∼15 000 deg 2 , but not as deeply as SPT-3G; see e. g. Calabrese et al. 2014 ) should measure the kSZ signal with dramatically increased precision. In contrast to the single-frequency analysis presented here, the deep multi-frequency data of these experiments could be combined to further isolate the kSZ signal from noise and tSZ contamination.
A joint analysis of pairwise kSZ measurements with other probes would then be a valuable ingredient for precision cosmology and cluster astrophysics: the combination of kSZ with X-ray and tSZ data could provide valuable insight into cluster profiles and the gas fraction, whereas a combination of the kSZ signal with other cosmological observables such as galaxy clustering and redshift-space distortions could measure the growth rate with high precision and hence tighten the constraints on dark energy or modified gravity.
The success of this project also relies critically on the expertise and dedication of the DES Data Management group. Funding for the DES Projects has been provided by the US Department of Energy, the US National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovacão, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey.
A1 Decomposition of the error budget We decompose the error budget making use of the different ingredients of the simulated mm-signal that are described in Section 5. 1, namely primary CMB, foregrounds, instrumental noise, and thermal SZ. To identify the strongest source of uncertainty, we measure τe for different combinations of the kSZ signal with the various contaminants. We use the same template fit as in the main analysis (see Section 4. 4) and compare the error on the optical depth, σ τe , for the different simulated data scenarios. This compresses the contribution of the individual components into a single number per simulated data scenario, allowing an easy comparison. We show in Table  A1  the error on τe for various combinations of simulated mm-signals. We find that the strongest increase of σ τe , and hence the largest contribution to the total error budget of our measurement, can be attributed to thermal SZ.
After discovering the pulsar, we produced an extended dataset covering observations until 2016 February 16 for use in follow-up spectral (Section 2. 3) and timing analyses (Section 3). The extended dataset was produced using the P8R2 SOURCE V6 IRFs, a lower zenith angle cutoff of 90 • , and a larger 15 • ROI. We calculated probability weights for photons within 5 • of the target source with gtsrcprob using the results 1 http://tinyurl. com/fermipulsars of a binned likelihood spectral analysis performed with pointlike. Our source model included all 3FGL sources within a radius 5 • larger than the ROI. The target source's spectrum was modeled with an exponentially cutoff power law. Its sky position and spectral parameters were free to vary, as were the spectra of 3FGL sources within 5 • , and the normalizations of the background models.
When producing the extended dataset, the spectral analysis was performed with gtlike, with the pulsar's position fixed at its preliminary timing position. The Galactic diffuse emission and isotropic diffuse background were modeled with the gll iem v06. fits  (Acero et al. 2016a ) and iso P8R2 SOURCE V6 v06. txt templates 2  respectively. For the blind search and subsequent timing analyses we performed minimum weight cutoffs to reduce the number of photons for computational speed. In the blind search, this cutoff was w min = 3. 9%, leaving w j = 2762. 7 "effective" photons. In the timing analysis, w min = 0. 7% and w j = 5284. 4.
Radio Observations On 2016 March 28, 2016 April 14 and 2016 June 18 we observed the pulsar timing position with the 64-m Parkes radio telescope for 2. 5 hr, 4. 3 hr and 1. 5 hr respectively using the H-OH receiver at a center frequency of 1369 MHz. With PDFB4 we recorded 256 MHz of bandwidth filtered into 512channel spectra with 256 µs sampling (80 µs for the third observation). The data were analyzed using standard pulsar search techniques implemented in PRESTO  (Ransom 2001). Folding the data with the gamma-ray ephemeris, we searched over dispersion measure (DM) from 0-2700 pc cm -3. We found no plausible pulsar candidates. Recent flux-calibrated observations using the same configuration show a root-mean-square noise level of 130 µJy hr -1 for a 32-bin pulse profile. With a detection threshold of one or more phase bins with S/N >8, then optimally our longest observation would have detected a source with a mean flux density >17 µJy.
We gratefully acknowledge the support of all volunteers who have donated CPU cycles to  Einstein@Home. We are especially grateful to  James Drews  of  UW-Madison, WI, USA  and  University of Houston ,  IT High Performance Computing, TX, USA , whose computers discovered PSR  J1208-6238. This work was supported by the  Max-Planck-Gesellschaft (MPG) , by the  Deutsche Forschungsgemeinschaft (DFG)  through an  Emmy Noether  research grant  PL 710/1-1  (PI:  Holger J. Pletsch ), and by  NSF  award  1104902. The Parkes radio telescope is part of the  Australia Telescope , which is funded by the  Commonwealth Government  for operation as a National Facility managed by  CSIRO. The  Fermi-LAT Collaboration  acknowledges support for LAT development, operation and data analysis from  NASA  and  DOE (United States) ,  CEA/Irfu  and  IN2P3/CNRS (France) ,  ASI  and  INFN (Italy) ,  MEXT ,  KEK , and  JAXA (Japan) , and the  K.
Experimental Configuration and Data Analysis The experiment was carried out at the Laboratori Nazionali di Legnaro (LNL) by bombarding a 136 Xe beam at E lab = 870 MeV, provided by the Tandem-ALPI complex, onto a 200 µg/cm 2 thick (and 99. 9% enriched) 208 Pb target, backed on 20 µg/cm 2 Carbon foil. The target was placed at 90 • with respect to the beam direction. PRISMA is a large-acceptance magnetic spectrometer, operating at LNL, designed for identification of reaction products of heavy ion collisions consisting of a Micro Channel Plate detector (MCP), a focusing quadrupole, a dipole magnet, a focal Multiwire Parallel Plate detector (MW-PPAC) and, finally, the ionization chambers (IC) (for ∆E-E measurement). The PRISMA set-up allows identification of the A and Z of the projectile-like fragments (PLF) of reaction within a resolution of 1/220 and 1/60, respectively.
Swift We reduced the 1. 1 ksec Swift/XRT observation taken on UT 2014 July 17 (MJD 56855) with the xrtpipeline FTOOLS software, version 6. 17. This observation was strictly simultaneous with NuSTAR OBSID 80001034002 and taken in the Windowed Timing mode. In order to avoid the piled-up center, the Swift extraction region was an annulus centered on the source with inner radius of 3 pixels (7 arcseconds) and outer radius of 20 pixels (47 arcseconds)  (Romano et al. 2006). We also used a background region with a radius of 30 pixels (71 arcseconds). Exposure maps and ancillary files were created with xrtexpomap and xrtmkarf, respectively. The data was then grouped with a minimum of 20 counts per bin and fit between 0. 7-10 keV. Below 0. 7 keV, the windowed timing mode is known to have calibration artifacts  16.
The authors thank the referee for their invaluable comments. ALK would like to thank the support for this work, which was provided by  NASA  through  Einstein Postdoctoral Fellowship  grant number  PF4-150125  awarded by the Chandra X-ray Center, operated by the  Smithsonian Astrophysical Observatory  for  NASA  under contract  NAS8-03060. PR acknowledges financial contribution from contract  ASI-INAF I/004/11/0  and  ASI-INAF I/037/12/0. JC thanks financial support from  ESA/PRODEX Nr. 90057. This work made use of data from the NuSTAR mission, a project led by the  California Institute of Technology , managed by the  Jet Propulsion Laboratory , and funded by the  National Aeronautics and Space Administration. This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the  ASI Science Data Center (ASDC, Italy)  and the  California Institute of Technology (USA).
Outside of the tabulated range of a and b the thermal scattering cross section is calculated using the short collision time approximation (MacFarlane and Muir, 1999): d 2 r SCT dXdE ¼ r b 2k B T ffiffiffiffiffiffiffiffiffi E 0 =E p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 4pT eff =T p exp À ða À jbjÞ 2 4a T T eff À b þ jbj 2 ( ) ð6Þ where T eff is the above mentioned effective temperature, which is also distributed in the ENDF/B-VII files. For light and heavy water there currently exist two models available in the different evaluated nuclear data libraries: one originally developed at General Atomic (the GA Model)  (Koppel and Houston, 1978; MacFarlane, 1994) , and one developed at IKE Stuttgart (the IKE Model)  (Mattes and Keinert, 2005). In the case of light water, both models treat Oxygen as a free gas at room temperature, whereas Hydrogen is computed in the Gaussian and incoherent approximation.
Data analysis In this section we describe the different steps in the processing of raw data carried out in this work. We analyzed detectors S137 and S139 in forward scattering and detector S100 in backward scattering direction. The detectors were selected in order to have a good separation of H, D and O peaks. In a former paper  (Rodríguez Palomino et al. , 2014)  we selected a detector so that the difference filter-out minus filter-in spectra showed a good baseline, free of the filter-dependent gamma background, while in the present case a heuristic method to correct background is employed. Table  2  shows the parameters of angle, position and filter thicknesses of the detectors. In the case of detector S100 placed at backscattering angle there are two filters (''thick" and ''thin"), arranged to be employed in the double-difference method  (Andreani et al.
Monte Carlo simulations We employed Monte Carlo simulations with a twofold objective: correcting the experimental data for multiple scattering attenuation and detector efficiency effects, and also helping in the analysis of the background as will be shown in Sections 4. 4 and 4. 5. A detailed account of the Monte Carlo procedure employed can be found in  Dawidowski et al. (2012) , where the reader is referred for further details. The Monte Carlo procedure requires the knowledge of the incident spectrum and the detector efficiency as above referred, together with instrumental parameters such as the sample and filter geometry and their total cross section as a function of the energy. The filter analyzer absorption cross section as a function of energy is read from absorption cross section tables, that can be obtained from ENDF/B-VII (2007).
The rationale of this procedure was double. On the one hand this kind of analysis is well suited to small-and medium size-neutron facilities, where the DINS technique proved to be feasible  (Cuello et al. , 2011) , although a small Table  4  Bound scattering cross sections fitted from the spectra of detectors S137, S139 and S100 (thin and thick filter). We show only the parameters for which a reasonable convergence of the least squares subroutine was attained. We also include the scattering cross sections determined from the eVS experiment performed at the Bariloche Electron LINAC (Argentina)  (Blostein et al. , 2009). The fitted values of the global constant K that relates the experimental data with the calculated cðt; hÞ are included. x  Oxygen Fig. 10. Bound-atom scattering cross sections for Hydrogen, Deuterium and Oxygen, determined from the Neutron Compton profiles measured with detectors S137, S139 and S100 (thin and thick filters).
Acknowledgments We gratefully acknowledge  L. Capararo  and  F. Y. Moreira  for the technical support, and  M. Schneebeli ,  P. D'Avanzo  and  A. Mancilla  for the  LINAC operation at Bariloche , and to  Rutherford Appleton Laboratory  ( J. Tomkinson ) for the invitation to share the beam time to make the experiments. Work partially supported by  ANPCyT  ( PICT 2011-534 ) and  CONICET  ( PIP 11220110100552 ) (Argentina).
The minimum PTV covered by the 95 % isodose line was 95 %. Dose constraints were set according to the GORTEC group (the French group of radiation oncology for head and neck cancer) (Table  2 ). Parotid sparing was not conducted if considered to the detriment of PTV coverage or other essential organs at risk (OARs). During the treatment course, set-up errors >5 mm were corrected by weekly in-room stereoscopic kV imaging. Informed consent was obtained from all patients. This study was approved by the institutional review board (ARTIX study NCT01874587).
Total cumulated dose estimations using deformable registration For all patients, the weekly CT images were first registered to the planning CT using a rigid transformation defined by six parameters (three translations and three rotations). The mean squared error was used as a similarity criterion. The cumulated dose estimate relied on deformable image registration, using the free-form deformation (FFD) method. The control points were iteratively displaced according to the considered metric. The dense deformation field was obtained by B-spline interpolation  [20]. The mutual information metric was used to handle the modified intensities between CT datasets caused by the presence of contrast agent. The geometric transformation obtained using both rigid and deformable registration was then applied to the weekly dose distributions in order to propagate each one to the planning CT0 dataset.
Linear regression model and nomogram to predict PG overdose PG overdose was calculated as the difference between the cumulated mean PG dose and the mean PG dose of the planning CT dataset (CT0). The following anatomical and dosimetric parameters have previously been described in the literature as correlating with PG overdose  [6, 10, 14, 15] , and were assessed for this correlation: CTV70 (cm3), PG volume (cm3), neck thickness (mm), PG-to-CTV70 distance, and mean PG dose. The PG-to-CTV70 distance was defined as the minimal Euclidean distance between the surfaces of the two contours. The difference between and ratio of each of these parameters computed from the two CTs (CT0 and each weekly CT, respectively) were also studied. The Pearson correlation was used to assess the correlation between variables significantly correlated with the PG overdose.
This method consisted in all patients but one (n = 19) being used to develop a PG overdose model (difference between cumulated PG dose and planned dose). A PG overdose prediction was then calculated for the one remaining patient using the model. This predicted PG overdose was then compared to the cumulated PG overdose. This step was repeated for each patient. The variance of each model parameters was calculated to estimate the model's stability and identify outliers. The mean squared error of the predicted values was calculated. Statistical analysis was carried out using the Statistical Package for the Social Sciences V. 20. 0, and R language and environment for statistical computing.
Weekly replanning for the patients at risk of PG overdose Using the nomogram, we identified patients predicted to receive at least one PG overdose, for whom we performed a weekly IMRT replanning on each weekly CT dataset, in accordance with the dose constraints described for the initial planning. PTV coverage did not significantly differ between the initial planning and the weekly replanning. The dose constraints specified for the OARs complied with the GORTEC recommendations for all replanning, as for the initial planning. The mean PG cumulated doses with replanning were compared to those without. The impact of the replanning on the risk of xerostomia was estimated by using the Lyman Kutcher Burman (LKB) model of normal tissue complication probability (NTCP) (n = 1, m = 0. 4, and median toxic dose [TD 50 ] = 39.
5 and 0. 9, respectively, p <0. 001), only the parameter with the highest r 2 was used (mean PG dose difference). The three parameters used for PG overdose prediction were CTV 70 on the planning CT dataset (CTV 70_CT0 ), the CTV 70 CT0-to-CT1 difference (ΔCTV 70 ), and the difference between the mean PG doses of CT1 and CT0 (ΔDosePG). Based on these parameters, the resulting linear regression model for PG overdose prediction, optimized for all patients, was: Fig. 3  Parotid gland overdose assessment. The mean dose difference was calculated between the estimated cumulated dose (without replanning) and the planned dose, in each parotid gland (PG) (ipsilateral and contralateral), for each of the 20 patients. A dose difference with a positive or negative value corresponded to a PG overdose or under-dose, respectively.