dataesr/scientific-paragraphs-categorization huggingface dataset:
To verify the assumptions made about the failing complex fields, the results of the IVD controls of 20 IMRT prostate plans of 2012 and 14 VMAT prostate plans of 2014-15 were confronted to the following plan parameters, determined from the DICOM plan using an in-house MATLAB v. 2. 014b software. 1. The modulation complexity, described by the Modulation Complexity Score (MCS),  (23) (24)  for IMRT and VMAT: (1) MCS beam = ∑ Z-1 , LSV CP i + LSV CP i+1 2 ( ) i=1 AAV CP i + AAV CP i+1 2 , MU i,i+1 MU beam with the Leaf Sequence Variability (LSV) per control point CP i , for a total number Z of control points: (2) LSV CP = left. ∑ n N-1 (pos max -|pos n -pos n+1 |) (N-1)×pos max [ ] right ∑ n N-1 (pos max -|pos n -pos n+1 |) (N-1)×pos max [ ] where pos n is the position of the left/right leaf n and pos max is the maximal position variability of the left/right leaf bank at control point CP i , for a total number N of open leaves.
ACKNOWLEDGMENTS We would like to thank the physicists, technicians, radio-oncologists and radiotherapists of our center for their patience and support during the different phases of the development and implementation in clinical routine of the EPIgray in vivo dosimetry system. A special thanks to the physicists  Laurent Bartolucci  and  Jean-Luc Dumas, PhD , for their helpful suggestions. We would also like to thank  DOSIsoft S. A. for their financial support and the successful cooperation, and in particular  Valerie Rousseau, PhD , for her technical support during the development and implementation in clinical routine. Finally, we would like to thank the reviewers and editors of the Journal of  Applied Clinical Medical Physics  for their constructive criticism and helpful suggestions.
No previous experimental information exists on the charmless hadronic decays being studied; theoretical predictions for the branching fraction of the Λ 0 b → Λπ + π -decay are in the range 10 -9 -10 -7  [22] [23] [24]. The paper is organised as follows. A description of the LHCb detector and the dataset used for the analysis is given in Sec. 2. The selection algorithms, the method to determine signal yields, and the systematic uncertainties on the results are discussed in Secs. 3-5. The measured branching fractions are presented in Sec. 6. Since significant signals are observed for the Λ 0 b → ΛK + π -and Λ 0 b → ΛK + K -channels, measurements of the phase-space integrated CP asymmetry parameters of these modes are reported in Sec. 7. Conclusions are given in Sec. 8.
Detector and dataset The analysis is based on pp collision data collected with the LHCb detector, corresponding to 1. 0 fb -1 at a centre of mass energy of 7 TeV in 2011, and 2. 0 fb -1 at a centre of mass energy of 8 TeV in 2012. The LHCb detector  [25, 26]  is a single-arm forward spectrometer covering the pseudorapidity range 2 < η < 5, designed for the study of particles containing b or c quarks. The detector includes a high-precision tracking system consisting of a silicon-strip vertex detector surrounding the pp interaction region, a large-area siliconstrip detector located upstream of a dipole magnet with a bending power of about 4 Tm, and three stations of silicon-strip detectors and straw drift tubes placed downstream of the magnet. The tracking system provides a measurement of momentum, p, of charged particles with a relative uncertainty that varies from 0.
The authors thank  M. Leone ,  L. Livietti , and  C. Somaschini , from  Polifab-Politecnico di Milano , for technical assistance. This work was funded by  Fondazione Cariplo  via the project  MAGISTER  (Grant No. 2013-0726 ). R. W. and R. C. thank  S. Behnke  and  C. Stemmler  for technical support at the MBE system as well as partial support by  EU  within the  FP7  Project  PASTRY  (GA No. 317746 ).
The Herschel Extragalactic Legacy Project (HELP) brings together a vast range of data from many astronomical observatories. Its main focus is on the Herschel data, which maps dust obscured star formation over 1300 deg 2. With this unprecedented combination of data sets, it is possible to investigate how the star formation vs stellar mass relation (main-sequence) of star-forming galaxies depends on environment. In this pilot study we explore this question between 0. 1 < z < 3. 2 using data in the COSMOS field. We estimate the local environment from a smoothed galaxy density field using the full photometric redshift probability distribution. We estimate star formation rates by stacking the SPIRE data from the Herschel Multi-tiered Extragalactic Survey (HerMES). Our analysis rules out the hypothesis that the main-sequence for starforming systems is independent of environment at 1.
For the SFR we use the FIR data from the Herschel Multi-tiered Extragalactic Survey (HerMES;  Oliver et al. 2012). In order to probe the environment and the stellar mass, we exploit the rich multiwavelength data and volume of the COSMOS field. The Herschel Extra-galactic Legacy Project (HELP,  Vaccari (2016) ,  Oliver et al. 2016, in prep. ) aims to collate and homogenize observations from many astronomical observatories to provide an integrated data set covering a wide range of wavelengths from the radio to the UV. The key focus of the HELP project is the data from the extra-galactic surveys from ESA's Herschel mission  (Pilbratt et al. 2010) , covering over 1300 deg 2. HELP will add value to these data in various ways, including providing selection functions and estimates of key physical parameters.
The data set will enable users to probe the evolution of galaxies across cosmic time and is intended to be easily accessible for the astronomical community. The aim is to provide a census of the galaxy population in the distant Universe, along with their distribution throughout the 3-dimensional space. Another key feature of HELP will be the generation of galaxy density maps. In this paper, we apply our chosen methodology for measuring density fields to publicly available data in the COSMOS field to explore the environmental dependence of star formation as probed by Herschel. The format of this paper is as follows. We describe the data we use in Section 2. We describe our methods of determining the environment of the galaxy (Section 3. 2), our stacking analysis (Section 3. 3) and how we obtained SFRs (Section 3.
The HerMES survey We use the SPIRE data  (Griffin et al. 2010 ) from the Her-MES  (Oliver et al. 2012)  survey to compute the SFRs of our sample of galaxies. We use all 250 µm, 350 µm and 500 µm SPIRE bands in the COSMOS field from the second data release, DR2 (5σ depth of  15. 9, 13. 3, 19. 1 mJy; at 250, 350 and 500 µm, respectively, Viero et al. 2015). The HerMES (and in future also the HELP) data can be obtained from the HeDAM database 1. One of the challenges at the longer wavelengths probed by SPIRE is extragalactic confusion (e. g. Nguyen et al. 2010) , whereby many sources detectable with higher resolution shorter wavelength imaging are located within a single SPIRE beam, and therefore appear as one SPIRE source. The SPIRE FWHM for 250, 350 and 500 µm is 18. 1, 25. 5, and 36. 6 arcsec, respectively  (Griffin et al.
SFR estimation Having determined the mean, normalised SPIRE flux densities in each SPIRE band, we estimate a total integrated FIR luminosity (and hence SFR) for each redhift and density bin. We find the best fit SED through a least-squares fit from the library of  Berta et al. (2013)  to the mean normalised flux densities in the three SPIRE bands. The SPIRE bands probe the peak of the SED for intermediate redshifts and this gives us the most accurate SED normalisation. PACS data  (Poglitsch et al. 2010 ) could be added and would probe the peak of the FIR emission better in our lowest redshift bin; however, due to differences in the map-making procedure (i. e. the non linear map making in PACS), and higher noise in the PACS data, we choose not to include these data in our analysis to avoid introducing biases into the sample  (Lutz et al.
We have omitted this error for our comparisons of different regions (see Appendix B for more detail about the error analysis). In the redshift range 1. 5 < z < 2 the declining toy model has a reduced χ 2 close to one, indicating a good fit, where the environment independent model has a reduced χ 2 greater than three. We conclude that our data at 1. 5 < z < 2 is inconsistent with the hypothesis of an environmental independent MS with significance at level of 1 per cent, measured using p-value. However, this is a small effect and all our data falls well within the 0. 2 dex intrinsic scatter of the SFRs in the MS. In the other redshift bins we cannot exclude the simple hypothesis. We can also use the stacked, normalised, SFRs to assign an estimated SFR to every galaxy (taking into account its stellar mass and the weights applied).
e. the "main-sequence") is the same for every environment. That hypothesis has been supported by other observations, although it is somewhat surprising theoretically, implying that environmental effects can change the relative proportion of galaxies that are star-forming or passive, but not the average SFR of the star-forming galaxies themselves (e. g. Peng et al. 2010). This implies that the environmental effects result in a rapid truncation of star formation  (Darvish et al. 2016). This tantalising result raises several questions, which are beyond the scope of this paper. For example, can we confirm this weak trend with better statistics including other fields with less cosmic variance, and where we can see a broader range of environments? Does our result depend on how we classify galaxies to be star-forming or quiescent? How do our results depend on the accuracy of the photometric redshifts, both within COSMOS and extending to regions with poorer phot-z estimate? This shows the exciting opportunities that will come from exploiting the whole HELP data set, which will enable such analysis using multiwavelength data over several fields.
CONCLUSIONS We have undertaken an investigation of the dust-obscured star formation activity as a function of environment and redshift. We constructed a galaxy density field using an adaptive kernel smoothing and exploiting the full photometric redshift probability distribution function from the deep optical, NIR and IRAC data in the COSMOS field. We characterised the density fields in terms of percentiles to facilitate comparisons between redshifts. We employed a "stacking" technique to estimate the normalisation of the "main-sequence" (i. e. the correlation between the SFR and stellar mass). This techniques fits the Herschel SPIRE data from HerMES to all galaxies with photometric redshifts and stellar masses in the same redshift bin simultaniously. A simple model in which the mean specific star formation rate for star-forming galaxies declines with increasing environmental density gives a better description at 0.
This works demonstrates the power of the Herschel SPIRE data when coupled with high-resolution data sets and demonstrates methodology that we will build upon to extend these studies to rarer higher density regions when exploiting the full 1300 deg 2 of data from the Herschel Extragalactic Legacy Project, HELP. 10 ). For every redshift and every SPIRE band we can see a clear correlation of mass with flux density, so this plot is effectively another way to show the MS.
ACKNOWLEDGEMENT space. In this calculation we assumed that we had the correct SED to transform from the SPIRE flux density to SFR. If instead we had chosen an SED with an FIR peak with an offset from the intrinsic one, our three SPIRE estimates would have given very different SFR values, allowing us to rule out this FIR peak location. But if the peak is only slightly wrong, or if all three SPIRE bands are longward of the FIR peak, then several SED templates (with different SFRs) would all give a reasonable fit. On the other hand, the K -correction, and the other corrections applied to obtain to our stacking list would still be roughly the same for these "good" SED fits. Because our corrections are roughly the same, we will find the same result (SPIRE flux density vs. density), and so we do not take this error in the SFR into account.
If we had picked the wrong SED, then all of our data points would move up or down together, leading to the same conclusion in whether or not the MS is dependent of environment. For the SFR-density in the COSMOS field we con-structed the errors based on a tile-selected jackknife over the map, in combination with the error on the SFR of the stacked galaxies and the error of the mass function, see  Ilbert et al. (2013). In this case, we have to take the error in the template into account, because we want to compare with previous results for the SFR-density relation. We quantified this uncertainty by not only running the SIMSTACK code for the best SED template, but also for the second to fifth best templates. These different templates give different SFR estimates. We constructed a weighted mean of these SFRs by weighting each SFR by the reduced χ 2 (on which we based our choice of best templates).
We thank the referee for very useful comments that improved the quality of the work. This project has received funding from the  European Unions   Horizon 2020 research and innovation programme  under grant agreement No  607254. This publication reflects only the authors view and the  European Union  is not responsible for any use that may be made of the information contained therein. Steven Duivenvoorden  acknowledges support from the  Science and Technology Facilities Council  (grant number  ST/M503836/1 ). Seb Oliver  acknowledges support from the  Science and Technology Facilities Council  (grant number  ST/L000652/1 ). Behnam Darvish  acknowledges financial support from  NASA  through the Astrophysics Data Analysis Program (ADAP), grant number  NNX12AE20G. E. Ibar acknowledges funding from  CONICYT/FONDECYT  postdoctoral project N • : 3130504.
Background estimation After the event selection the background is composed primarily, in order of importance, of W+jets, t t dilepton, single top, and QCD multijet events. The W+jets background is obtained from MC simulation with normalization and heavy-flavor content adjusted in data control regions. The t t dilepton background is determined as a fraction of the full t t sample predicted by MC simulation. QCD multijet events are estimated with a fully data-driven method. Single top production as well as minor backgrounds (Z+jets and diboson) are determined from MC simulation normalized to the best available theoretical calculation of their cross-sections. The W+jets background estimate uses as a starting point the Alpgen+Pythia samples normalized to the inclusive W boson NNLO cross-section.
The correlation between each systematic uncertainty's effect on the signal and background spectra is taken into account. For the t t generator, T,parton. The total uncertainty (band) is shown along with the effect of the dominant uncertainties. The components "Large-R (JES) stat. " and "Large-R (JES) data vs MC" are, respectively, the statistical uncertainty and the systematic uncertainty associated with the difference in jet response between data and MC simulation when balancing p T in photon+jet events. parton shower, and ISR/FSR uncertainties, a systematic uncertainty variation is defined as the difference between the generated and unfolded cross-section of a given generator, with unfolding corrections extracted with an alternative generator (or alternative generator setting). The PDF uncertainty is computed by unfolding the nominal sample with correction factors extracted by reweighting the nominal sample at the hard-process level for each variation of the PDF.
05. Better agreement with data is obtained when using the HERAPDF set instead of CT10, which reduces the difference between    data and MC simulation by up to about 20%. The Powheg+Pythia prediction that provides the best description of the data is the one that simultaneously employs the HERAPDF set and h damp = m top , corresponding to a p-value of 0. 31 at particle level. The measured parton-level cross-section is compared to the prediction of the parton-level NLO MCFM generator  [95] , which is interfaced with Applgrid  [96]  to convolve the perturbative coefficients with the strong coupling and the PDF. The inclusive cross-section computed by MCFM is used to normalize the prediction and no electroweak corrections are applied. Several PDF sets are compared: CT10, MSTW, NNPDF, and HERAPDF.
Conclusions The differential t t production cross-section in √ s = 8 TeV pp collisions has been measured as a function of the hadronically decaying top quark p T in a high-p T regime, using a dataset corresponding to   an integrated luminosity of 20. 3 fb -1 collected by the ATLAS detector at the LHC. Boosted hadronically decaying top quarks with p T > 300 GeV are reconstructed within large-R jets and identified using jet substructure techniques. The measured p T spectrum is extended in this analysis relative to previous measurements. A particle-level cross-section is measured in a fiducial region that closely follows the event selection. The measurement uncertainty ranges from 13% to 29% and is generally dominated by the uncertainty on the jet energy scale of large-R jets. A parton-level cross-section is also reported, with larger systematic uncertainties due to its greater reliance on t t MC generators to correct the data.
The measured cross-sections are compared to the predictions of several NLO and LO matrix-element generators normalized to NNLO+NNLL QCD calculations, and using various PDF sets. Previous measurements suggest that the top quark p T spectrum is well predicted at low p T by NLO and matrixelement MC generators, both in normalization and shape, but that their predictions exceed the data at high p T. The current analysis, focused on the boosted topology and extended to higher p T values, also observes such a trend. However, a statistical analysis shows that the measurements are compatible with the majority of MC generator predictions within the quoted uncertainties. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide.
INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Sk 0142odowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; the Royal Society and Leverhulme Trust, United Kingdom.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel;.
Acknowledgements We are all grateful to  Gavin Salam  and  Andrzej Siodmok  for helpful discussions. MD and GS both thank each other's institutions for hospitality as this work was being carried out. MD's work is supported in part by the  Lancaster-Manchester-Sheffield Consortium for Fundamental Physics  under  STFC  grant  ST/L000520/1. GS's work is supported in part by the  French Agence Nationale de la Recherche , under grant  ANR-15-CE31-0016  and by the  ERC  advanced grant Higgs@LHC.
In this paper we investigate the impact of different star formation histories (SFHs) on the relation between stellar mass (M * ) and star formation rate (SFR) using a sample of galaxies with reliable spectroscopic redshift z spec > 2 drawn from the VIMOS Ultra-Deep Survey (VUDS). We produce an extensive database of dusty model galaxies, calculated starting from the new library of single stellar population (SSPs) models presented in Cassarà et al. (  2013 ) and weighted by a set of 28 different star formation histories based on the Schmidt function, and characterized by different ratios of the gas infall time scale τ infall to the star formation efficiency ν. The treatment of dust extinction and re-emission has been carried out by means of the radiative transfer calculation. The spectral energy distribution (SED) fitting technique is performed by using GOSSIP+, a tool able to combine both photometric and spectroscopic information to extract the best value of the physical quantities of interest, and to consider the Intergalactic Medium (IGM) attenuation as a free parameter.
2001; Guo et al. 2012 , just to mention a few). When modeling the SED of galaxies, some parametrization of the SFH must be used  (Ilbert et al. 2010; Lee et al. 2010; Wuyts et al. 2011; Ilbert et al. 2013). The SFH can in principle be arbitrarily complex, but simple forms are of-A&A proofs: manuscript no. Cassara2016_astroph ten adopted: one of the frequently used form is a declining exponential model, where SFH ∝ exp(-t/τ), as would be predicted from a closed box model of galaxy evolution  (Schmidt 1959; Tinsley 1980). It is worth recalling that the closed-box model of galaxy evolution, applied to study the chemical history of the solar neighborhood, fails to explain the metallicity distribution observed among old field stars, giving rise to the so called Gdwarfs problem. Models with infall are able to solve this issue, since they avoid the excess of very low metal stars: the metallicity increases faster, and few stars are formed at very low metallicities  (Chiosi 1980; Bressan et al.
Tasca et al. (2015)  recently presented a study on the evolution with redshift of the SFR-M * relation and of the sSFR of a sample of 4531 galaxies from the VUDS survey, with spectroscopic redshifts between 2 and 6. 5. The values of M * and SFR have been obtained by fitting all the available multiwavelenghts data with Le Phare code  (Arnouts et al. 1999; Ilbert et al. 2006 ) and using a range of templates coming from the  Bruzual & Charlot (2003)  models. The assumed IMF is the Chabrier IMF, while the adopted SFHs are exponentially declining (S FR ∝ e -t/τ ) and two delayed SFHs models with peaks at 1 and 3 Gyrs. They conclude that the logS FR -logM relation for star forming galaxies (SFGs) remains linear up to z=5 but the SFR increases at a fixed mass with increasing redshift. For stellar masses M * ≥ 10 10 M ⊙ the SFR increases by a factor 1.