dataesr/scientific-paragraphs-categorization huggingface dataset:
For leptonic models, X-ray spectra from the Suzaku observations are also fitted simultaneously with the gamma-ray data. We used the radiative code and Markov Chain Monte Carlo (MCMC) fitting routines of naima 5   (Zabalza 2015)  to derive the present-age particle distribution, using the parametrisation of neutral pion decay by  Kafexhiu et al. (2014) , and of IC up-scattering of diluted blackbody radiation by  Khangulyan et al. (2014). For the leptonic models, the magnetic field and exponential energy cutoff are treated as independent parameters. The magnetic field is constrained by the ratio of synchrotron to IC flux magnitude, whereas the cut-off in the parent electron spectrum is constrained by the cut-off in the VHE part of the IC gamma-ray spectrum. In the MCMC fit, the seed photon fields considered for the IC emission are the cosmic microwave background radiation and a far-infrared component with temperature T = 26.
80 ± 0. 12) × 10 49 (n H /1 cm -3 ) -1 erg above 1 TeV is required to explain the measured gamma-ray flux. Fig. 5 : Comparison of hadronic and leptonic models to the data. Top left: the hadronic gamma-ray model obtained with our broadband fit is compared to data. Bottom left: the same plot of the leptonic gamma-ray model compared to data including lower-energy X-rays and radio data. The thick blue and red lines indicate the maximum-likelihood models, and the grey lines surrounding them are the models for 100 samples of the MCMC chain and serve to illustrate the fit uncertainties. The energy flux data points shown from high to low energy are the H. E. S. S. and Fermi-LAT gamma-ray data as solid and open circles, the Suzaku X-ray data and ATCA radio data  (Lazendic et al. 2004 ). The latter flux was determined for the northwest part of the SNR shell only and was scaled up by a factor of two here to represent the whole SNR.
Owing to this ad hoc scaling, these points are not included in the fit, but are shown for illustration only. Right-hand side: both leptonic and hadronic models are compared to the Fermi-LAT and H. E. S. S. data points including statistical and systematic uncertainties. In addition to the preferred best-fit models of a broken power law with a cut-off (BPL), a power law without cut-off is also shown for the leptonic model to demonstrate that this model cannot describe the Fermi-LAT gamma-ray data. As in the hadronic scenario, the observed gamma-ray spectrum cannot be explained with an electron population described by a single power law. This is clearly seen on the right-hand side of Fig. 5 , where the best-fit power-law electron model is shown to be incompatible with the gamma-ray data even when taking all uncertainties into account.
6 : Gamma-ray model curves and parent particle energy spectra. On the left, the best-fit electron and proton gamma-ray models (broken power laws with exponential cut-offs) are compared to the Fermi-LAT and H. E. S. S. data. The data points and model curves are the same as in Fig. 5. On the right, the corresponding best-fit parent particle energy spectra are shown. The electron model is derived from a combined fit to both the X-ray and gamma-ray data.
The broadband emission spectra of RX J1713. 7-3946 from various regions are fit with present age parent particle spectra in both a hadronic and leptonic scenario, using Suzaku X-ray and H. E. S. S. gamma-ray data. From the resolved spectra in the 29 small boxes in the leptonic scenario, we derive magnetic field, energy cut-off, and particle index maps of the SNR. For the latter parameter, we do the same for the hadronic scenario. The leptonic and hadronic parent particle spectra of the entire remnant are also derived without further detailed assumptions about the acceleration process. These particle spectra reveal that the Fermi-LAT and H. E. S. S. gamma-ray data require a twocomponent power-law with a break at 1-3 TeV, challenging our standard ideas about diffusive particle acceleration in shocks.
In either leptonic or hadronic scenarios, approaches more involved than one or two zone models are needed to explain such a spectral shape. Neither of the two scenarios (leptonic or hadronic), or a mix of both, can currently be concluded to explain the data unambiguously. Either better gamma-ray measurements with the future CTA, with much improved angular resolution and much higher energy coverage, or high sensitivity VHE neutrino measurements will eventually settle this case for RX J1713. 7-3946. Comparing the gamma-ray to the XMM-Newton X-ray image of RX J1713. 7-3946, we find significant differences between these two energy regimes. As concluded before by  Tanaka et al. (2008) , the bright X-ray hotspots in the western part of the shell appear relatively brighter than the H. E. S. S. gamma-ray data.
Acknowledgments The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  French Ministry for Research , the  CNRS-IN2P3  and the  Astroparticle Interdisciplinary Programme of the CNRS , the  U. K. Science and Technology Facilities Council (STFC) , the  IPNP of the Charles University , the  Czech Science Foundation , the  Polish Ministry of Science and Higher Education , the South.
2017a ). These data revealed lowfrequency photospheric variability due to travelling gravity waves, propagating into the wind. A similar study for the more evolved B1Ia supergiant HD 2905 had to rely on low-cadence Hipparcos photometry and ground-based spectroscopy and revealed similar variability  (Simón-Díaz et al. 2018) , although the wind behaviour is more dominant for that star. In this paper, we report the detection of low-frequency variability in K2 halo photometry of the bright blue supergiant ρ Leo  (HD 91316, B1Iab, V=3. 87). We also performed HERMES spectroscopy of the star with the aim to assess its asteroseismic potential. We present the target in Section 2, each of the two new data sets in Sections 3 and 4, and discuss our interpretation of ρ Leo's variability in Section 5.
K2 HALO PHOTOMETRY Following the idea of scattered-light Kepler photometry of the blue supergiant HD 188209 in the nominal Field-of-View of the mission  (Aerts et al. 2017a) , the technique of halo photometry was developed and is meanwhile well established for the follow-up K2 mission  (Pope et al. 2016; White et al. 2017). We hence proposed ρ Leo for K2 halo photometry during Campaign 14 of the mission. The K2 data set of ρ Leo consists of long-cadence (29. 4 min) data covering 79. 64 d. We used the K2 pixel data as provided in MAST 1 and deduced halo photometric light curves by adopting four different masks. The four custom halo aperture masks were constructed from the stacked images, where the saturated pixels centered on the star, as well as the very outer regions, were avoided. We considered the four masks shown in Fig.
The K2 data set has a time base of 79. 64 d, leading to a frequency resolution (Rayleigh limit) of 0. 0126 d -1. All frequency analyses of the four light curves shown in Fig. 1  give the same conclusions; in the rest of the paper we show the results for the full line in Fig. 1  based on the mask in the upper left panel of Fig. A1. Scargle amplitude spectra of the K2 and Hipparcos photometry are shown in Fig. 2. No significant variability due to the star is detected above 6 d -1. It can be seen that the Hipparcos data were of insufficient quality to detect ρ Leo's photometric variability, but that the K2 data reveals periodic signal. The dominant frequency occurs at f 1 = 0. 0397 ± 0. 0002 d -1 with an amplitude of 7. 94 ± 0. 28 mmag. The statistical frequency error is far below the uncertainty due to the low resolving power of 0.
We use these two line diagnostics to highlight that similar variability to that in the K2 photometry is also present in the low-cadence optical spectroscopy. These two quantities are computed as the zeroth-and first-order moments of the lines, where EW captures temperature variations and v centroid velocity variations  (Aerts et al. 1992 ). Figures  4  and 5  show the only "dense" part of these otherwise sparsely sampled data and reveal (quasi-)periodicity. The Hα line, formed at the base of the stellar wind, shows complex and large-amplitude variations with a dominant time scale of ∼ 30 d in v and about half this period in EW. To further demonstrate the variability in Hα, we show 20 epochs of spectroscopic observations in Fig. 6 , which vary between strong absorption and weak emission. The time scale and amplitude of this variability differ to that of the photospheric Si III 4567Å line, which can also be seen in Figs  4  and 5.
2018). A more detailed analysis using continuing but higher-cadence spectroscopy will be provided in a future follow-up paper. For the purposes of the present paper, the photospheric line Si III 4567Å and the Hα wind line are fully representative and sufficient to probe the variability mechanism in the photosphere and wind of ρ Leo. We now turn to the Fourier analysis of the EW and v of the Si III 4567Å and the Hα lines. Scargle amplitude spectra of these four full data sets are heavily affected by daily aliasing but the EW of both lines are dominated by a frequency close to f 1 and 2 f 1 , within the Rayleigh limit of the K2 data. This is also the case for the Hα v data; the Si v data indicate a dominant low-amplitude (  1. 6 km s -1 ) frequency near 0. 43 d -1. In each of the individual data sets, these frequencies reach low significance, typically between 2 and 4 σ.
As in  Aerts et al. (2017a) , we exploit the full potential of the totally independent K2 and HERMES data sets by "merging" the information in the Fourier domain. This is done by computing harmonic geometric means of the normalised dimensionless individual Scargle amplitude spectra, where we give equal weight to the K2, Hα, and Si III 4567Å data. In this way, we combine the highcadence uninterrupted K2 data with time base of 80 d with the lowcadence aliased HERMES photospheric and wind data with time base of 1800 d. This has the aim to illustrate which frequencies are present in the multiple datasets, but we refrain from any formal statistical interpretation in terms of significance. The result is shown in Fig. 8  and leads to a dominant frequency based on EW of f rot = 0. 0373 d -1 , well within the K2 Rayleigh limit of f 1.
The dominant frequency for v is slightly different. This is not surprising as it is determined by the velocity rather than the temperature variations in the photosphere. As this frequency gives a much poorer fit to the K2 data, we select the frequency on the basis of EW and interpret f rot as the best estimate of the rotation frequency of the star (with corresponding rotation period of 26. 8 d). Harmonics  of f rot occur in both the v and EW multiplied Scargle amplitude spectra. The latter confirm the findings based on the K2 data alone and revealed by Fig. 3 : the dominant K2 frequency is due to cyclic variabillity caused by rotational modulation by an aspherical stellar wind and reveals fine structure due to a variable amplitude and frequency. This is illustrated quantitatively in Fig. 9 , where we show the amplitude and phase of f rot computed from the K2 data follow-  ing the methodology of  Bowman et al.
Both of them also occur in the K2 data, at low amplitude. This suggests a different astrophysical cause compared to f rot and we interpret these frequencies, along with several others in Fig. 8  that peak above 0. 2, in Fig. 3 , as due to gravity waves -whether this photospheric variability is caused by coherent gravity-mode oscillations or convectively-driven travelling gravity waves, or a combination thereof, cannot be deduced from the K2 data due to the too limited time base of 80 d. The frequency spectra derived from numerical simulations of gravity waves in massive stars have a similar morphology to that shown in Fig. 8   (Rogers et al. 2013; Aerts & Rogers 2015; Aerts et al. 2017a; Simón-Díaz et al. 2018) , which supports this interpretation as the cause of the photospheric variability in ρ Leo.
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant agreement N • 670519: MAMSIE). S. S-D. acknowledges funding by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under the grants  AYA2010-21697-C05-04 ,  AYA2012-39364-C02-01 , and Severo Ochoa SEV-2011-0187. P. G. B. acknowledges support through the  MINECO-programme 'Juan de la Cierva Incorporacion'  ( IJCI-2015-26034 ). D. H. acknowledges support by the  Australian Research Council's Discovery Projects funding scheme  (project number  DE140101364 ) and support by the  National Aeronautics and Space Administration  under Grant  NNX14AB92G  issued through the  Kepler Participating Scientist Program.
Acknowledgments. François Gay-Balmaz  is partially supported by the  ANR  project  GEOMFLUID ,  ANR-14-CE23-0002-01 ;  Hiroaki Yoshimura  is partially supported by  JSPS   Grant-in-Aid for Scientific Research  ( 26400408 ,  16KT0024 ,  17H01097 ), the  MEXT  " Top Global University Project " and  Waseda University  ( SR 2017K-167 ).
OBSERVATIONS HD 95086 was first observed by two ALMA programmes in cycle 2: #2013. 1. 00773. S (PI: Su; hereafter referred to as data set A) and #2013. 1. 00612. S (PI: Booth; hereafter referred to as data set B). In both cases, the observations were taken in band 6 whilst the array was in a compact configuration. The total on-source observation time for the combined 1 Radial distances quoted from papers using the Hipparcos or Gaia Data Release 1 distances have been adjusted to the new distance throughout this paper. data sets is 9. 1 hours. Further details of the observations along with analysis of the continuum data can be found in  Su et al. (2017). Both data sets cover the CO J=2-1 line (which has a rest frequency of 230. 538 GHz) with one spectral window, which has 3840 channels of width 0.
A. 00021. T (PI: Ho). These data sets were also reduced using casa version 4. 7. 2 in pipeline mode. The band 3 data were for a total on-source observing time of 55 minutes. We present this here for the first time and show the continuum image in figure  1 , created using CLEAN with natural weights. This observation also included a spectral window covering the CO J=1-0 line (which has a rest frequency of 115. 271 GHz) with 3840 channels of width 0. 63 km s -1 or 0. 24 MHz. The band 7 data were for a total on-source observing time of 6 minutes and is discussed in detail in  Zapata et al. (2018). It included a spectral window covering the CO J=3-2 line (which has a rest frequency of 345. 796 GHz) with 3840 channels of width 0. 42 km s -1 or 0. 49 MHz. As with the band 6 data, the continuum was subtracted from each of these and the visibilities were inverted creating a data cube with an RMS per spectral channel of 2.
CO J=2-1 analysis Although there is no obvious CO detection in the dirty data cube, it is possible that there may still be evidence for CO if we make use of spatial and spectral filters as  Matrà et al. (2015 Matrà et al. ( , 2017b) )  have done for Fomalhaut. We start by looking for evidence of CO J=2-1 emission in the band 6 combined data as this is the deepest dataset. The top plot of figure  2  shows the spectrum obtained by summing over all pixels with a signal-to-noise ratio of at least 10 in the continuum image (this covers radii of ∼0. 5 to ∼5 from the star) 2. The radial velocity given is the barycentric radial velocity set such that a CO line with velocity equal to the Solar System's barycentric velocity would appear at 0 km s -1. If CO is present in the disc, it should have a velocity consistent with that of the star.
RESOLVED SPECTRAL INDEX MAP In addition to probing multiple gas lines, observing the disc at multiple ALMA wavelengths allows us to determine the spectral index of the dust across the image. At submillimetre wavelengths (i. e. in the Rayleigh-Jeans limit), the flux density, Fν , is a power law function of the wavelength λ in the form Fν ∝ λ -α , where α is the spectral index. If the grains emit as pure blackbodies, they would have a spectral index of 2. In reality, dust grains are inefficient emitters at wavelengths much greater than their size and so we expect deviations from this at sub-millimetre wavelengths. To determine the spectral index we create CLEAN, naturally weighted images of the continuum emission for each pair of spectral windows. This gives us seven images (two in band 3, three in band 6 due to the overlap between datasets A and B and two in band 7) at frequencies of  102, 114, 216, 231, 246, 333 and 345  GHz.
CONCLUSIONS In this paper we search for CO gas in the debris disc around the star HD 95086 using ALMA. We find a tentative signal for the J=2-1 line. This candidate line has a peak of 4. 3±1. 4 mJy at a velocity of 8. 5±0. 2 km s -1. This velocity is consistent with the stellar radial velocity found by  Madsen et al. (2002) , the significance of the line is higher when spectro-spatially filtering compared to just spatially filtering and there is a clear rise in the radial profile consistent with the location of the belt as seen in continuum emission. We therefore consider this to be a tentative detection of CO gas in the HD 95086 system, although it is clear that more observations are needed to confirm this. We find that the data are best matched by gas moving towards us from the East ansa and away at the West ansa.
Assuming the gas is orbiting in the same direction as the known planet (clockwise), then we can say that the South side of the disc and the planet are inclined away from us and the North side is the near side. The integrated line flux found is 9. 5±3. 6 mJy km s -1 , corresponding to a CO mass of (1. 4-13)×10 -6 M⊕, which would imply a cometary mass fraction of 5. 7-35. 7%. This is consistent with second generation production through the collisional cascade as derived by  Kral et al. (2017). We did not find any evidence for CO J=3-2 or J=1-0 emission. This is unsurprising given the much lower sensitivity of the band 3 and band 7 datasets and so does not give us any strong constraints on the CO line ratios. Whilst it is hard to make predictions for how deep we would need to go to detect the CO emission at these bands as we do not know for sure what the line ratio is, we predict that CO could be detected in either band 7 or band 3 observations within a few hours depending on the excitation regime.
ACKNOWLEDGEMENTS The authors thank  Steve Ertel ,  Sarah Morrison  and  Renu Malhotra  for providing comments on a draft of the manuscript and also thank the referee for a thorough and constructive review. MB acknowledges support from the  Deutsche Forschungsgemeinschaft  through project  Kr 2164/15-1. LM acknowledges support from the  Smithsonian Institution as a Submillimeter Array (SMA ) Fellow. QK acknowledges support from  STFC  via the  Institute of Astronomy, Cambridge  Consolidated Grant. AMH acknowledges support from  National Science Foundation (NSF)  grant  AST-1412647. MAM acknowledges support from a  NSF   Astronomy and Astrophysics Postdoctoral Fellowship  under Award No. AST-1701406. TL acknowledges support from the  Deutsche Forschungsgemeinschaft  through project  Lo 1715/2-1.
Reduced cross sections are obtained in the kinematic range of negative four-momentum transfer squared of the photon 2. 5 GeV 2 ≤ Q 2 ≤ 2000 GeV 2 and Bjorken scaling variable 3 • 10 -5 ≤ x Bj ≤ 5 • 10 -2. The combination method accounts for the correlations of the statistical and systematic uncertainties among the different datasets. Perturbative QCD calculations are compared to the combined data. A next-to-leading order QCD analysis is performed using these data together.
In general, the different methods explore different regions of the heavy-quark phase space and show different dependencies on sources of systematic uncertainties. Therefore, by using different tagging techniques a more complete picture of heavy-flavour production is obtained. In this paper, a simultaneous combination of charm and beauty production cross-section measurements is presented. This analysis is an extension of the previous H1 and ZEUS combination of charm measurements in DIS  [42] , including new charm and beauty data  [13, 14, 16, 19, [21] [22] [23]  and extracting combined beauty cross sections for the first time. As a result, a single consistent dataset from HERA of reduced charm and beauty cross sections in DIS is obtained, including all correlations. This dataset covers the kinematic range of photon virtuality 2.
5 GeV 2 ≤ Q 2 ≤ 2000 GeV 2 and Bjorken scaling variable 3 • 10 -5 ≤ x Bj ≤ 5 • 10 -2. The procedure follows the method used previously  [42] [43] [44] [45] [46] [47]. The correlated systematic uncertainties and the normalisation of the different measurements are accounted for such that one consistent dataset is obtained. Since different experimental techniques of charm and beauty tagging have been employed using different detectors and methods of kinematic reconstruction, this combination leads to a significant reduction of statistical and systematic uncertainties with respect to the individual measurements. The simultaneous combination of charm and beauty cross-section measurements reduces the correlations between them and hence also the uncertainties. The combined reduced charm cross sections of the previous analysis  [42]  are superseded by the new results presented in this paper.
The new data are subjected to a QCD analysis together with the final inclusive DIS cross-section data from HERA  [48]  allowing for the determination at NLO of the running charm-and beauty-quark masses, as defined from the QCD Lagrangian in the modified minimum-subtraction (MS) scheme. The paper is organised as follows. In Sect. 2, the reduced heavy-flavour cross section is defined and the theoretical framework is briefly introduced. The heavy-flavour tagging methods, the data samples and the combination method are presented in Sect. 3. The resulting reduced charm and beauty cross sections are presented in Sects. 4 and 5, where they are compared with theoretical calculations based on existing PDF sets and with existing predictions at NLO and at NNLO in the FFNS and VFNS. In Sect. 6, the NLO QCD analysis is described and the measurement of the running masses of the charm and beauty quarks in the MS scheme at NLO is presented.
Data samples The H1  [60] [61] [62]  and ZEUS  [63]  detectors were general purpose instruments which consisted of tracking systems surrounded by electromagnetic and hadronic calorimeters and muon detectors, ensuring close to 4π coverage of the ep interaction region. Both detectors were equipped with highresolution silicon vertex detectors  [64, 65]. The datasets included in the combination are listed in Table  1. The data have been obtained from both the HERA I (in the years 1992-2000) and HERA II (in the years 2003-2007) data-taking periods. The combination includes measurements using different tagging techniques: the reconstruction of particular decays of D mesons  [4, 6, 10, 12, 15, [20] [21] [22]  (datasets 2-7, 9, 10), the inclusive analysis of tracks exploiting lifetime information  [14, 23]  (datasets 1, 11) and the reconstruction of electrons and muons from heavy-flavour semileptonic decays  [13, 16, 19]  (datasets  8, 12, 13).