dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements 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  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA,.
Acknowledgements The authors are grateful to  Camille Aron ,  Alejandra Castro ,  Geoffrey Compère ,  Diego Hofman ,  David Kutasov ,  Finn Larsen ,  Ioannis Papadimitriou ,  Herman Verlinde  and  Konstatin Zarembo  for interesting conversations and  Geoffrey Compère  and  Andrew Strominger  for comments on the draft. M. G. would like to thank the  Tsinghua Sanya International Mathematics Forum  for its hospitality while this work was being completed. The work of M. G. was supported by the  ERC   Starting Grant 679278 Emergent-BH , the  Knut and Alice Wallenberg Foundation  under grant  113410212  ( as Wallenberg Academy Fellow ) and the  Swedish Research Council  grant number  2015-05333. The work of A. B. is supported by the  ANR  grant  Black-dS-String   ANR-16-CE31-0004-01  and the  CEA Enhanced Eurotalents Fellowship.
2018). The profiles of the detected absorption features were modelled to investigate the molecular content of the material along the line of sight. The angular resolution of these previous studies was not high enough to resolve the continuum structure of Sgr B2(N). For instance,  Corby et al. (2018)  investigated simple molecules along the line of sight to Sgr B2 using the Green Bank Telescope. Their data covered the frequency range between 1 and 50 GHz with a resolution between 13 and 15. Corby et al. (2015)  performed a spectral survey of Sgr B2 with the Australia Telescope Compact Array (ATCA) between 30 and 50 GHz with an angular resolution of 5-10 that starts to resolve the continuum emission of Sgr B2(N). They reported variations in the column densities of several molecules seen in absorption across the field of view of their observations but they did not have enough resolution elements to perform a detailed study of the spatial structure of the clouds seen in absorption along the line of sight.
Sun In this paper we investigate the spatial structure of the molecular clouds traced by several molecules detected in absorption along the line of sight to Sgr B2 at much better angular resolution, namely 1. 6. In Sect. 2 we briefly describe the dataset we used for this work. The different techniques we adopted to analyse the data are presented in Sect 3. We present the results Article number, page 2 of 83  Thiel et al. :  Structure of diffuse and translucent clouds towards Sgr B2 in Sect. 4 and discuss them in Sect. 5. We give a summary in Sect. 6.
Observations We analysed the absorption lines detected in the EMoCA survey  (Belloche et al. 2016 ). This spectral line survey was observed with ALMA in Cycles 0 and 1. It was pointed towards Sgr B2(N) with the phase centre located half way between the two main hot cores N1 and N2 at EQ J2000: 17 h 47 m 19. 87 s , -28 • 22 16 (see Fig. 1 ). The survey covers the frequency range from 84. 1 to 114. 4 GHz with a spectral resolution of 488 kHz (1. 7 to 1. 3 km s -1 ) at a median angular resolution of 1. 6. The median largest angular scale is 21. 4. The average noise level is ~3 mJy beam -1 per channel. Details about the calibration and deconvolution of the data are reported in  Belloche et al. (2016). We also analyse the emission lines of the molecules HCO + , HNC, CS, and 13 CO detected in the 3 mm imaging spectral survey of Sgr B2 performed by  Jones et al.
(2008). The observations were carried out with the 22 m Mopra Millimetre Telescope in June 2006 in on-the-fly mode, covering an area of 5 by 5 centred on the J2000 equatorial position 17 h 47 m 19. 8 s , -28 • 22 17 which is close to Sgr B2(N). The survey covers the full frequency range between 82 and 114 GHz with a spectral resolution of 2. 2 MHz (6. 4 km s -1 at 100 GHz). Additional narrowband spectra with a high resolution of 33 kHz (0. 10 km s -1 at 100 GHz) were also taken. The angular resolution of the data is about 36 and the RMS noise level of the broad-band spectra in main-beam temperature scale is 0. 12-0. 42 K depending on the frequency.
Selected data sample To have enough sensitivity, we analyse the absorption features towards positions where the continuum emission is brighter than four times the RMS noise level. The noise level is derived from a Gaussian fit to the flux density distribution of all pixels in the continuum map not corrected for primary beam attenuation. For this, we use the command go noise in the GILDAS package GREG 1. Positions close to the hot cores Sgr B2(N1) and Sgr B2(N2) have spectra full of emission lines of organic molecules (e. g. Bonfand et al. 2017). Therefore, in order to minimise the contamination of the absorption features by emission lines, we perform the analysis towards the positions that are far enough from these hot cores by excluding pixels inside ellipses drawn around them. Because of the slightly different beams and noise levels in the different spectral windows (see  Belloche et al.
Modelling In order to identify and characterise the velocity components present in the absorption spectra, we fit synthetic spectra consisting of a collection of Gaussian opacity distributions. We model the spectra with Weeds  (Maret et al. 2011 ) which solves the radiative transfer equation under the assumption of local thermodynamic equilibrium and takes into account the finite angular resolution of the observations. Because our data sample contains several hundreds of spectra per molecule, the spectra are fitted automatically. For this, we use the fitting routine MCWeeds  (Giannetti et al. 2017) , which combines the python package PyMC2  (Patil et al. 2010)  and Weeds  (Maret et al. 2011). MCWeeds adjusts the parameters for a given number of velocity components and delivers the best result along with uncertainties.
Automatisation programme We wrote a python programme that searches for appropriate initial guesses and runs the minimisation with MCWeeds in an automatic way for all selected positions. It also automatically determines the number of velocity components that are required to 3 see https://scipy. org Fig. 3. a ALMA spectrum of ortho c-C 3 H 2 2 1,2 -1 0,1 at 85338. 896 MHz towards the UCHII region K4  (Gaume et al. 1995). The spectrum in magenta is the synthetic spectrum obtained with MCWeeds. The blue lines show the central velocities of the fitted components. The dotted line represents the continuum level and the dashed line marks the 4σ line detection threshold. The grey area marks the velocity range of the envelope of Sgr B2 ( LSR > 42 km s -1 ). b Residuals, that is the difference between the observed and synthetic spectra.
Principal component analysis To search for correlations or anti-correlations between the opacity maps of the different molecules, we use the principal component analysis (PCA) (see, e. g. Heyer & Peter Schloerb 1997; Neufeld et al. 2015; Spezzano et al. 2017; Gratier et al. 2017). The PCA applies an orthogonal transformation to a dataset to produce a set of components which are linearly uncorrelated, the so-called principal components (PC). The PCs are orthogonal to each other and make up a new coordinate system to which the data are transformed. The first PC goes in the direction of the largest variance in the data. The number of PCs that are considered has to be smaller than or equal to the number of dimensions of the original data set. In our case the number n of molecules used for the PCA is the dimension of the data set and also the number of calculated PCs.
We apply the PCA to opacity maps at a given velocity. In our case, the original data set consists of one-dimensional arrays, one for each molecule, which contain the opacities of the selected pixels. Before starting the PCA, the array a i of each molecule i is normalised by subtracting the mean āi and by dividing by the standard deviation σ i (e. g. Neufeld et al. 2015) : a i, j,normed = a i, j -āi σ i , (9) with j the pixel position in the array. After this preparation, the PCA is performed with the Python package scikit-learn  (Pedregosa et al. 2011). The procedure computes the principal components as well as the eigenvalues of the decomposition. The powers (eigenvalue divided by sum of eigenvalues) give the contribution of the different components calculated from the eigenvalues. To determine the contribution C i,k of each principal component PC k to each molecule array a i,normed , the following system of linear equations has to be solved: a i,normed = b i k C i,k • PC k , (10) with b i a constant factor, the normalisation condition k C 2 i,k = 1, and with the principal components having a length of 1 and a standard deviation of 1.
Identification of molecules We performed the identification of the molecules on the basis of the spectroscopic information provided in the Cologne Database for Molecular Spectroscopy (CDMS,  Endres et al. 2016; Müller et al. 2005 Müller et al. , 2001) )  and the Jet Propulsion Laboratory (JPL) molecular spectroscopy catalogue  (Pickett et al. 1998). In total, we identified 19 molecules seen in absorption in the diffuse and translucent molecular clouds along the line of sight to Sgr B2(N): C 13 O, CS, CN, SiO, SO, HCO + , HOC + , HCN, HNC, CCH, N 2 H + , HNCO, H 2 CS, c-C 3 H 2 , HC 3 N, CH 3 OH, CH 3 CN, NH 2 CHO, and CH 3 CHO. We also detected the following less abundant isotopologues: C 18 O, C 17 O, C 34 S, 13 CS, C 33 S, 13 CN, 29 SiO, 30 SiO, H 13 CO + , HC 18 O + , H 13 CN, HC 15 N, HN 13 C, H 15 NC, and 13 CH 3 OH.
None of the components at velocities below -30 km s -1 are detected in emission in the HCO + , HNC, and CS Mopra spectra, but these species show emission at velocities above ∼ -30 km s -1 , which may be at least in part associated with the absorption components seen in the ALMA spectra. We perform non-LTE radiative transfer calculations with RADEX (van der  Tak et al. 2007 ) to estimate the densities and kinetic temperatures that are consistent with the emission seen with Mopra, or its upper limits. We take the spectroscopic parameters and collisional rates with H 2 from the Leiden Atomic and Molecular Database (LAMDA,  Schöier et al. 2005)  for HCO +  (Botschwina et al. 1993; Flower 1999; Schöier et al. 2005) , CS (CDMS, and  Lique et al. 2006) , HNC (CDMS, and  Dumouchel et al. 2010) , and 13 CO (CDMS, JPL, and Goorvitch 1994;  Cazzoli et al.
In this plot, the H 2 column densities are derived from the HCO + column densities assuming the same nonuniform HCO + abundance profile as in Sect. 5. 1. Overall, we see that CCH is well correlated with H 2 for both diffuse and translucent clouds. The slope of unity indicates that it can be used as a good tracer of H 2 up to A v = 5 mag at least. The correlation is a bit tighter for the GC translucent clouds than for the ones located in the disk of our Galaxy, with a slope slightly higher and lower than unity for the former and latter, respectively. Figure  M. 2 shows the same plot for c-C 3 H 2. If we take all data together, there is an overall correlation between c-C 3 H 2 and H 2 with a slope close to unity, but a fit limited to the ALMA data only yields a much flatter correlation, with a large dispersion.
c-C 3 H 2 does not seem to correlate as tightly with H 2 as CCH in the translucent regime. This larger dispersion is dominated by the clouds located in the Galactic disk for which there is no correlation between c-C 3 H 2 and H 2. The correlation is tighter for the GC clouds with, however, a slope higher than unity. In both cases, c-C 3 H 2 thus does not appear as a good tracer of H 2. The column densities of CCH and c-C 3 H 2 are plotted against each other in Fig. M. 3. Here also, while there is an overall correlation with a slope close to unity for the full sample of diffuse and translucent clouds, the fit limited to the ALMA data departs significantly from a slope of unity. Therefore, our data do not reveal a tight correlation between CCH and c-C 3 H 2 beyond the range of C 3 H 2 column densities explored by  Lucas & Liszt (2000)  and  Gerin et al.
The median value of Category I, 5. 4 km s -1 (Table  2 ), lies at the upper edge of these ranges for the FWHM. If we look at the width distribution of Category I (see Fig. 4a ) the majority (75%) of the velocity components have FWHM smaller than about 7. 5 km s -1. For larger widths the number of velocity components is decreasing. This tail could be caused by overlapping velocity components that could not be fitted separately. The largest FWHM derived from our fits is 20 km s     2011 ) and the black ones from  Lucas & Liszt (1996 , 2000). The dashed lines are linear fits to the following samples: all data (blue), ALMA data only (black), ALMA GC components only (magenta), and ALMA disk components only (green). The dotted-dashed lines are fits with a slope of unity. The fits results are given in the upper left corner with their uncertainties.
Solenoidal forcing causes smaller density contrasts  (Federrath et al. 2008) , which results in more homogeneous cloud structures. This is consistent with the lack of substructures that we noticed on the basis of our analysis of the twopoint autocorrelation functions of the opacity maps. Studies of three translucent high-latitude clouds, MBM 16, MBM 40, and MBM 3, show that the motions inside the clouds are highly correlated in clumps which have sizes of about 0. 5 pc  (Magnani et al. 1993; Shore et al. 2003 Shore et al. , 2006 Shore et al. , 2007)). This scale corresponds to the extent of the field of view we have in our ALMA data for the most distant clouds. The clumps in the translucent high-latitude clouds are located in a more diffuse structure. They are not self-gravitating and do not show starforming activity.
2005; Halfen et al. 2017). As a further verification, we used the EMoCA data to measure the 12 C 13 C isotopic ratio of the velocity components at -2. 6, 3. 7, and 8 km s -1 (covering the velocity range from -5 to 10 km s -1 ). We used the absorption spectra of CN and 13 CN and derived isotopic ratios of 12 C 13 C = 20±3 for these clouds, confirming once more their location in the GC. This value is robust against optical depth effects because we used the hyperfine components of CN to derive the ratio. Liszt & Gerin (2018)  determined a higher isotopic ratio (  12 C 13 C = 64 ± 4) towards J1774 using HCO + isotopologues in the velocity range of -33 to 13 km s -1 , which is dominated by an absorption feature between -10 km s -1 and 10 km s -1. The continuum source J1774 is located at small longitudes near the Galactic centre, with a separation of about 3 • from Sgr B2.
7. A principal component analysis indicates in most cases a homogeneous distribution of the molecules. Only two of the six investigated velocity components show a structure smaller than the field of view. Our analysis of the structure of the translucent clouds detected in absorption towards Sgr B2 is limited by two factors: the limited size of the continuum background emission, which results from the spatial filtering of the interferometer, and the finite angular resolution. Combining these ALMA main array data with measurements performed with the ACA would allow us to extend the analysis to scales larger than 15. 1. Baseline subtraction for each position using the command base in the GILDAS package CLASS. This yields the continuum level and the spectral line noise level of each spectrum. 2.
Acknowledgements. We thank  Volker Ossenkopf-Okada  and  Nicola Schneider  for fruitful discussions about PDFs. We thank the referee,  Harvey Liszt , for his insightful comments. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2011. 0. 00017. S, ADS/JAO. ALMA# 2012. 1. 00012. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The interferometric data are available in the ALMA archive at https://almascience. eso. org/aq/. DC acknowledges support by the  Deutsche Forschungsgemeinschaft, DFG , through project number  SFB956C.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  824093. L. S. is supported by the grant  2017/26/M/ST2/01074  of the National Science Center in Poland. L. S. also thanks the  French LABEX P2IO , the  French GDR QCD  and the  LPT  for support.
The analysis and combination of data from different gamma-ray instruments involves the use of collaboration proprietary software and case-bycase methods. The effort of defining a common data format for high-level data, namely event lists and instrument response functions (IRFs), has recently started for very-high-energy gamma-ray instruments, driven by the upcoming Cherenkov Telescope Array (CTA). In this work we implemented this prototypical data format for a small set of MAGIC, VERITAS, FACT, and H. E. S. S. Crab nebula observations, and we analyzed them with the open-source gammapy software package. By combining data from Fermi-LAT, and from four of the currently operating imaging atmospheric Cherenkov telescopes, we produced a joint maximum likelihood fit of the Crab nebula spectrum. Aspects of the statistical errors and the evaluation of systematic uncertainty are also commented upon, along with the release format of spectral measurements.
Introduction The opening of new astronomical windows at different wavelengths in the last decades has made evident that many astrophysical puzzles could be solved only by combining images obtained by different facilities. In the late 1970s a common format was developed to facilitate the image interchange between observatories, hence overcoming incompatibilities between the numerous operating systems. The Flexible Image Transport System (FITS) format was standardized in 1980  (Wells et al. 1981)  and formally endorsed in 1982 by the International Astronomical Union (IAU), that in 1988 formed a FITS working group (IAUFWG) entrusted to maintain the format and review future extensions. In the mid-1990s the NASA high energy astrophysics science archive research center (HEASARC) FITS Working Group, also known as the OGIP (Office of Guest Investigator Programs), promoted multi-mission standards for the format of FITS data files in high-energy astrophysics and produced a number of documents and recommendations that were subsequently incorporated into the FITS standard format definition.
Since its conception, the FITS format has been been updated regularly to address new types of metadata conventions, the diversity of research projects and data product types. The last version of the FITS Standard document (4. 0) was released in 2018 1. Today the FITS format is in widespread use among astronomers of all observing bands, from radio frequencies to gamma rays. For instance, the high-energy gamma-ray (HE, E>100 MeV) Large Area Telescope (LAT,  Atwood et al. 2009) , on board of the Fermi satellite, publicly releases all its high-level analysis data in FITS format, that, processed with the science tools, are used to obtain scientific products Send offprint requests to: C. Nigro, cosimo. nigro@desy. de 1 https://fits. gsfc. nasa. gov/fits_standard. html as spectra, light-curves and sky-maps.
2006) , the First G-APD Cherenkov Telescope  (FACT, Anderhub et al. 2013 ) and the High Energy Stereoscopic System (H. E. S. S. ,  Hinton et al. 2004). Each of them is described in section 2. A new era in VHE gamma-ray astronomy is expected to start with the future Cherenkov Telescope Array (CTA,  Acharya et al. 2013) , the next generation IACT instrument, which is currently under construction. The future operation of CTA as an observatory calls for its data format and analysis software to be available to a wide astronomical community. This requirement led to a standardization of the IACT data format, adopting the FITS standard, and the development of open-source science tools, initiating the integration of the VHE discipline into multi-instrument astronomy. A first attempt to define a common data format for the VHE gamma-ray data is being carried out within the "Data for-mats for gamma-ray astronomy" 3  forum  (Deil et al.
2017b (Deil et al. , 2018)). This is a community effort clustering together members of different IACT collaborations with CTA as driving force. In this paper we implement this prototypical data format for data samples by MAGIC, VERITAS, FACT, and H. E. S. S. and we combine, for the first time, observations by Fermi-LAT and these four IACTs relying for the scientific analysis solely on open-source software, in particular on the gammapy 4  project  (Donath et al. 2015; Deil et al. 2017a). We provide the reader not only with the datasets but also with all the scripts and an interactive environment to reproduce all the results. They are available at https://github. com/ open-gamma-ray-astro/joint-crab and will be referred to, from now on, as online material. This allows to fully reproduce the results presented in the paper.
The Crab nebula is selected as a target source for this work, being the reference source in the VHE gamma-ray astronomy  (Aharonian et al. 2004 (Aharonian et al. , 2006;; Albert et al. 2008; Aleksić et al. 2012; Aleksić et al. 2016 ) due to its brightness, apparent flux steadiness and visibility from all the considered observatories. The paper is structured as follows: we describe the selected datasets in Section 2, the process of extracting the spectral information with some considerations on the handling of statistical and systematic uncertainties in Section 3. In Section 4 we present the resources we use to ensure the analysis reproducibility, and in Section 5 we provide some prospects for the reuse of the methodologies of data release discussed and implemented in this work. This report is a technical paper to show the ease of multi-instrument results once the format standardization is reached: we do not seek to draw any scientific conclusion on the physics of the Crab nebula, and of pulsar wind nebulae, in general.
Datasets Differently than other astronomical telescopes, instruments for gamma astronomy cannot directly scatter or reflect gamma rays, being photon-matter interactions dominated by pair production for E γ > 30 MeV. The experimental techniques, either space-borne or ground-based  (Funk 2015) , rely on the direct detection of secondary charged particles or on the indirect detection of the Cherenkov emission of a cascade of charged secondaries they produce in the atmosphere. A detection, or event, cannot be unambiguously discriminated from the irreducible charged cosmic ray background, but can only be classified with a certain probability as a primary photon. Gammaray astronomy could therefore be labelled as "event-based" in contrast to the "image-based" astronomy where chargecoupled devices (CCDs) act as detectors.