dataesr/scientific-paragraphs-categorization huggingface dataset:
ipynb in the online material. For this publication we will use all the datasets to perform a point-like analysis. In the IACT terminology, event lists and IRFs are dubbed Data Level 3 (DL3) products  (Contreras et al. 2015). The datasets used in this work include observations of the Crab nebula by Fermi-LAT, MAGIC, VERITAS, FACT, and H. E. S. S. following the format specifications available in the "Data formats for gamma-ray astronomy" forum  (Deil et al. 2017b ). The IACT DL3 datasets were produced with proprietary codes that extracted the event lists, and IRFs, and saved them in the requested format  5. They are released in chunks, typically of 20-30 minutes, of data acquisition, named runs. IACTs are ground-based, pointing instruments and their response varies with the observing conditions (atmospheric transmission, zenith angle, night sky background level, position of the source in the FoV) therefore their data come with per-run IRFs.
The Fermi-LAT telescope, orbiting around the Earth at ∼ 600 km, is generally operating in survey mode and has a stable set of IRFs shipped with the science tools. We use the Fermi-LAT science tools and gammapy to make these datasets spec-compliant. This work constitutes the first joint release of datasets from different instruments in VHE gammaray astronomy. An interactive notebook illustrating the content of the datasets, 1_data. ipynb, is available in the online material. The datasets are presented in what follows in order of increasing instrument energy threshold (see Table  1 ). Fig. 1. Histograms of the estimated mean number of signal events from the Crab nebula, number of excess events, vs estimated energy for each dataset.
MAGIC MAGIC is a system of two 17-m diameter IACTs, with 3. 5 • FoV located on the Canary island of La Palma, Spain at the Roque de Los Muchachos Observatory (2200 m above sea level). The first telescope worked in stand-alone mode from 2004 until 2009, when a second one started operations. In 2012 the two MAGIC telescopes underwent a major upgrade 6 https://fermi. gsfc. nasa. gov/ssc/data/analysis/ documentation/Pass8_edisp_usage. html of the readout systems and the camera of the first telescope  (Aleksić et al. 2016 ) leading to a significant improvement of the instrument performance  (Aleksić et al. 2016). The FITS data released by the MAGIC collaboration includes 40 min of Crab nebula observations and their corresponding IRFs. The data were recorded in 2013, after the aforementioned major upgrade, at small zenith angles (< 30 • ) in wobble mode  (Fomin et al.
1994)  with the source sitting at an offset of 0. 4 • from the FoV center. They are chosen from the data sample used for the performance evaluation in  Aleksić et al. (2016). The IRFs released by the MAGIC collaboration were generated using the MARS software  (Zanin et al. 2013) , for a pointlike source response: i. e. they are calculated for a simulated source at 0. 4 • offset, applying a directional cut on the events direction of 0. 14 • around the source position.
VERITAS VERITAS consists of four 12-m diameter IACTs  (Holder et al. 2006 ), with 3. 5 • FoV based at the Fred Lawrence Whipple Observatory in Southern Arizona, USA. Since the start of full array operations in 2007, the sensitivity of VERITAS was improved by two major upgrades: the relocation of one of its telescopes in 2009  (Perkins et al. 2009 ) and the upgrade of the camera with higher quantum efficiency photomultiplier tubes in 2012  (Kieda et al. 2013). The DL3 data released by the VERITAS collaboration includes 40 min of archival observations of the Crab nebula taken in 2011, after the telescope relocation, but prior to the camera upgrade. These observations were carried out in wobble mode with the standard offset of 0. 5 • at small zenith angles (< 20 • ). The released IRFs are valid for the analysis of point-like sources taken with the standard offset angle and were generated from events surviving the standard directional cut of 0.
Data analysis In this section we present a spectral analysis of the gammaray datasets described in Section 2. First, the gamma-ray event data and IRFs are reduced for each instrument (Section 3. 1). Then, in Section 3. 2, we preform a spectral likelihood fit, under the assumption of a log-parabola analytic model, for each datasets separately, and for all the datasets together (joint fit). Finally, we present an analysis that includes a systematic error term, representing the uncertainty on the energy scale of each instrument, in a modified likelihood function.
Spectrum extraction In order to estimate the energy spectrum of a gamma-ray source ( dφ dE (E; Λ), with Λ a the set of spectral parameters), a binned maximum likelihood method, with n E bins in estimated energy E is used. The observed data D for such a likelihood function are the number of events in a circular signal region (labeled as ON) containing the gamma-ray source and in a control region (labeled as OFF) measuring the background to be subtracted from the ON. Considering n runs observation runs from n instr different instruments (or datasets), we can write the likelihood as: L(Λ|D) = n instr i=1 L i (Λ|{N on,i jk , N off,i jk } j=1,. ,n runs ;k=1,. ,n E ) (1) with each instrument contributing with a term: L i (Λ|{N on,i jk , N off,i jk } j=1,. ,n runs ;k=1,. ,n E ) = n runs j=1 n E k=1 Pois(g i jk (Λ) + b i jk ; N on,i jk ) × Pois(b i jk /α i j ; N off,i jk ), (2) where: -N on,i jk and N off,i jk are the number of observed events within the ON and OFF regions, respectively, in the energy bin k in the run j for the i-th instrument.
They are both characterized by a Poisson distribution; -g i jk (Λ) and b i jk are the expected number of signal and background events, respectively, in the energy bin k in the run j for the i-th instrument. g i jk is computed with the forward folding technique: for a point-like analysis the assumed spectrum dφ dE is convolved with the effective area and energy dispersion IRFs component. b i jk , in absence of a background spectral model, is treated as a nuisance parameter and fixed to the value returning ∂L ∂b i jk = 0, for the mathematical details of g i jk and b i jk evaluation see Appendix A in  Piron et al. (2001). 8 https://www. mpi-hd. mpg. de/hfm/HESS/pages/ dl3-dr1/ -α i j is the ON to OFF exposures ratio, constant with energy in our case, in the run j for the i-th instrument. The size of the circular ON region per each dataset is given as R on in Table  1 , the OFF region can be defined either as multiple regions mirroring the ON symmetrically with respect to the telescope pointing position (reflected regions background  Aharonian et al.
2001; Berge, D. et al. 2007)  or as a ring around the source position (ring background method in  Berge, D. et al. 2007 ). Given the wobble mode observation strategy, and the small FoV, the reflected regions method is naturally suitable for the IACTs datasets. On the other hand, the ring background method is used for the Fermi-LAT datasets, in this analysis with a circular signal region of 0. 3 • radius and a background ring with inner and outer radius of 1 • and 2 • , respectively. We choose, for all the instruments, 20 bins per decade for the estimated energy between 10 GeV and 100 TeV. For a given instrument i and run j the likelihood values are not computed in the energy bins outside the range [E min , E max ] given in Table  1. The choice of the energy range for Fermi-LAT is already discussed in Section 2.
1. For the IACT datasets, E min is a safe energy threshold for the spectrum extraction computed by the collaboration software and hard coded in the DL3 files. It is mostly dependent on the experiment performance and on the zenith angle of the observations. FACT has an energy threshold of 450 GeV, higher than MAGIC and VERITAS despite the observations carried out in the same zenith angle range, due to its limited light-collection area and the single telescope observations. The larger zenith angle of the H. E. S. S. datasets is due to the low altitude at which the source culminates at the H. E. S. S. site. This yields an energy threshold of ∼ 700 GeV, higher than any other IACT. The maximum energy E max is fixed to 30 TeV for all the IACTs and it is chosen to cover the whole energy range containing events.
Spectral model fit We assumed a spectral model of log-parabolic form: dφ dE = φ 0 E E 0 -Γ-β log 10 E E 0 , (3) since it was suggested as the best-function approximation for the Crab nebula spectrum in such a wide energy range (Alek-   2. Crab nebula SED for individual instrument fits and from the joint fit. Single-instrument results are represented with dashed lines, the fit of all the datasets together, labeled as joint, is represented as a thick, solid red line. The shaded areas represent the SED error bands whose calculation is explained in Section 3. 2. The dotted line shows the model in  Meyer et al. (2010). sić et al. 2015). The spectral parameters Λ = (φ 0 , Γ, β) are left free to vary in the fit while the reference energy E 0 is fixed at the value of 1 TeV. We refer to the result of the maximum likelihood using all the instrument datasets as joint fit.
As a consistency check we also fitted each instrument dataset separately (i fixed in Eq. 1). E 0 is usually chosen to minimize the correlation between the other spectral parameters. In this work, in order to directly compare the parameters Λ also for the fit with the individual instrument datasets, the reference energy E 0 is kept fixed at the same value of 1 TeV. This introduces larger errors and correlation for the datasets for which such value is close to one of the extremes of its energy range. The resulting spectral energy distributions (SEDs, E 2 dφ/dE) are shown in Fig. 2 , together with a theoretical model taken from  Meyer et al. (2010). The values of the fit parameters are listed in Table  2. The joint fit inherently comes with an increase in statistical power, as evidenced by the shrinking of the confidence contours of the fitted spectral parameters for the joint fit in Fig.
We assume µ = Λ = ( φ0 , Γ, β), values of the fitted parameters and Σ = V Λ, covariance of the fitted parameters. We sample this distribution and compute the spectrum realization corresponding to each sampled Λ. The ±1 σ uncertainty on the fitted spectrum is estimated by taking, at a given energy, the quantiles of the fluxes distribution that returns a 68% containment of all the realizations. The upper and lower limits of the error band estimated with our method are plotted in black against 100 realizations of the spectrum with sampled parameters in Fig. 4 , for the example case of the VERITAS datasets.
Systematic uncertainties on different energy scales Spectral measurements in gamma-ray astronomy are affected by multiple sources of systematic uncertainty. The DL3 data contains systematic uncertainties that originate from an imperfect modeling of the atmosphere, telescopes and event reconstruction, resulting in a shift of the reconstructed energy scale and errors in the assumed IRF shapes. A second source of systematic error comes from the data reduction and model fitting, e. g. due to energy binning and interpolation effects, as well as from source morphology and spectral shape assumptions. Generally two approaches are used to evaluate and report systematic errors  (Conrad et al. 2003; Barlow 2017 ): multiple analyses / bracketing as in  (Aharonian et al. 2006; Aleksić et al. 2012)  or modified likelihood with nuisance parameters  (Dickinson & Conrad 2013; Dembinski et al.
This parameter is defined as z i = Ẽ-E E = Ẽ E -1, with Ẽ being the energy reported by an instrument and E the actual energy of each single event. The apparent spectral model we aim to fit for a single instrument would then be: d φ d Ẽ = dφ dE dE d Ẽ = φ 0 E/(1 + z) E 0 -Γ+β log 10 E/(1+z) E 0 1 1 + z (4) and the overall joint likelihood is modified in: L(Λ|D) = n instr i=1 L i (Λ|{N on,i jk , N off,i jk } j=1,. ,n runs ;k=1,. ,n E ) × N(z i ; 0, δ 2 i ) (5) where now the energy biases are included in the spectral parameters to be fitted: Λ = (φ 0 , Γ, β, z 1 ,. , z n isntr ) and the energy spectrum d φ/d Ẽ in Eq. 4 is used to predict the g i jk via forward folding. As in Eq. 1, i runs over the instruments, j on A&A proofs: manuscript no. jointcrab 3 4 5 0 /(  10   Error estimation methods for the measured SED using the VERITAS dataset, as example case.
The solid black lines display the upper and lower limits of the error band estimated with the multivariate sampling. They represent the 68% containment of 500 spectral realizations (100 displayed as gray lines) whose parameters are sampled from a multivariate distribution defined by the fit results. the runs and k on the energy bin. The inclusion of the energy biases allows, in addition to the variation of the global spectral parameters φ 0 , Γ and β (the same for all datasets), also an instrument-dependent energy adjustment (a shift) of the assumed model, through the individual z i. This shift is not arbitrary: it is, in fact, constrained by its Gaussian distribution with a standard deviation given by the systematic uncertainty on the energy scale provided by the single experiment, δ i. Hereafter we will refer to this likelihood fit as stat.
Reproducibility This work presents a first reproducible multi-instrument gamma-ray analysis, achieved by using the common DL3 data format and the open-source gammapy software package. We provide public access to the DL3 observational data, scripts used and obtained results with the GitHub repository mentioned in the introduction, along with a Docker container 9  on DockerHub, and a Zenodo record  (Nigro et al. 2018) , that provides a Digital Object Identifier (DOI). The user access to the repository hosting data and analysis scripts represents a necessary, but not sufficient condition to accomplish the exact reproducibility of the results. We deliver a conda 10  configuration file to build a virtual computing environment, defined with a special care in order to address the internal dependencies among the versions of the software used.
Furthermore, since the availability of all the external software dependencies is not assured in the future, we also provide a joint-crab docker container, to guarantee a mid-term preservation of the reproducibility. The main results published in this work may be reproduced executing the make. py command. This script works as a documented command line interface tool, wrapping a set of actions in different option commands that either extract or run the likelihood minimization or reproduce the figures presented in the paper. The documentation is provided in the form of Jupyter notebooks. These notebooks can also be run through Binder 11  public service to access via web browser the whole joint-crab working execution environment in the Binder cloud infrastructure. The Zenodo joint-crab record, the jointcrab docker container, and the joint-crab working environment in Binder may be all synchronized if needed, with the content present in the joint-crab GitHub repository.
Therefore, if eventual improved versions of the joint-crab bundle are needed (i. e. comments from referees, improved algorithms or analysis methods, etc. ), they may be published in the GitHub repository and then propagated from GitHub to the other joint-crab repositories in Zenodo, DockerHub and Binder. All these versions would be kept synchronized in their respective repositories. 5. Likelihood contours corresponding to 68 % probability content for the fitted spectral parameters (φ 0 , Γ, β), for the likelihood in Eq. 1 (red) and the likelihood in Eq. 5 (blue). Results from the individual instruments with the likelihood in Eq. 1 are displayed in gray.
E. S. S. , made available. It is worth noting that even the more exhaustive fullenclosure IRFs format may require further development as the current radial offset dependency does not account for a possible non-azimuthal symmetry of the instrument acceptance  (Prandini et al. 2015). On a more general note, the objective of this publication is also to remark a novel approach to gamma-ray science, summarized through the three essential concepts of: common data-format, open-source software and reproducible results. We illustrate that a common data-format allows naturally multi-instrument analysis. Generating data samples compliant with the prototypical DL3 format defined in the "Data formats for gamma-ray astronomy" forum, we perform, for the first time, a spectral analysis of the Crab nebula using data from Fermi-LAT and four currently operating IACTs by using the gammapy software package.
Open-source software will be a key asset for the upcoming CTA, which, as an open-observatory, will share its observation time and data with the wider astronomical community. Reproducible results are seamlessly achieved once the data and software are publicly available. There are several tools and platforms on the market that can be used for this purpose. In particular, with the on-line material attached to this issue, we show a practical example of how a future gamma-ray publication can be released with long-term solutions. A Git repository suffices for the first period after the publication, whereas a Docker accounts for the eventual loss of maintenance of the software packages needed for the analysis. We also provided some considerations on analysis procedures related to spectral analysis commonly performed in the VHE IACT-related astronomy.
We proposed a method for computing error bands on the measured SED based on the sampling of a multivariate distribution, along with a method to account for the systematic uncertainties on the energy scales of different gamma-ray instruments while performing a joint fit of their data. We also pointed-out the advantages of publishing the outputs of the spectrum extraction, i. e. the distribution of the signal and background events and the IRFs (alike the OGIP spectral data in the joint-crab repository), instead of the spectral points. Mainly this grants the possibility to successively construct a likelihood using an arbitrary theoretical spectral model.
Acknowledgements. This work was supported by the  Young Investigators Program of the Helmholtz Association , by the  Deutsche Forschungsgemeinschaft (DFG)  within the  Collaborative Research Center SFB 876  " Providing Information by Resource-Constrained Analysis ", project  C3  and by the  European Commision  through the  ASTERICS Horizon2020 project (id 653477 ). We would like to thank the H. E. S. S. , MAGIC, VERITAS and  FACT  collaborations for releasing the data that were used. This work made use of astropy.
Systematic uncertainties of the amplitude analysis Several sources of systematic uncertainty that affect the results of the amplitude analysis are considered and discussed in the following. Fit method. Biases induced by the fitting method are evaluated with a large ensemble of pseudoexperiments. For each signal decay, samples with the same yield of signal observed in data (see Table  3 ) are generated according to the PDF of Eq. (  8 ) with inputs set to the results summarised in Table  4. The use of the weights defined in Eq. (  9 ) to account the detector acceptance would require a full simulation and, instead, a parametric efficiency is considered. For each observable, the mean deviation of the result from the input value is assigned as a systematic uncertainty. Description of the kinematic acceptance.
Decay mode B 0 → (K + π -)(K -π + ) Observable f f ⊥ |A + S | 2 |A SS | 2 S- → (K + π -)(K -π + ) Observable f f ⊥ |A + S | 2 |A SS | 2 S- B 0 → K * 0 K * 0 decay, the Γ H = Γ L approximation is adopted. The detection efficiency is determined from simulation for each channel separately for the different categories discussed in Sect. 6: year of data taking, trigger type and, in addition, the LHCb magnet polarity. An exception is applied to the particle-identification selection whose efficiency is determined from large control samples of D * + → D 0 π + , D 0 → K -π + decays. Differences in kinematics and detector occupancy between the control samples and the signal data are accounted for in this particle-identification efficiency study  [39, 40]. The different sources of systematic uncertainty in the branching fraction determination are discussed below.
The telluric corrections was performed with molecfit, a software tool designed to remove atmospheric absorption features from astronomical spectra  (Smette et al. 2015). The 1-dimensional spectrum extraction was performed over a region of ±2 from the peak value of the spectrum. A flux correction was applied to take into account the regions outside these limits by fitting a Gaussian profile to the cross order profile and integrating over the fit to obtain the total flux. The spectra from the three X-Shooter arms were combined matching them to the bluest spectrum, so the UBV arm is anchoring the overall flux calibration. The final products are shown in Fig. 1.
Summary In this paper we presented new high quality UV-VIS-NIR spectroscopy of a set of well studied galaxies spanning a large range of ages, metallicities and mass, and ranging from ellipticals to spirals (Fig. 5 ). This is matching our aim of creating an atlas of spectra for a variety of galaxies to be used as template to investigate their spectral properties homogeneously from the optical to the NIR. Our X-Shooter dataset of combined optical-NIR range sampling the same spatial region of the galaxies minimize any systematic. The combination of wide spectral coverage and high S/N in our data is unique and unprecedented. Table  3. Central values of the velocity, dispersion, line-strength indices, age and metallicity of the sample galaxies measured within an aperture of radius 0. 3 r e. Galaxy V H σ V Fe [MgFe] Mg 2 Mg b Hβ T [Z/H] ID [km s -1 ] [km s -1 ] [Å] [Å] [mag] [Å] [Å] [Gyr] [dex] NGC 0584 1830.
6±1. 0 203. 9±4. 9 3. 002±0. Such as a dataset of spectra will be crucial to address important questions of the modern investigation concerning galaxy formation and evolution. Some relevant topics among others are the study of the slope of the IMF, the role of the AGB stars in tracing young stellar populations, the galaxy continuum and the absorption features in the IR as complementary tools, with respect to   3  and A. 1 -A. 4). For uniformity we only show literature sources that include multiple galaxies from our sample. The plot intervals reflect the ranges of values from our measurements. Bottom panels: Difference between our heliocentric velocities and velocity dispersion in the central region of the sample galaxies and those from literature as a function of the literature values. The average difference of 39 measurements after removing three outliers is consistent with zero (-7±29 km s -1 ).
the optical range, in deriving galaxy stellar populations. These spectra will also be very extremely useful to be used as templates for stellar population modeling. We also discussed a number of tests for the data quality validation to confirm the goodness and consistency of our galaxy spectra. As consistency check, in Section 4, we compared the spectra of our galaxies with literature flux calibrated spectra finding a good match between them. For two galaxies of our sample, NGC584 and NGC3379, we estimated the quality of our data processing in recovering the continuum shape of the spectra. To perform this step we compared synthetic magnitudes derived from our spectra with observed apparent magnitudes reported by  Brown et al. (2014). The results indicate a good agreement for both galaxies.
2): morphological classification from Lyon Extragalactic Database (HyperLEDA; Makarov et al. 2014); (3): numerical morphological type from HyperLEDA; (4): major and minor isophotal diameter measured at a surface-brightness level of µ B = 25 mag arcsec -2 from HyperLEDA; (5): radial velocity with respect to the cosmic microwave background (CMB) reference frame from HyperLEDA; (6): total observed blue magnitude B T from HyperLEDA; (7): Galactic extinction in B-band from HyperLEDA; (8): Internal extinction due to inclination in B-band from HyperLEDA;.