dataesr/scientific-paragraphs-categorization huggingface dataset:
As the considered ATLAS study includes a large set of signal regions each dedicated to a different jet multiplicity, it is sensitive to this simplified model that has moreover not been covered the result interpretations performed in the experimental publication. As a last example, we study the phenomenology of a simplified dark matter model in which a Dirac fermion dark matter candidate couples to the SM via interactions with an s-channel spin-1 mediator  [13, 14]. This model is known to be reachable via standard LHC monojet and multijet plus missing transverse energy searches for dark matter. We extract up-to-date bounds on the model by reinterpreting the results of the ATLAS search of ref. [12]  that analyses the full Run 2 ATLAS dataset. This search includes signal regions dedicated to both the monojet and the multijet plus missing energy signatures, so that it consists in an excellent probe for dark matter models.
LHC recasting with MadAnalysis 5 The MadAnalysis 5 package  [15, 16]  is a framework dedicated to new physics phenomenology. Whilst the first aim of the programme was to facilitate the design and the implementation of analyses targeting a given collider signal of physics beyond the Standard Model, and how to unravel it from the background, more recently it has been extended by LHC reinterpretation capabilities  [3, 8]. This feature allows the user to derive the sensitivity of the LHC to any collider signal obtained by matching hardscattering matrix elements with parton showers, based on the ensemble of analyses that have implemented in the MadAnalysis 5 public analysis database (PAD)  [3]  1. For each of these analyses, the code simulates the experimental strategies (which includes both the simulation of the detector response and the selection) to predict the number of signal events that should populate the analysis signal regions.
It then compares the results with both data and the SM expectation, so that conclusive statements could be drawn. As in all recasting codes relying on the same method  [2, 4, 5] , the uncertainty on the signal is ignored although it could be relevant  [7]. With the release of MadAnalysis 5 version v1. 8, the user has now the possibility to deal with various classes of signal uncertainties and to extrapolate any reinterpretation result to higher luminosities. This section documents all these new functionalities. Section 2. 1 briefly summarises how to install MadAnalysis 5, get the code running and download a local copy of its public analysis database. Section 2. 2 details how the code can be used to reinterpret the results of a specific LHC analysis. A more extensive and longer version of this information on MadAnalysis 5 installation and running procedures can be found in ref.
[8]. Section 2. 3 is dedicated to the new methods that have been developed in the context of this work, and which are available from MadAnalysis 5 version v1. 8 onwards. We also introduce in this section several new optional features that can be used for the design of the analysis. info files. One such file accompanies each analysis of the database and contains information on the observation and the SM expectation of the different analysis signal regions. In section 2. 4, we describe the corresponding modifications of the output format relevant for a recasting run of Mad-Analysis 5.
Prerequisites and installation MadAnalysis 5 is compatible with most recent Unixbased operating systems, and requires the GNU G++ or CLang compiler, a Python 2. 7 installation (or more recent, but not a Python 3 one) and GMake. In order for the recasting functionalities to be enabled, the user must ensure that the SciPy library is present, as it allows for limit computations, and that the Delphes 3 package  [17]  is locally available within the MadAnalysis 5 installation. The latter, which requires the Root framework  [18]  and the FastJet programme  [19] , is internally called by MadAnalysis 5 to deal with the simulation of the response of the LHC detectors and to reconstruct the events. Moreover, reading compressed event files can only be performed if the Zlib library is available. The latest version of MadAnalysis 5 can be downloaded from LaunchPad 2 , where it is provided as a tarball named ma5 v<xxx>.
tgz, that contains all MadAnalysis 5 source files (<xxx> standing for the version number). After unpacking the tarball, the code can be started by issuing in a shell. /bin/ma5 -R where the -R options enforces the reco mode of Mad-Analysis 5, that is relevant for LHC recasting. The programme begins with checking the presence of all mandatory packages and determining which of the optional packages are available. The MadAnalysis 5 command-line interface is then initialised and the user is prompted to type in commands. In the case where any of the Zlib or Delphes 3 package would not be found by MadAnalysis 5, they can be installed locally by typing, directly in the MadAnalysis 5 interpreter,.
gz> on the user system, has to be imported. This is achieved by issuing the commands set main. recast = on import <path-to-events. hepmc. gz> as <label> The second command defines a dataset identified by the label <label> that here solely includes the imported sample. Several event files can be imported and collected either under a unique dataset (by using the same <label> for each call to the import command) or split into different datasets (by employing different labels). When studying the signal under consideration, MadAnalysis 5 will run over all defined datasets and imported event files. In addition, the user can activate the storage of the Root file(s) generated by Delphes 3 by issuing the command, set main. recast. store_root = <status> where <status> can take the True or False value, and directly provide a predefined recasting card (available on the system at <path-to-a-card>), through set main.
recast. card_path = <path-to-a-card> In the case where no card is provided, MadAnalysis 5 creates a consistent new card with one entry for each of the available analyses. Such an entry is of the form <tag> <type> <switch> <detector> # <comment> The <tag> label corresponds to the filename of the C++ code associated with the considered analysis (located in the Build/SampleAnalyzer/User/Analyzer subdirectory of the PAD installation in tools/PAD), the <type> label indicates whether the PADForMA5tune (v1. 1) or PAD (v1. 2) recasting infrastructure should be used and the <switch> tag (to be set to on or off) drives whether the analysis has to be recast. The name of the Delphes 3 card to use (see the Input/Cards subdirectory of the PAD installation) is passed as <detector>, and <comment> consists of an optional comment (usually briefly describing the analysis).
The run is finally started by typing in the interpreter, submit Firstly, MadAnalysis 5 simulates the detector impact on the input events, for each of the necessary Delphes 3 cards according to the analyses that have been switched on in the recasting card. Next, the code derives how the different signal regions are populated by the signal events and finally computes, by means of the CL s prescription  [20] , the corresponding exclusion limits, signal region by signal region. This is achieved by a comparison of the results with the information on the SM background and data available from the different info files shipped with the PAD. The output information is collected into a folder named ANALYSIS_X, where X stands for the next available positive integer (in terms of non-existing directories).
On top of basic details about the run itself, this folder contains the recasting results that are located in the ANALYSIS_X/Output folder. The latter includes the CLs_output_summary. dat file that concisely summarises all the results of the run. A more extensive version of these results can be found in the set of subfolders named after the labels of the imported datasets. The CLs_output_summary. dat file contains one line for each signal region of each reinterpreted analysis, and this for each of the datasets under consideration. Each of these lines follows the format <set> <tag> <SR> <exp> <obs> || <eff> <stat> where the <set> and <tag> elements respectively consist in the names of the dataset and analysis relevant for the considered line of the output file. The <SR> entry relates to one of the analysis signal regions, the exact name being the one defined in the analysis C++ source code.
The <exp> and <obs> quantities are the expected and observed cross-section values for which the signal modelled by the events stored within the dataset <set> is excluded by the signal region <SR> of the analysis <tag> at the 95% confidence level. In the former case, the code makes use of the SM expectation to predict the number of events populating the signal region <SR>, whilst in the latter case, data is used. Finally, the <eff> and <stat> entries respectively refer to the corresponding selection efficiency and the associated statistical error. The user has the option to specify the cross section corresponding to the investigated signal by issuing, in the MadAnalysis 5 interpreter, set <label>. xsection = <value> prior to the call to the submit command. Following this syntax, <label> stands for one of the labels of the considered datasets and <value> for the associated cross-section value, in pb.
In this case, the confidence level at which the analysed signal is excluded is included in the output summary file (before the double vertical line). The Output folder additionally contains a specific subfolder for each of the defined datasets. Such a directory contains a file named CLs_output. dat that includes the same information as in the CLs_output_summary. dat file, following the same syntax, but restricted to a specific dataset. A second file encoded into the SAF format  [15]  and named <label>. saf (<label> being the dataset name) contains general information on the dataset organised according to an XML-like structure. The latter relies on three classes of elements, namely <SampleGlobalInfo>, <FileInfo> and <SampleDetailedInfo>. The first of these contains global information on the dataset, such as its cross section (xsec), the associated error (xsec_err), the number of events (nev) or the sum of the positive and negative event weights (sum_w+ and sum_w-).
</SampleGlobalInfo> where the numerical values have been omitted for clarity. The <FileInfo> element sequentially provides the paths to the different event files included in the dataset, while detailed information on each file is provided within the <SampleDetailedInfo> XML root element, in a similar manner as for the sample global information (with one line for each file). Furthermore, the dataset output directory includes a RecoEvents folder dedicated to the storage of Delphes 3 output files (one file for each considered detector parameterisation), provided that the corresponding option has been turned on (see above), as well as one folder for each of the recast analyses. Each of these folders contains one SAF file listing all signal regions implemented in the associated analysis, as well as two subfolders Cutflows and Histograms.
Histograms are all collected into the file histos. saf, that is also organised according to an XML-like structure relying on several <Histo> elements. Each of these corresponds to one of the histograms implemented in the analysis. A <Histo> element includes the definition of the histogram (provided within the <Description> element), general statistics (as part of the <Statistics> element) and the histogram data itself (within the <Data> element). where all bin values are omitted and the two columns respectively refer to events with positive (first column) and negative (second column) weights. The underflow and overflow bins are also included. To close this section, we detail below how limits on a given signal are derived by MadAnalysis 5, using the CL s prescription. The output file generated by the code contains three numbers associated with those limits, the expected and observed cross sections excluded at the 95% confidence level, σ exp 95 and σ obs 95 , as well as the confidence level at which the input signal is excluded.
Those numbers are extracted on the basis of the information available from the. info file, shipped with each recast analysis and that contains, for each signal region, the number of expected SM events n b , the associated error ∆n b and the observed number of events populating the signal region n obs. As said above, starting from the input event file, MadAnalysis 5 simulates the response of the LHC detector, applies the analysis selection, and estimates how the different signal regions are populated. In this way, for each signal region, the number of signal events n s is known. This enables the computation of the background-only and signal-plus-background probabilities p b and p b+s and to further derive the related CL s exclusion. In practice, the code considers a number of toy experiments (the default being 100000 that can be changed by issuing, in the MadAnalysis 5 interpreter and before the call to the submit method, set main.
Including signal uncertainties and extrapolation to higher luminosities In the procedure described in the previous section, any error on the signal is ignored, both concerning the usual theory uncertainties (scale variations, parton densities) and the systematics, mostly stemming from more experimental aspects. In particular, with the constantly growing mass bounds on hypothetical new particles, the scale entering the relevant hard-scattering processes is larger and larger, so that theoretical errors could start to impact the derived limits in an important and non-negligible manner. Starting from version v1. 8 onwards, MadAnalysis 5 offers the user a way to account for both the theoretical and systematical errors on the signal when a limit calculation is performed. The scale and parton density (PDF) uncertainties can be entered, within the MadAnalysis 5 interpreter, similarly to the cross section associated with a given dataset (see section 2.
2), set <label>. xsection = <xsec_val> set <label>. scale_variation = <scale> set <label>. pdf_variation = <pdf> where <label> stands for the label defining the signal dataset. In this case, the signal cross section σ s is provided through the xsection attribute of the dataset, as described in the previous section, while the scale and parton density uncertainties ∆σ scales and ∆σ PDF are given through the scale_variation and pdf_variation attributes. The errors are symmetric with respect to the central value σ s , and their value (given by <scale> and <pdf> in the above example) must be inputted as the absolute values of the relative errors on the cross section (i. e. as positive floating-point numbers). Asymmetric errors can also be provided, the upper and lower uncertainties being independently fixed by issuing, in the MadAnalysis 5 interpreter, set <label>.
In the above snippet, the user fixed an asymmetric level of systematics (for the sake of the example) indicated by the <lvl_up> and <lvl_dn> tags. In cases where the band would have a vanishing size, the uncertainty information is not written to the output file. This could be due either to negligibly small uncertainties, to the fact that for the considered region, the signal is excluded regardless the level of systematics (at the 100% confidence level), or to the region not targeting the signal at all (the corresponding selection efficiency being close to zero). The CLs_output. dat dataset-specific files present in the output subdirectory associated with each imported dataset all contain similar modifications. In case of extrapolations to different luminosities, copies of this file named CLs_output_lumi_<lumi>.
Finally, our results show that the NLO K-factor K NLO is of about 1. 6 -1. 7, a typical value for a strong supersymmetric production process, and features a significant gluino mass dependence. The latter originates from the quark-antiquark contributions to the cross section that become relatively larger with respect to the gluon fusion ones with increasing Bjorken-x values  [35]. We then predict, for several (m g , m χ0 1 ) configurations, how the signal events would populate the different signal regions of the ATLAS-SUSY-2016-07 search for supersymmetry  [11]. In practice, we use the corresponding recast analysis as implemented in the MadAnalysis 5 public database  [36] , together with the appropriate Delphes 3 configuration for the simulation of the detector response. In this analysis, the ATLAS collaboration investigates the potential of a signature featuring multiple jets and missing transverse energy through two approaches.
The second one is based on the recursive jigsaw reconstruction technique  [37]. Whilst all M eff -based signal regions have been implemented in MadAnalysis 5, the recursive jigsaw reconstruction ones have been ignored due to the lack of information allowing for their proper recasting. They are thus ignored in the following study as well. Our results are presented in figure  3  in the form of exclusion contours in the (m g , m χ0 1 ) mass plane, to which we supplement the values of the signal cross section that are excluded at the 95% confidence level through a colour code. The exclusion contours and excluded cross sections at the 95% confidence level are extracted by means of Gaussian process regression with a conservative amount of data as implemented in the Excursion package  [38]. We compare our predictions (the solid blue line), obtained with the setup described above, with the official ATLAS limits (the green line) as originating from the M eff -based signal region yielding the best expectation.
They could hence be seen as a conservative theory estimate for the LHC sensitivity at 139 fb -1 , when estimated from official 36 fb -1 results. The ATLAS-SUSY-2016-07 analysis has been updated last summer as the ATLAS-CONF-2019-040 analysis  [12] , so that the most recent and stringent ATLAS limits on the considered gluino simplified model now encompass the analysis of the full LHC Run 2 dataset. On the other hand, the updated analysis has been recently added to the PAD  [42] , so that it can be used within the Mad-Analysis 5 framework for reinterpretation studies. The corresponding 95% confidence level contour is shown on the left panel of figure  5  (solid blue line), together with the uncertainty band stemming from combining the scale and PDF uncertainties in quadrature. In addition, we also present predictions for the bounds as obtained from an extrapolations of early Run 2 results focusing on 36 fb -1 of LHC data.
L = L SM + L kin + Xγ µ g V X + g A X γ 5 X Y µ + q qγ µ g V q + g A q γ 5 q Y µ (8) In our analysis, we focus on two further simplified scenarios originating from that model. In a first case (that we label S1), the mediator couplings are taken as of a vectorial nature, whilst in the second case (that we label S2), they are taken as of an axial-vectorial nature. In other words, the two scenarios are defined as S1 : g A q = g A X = 0 ; S2 : g V q = g V X = 0. (9) In order to study the sensitivity of the LHC to these two classes of scenarios, we make use of the publicly available 6  implementation of the model in the FeynRules package  [33]  introduced in ref. [13] , as well as of the corresponding public UFO  [46]  library. As in the previous sections, hard scattering events are generated at the NLO accuracy in QCD with MG5 aMC  [22]  and then matched with parton showering and hadronisation as performed by Pythia  [27].
e. in the S2 scenario), as already mentioned earlier in this section. We then evaluate the cross section value σ 95 that is excuded at the 95% confidence level (third and sixth columns of the table) by reinterpreting the results of the ATLAS-CONF-2019-040 analysis. We observe that small dark matter masses are excluded already with the full Run 2 dataset, cross sections as small as 100 fb being excluded regardless of the DM mass. Moving on with a scenario in which the couplings satisfy eqs. (  9 ) and  (11) , we translate the bounds that we have obtained into bounds on a universal coupling. The latter is found to be of at most in the 0. 5-0. 7 range once one lies in a configuration below threshold (2m X < m Y ), and is mostly unconstrained for larger DM mass values. As for the previous class of scenarios in which the DM mass was fixed and the mediator mass was varying, 3000 fb -1 will not improve the limits much, as the analysis being dominated by the systematics.
Acknowledgements JYA acknowledges the hospitality of the  LPTHE (Sorbonne Université ) and  IPPP (Durham University ) where parts of this work were completed. MF acknowledges the  NSERC  for partial financial support under grant number  SAP105354. BF has been supported by the  LABEX ILP  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ).
Flowsort FlowSort is a variant of an outranking MCDM for sorting problems, PROMETHEE  (Brans, 1982; Brans & Vincke, 1985). As it is from the same outranking family as PROMETHEE, it inherits PROMETHEE's properties. It requires only few parameters and is easy for non-technical persons to understand and use; user-friendly software are available such as Smart Picker  (Nemery, Ishizaka, Camargo, & Morel, 2012) , D-Sight  (Hayez, De Smet, & Bonney, 2012)  and Visual Promethee. Scores do not need to be normalised, so evaluations of specific criteria can be expressed in specific units. This avoids the drawback of rankings being dependent on the normalisation method selected  (Ishizaka & Nemery, 2011; Tofallis, 2008). Preference degree. For each criterion the decision-maker selects a preference function, such as the linear, step or Gaussian preference function  (Brans & Vincke, 1985).
Virtual 3D models were generated using Avizo 9. 0. 1 software (FEI Visualization Sciences Group). The fossil sample consists of one left bony labyrinth belonging to the late Miocene stem-hominoid Oreopithecus bambolii  (Rook et al. , 2004)  and of two right inner ear that have been virtually extracted from the Australopithecus specimens StW 573 and StW 578 from Sterkfontein  (Beaudet et al. , 2019b). The vestibular apparatus was separated from the cochlea by cutting right under the oval window and the saccule, and filling the resulting hole with a flat surface in Geomagic Studio 2014 software (3D Systems).
, 2012b). Prior to the analysis, the unscaled vestibular surfaces were aligned and scaled using the 'Align Surface' module of Avizo 9. 0. Then, deformations between surfaces were mathematically modeled as a diffeomorphism (i. e. , a one-to-one deformation of the 3D space that is smooth, invertible, and with a smooth inverse), and a set of momenta (vectors representing the flow of deformations from the initial position of the control points on the template to the target shape) were estimated with Deformetrica 3 software. Due to its high-demanding computational power, analyses were run in the CALMIP supercomputing center (Toulouse, France). We inspected interspecific major patterns of shape variation by means of between-group principal components analysis (bgPCA) of the deformation-based shape residuals, using major clades (i.
Phylomorphospace and ancestral states analysis To quantify major patterns of vestibular shape variation along the branches of the phylogeny we relied on a phylomorphospace approach  (Sidlauskas, 2008) , which allows us to intuitively visualize the extent and direction of the inferred shape change by means of branch length and orientation. This method projects the phylogenetic tree (Figure  13 ) onto the tangent space computed from the bgPCA and estimates the position in the morphospace of the internal nodes (ancestral morphologies) via a maximum likelihood (ML) method for continuous characters  (Felsenstein, 1988; Schluter et al. , 1997)  using the 'fastAnc' function of phytools v0. 6-60 R package  (Revell, 2012). Subsequently, the bgPC scores of the ancestral states are rotated and translated from the shape data back into the configuration space for interpolation and 3D visualization using Deformetrica 3 software.