dataesr/scientific-paragraphs-categorization huggingface dataset:
Weighted edges for sensitivity optimisation In generating the set of allowed paths, we have been concerned only with SR-exclusivity and treated all graph edges (and hence SRs) as of equivalent value, within the fixed DAG ordering provided. But in our physics application, of course, this is not the case: for each specific BSM model, some signal regions will be more sensitive than others. For example, leptophilic models naturally tend to see most sensitivity in SRs with multilepton signatures; models with enhanced couplings to the third generation have most impact on tand b-quark and τ-lepton signatures; and dark-matter models favour jet + missing transverse-energy signatures. In addition, when not all SRs have the same integrated luminosity, SRs in high-luminosity datasets are naturally more sensitive than those in low-statistics ones.
T1 simplified-model combination Following the method described in Section 2, an overlap matrix was constructed from the selection of analyses available in SMODELS and MADANALYSIS 5. Using the SMODELS database, an exclusivity matrix of 393 SRs was created with a 1% threshold of maximum overlap. The first test of the TACO formalism was to compare the combination results to the validation  plots for analyses and topologies available in the SMODELS database, chosen as this checks for consistency within a model-space completely understood and mapped by SMODELS. The first simplified model chosen was the "T1" topology, which is a simplified version of gluino pair production in which each gluino undergoes a three-body decay g → qq χ0 1 to a light-flavor quark-antiquark pair plus the lightest stable particle (LSP) χ0 1.
This selection and ordering of the signal regions was propagated to the exclusivity matrix, and the WHDFS SRselection algorithm was applied. Figure  5  shows that the combined result pushes the exclusion line beyond that of the best performing analysis available in the current SMODELS database by approximately 150 GeV. Looking deeper into the combined results, Figure  6 (a) shows the distribution of starting (i. e. lowest) SR-indices over the set of maximum-sensitivity combinations. This confirms the statements made in Section 3 that the efficiency of the path-finding would be greatly increased by sorting the exclusivity matrix by individual SR sensitivities. The histogram shows that, when ordered, the optimum combination is typically seen early in the iteration process, allowing many later path-sets to be vetoed when there is no prospect of their completions beating the current best.
pMSSM-19 reinterpretation With the machinery in place to construct the exclusivity matrix from SRs based on a given model-point, it was now possible to extend the analysis to increasingly complex models. The 19-parameter phenomenological Minimal Supersymmetric Standard Model (pMSSM-19) was chosen as a testing ground for reinterpretation, as a model with considerably more degrees of freedom than typically studied in experimental publications. We used data points sourced from the ATLAS 2015 pMSSM-19 scan paper  [41] , independently for that paper's two scenarios of bino-like and wino-like LSPs. These points were identified by ATLAS according to their model viability compared to 8 TeV ATLAS data, and hence provide an a priori interesting set for reevaluation against 13 TeV LHC data. p-values from 13 TeV LHC Run-2 data analyses were calculated from the first (randomly ordered) 27 000 model points in the bino and wino scenarios separately, run through the SMOD-ELS analysis chain and discarding those points which lay outside the bounds of the SMODELS efficiency maps, with ∼20 000 points surviving in each run.
LLRs were calculated using the SMODELS best-single-expected SR-selection process and the best-expected-combination results. The resulting p-value distributions from the pMSSM-19 bino-LSP reinterpretation are shown in Figure  8. The histograms show that in both the (a) expected and (b) observed cases the combination procedure moves a significant fraction of points from below the 95% exclusion into the excluded category, an increase in exclusion fraction from approximately 35% to 70% of all points in the ATLAS set of bino-like models. This mean shift from single to combined could be seen as confirmation of the exclusionary power of the TACO approach, but before making any conclusions it is prudent to review exactly how the model points are behaving on a bin-to-bin level. To this end, Figure  9  shows the transition matrices (also known as stochastic matrices) for the pMSSM-19 bino dataset, showing the probability of a model point "moving" between p-value bins depending on whether the single-SR or combined-SR LLR construction method is used.
All analyses considered are integrated in the MADANALYSIS 5 Public Analysis Database  [44] , the recast codes and their detailed validation notes being available from Refs. [45] [46] [47] [48] [49]  and on the database webpage. 2 o estimate the individual exclusion limits originating from each analysis, we used MAD-GRAPH5_aMC@NLO v2. 6. 5  [29]  to generate hard-scattering events at leading-order (LO) accuracy. We grouped the three different contributions to the signal in two sets according to the parton-level jet multiplicity. A first matrix-element describes the production of a pair of dark-matter states with a single hard jet (pp → χχ j), and a second one concerns mediator pair-production and decay (pp → Y Y * → χ jχ j). The associated production of a dark-matter particle with a mediator is hence included in the first subprocess, as it yields the same finalstate (pp → χ Y → χ(χ j) with the intermediate mediator Y being on-shell).
Conclusions and outlook In this paper we have argued that maximising the BSM-search power of the LHC and other colliders, in the light of Run 2's significant list of null-result direct searches, necessitates combination of analyses for sensitivity to more subtle dispersed-signal models than have so far been considered. But combination cannot be performed naively, due to overlaps in analyses' event acceptance. Short of many-year, top-down coordination within experimental collaborations to forbid phase-space overlaps (with implicit prioritisation of some analyses over others) or public lists of which collider-event numbers entered which signal regions across all published analyses, a post hoc method is needed to estimate the extent of such overlaps. In this paper we have presented the TACO method for this estimation, using the SMODELS and MADANALYSIS 5 analysis databases to guide simulated-event population of all recastable signal regions.
The expected best-sensitivity combination can then be efficiently identified by a weighted hereditary depth-first search (WHDFS) algorithm, in direct analogy to a weighted longest-path problem. Code for both the overlap estimation and SR-combination aspects of this paper is publicly available from the https://gitlab. com/t-a-c-o/taco_code online repository. Computation of such SR-subsets using the TACO WHDFS algorithm is a practical alternative to direct use of the overlap matrix to compute a correlated χ 2 or other measure across all signal regions, given the latter approach's high risk of numerical instabilities in covariance inversion, and the huge computational cost of simultaneously likelihood-profiling over a full set of SRs. This graph-based approach hence has potential not just as a route to composite likelihoods in reinterpretation, but also as a dimension-reduction technique for visualising and interpreting how dominant categories of analyses and signal-regions evolve through model spaces.
Acknowledgements AB and JY thank the  Royal Society  for studentship funding under  URF Enhancement Grant   RGF\EA\180252 , and  STFC  for support under Consolidated Grant  ST/S000887/1. The research of  HRG  is funded by the  Italian PRIN  grant  20172LNEEZ. The authors thank the  Les Houches Physics at the Terascale series  for its 2019 edition during which this work originated, and we look forward to the return of collider-physics to the academically productive slopes of the Chamonix valley. Our further thanks to  Sabine Kraml  and others at  LPSC Grenoble  for generously bringing the authors together to invigorate this work, with support from the  IN2P3  project " Théorie -BSMGA ".
0NLO was used in the matrix-element (ME) calculations and the AZNLO tune  [34]  of Pythia 8 parameters was used in the MC generation. The EvtGen v1. 6. 0 program  [35]  was used for the properties of the bottom and charm hadron decays. The Powheg prediction is accurate to NLO in QCD corrections and tuned to match calculations with effects due to finite heavy-quark masses and soft-gluon resummations up to next-to-next-to-leading-logarithm (NNLL). The MC prediction was normalized to a cross-section calculated up to the full NLO in QCD and EW couplings  [36, 37]  with an additional approximate next-to-next-to-leading order (NNLO) QCD correction  [38]. The sample normalization accounts for the decay branching ratios calculated with Hdecay  [39] [40] [41]  and PROPHECY4F  [42] [43] [44]. Systematic variations were generated as event weights to account for uncertainties associated with PDFs (using the 30 variations of the PDF4LHC15 PDF set  [45] ), 𝛼 𝑠 (using 0.
The Mad-Graph5_aMC@NLO generator implements the CKKW scheme  [51]  to remove overlaps between the ME and the PS and calculates the matrix element of the full process, including the Higgs boson decay by MadSpin  [52]. This calculation is accurate to NLO in QCD corrections and utilizes the NNPDF30NLO  [53]  PDF set. The EvtGen v1. 7. 0 program was used to simulate the bottom and charm hadron decays. The H7UE set of tuned Herwig 7 parameters and the MMHT2014LO PDF set were used for the UE. This sample is referred to as MG5+Herwig 7 production. Another set of three distinct predictions for the signal process were produced at LO and NLO accuracies in the strong and electroweak couplings using the VBFNLO generator version 2. 7. 1  [54]. In contrast to the other MC samples, the events generated by VBFNLO were produced only at particle or parton levels.
Background Monte Carlo samples The Higgs boson production and decay into a pair of 𝑊 bosons were simulated for the ggF and associated 𝑊 𝐻/𝑍 𝐻 production modes (collectively referred to as 𝑉 𝐻). The simulation of the ggF Higgs boson production used the Powheg method  [28] [29] [30]  for merging the NLO Higgs boson + jet cross-section with the PS and the MiNLO method  [57] [58] [59]  to simultaneously achieve NLO accuracy for the inclusive Higgs boson production. The PS and non-perturbative effects were simulated using Pythia 8. 212. In a second step, a reweighting procedure (NNLOPS  [60, 61] ), exploiting the Higgs boson rapidity distribution, was applied using HNNLO program  [62, 63]  to achieve NNLO accuracy in the strong coupling constant. The cross-section was normalized to next-to-next-to-next-to-leading order (N 3 LO) in the strong coupling, and included NLO EW corrections  [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74].
To assess the impact of the PS model and its matching to the ME, an independent sample of ggF events was simulated with Powheg interfaced to Herwig 7. Higgs boson production in association with a vector boson, 𝑉 𝐻, was simulated using POWHEG-BOX v2 MiNLO interfaced with Pythia 8 for PS and non-perturbative effects. The prediction is accurate to NLO for the production of 𝑉 𝐻 plus one jet. The MC prediction was normalized to cross-sections calculated at NNLO in the strong coupling with NLO electroweak corrections  [75] [76] [77] [78] [79]. The ggF Higgs boson samples were generated with NNPDF3. 0NNLO PDFs in the ME while the 𝑉 𝐻 samples with NNPDF3. 0NLO PDFs. Both sets of processes used the AZNLO tune of Pythia 8 parameters and the EvtGen v1. 2. 1 program  [35]  to simulate the properties of the bottom and charm hadron decays.
The shapes of signal and irreducible background processes, i. e. , 𝑡 t and non-resonant 𝑉𝑉, 𝑍/𝛾 * +jets, and ggF Higgs boson, in the discriminant distributions are obtained from the simulated samples described in Section 3. Their yields are determined in the SR from the fit, while the yield of the reducible mis-Id background is estimated in data from a dedicated control region as described in Section 6. Its overall normalization and shape are constrained within systematic uncertainties in the fit. The extended likelihood L consists of a product of likelihood functions (Ps) of the SR yield and those of the two CRs: L =P (𝑁 SR data |𝑁 SR signal + 𝑛 bkg ∑︁ 𝑘=1 𝜇 bkg-𝑘 • 𝑁 SR bkg-𝑘 + 𝑚 bkg ∑︁ 𝑗=1 𝑁 SR bkg-𝑗 )• 2 𝑛=1 P (𝑁 CR-𝑛 data |𝑁 CR-𝑛 signal + 𝑛 bkg ∑︁ 𝑘=1 𝜇 bkg-𝑘 • 𝑁 CR-𝑛 bkg-𝑘 + 𝑚 bkg ∑︁ 𝑗=1 𝑁 CR-𝑛 bkg-𝑗 ) • 𝑛 𝜃 𝑖 𝑖=1 N ( θ𝑖 |𝜃 𝑖 ) (2) where 𝑁 SR data and 𝑁 CR-𝑛 data are the numbers of events observed in data in the signal region and control region CR-𝑛 (the ggF CR or the 𝑍/𝛾 * +jets CR), respectively, and 𝑁 SR signal and 𝑁 CR-𝑛 signal are the expected signal yields in the SR and CR-𝑛, respectively.
1. Templates are smoothed to minimize statistical fluctuations in the MC samples, using the Gaussian distribution kernel density approximation implemented in the RooKeysPdf algorithm in the RooFit package  [113]. The normalization of the sum of top-quark and non-resonant 𝑉𝑉 processes (𝜇 top+𝑉𝑉 ) is determined in the signal region by leveraging the discrimination power of the multivariate discriminants. The inclusion of the two control regions in the fit allows the normalizations of the 𝑍/𝛾 * +jets (𝜇 𝑍+jets ) and ggF Higgs boson (𝜇 ggF ) backgrounds to be constrained by data in the SR as well as in their respective CRs. The normalization parameters of all other processes are fixed to their respective theoretical predictions, or to the data-driven estimate for the case of the mis-Id background.
Multivariate discriminants Several BDTs are utilized in the analysis and are trained independently to discriminate between one process type against others. The BDT algorithms implemented in the TMVA package  [112]  are used. Their scores are in the [-1, 1] range and reflect the compatibility between an event and the process for which the BDT is trained, with the purity increasing as the score value increases. The BDTs were trained on MC samples and tested on statistically independent MC events. The BDT hyperparameters were tuned to maximize the area under the receiver operating characteristic (ROC) curve while avoiding overtraining by ensuring that the Kolmogorov-Smirnov test score between training and test samples was high. For each BDT, several variables were studied to be used as inputs to the BDT training, but only variables significantly improving the discrimination power (i.
Dedicated checks were carried out to assess the dependence of the extrapolation factors on the number of jets reconstructed in the event, the distance between the lepton candidate and a nearby jet, and pile-up. No significant dependence was found. An MC-based test was also carried out, in which the same mis-Id background estimate technique as used in data for the measurements of the integrated and differential cross-sections is applied on MC 𝑊+jets samples using two different MC generators (see Section 3). No bias was observed beyond statistical effects in the MC samples. Therefore, no systematic uncertainty was assigned to such an effect. Finally, uncertainties may arise from the unfolding procedure used. Tests with MC simulations and ensemble tests with varying underlying cross-sections show that the method is successful in retrieving the particle-level distribution in the fiducial region from the reconstructed distribution in the signal region.
e. , outside the limit ranges. This effect is due to a dependence of the fiducial selection efficiency on the EFT parameters for extreme values of these couplings and not to a data unfolding bias. The associated bias was studied and its effect was assigned as an uncertainty to the EFT parameterization for those couplings. It was found to have negligible impact on the limits, i. e. , about an order of magnitude smaller than the impact of systematic uncertainties assigned to the signal modeling. The contributions of the pure-SM, the interference and the pure dimension-six terms to the differential cross-sections in Eq. (  7 ) are generated at leading-order in perturbative QCD and EW processes using the MadGraph5_aMC@NLO 2. 7. 2 generator. The anomalous interactions from the dimension-six operators are provided by the SMEFTsim package  [124, 127]  in the 𝑈 (3) 5 flavor symmetric limit with the cut-off scale set to Λ = 1 TeV.
The events are simulated using the NNPDF30NLO PDF set  [53]  and the 𝐺 𝐹 EW scheme. The MadGraph5_aMC@NLO samples are interfaced to the Pythia 8. 230 program with the dipole recoil approximation and the A14 set of tuned parameters for parton showering, hadronization, and multiple parton scattering. The pure-SM prediction was validated against VBFNLO@LO+Pythia 8. An assumption is made that higher-order QCD corrections, applied in a multiplicative way, are similar for the SM prediction and the one for anomalous interactions. Therefore, the parameterizations of the cross-sections as a function of each Wilson coefficient are expected to have small effects associated to missing higher-order corrections  [128]. The higher-order calculation is taken to be the prediction from Powheg+Pythia 8, see Section 3.
Conclusion Integrated and differential fiducial cross-section measurements for the vector-boson fusion production of the Higgs boson in the 𝐻→ 𝑊𝑊 * → 𝑒𝜈𝜇𝜈 final state are presented using proton-proton collision data collected by the ATLAS experiment at a center-of-mass energy of √ 𝑠 = 13 TeV and with an integrated luminosity of 139 fb -1. The data are selected by requiring one electron and one muon from the decay of 𝑊 bosons along with two energetic jets in the final state and are corrected to particle level. The measured integrated fiducial cross-section is 𝜎 fid = 1. 68 ± 0. 33 (stat) ± 0. 23 (sys) fb. It is found to be consistent with SM predictions that include NLO QCD and NLO electroweak corrections and with LO QCD calculations that implement parton showering. The fiducial cross-section is also measured differentially as a function of kinematic and angular variables of the final-state charged leptons and jets that are sensitive to the Higgs boson production and decay.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and CRC, Canada;  PRIMUS 21/SCI/017  and  UNCE SCI /013 ,  Czech Republic ;  COST ,  ERC ,  ERDF ,  Horizon 2020 ,  ICSC-NextGenerationEU  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France ;  DFG  and  AvH Foundation, Germany ;  Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece ;  BSF-NSF  and  MINERVA, Israel ;  Norwegian Financial Mechanism 2014-2021 ,  Norway ;  NCN  and  NAWA ,  Poland ;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya  and  PROMETEO  and  GenT Programmes Generalitat Valenciana ,  Spain ;  Göran Gustafssons Stiftelse ,  Sweden ;  The Royal Society  and  Leverhulme Trust, United Kingdom.
Acknowledgements We acknowledge the support of the  International Associated Laboratory in Photonics between France and Australia (LIA ALPhFA) , the  Agence Nationale de la Recherche  ( MIRSICOMB ,  ANR-17-CE24-0028 ;  MetaOnDemand ,  ANR-20-CE24-0013  and  MIRthFUL ,  ANR-21-CE24-0005 ), the  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Actions (MSCA)  ( REDI , grant agreement No  101034328  and  ECLAUSion , grant agreement No  801512 ).
The NASA Compton Gamma-Ray Observatory  (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000)  with the COMPTEL instrument provided a sky image of the 26 Al γ-ray line, which showed a structured 26 Al emission, extended along the plane of the Galaxy  [14] [15] [16]  (figure  2 ). This image was obtained from measurements taken over years 1991-2000 throughout the sky. It uses a maximum-entropy regularization together with the likelihood of the imageʼs projected data fitting the measurement, and varies the image iteratively until a best image is found to fit the measured events. Such forward convolution analysis is required, as the direct inversion of measured data is not possible; the signal of the 26 Al sky is translated into the data space of measured events by the instrument response function, which includes Compton scattering (as a probabilistic process), and is singular (hence cannot be inverted).
E x (keV) J a Spectroscopic factor C 2 S = 0. 022 from Endt and Rolfs  [192] , single-particle proton width calculated from the parameters given by Iliadis  [193]. An uncertainty on the spectroscopic factor of 20% has been assumed. b Expectation and variance of the resonance strength using ωγ = 4. 5 × 10 -22 and a factor 1. 5 uncertainty in  [184]. c Expectation and variance of the resonance strength using ωγ = 2. 1 × 10 -11 and a factor 1. 5 uncertainty in  [184]. d This resonance is given as f 0 = 0. 71 in  [182]  citing then yet-to-be-published paper by Endt, de Wit and Alderliesten which is presumably  [194]. The decay branches listed in  [194]  give f 0 = 0. 0041  (7). The listed γ-ray branches in the ENSDF database give f 0 = 0. 0042  (6). We adopt f 0 = 0. 0042  (6). This resonance is rather weak for both radiative capture to the ground state and isomeric state and the astrophysical impact of this reassignment is negligible.
For example, the f 0 value of the E r = 95 keV resonance was revised from f 0 = 0. 52 ± 0. 02(stat. ) ± 0. 06 (syst. ) [186]  to f 0 = 0. 76  (10)   [189]. Similarly, on the basis of the γ-ray branching information stored in the ENSDF database, the f 0 value for the astrophysically unimportant E r = 109 keV resonance has values ranging from f 0 = 0. 0041  (7)  to f 0 = 0. 71 depending on the source. New, independent measurements of these ground state branching fractions are advisable in order to validate the existing results. (Left) The fractional contribution of each resonance to the 25 Mg(p, γ) 26 Al g based on the resonance parameters given in table 1. (Right) Contour heatmap of the current reaction rate uncertainties. The thick (thin) black curves are the 68% (95%) coverage limits. The green curves are median (solid) and 68% coverages (broken) for the 25 Mg(p, γ) 26 Al g reaction rate from the compilation of Iliadis et al  [213].
The (n, p 1 ) channel is thought to be astrophysically dominant, but it is not accessible in time-reversed experiments. Recently, there have been new direct measurements of 26 Al(n, p) 26 Mg and 26 Al(n, α) 23 Na reactions at the new high neutron flux beamline EAR-2 at n_TOF CERN, and at the GELINA facility  [248, 249]. Both reaction cross sections were measured up to about 150 keV neutron energy, extending the previously available experimental range for energy dependent data. Resonance strengths of several resonances were provided for the first time. Astrophysical reaction rates, including all relevant branches could be deduced from the data up to about 0. 6 GK stellar temperature. Resonance strengths obtained for the 26 Al(n, α) 23 Na reaction in  [249]  agree well with previous data by De Smet et al  [246] , leading to a good agreement of astrophysical reaction rates at low stellar temperatures.