dataesr/scientific-paragraphs-categorization huggingface dataset:
722104 ), the  Fulbright-Cottrell Award  and from  BMBF  (contract  05H18MGCA1 ). DR acknowledges support from the  German-American Fulbright Commission  allowing him to stay at  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. VT also thanks the  CNRS  and the  IPhT  for financial support and hospitality during the early stages of this work. GS is supported in part by the  French Agence Nationale de la Recherche , under grant  ANR-15-CE31-0016.
We also conducted a Principal Component Analysis (PCA) of the Neptune and Uranus data and found that in the 1. 57 -1. 60 µm range, some of the Empirical Orthogonal Functions (EOFs) mapped closely to physically significant quantities, with one being strongly correlated with the modelled H 2 S signal and clearly mapping the spatial dependence of its spectral detectability. Just as for Uranus, the detection of H 2 S at the cloud tops constrains the deep bulk sulphur/nitrogen abundance to exceed unity (i. e. > 4. 4 -5. 0 times the solar value) in Neptune's bulk atmosphere, provided that ammonia is not sequestered at great depths, and places a lower limit on its mole fraction below the observed cloud of (0. 4 -1. 3) ×10 -5. The detection of gaseous H 2 S at these pressure levels adds to the weight of evidence that the principal constituent of the 2.
Spectral Data Sources The main gaseous absorber in the H-band (i. e. 1. 45 -1. 8 µm) in Uranus's and Neptune's spectra is methane. The best available source of methane line data at low temperature in this range is the "WKLMC@80K+"  (Campargue et al. 2013 ) line database, and its efficacy in modelling the near-IR spectra of Uranus was shown by  Irwin et al. (2018). Hence, we used these line data again in this study. For line shape we used a Voigt function, but with a sub-Lorentzian correction far from line centre as recommended for H 2 -broadening conditions by  Hartmann et al. (2002). For hydrogen sulphide (H 2 S) and ammonia (NH 3 ) we used line data from HITRAN 2012  (Rothman et al. 2013 ), including their line widths and their temperature exponents, which were reported by  Irwin et al. (2018)  to be all that was available.
Alternatively, it may be that our assumption of using the same particle size distribution to model the reflection at all altitudes is not appropriate for Neptune, which clearly has a higher particle density in the upper troposphere/lower stratosphere than Uranus. A further possibility is that there is some other error in the photometric correction. To test for this latter possibility we compared our Gemini-North/NIFS spectra with observations made with VLT/SINFONI in 2013  (Irwin et al. 2016). Figure  11  compares the spectra measured by VLT/SINFONI and Gemini/NIFS near disc centre (area '4' for Gemini/NIFS). Aside from the lower spectral resolution of the VLT/SINFONI data (R=3000, compared with R=5290 for NIFS), there is an excellent correspondence between the two sets of observations, taken four years apart from each other and calibrated independently, including in the poorly modelled regions near 1.
577 and 1. 59 µm. Hence, the discrepancies between the modelled and measured spectra for Neptune seem to be real. It is clear that Neptune has more reflection from upper level hazes than Uranus and one final possibility for explaining the discrepancy is that the "WKLMC@80K+"  (Campargue et al. 2013 ) line data may be less accurate at modelling methane absorption at the cooler, lower pressures of Neptune's haze layers. However, until the cause of the modelling discrepancies is isolated we must be slightly more cautious in our confidence of detection of H 2 S in Neptune's atmosphere than we are of its detection in Uranus's atmosphere.
Acknowledgements. We thank the referee for his comments and suggestions. The authors also wish to thank  Jonathan Ferreira ,  Pierre-Olivier Petrucci  and  Jean-Pierre Lasota-Hirszowicz  for the very fruitful discussions. NS acknowledges financial support from the pole PAGE of the  Université Grenoble Alpes. This work was granted access to the  HPC resources of IDRIS  under the allocation A0040402231 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). Some of the computations presented in this paper were performed using the Froggy platform of the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the  Rhône-Alpes region  ( GRANT CPER07_13 CIRA ), the OSUG@2020 labex (reference  ANR10 LABX56 ) and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment The ATLAS Collaboration This paper describes a strategy for a general search used by the ATLAS Collaboration to find potential indications of new physics. Events are classified according to their final state into many event classes. For each event class an automated search algorithm tests whether the data are compatible with the Monte Carlo simulated expectation in several distributions sensitive to the effects of new physics. The significance of a deviation is quantified using pseudo-experiments. A data selection with a significant deviation defines a signal region for a dedicated follow-up analysis with an improved background expectation. The analysis of the data-derived signal regions on a new dataset allows a statistical interpretation without the large look-elsewhere effect.
The sensitivity of the approach is discussed using Standard Model processes and benchmark signals of new physics. As an example, results are shown for 3. 2 fb -1 of proton-proton collision data at a centre-of-mass energy of 13 TeV collected with the ATLAS detector at the LHC in 2015, in which more than 700 event classes and more than 10 5 regions have been analysed. No significant deviations are found and consequently no data-derived signal regions for a follow-up analysis have been defined.
Introduction Direct searches for unknown particles and interactions are one of the primary objectives of the physics programme at the Large Hadron Collider (LHC). The ATLAS experiment at the LHC has thoroughly analysed the Run 1 pp collision dataset (recorded in 2010-2012) and roughly a quarter of the expected  Run 2 dataset (2015 Run 2 dataset ( -2018)). No evidence of physics beyond the Standard Model (SM) has been found in any of the searches performed so far. Searches that have been performed to date do not fully cover the enormous parameter space of masses, cross-sections and decay channels of possible new particles. Signals might be hidden in kinematic regimes and final states that have remained unexplored. This motivates a model-independent1 analysis to search for physics beyond the Standard Model (BSM) in a structured, global and automated way, where many of the final states not yet covered can be probed.
General searches without an explicit BSM signal assumption have been been performed by the DØ Collaboration  [1] [2] [3] [4]  at the Tevatron, by the H1 Collaboration  [5, 6]  at HERA, and by the CDF Collaboration  [7, 8]  at the Tevatron. At the LHC, preliminary versions of such searches have been performed by the ATLAS Collaboration at √ s = 7, 8 and 13 TeV, and by the CMS Collaboration at √ s = 7 and 8 TeV. This paper outlines a strategy employed by the ATLAS Collaboration to search in a systematic and (quasi-)model-independent way for deviations of the data from the SM prediction. This approach assumes only generic features of the potential BSM signals. Signal events are expected to have reconstructed objects with relatively large momentum transverse to the beam axis. The main objective of this strategy is not to finally assess the exact level of significance of a deviation with all available data, but rather to identify with a first dataset those phase-space regions where significant deviations of the data from SM prediction are present for a further dedicated analysis.
The observation of one or more significant deviations in some phase-space region(s) serves as a trigger to perform dedicated and model-dependent analyses where these 'data-derived' phase-space region(s) can be used as signal regions. Such an analysis can then determine the level of significance using a second dataset. The main advantage of this procedure is that it allows a large number of phase-space regions to be tested with the available resources, thereby minimizing the possibility of missing a signal for new physics, while simultaneously maintaining a low false discovery rate by testing the data-derived signal region(s) on an independent dataset in a dedicated analysis. The dedicated analysis with data-derived signal regions also allows an improved background prediction. In this approach, events are first classified into different (exclusive) categories, labelled with the multiplicity of final-state objects (e.
g. muons, electrons, jets, missing transverse momentum, etc. ) in an event. These final-state categories are then automatically analysed for deviations of the data from the SM prediction in several BSM-sensitive distributions using an algorithm that locates the region of largest excess or deficit. Sensitivity tests for specific signal models are performed to demonstrate the effectiveness of this approach. The methodology has been applied to a subset of the √ s = 13 TeV proton-proton collision data as reported in this paper. The data were collected with the ATLAS detector in 2015, and correspond to an integrated luminosity of 3. 2 fb - 1. The paper is organized as follows: the general analysis strategy is outlined in Section 2, while Section 3 provides specific details about its application to the ATLAS 2015 pp collision dataset.
Strategy The analysis strategy assumes that a signal of unknown origin can be revealed as a statistically significant deviation of the event counts in the data from the expectation in a specific data selection. A data selection can be any set of requirements on objects or variables needed to define a signal region (e. g. an event class or a specific range in one or multiple observables). In order to search for these signals a large variety of data selections need to be tested. This requires a high degree of automation and a categorization of the data events according to their main features. The main objective of this analysis is to identify selections for which the data deviates significantly from the SM expectation. These selections can then be applied as data-derived signal regions in a dedicated analysis to determine the level of significance using a new dataset.
Step 1: Data selection and Monte Carlo simulation The recorded data are reconstructed via the ATLAS software chain. Events are selected by applying event-quality and trigger criteria, and are classified according to the type and multiplicity of reconstructed objects with high transverse momentum (p T ). Objects that can be considered in the classification are those typically used to characterize hadron collisions such as electrons, muons, τ-leptons, photons, jets, b-tagged jets and missing transverse momentum. More complex objects, which were not implemented in the example described in Section 3, could also be considered. Examples are resonances reconstructed by a specific decay (e. g. Z or Higgs bosons decaying into two or four isolated leptons respectively, or decaying hadronically and giving rise to large radius jets with substructure) and displaced vertices.
Data selections are not considered in the scan if large uncertainties in the expectation arise due to a lack of MC events, or from large systematic uncertainties. To avoid overlooking potential excesses in these selections the p 0 -values of selections with more than three data events are monitored separately. Single outstanding events with atypical object multiplicities (e. g. events with 12 muons) are visible as an event class. Single outstanding events in the scanned distributions are monitored separately. The result of scanning the distributions for all event classes is a list of data selections, one per event class containing the largest deviation in that class, and their local statistical significance. Details of the procedure and the statistical algorithm used for the 2015 dataset are explained in Section 3.
From Figure  1  it follows for instance that 2% of the pseudo-experiments have at least three p channel -values smaller than 10 -4. Consequently, the probability of obtaining a third smallest p channel -value smaller than 10 -4 from data in the absence of a signal is about 2%, or P exp,3 (10 -4 ) = 0. 02 In Figure  1  a horizontal dotted line is drawn at a fraction of pseudo-experiments of 5% and corresponding vertical dotted lines are drawn at the three p min thresholds. The observation of one, two or three p channelvalues in data below the corresponding p min threshold, i. e. an observation with a P exp,i < 0. 05, promotes the selections that yielded these deviations to signal regions that can be tested in a new dataset.
2. 7 Step 7 (only in the case of P exp,i < 5%): Dedicated analysis of deviation Dedicated analysis on original dataset Deviations are investigated using methods similar to those of a conventional analysis. In particular, the background prediction is determined using control selections to control and validate the background modelling. Such a procedure further constrains the background expectation and uncertainty, and reduces the dependence on simulation. If such a re-analysis of the region results in an insignificant deviation, it can be inferred that the deviation seen before was due to mismodellings or not well-enough understood backgrounds.
Dedicated analysis on an independent dataset If a deviation persists in a dedicated analysis using the original dataset, the data selection in which the deviation is observed defines a data-derived signal region that is tested in an independent new dataset with a similar or larger integrated luminosity. At this point, a particular model of new physics can be used to interpret the result of testing the data-derived signal region. Since the signal region is known, the corresponding data can be excluded ('blinded') from the analysis until the very end to minimize any possible bias in the analysis. Additionally, since only a few optimized hypothesis tests are performed on the independent dataset, the large look-elsewhere effect due to the large number of hypothesis tests performed in step 3 is not present in the dedicated analysis of the signal region(s).
• Since this analysis is not optimized for a specific class of BSM signals, a dedicated analysis optimized for a given BSM signal achieves a larger sensitivity to that signal. The enormous parameter space of possible signals makes an optimized search for each of them impossible. • The large number of data selections introduce a large look-elsewhere effect, which reduces the significance of a real signal. Step 7 circumvents this problem since the final discovery significance is determined with a dedicated analysis of one or a few data selection(s) and a statistically independent dataset. This can yield an improved signal sensitivity if the background uncertainty can be constrained in the dedicated analysis. • Despite being broad, the procedure might miss a certain signal because it does not show a localized excess in one of the studied distributions.
ATLAS detector and dataset The ATLAS detector  [11]  is a multipurpose particle physics detector with a forward-backward symmetric cylindrical geometry and a coverage of nearly 4π in solid angle. 3  The inner tracking detector (ID) consists of silicon pixel and microstrip detectors covering the pseudorapidity region |η| < 2. 5, surrounded by a straw-tube transition radiation tracker which enhances electron identification in the region |η| < 2. 0. Between Run 1 and Run 2, a new inner pixel layer, the insertable B-layer  [12] , was inserted at a mean sensor radius of 3. 3 cm. The inner detector is surrounded by a thin superconducting solenoid providing an axial 2 T magnetic field and by a fine-granularity lead/liquid-argon (LAr) electromagnetic calorimeter covering |η| < 3. 2. A steel/scintillator-tile calorimeter provides hadronic coverage in the central pseudorapidity range (|η| < 1.
7). The endcap and forward calorimeter coverage (1. 5 < |η| < 4. 9) is completed by LAr active layers with either copper or tungsten as the absorber material. An extensive muon spectrometer with an air-core toroid magnet system surrounds the calorimeters. Three layers of high-precision tracking chambers provide coverage in the range |η| < 2. 7, while dedicated fast chambers provide a muon trigger in the region |η| < 2. 4. The ATLAS trigger system consists of a hardware-based level-1 trigger followed by a software-based high-level trigger  [13]. The data used in this analysis were collected by the ATLAS detector during 2015 in pp collisions at the LHC with a centre-of-mass energy of 13 TeV and a 25 ns bunch crossing interval. After applying quality criteria for the beam, data and detector, the available dataset corresponds to an integrated luminosity of 3.
This is done for 27 event classes among which the smallest p 0 -value observed in a discarded region is 0. 01. To model the analysis of discarded regions in pseudo-experiments, regions are allowed to have larger uncertainties if they fulfil the N obs > 3 criterion. In addition to monitoring regions discarded due to a total uncertainty in excess of 100%, regions discarded due to N SM < 0. 01 but with N obs > 3 would also be monitored individually; however, no such region has been observed. Tables  4  and 5  list the three event classes with the largest deviations in the m inv and m eff scans respectively. The largest deviation reported by a dedicated search using the same dataset was observed in an inclusive diphoton data selection at a diphoton mass of around 750 GeV with a local significance of 3.
An exclusion sensitivity at the 95% CL in a dedicated search roughly corresponds to a single class having a p 0 -value for a discovery test smaller than 0. 05. Consequently, the sensitivity to a benchmark signal corresponding to a given particle mass should be compared with the discovery sensitivity of other searches for a Z boson or gluino. As previously described, a deviation for which P exp,i < 5% promotes the selection to a signal region for a dedicated analysis. By applying this sensitivity criterion, it can be seen in Figure  8  that this search is sensitive to a Z boson with a mass of about 2. 5 TeV as more than 90% of the signal-injected pseudoexperiments show a deviation for which P exp,1 < 5%. Similar sensitivity is expected for a gluino with a mass of about 1 TeV. The probability of discovering a new-physics signal in a new dataset with a dedicated search in the selected event classes is estimated in the next section.
Sensitivity of a second independent dataset In step 7 a dedicated analysis of a deviation is performed on an independent dataset. The sensitivity of step 7 is evaluated with pseudo-experiments. A first pseudo-experiment emulates the original dataset on which this analysis is performed. The scan algorithm is applied after which eight different cases can be distinguished where P exp,i is either larger or smaller than 5% for i = 1, 2, 3. In seven cases at least one P exp,i < 5% and a new independent pseudoexperiment is generated to emulate a new independent dataset with the same integrated luminosity. The one, two or three data selections for which P exp,i < 5% are applied to the second pseudo-experiment to obtain the p 0 -values for these selections. Although the systematic uncertainties may be reduced by applying data-driven estimates of the background, they were assumed to have the same size in the second pseudo-experiment to make a conservative estimate.
The systematic uncertainties are also expected to be partially correlated between two datasets but here they were assumed to be uncorrelated. In four of the seven cases P exp,1 < 5% and these cases are grouped together into a 'one signal region' class. This class shows the sensitivity when there would be only a single data-derived signal region. The case where only P exp,3 < 5% is called the 'three signal region' class. The two remaining cases where P exp,2 < 5% define the 'two signal region' class. These classes show cases when a data-derived signal region is found only by a combination of two and three regions. χ + t t → g SM + 1 2 signal regions SM-only = 1. 0 TeV g m σ 5 σ 5 σ 5 3 (b) Figure  9 : The fraction of cases in which the first pseudo-experiment has P exp,i < 5% and triggers a second pseudoexperiment which yields a value forn k=1 log 10 (p 0k ) smaller than a given value (n k=1 log 10 (p min,k )).
g. for higher Z masses) would raise the number of cases in the n = 2 and 3 categories. A Z boson with a mass of 2. 5 TeV would yield a discovery in almost all cases. In the case of a 1. 0 TeV gluino the sensitivity is about 5σ. The sensitivity increases to about 1. 1 TeV if the integrated luminosity of the two datasets combined is increased to about 10 fb -1 by doubling the size of the second dataset to 6. 4 fb -1. ATLAS has determined the discovery sensitivity of the dedicated searches for gluinos decaying to quarks, a W boson and a neutralino. This dedicated search estimates a local significance (i. e. not corrected for trial factors of the dedicated searches) of 5σ with a luminosity of 10 fb -1 for gluinos with a mass of 1. 35 TeV assuming a systematic uncertainty of 25%  [51]. It should be noted that, with this strategy, these signals are found without any a priori assumptions about the model, including the mass and the decay chain of the gluinos or the Z boson.
Step 6: Results In step 6 the p channel -values found in the analysis of the 2015 ATLAS data are interpreted by comparing them with the p channel -values found in the pseudo-experiments. Figure  10 : The fractions of pseudo-experiments (P exp,i (p min )) which have at least one, two or three p channel -values (circular, square, and rhombic markers respectively) smaller than a given threshold (p min ) in the scans of (a) the m inv distributions, and of (b) the m eff distributions. The coloured arrows represent the three smallest p channel -values observed in data. Dashed lines are drawn at P exp,i = 5% and at the p min -values corresponding to 1σ, 2σ, 3σ, 4σ and 5σ local significances. Details of the deviations can be found in Tables  4  and 5. Figure  10  shows the fractions of pseudo-experiments that have at least one, two or three p channel -values below a given threshold (p min ) in the scans of the m inv and the m eff distributions.
The statistical tests in both distributions for the three leading p channel -values are all consistent at the P exp,i > 50% level with the SM expectation of p channel -values obtained from pseudo-experiments. Changing the size of the theoretical shape uncertainties by a factor of two leads to a change in the three smallest p channel -values of a factor of two. It therefore does not lead to an appreciable change in the result. In conclusion, no significant deviations are found in the 2015 dataset and consequently no dedicated analysis using data-derived signal regions (step 7) is initiated.