dataesr/scientific-paragraphs-categorization huggingface dataset:
The weights of the various topologies, computed as production cross section times branching ratios, are then compared against a large database of experimental constraints. This procedure takes advantage of the large number of simplified models already constrained by official ATLAS and CMS results and does not require Monte Carlo event simulation, thus providing a fast way of confronting a full BSM model with the LHC constraints. Furthermore, "missing" topologies, which are not covered by any of the experimental constraints, are also identified and provided as an output of SModelS. The tool can be used for testing any BSM scenario with a Z 2 -like symmetry as long as all heavier odd particles (cascade-)decay promptly to the lightest one, which should be electrically and colour neutral. 1  It has been applied to a number of minimal and non-minimal supersymmetric (SUSY) models in  [9, [12] [13] [14]  but may also be used for non-SUSY models, see e.
The points excluded by ATLAS are the center of interest of our study: our aim is to compare the exclusion coverage obtained using SMS results only to that from full event simulation. (In the following we mean by "coverage" the fraction of points excluded by ATLAS which is also excluded by SModelS. ) We restrict our analysis to the sets with bino-like or higgsino-like LSP, neglecting points with a wino-like LSP, as most of them lead to a displaced vertex signature, which cannot be studied with the current version of SModelS. We further remove points from the bino-and higgsino-like LSP data sets if they contain any long lived sparticles-this concerns however only a small number of points. Likewise, points which ATLAS found to be excluded only by heavy Higgs searches are also not considered here, as such searches are not treated in SModelS for the time being.
e. σ × BR) of the SMS components of each point are rescaled by the corresponding efficiencies (see  [10]  for more details) and matched with the experimental results in the database. In case of UL maps, this is a direct comparison of individual weights and the cross section upper limit for a given simplified model component or topology. In case of EMs, the weights of several topologies can be combined and may contribute to a specific signal region of a given analysis; it is then the combined signal cross section for the most sensitive signal region (i. e. the signal region with the best expected limit) which is compared against the experimental limit. Hence using efficiency maps can significantly improve the constraining power of simplified models. See the SModelS v1. 1. 1 manual  [10]  for a detailed explanation of the procedure.
For a fair comparison with  [7] , we employ only the 8 TeV results in the v1. 1. 1 database. In order to maximise the coverage by SMS, we consider however also CMS 8 TeV results, as they may give complementary constraints. This is justified because ATLAS and CMS SUSY searches largely consider the same final states and have very similar reach. We also note that the official ATLAS and CMS Run 1 results available in SModelS were augmented with several 'home-grown' EMs in the v1. 1. 1 database to increase the coverage, and we further extend this database with Fastlim-1. 0 [33]  EMs as explained in  [10]. The complete list of analyses and results included in the v1. 1. 1 database can be consulted at  [34]. A comparison of the analyses considered in  [7]  and the SMS results included in SModelS v1.
1. 1 is given in Table  1. The analyses covered by the Fastlim EMs are listed in Table  2. Here note that in SModelS v1. 1. 1 efficiencies with a relative statistical uncertainty greater than 25% are set to zero and, moreover, zero-only EMs are discarded per default. Therefore, from the 264 EMs of Fastlim-1. 0, which are based on 11 ATLAS conference notes, used in practice are 163 EMs from 9 conference notes. The CMS analyses included in the v1. 1. 1 database are listed in Table  3. SModelS reports its results in the form of r-values, defined as the ratio of the theory prediction over the observed 95% confidence level (CL) upper limit, for each experimental constraint that is matched in the database. We consider as excluded all points for which at least one r-value equals or exceeds unity (r max ≥ 1).
Exclusion compared to ATLAS As a first overview of our results, we list in Table  4  the total number of points studied, the number of points that can be excluded by SModelS (r max ≥ 1) when using only the UL results in the database, and the number of points that can be excluded when using the full 8 TeV database, that is including EM results. We see that the coverage of bino-like LSP scenarios can be improved by using EMs, increasing from 44% (UL results only) to 55% (full database). Similarly, the coverage for the higgsino-like LSP scenarios is improved from 55% to 63%. Table  4 : Summary of results, listing the number of ATLAS-excluded pMSSM points tested in this study, the number of points excluded by SModelS when using UL-type results only, and the number of points excluded when using the full 8 TeV database including EM-type results.
Bino-like LSP The improvement in coverage due to EMs largely happens for light to intermediate gluino masses, as illustrated in Figure  1. These scenarios benefit from the fact that EMs allow us to combine the signal for all topologies contributing to the same signal region before comparing against an overall cross section limit, hence increasing the constraining power. Moreover, some asymmetric topologies are included in the EM-type results (from Fastlim) but not in the UL-type results in the database. Figure  1  also shows the importance of the Fastlim and our 'home-grown' EMs with respect to the official ATLAS and CMS SMS results. We note that the Fastlim maps are particularly relevant for constraining gluinos in the intermediate mass range decaying to higgsino-like EW-inos, which is typical for the natural SUSY case they have been derived for.
Gluinos It is striking that there are many points with light gluinos which cannot be excluded by the SMS results in the SModelS database. To understand this better we show in Figure  2  the coverage in the gluino vs. neutralino mass plane. For comparison with the "naive" SMS expectation, the exclusion line obtained in  [35]  for a simplified model where pair-produced gluinos decay exclusively as g → qq χ0 1 is also drawn in Figure  2. We see that light gluinos escape SMS limits especially in the compressed region where monojet-type searches become important. This is in agreement with the simplified model exclusion line. Moreover, while the coverage is good for very light gluinos up to about 600 GeV, it drops for intermediate gluino masses around 1 TeV and higher, as can also be observed in Figure  1.
Fraction of Higgsino LSP ATLAS excluded points excluded by SModelS Figure  2 : Coverage in the gluino vs. neutralino mass plane, for gluino masses up to 2 TeV, for bino-like LSP scenarios (left) and higgsino-like LSP scenarios (right). The color code indicates the fraction of points excluded by SModelS, the text gives the total number of points tested in each bin. For comparison, the 95% CL exclusion line for the g → qq χ0 1 simplified model from  [35]  is drawn in black. pronounced in the bino-like LSP scenario. Concretely, the coverage of bino-like LSP scenarios is 80% when considering only points with light gluinos (m g < 600 GeV), but drops to 60% when considering all points with m g < 1400 GeV. Similarly the coverage of higgsino-like LSP scenarios drops from 97% (m g < 600 GeV) to 74% (m g < 1400 GeV).
Such results cannot be used for reinterpretation in generic scenarios where typically the gluino mass differs from the squark masses, and where the relative importance of the various production and decay channels will be different from the minimal gluino-squark model description. The importance of the 3 jets + E miss T topology is corroborated in Figure  7 , which shows the five most important missing topologies for points with light gluinos below 1. 5 TeV. 6 The leading missing topology for both the bino-and the higgsino-LSP datasets is indeed 3 jets + E miss T from gluino-squark associated production as discussed above, see the yellow points in Figure  7  which cover a wide range of gluino and LSP masses. Gluino-squark associated production also leads to the 5 jets + E miss T missing topology; in this case all squarks are heavier than the gluino and decay via q → qg, and the gluino then decays further to two jets and the χ0 1.
) Other topologies like 2b2t+jet+E miss T (from pp → g q, q → qg, g → tbχ + 1 in the higgsino-LSP case) or long cascades with 4b+jet+E miss T or 3 jets+2W +E miss T also show up in Figure  7 , but are much less often the missing topology with highest cross section. The corresponding diagrams, SUSY processes and labelling in SModelS notation can be found in Appendix A. We note that all these missing topologies could be constrained from the jets+E miss T searches, if the appropriate SMS interpretations were available. For instance, a limit of 40, 20, 10 fb on the 3 jets + E miss T missing topology cross section would exclude additional 4846, 5799, 6599 (1377, 1948, 2637) points of the bino-like (higgsino-like) LSP dataset. We have explicitly checked a couple of representative SModelS-allowed points with a high 3 jets + E miss T cross section and verified that including the efficiencies for the relevant gluino-squark simplified model would indeed exclude these points.
Fraction of Higgsino LSP ATLAS excluded points excluded by SModelS Figure  8 : Coverage in the stop vs. neutralino mass plane, for t1 masses up to 800 GeV, for bino-like LSP scenarios (left) and higgsino-like LSP scenarios (right). The color code indicates the fraction of points excluded by SModelS as compared to ATLAS, while the text gives the total number of points tested in each bin. For comparison, the black lines are the 95% CL exclusion curves for the t1 → t χ0 1 simplified model from  [44]  (0-lepton mode, full line) and  [45]  (1-lepton mode, dashed line). For stops, we observe an excellent coverage in the higgsino-LSP case when compared to the official exclusion curves. (A slightly stronger exclusion is obtained by the combination of the 0-lepton and 1-lepton analyses  [50] , but no UL maps are available for the combination.
It is relevant to stress that the branching ratios shown in Figure  10  only consider visible decays. In particular the higgsino-like LSP dataset contains many points where sbottom branching ratios are shared out in b → b χ0 i =1 and t χ-decays (contributing to the reduced coverage for m b1 500 GeV seen in Figure  9 ) but the subsequent EW-ino decays are invisible because of mass compression. This leads to the patch of grey points just below the exclusion curve in the right plot of Figure  10. Regardless of this, the conclusion from Figure  10  is that EM results for stops and sbottoms decaying through an intermediate particle (leading to final states with additional W , Z or h bosons) would be highly desirable.
Finally, a small fraction of points with min(m l) = 250-300 GeV and  Fraction of Bino+Higgsino LSP ATLAS EW excluded points excluded by SModelS EW  Figure 11 : Coverage in the chargino vs. neutralino mass plane, for χ± 1 masses up to 700 GeV. Here, bino-like and higgsino-like LSP scenarios have been combined to increase the number of points. The plot on the left considers all analyses, the plot on the right only EW analyses. The color code indicates the fraction of points excluded by SModelS as compared to ATLAS, while the text gives the total number of points tested in each bin. For comparison, the exclusion line from the 3-leptons analysis  [54]  is shown in red and from the combination paper  [84]  is drawn in black. light LSP escape exclusion in SModelS because the sleptons partly undergo cascade decays via heavier EW-inos.
Figure12: Coverage in the plane of lightest slepton (first two generations) vs. LSP mass, for l masses up to 700 GeV. Here, bino-like and higgsino-like LSP scenarios have been combined to increase the number of points. The plot on the left considers all analyses, the plot on the right only EW analyses. The color code indicates the fraction of points excluded by SModelS as compared to ATLAS, while the text gives the total number of points tested in each bin. The exclusion lines for lR (red) and lL (black) are also shown for comparison.
Example for the impact of a 3 jets + E miss T simplified model In order to illustrate the importance of asymmetric topologies, we analyse here in more detail one of the ATLAS-excluded points with a light gluino which has not been excluded by the SMS results. The pMSSM point we consider is no. 192342466 of the bino-LSP dataset; it has light gluinos and a highly split spectrum of squarks with light qL. Concretely, m χ0 1 = 666, m g = 712, m ũL = 758, m dL = m sL = 761, m dR = 1343, m ũR = 3968, where all values are in GeV. Stops and sbottoms are heavy with m t1 , b1 ≈ 1. 4 TeV and m t2 , b2 ≈ 3. 3 TeV. The wino-and higgsino-like EW-inos have masses around 3. 5 TeV.
[49]tion of Higgsino LSP ATLAS excluded points excluded by SModelSFigure9: Coverage in the sbottom vs. neutralino mass plane, for b1 masses up to 800 GeV, for bino-like LSP scenarios (left) and higgsino-like LSP scenarios (right). The color code indicates the fraction of points excluded by SModelS as compared to ATLAS, while the text gives the total number of points tested in each bin. The black line is the 95% CL exclusion line for the b1 → b χ01 simplified model from[49].
Acknowledgements We thank the  ATLAS  and  CMS SUSY groups  for providing a plethora of SMS cross section upper limits and efficiency maps in digital format. Moreover, we are particularly grateful for the detailed material on the ATLAS pMSSM study available on HepDATA, which made the analysis presented in this paper possible. This research was supported in part by the  ANR  project  DMASTROLHC  grant no. ANR-12-BS05-0006 , the  IN2P3  project " Théorie -LHCiTools " and the  CNRS-FAPESP  collaboration  PRC275431. F. A. is supported by the  Austrian FWF , project  P26896-N27 , Su. K. by the " New Frontiers" program  of the  Austrian Academy of Sciences , U. L. by the " Investissements d'avenir, Labex ENIGMASS ", and A. L. by the  São Paulo Research Foundation (FAPESP) , projects  2015/20570-1  and  2016/50338-6.
1 B 1 Example for the impact of a 3 jets + E miss T simplified model In order to illustrate the importance of asymmetric topologies, we analyse here in more detail one of the ATLAS-excluded points with a light gluino which has not been excluded by the SMS results. The pMSSM point we consider is no. 192342466 of the bino-LSP dataset; it has light gluinos and a highly split spectrum of squarks with light qL. Concretely, m χ0 1 = 666, m g = 712, m ũL = 758, m dL = m sL = 761, m dR = 1343, m ũR = 3968, where all values are in GeV. Stops and sbottoms are heavy with m t1 , b1 ≈ 1. 4 TeV and m t2 , b2 ≈ 3. 3 TeV. The wino-and higgsino-like EW-inos have masses around 3. 5 TeV.
[49]tion of Higgsino LSP ATLAS excluded points excluded by SModelSFigure9: Coverage in the sbottom vs. neutralino mass plane, for b1 masses up to 800 GeV, for bino-like LSP scenarios (left) and higgsino-like LSP scenarios (right). The color code indicates the fraction of points excluded by SModelS as compared to ATLAS, while the text gives the total number of points tested in each bin. The black line is the 95% CL exclusion line for the b1 → b χ01 simplified model from[49]. Fraction of Bino LSP ATLAS excluded points excluded by SModelS 38 91 132 50 29 44 124 35 39 40 41 38 21 36 36 40 39 29 46 45 53 42 53 46 45 χ 0 1 [GeV] 350 400 1 14 18 1 32 25 26 44 39 24 40 40 26 54 47 33 44 45 36 44 45 32 52 59 39 0. 8 0. 9 1 96 21 25 42 43 49 30 50 44 m 300 5 34 56 57 58 46 42 57 64 36 64 28 39 40 48 47 39 38 56 1 15 39 62 69 62 75 66 76 70 0.
Long-term monitoring dataset: Rivers, Vilaine Bay, Bay of Biscay River d ischarge measurements at Montjean-sur-Loire on the Loire and Rieu x on the Vilaine (Fig. 1 ) were ext racted fro m the French hydrologic "Banque Hydro" database (http://www. hydro. eaufrance. fr/). These two gauging stations serve as reference for calculat ing river inputs  (Romero et al. , 2013; Ménesguen et al. , 2018b). Nutrients and phytoplankton biomass data (dissolved inorganic phosphorus concentrations, DIP; dissolved inorganic nitrogen concentrations, DIN, dissolved silicate concentrations, DSi and ch lorophyll a concentrations, Chl a) in rivers were furnished by the Loire-Brittany River Basin Authority (http://osur. eau-loire-bretagne. fr/exportosur/Accueil). DIP, DIN and Chl a data came fro m Sainte-Luce-sur-Lo ire on the Loire and Rieu x on the Vilaine, and DSi fro m Montjean -sur-Lo ire on the Loire and Férel on the Vilaine (Fig.
Data pre-processing Most of DIN concentrations measured in fresh and marine waters consisted main ly of nitrate (>90%;  Garnier et al. , 2010; Ménesguen et al. , 2018b). Therefore, DIN, defined as the sum of nitrate, nitrite and ammoniu m, was used for trend analysis. DIN and DIP loads from rivers were calculated using averaged monthly discharge and individual monthly nutrient concentrations  (Ro mero et al. , 2013). DSi in rivers was not analy zed for trends because of the short data period (none before 2002). Dataset in the Bay of Biscay were not included for trend analysis due to the short periods and irregularity of sampling. Of all the micro-phytoplankton classes, genera and species identified in Vilaine Bay, only total counts of diato ms (Bacillariophyceae) and dinoflagellates (Dinophyceae) were used in this work to account for the role of DSi.
Other microphytoplankton classes (Dictyophyceae, Prasinophyceae, Cyanophyceae, Chrysophyceae and Raphidophyceae) together represented only 10 to 15 % of total counts. Prior to analysis, all datasets were examined using time scaled scatter plots. Only for DIP, it showed periods during which a limited set of values appeared repeatedly (Fig. S1 ), wh ich were due to analytical problem (Lo ire -Brittany River Basin Authority, S. Jolly, pers. comm. ). Consequently, these suspect data were not taken into account to avoid misinterpretation. The removed DIP datasets represented 29% and 31% of the total nu mber of data, corresponding to respectively the period  1980 -1989 in the Loire, and 1980 -1989 and 2009 -2011  in the Vilaine, but not change the general trend observed.
Time-series analyses The time-series were modeled using Dynamic Linear Models (DLM;  West and Harrison, 1997)  with the dlm package  (Petris, 2010)  in R software (R core team 2016). Th is approach is also referred to as State Space Time Series Analysis, Structural Time Series Model, Unobserved Co mponent Model  (Harvey et al. , 1998)  or Dynamic Harmon ic Regression  (Taylor et al. , 2007). This method, associated with the Kalman filter  (Kalman, 1960) , was also described in  Minaudo et al. (2015)  and  Halliday et al. (2012). The model deco mposes an observed time-series into component parts, typically trend, seasonal component (i. e. , seasonality) and residual as white noise. DLM is particularly suitable for environ mental data characterized by outliers, irregular samp ling frequency and missing data  (Hernández-Fariñas et al.
, 2014). The sequential DLM approach trivially accounts for them: absence of data leads to no change in distributions for model parameters, which look like interpolations. The model used in the current study is a second order polynomial trend, wh ich allows adapting trend up to quadratic, and a two harmonics trigonometric seasonal component, which allow adapting to seasonality up to bimodal. The time units based on sampling frequencies were weekly for VB t ime series, and monthly and bimonthly for the rivers. A Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-406 Manuscript under review for journal Biogeosciences Discussion started: 19 September 2018 c Author(s) 2018. CC BY 4. 0 License. variance-stabilizing log t ransformat ion was applied to all variab les: base was e, except for phytoplankton counts and their ratios, for which the base was 10.
The MK test was also used to corroborate seasonal trends. The seasons were divided as follows: winter (JFM), spring (AMJ), summer (JAS), and autumn (OND). The MK test was applied to the common time segment of all river and Vilaine Bay variables  (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013). Spearman Correlations were co mputed for annual median values in order to analyze relationships among variables, and tested using STATGRAPHIC CENTURION software.
Acknowledgements This study was funded by  The Loire -Brittany Water Agency (AELB). Authors are grateful to  Sylvain Jolly  fro m A ELB for providing datasets of the Vilaine and the Lo ire. We thank IFREM  ER-LER/M PL  staff for their technical contribution, especially  Karine Co llin ,  Yoann Le Merrer ,  Mireille Fortune ,  M ichël Retho  ,  Raoul Gabellec ,  Jacky Chauvin ,  Isabelle Truquet  and  Anne Schmitt. We thank  A lice Mellor  for the English corrections. Authors are grateful to  Drs. F. Gerald Plu  mley and  Anniet M. Laverman  for kindly reviewing befo re submission. Authors acknowledge IFREM ER and the  Regional Council of the Région des Pays de la Loire  for the PhD funding of W. Ratmaya.
Data availability All data used in this study are available in the following online data bases: French National Observation Network for Phytoplankton and Hydrology in coastal waters (http://www. seanoe. org/data/00361/47248/), French Oceanographic Cruises PELGAS surveys (http://campagnes. flotteoceanographique. fr/series/18/), Lo ire-Brittany River Basin Authority (http://osur. eau-loire-b retagne. fr/exportosur/Accueil), French hydrologic database (http://www. hydro. eaufrance. fr/), ICES Oceanographic database (http://ocean. ices. dk/HydChem/HydChem. aspx?plot=yes ).
(f ) Mass spectrometry analysis Ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) mass spectrometry was carried out using an UltiMate 3000 UPLC system (Thermo Scientific), controlled by the CHROMELEON software (Thermo Scientific) coupled to a maXis quadrupole time of flight mass spectrometer (Bruker Daltonics), using the Otof Control and Hystar software packages (Bruker Daltonics) and equipped with an electrospray ionization (ESI) source. UPLC conditions of analysis were as follows: 1. 7-mm C18 (50 Â 2. 1-mm) ultra-high performance liquid chromatography column (Phenomenex); column temperature, 408C; flow rate, 0. nebulizer gas (nitrogen) pressure, 2 bar; capillary voltage, 4500 V; ion source temperature, 1808C; dry gas flow, 9 l min 21 ; spectra rate acquisition, 10 spectra s 21.
(g) Analysis of LC-MS/MS data The Bruker w Daltonics Find Molecular Features algorithm was used to identify molecular features in each sample (Bruker w DATA ANALYSIS software v. 4. 2 build 4. 2. 395. 0) using the following parameters: a signal-to-noise threshold of 5, correlation coefficient threshold of 0. 7, a minimum compound length of 8 spectra and a smoothing width of 2. The number of detected molecular features in each experiment were: 2150 (UVR experiment), 5769 (timeline experiment), 8700 (in situ interactions) and 6500 (molecular cartography of in situ interactions). Peak area was normalized to the total abundance of all molecules in the sample. Spectral data were converted to mzXML using COMPASSXPORT (Bruker, Daltonics, Bremen, Germany) and files were uploaded to the Global Natural Products Social Molecular Network (GNPS) database  [26]  and deposited to Massive ID under the reference MSV000080662.