dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. SP acknowledges the funding by  CNES  through the MEDOC data and operations center. This work used data provided by the MEDOC data and operations center (CNES /  CNRS / Univ. Paris-Sud ), http://medoc. ias. u-psud. fr/. GDZ acknowledges support by  STFC (UK)  via the consolidated grant of the DAMTP atomic astrophysics group at the  University of Cambridge. This work was carried out as part of the research of the ISSI international team n. 418, Linking the  Sun  to the Heliosphere using Composition Data and Modelling. CHIANTI is a collaborative project involving  George Mason University , the  University of Michigan (USA)  and the  University of Cambridge (UK). SUMER is financially supported by  DLR ,  CNES ,  NASA , and the  ESA PRODEX program  (Swiss contribution). SOHO is a mission of international cooperation between  ESA  and  NASA.
Computational time estimation We estimated the computational time related to IEPOX-SOA simulation for the full chemistry and the different parameterizations. The box model was used for estimating the time needed for chemistry calculation using chemical reactions and dry depositions in Table  S1. All the parameterizations showed much faster integration time compared to the full chemistry. For estimation within GEOS-Chem, we used the Gprof function profiling program and categorized the results according to four major processes (chemistry, transport, dry deposition, and wet deposition), as shown in Table  1. One of the main advantages of using a function profiling program is that all of the timings are estimated at once without the need for multiple simulations. Because model computational time varies between individual executions even for the same machine and code  (Philip et al.
Detailed mechanistic studies in the laboratory, often aided by new mass spectrometry instrumentation with higher molecular detail, are leading to the development of many detailed SOA mechanisms, which will challenge global and especially climate models with their increased computational cost. The method developed in this study can be used to simplify other SOA mechanisms, allowing more accurate SOA simulations while limiting computational cost. Code and data availability. The KinSim box model can be downloaded from http://tinyurl. com/kinsim-release (last access: 9 July 2019; preferred, due to updates) or from the Supplement (https://pubs. acs. org/doi/suppl/10. 1021/acs. jchemed. 9b00033/ suppl_file/ed9b00033_si_001. zip, last access: 9 July 2019) of  Peng and Jimenez (2019). The different KinSim chemical mechanisms used for the box model are available in the Supplement of this paper and also at https://tinyurl.
com/kinsim-cases (last access: 9 July 2019). They can be directly loaded into KinSim to reproduce the calculations in this work. GEOS-Chem v11-02-rc and meteorological data can be downloaded from the GEOS-Chem wiki (http://wiki. seas. harvard. edu/geos-chem/index. php/ Downloading_GEOS-Chem_data_directories, last access: 9 July 2019). GEOS-Chem code modifications for new parameterizations and global model data are available upon email request (duseong. jo@colorado. edu). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-12-2983-2019-supplement. Author contributions. JLJ, AH, LKE, and DSJ designed the research. ZP developed the KinSim box model and supported the implementation of the full IEPOX-SOA chemistry in it. WH conducted the IEPOX reactive uptake calculation within Igor Pro.
Acknowledgements. This publication was supported by  US EPA   STAR 83587701-0 ,  NOAA NA18OAR4310113 ,  DOE  ( BER/ASR) DE-SC0016559 ,  NSF   AGS-1822664 , and the  European Research Council  (grant no. 819169 ). It has not been formally reviewed by  EPA. We thank  Prasad Kasibhatla  for useful discussions. We thank the two anonymous referees for their helpful comments. Financial support. This research has been supported by the  U. S. Environmental Protection Agency  (grant no. STAR 83587701-0 ), the  National Oceanic and Atmospheric Administration  (grant no. NA18OAR4310113 ), the  U. S. Department of Energy, Office of Science  (grant no. DE-SC0016559 ), the  National Science Foundation  (grant no. AGS-1822664 ), and the  European Research Council (Horizon 2020  (grant no. 819169 )). Review statement.
Stellar mass To determine the host galaxy stellar masses we used photometric measurements (typically covering the visible to near-infrared wavelength range) from the literature, complemented with new values that we measured from archival data for GRB 061007, GRB 100615A and GRB 090201. All of the values and references are reported in the appendix (Tables A. 1 and A. 2). We fit the available observational constraints (excluding nondetections 1 ) on the emission-line fluxes and broad-band photometry of the galaxies in our sample using the Bayesian spectral interpretation tool beagle  (Chevallard & Charlot 2016; version 11. 3). The version of beagle we use relies on the models of  Gutkin et al. (2016) , who follow the prescription of  Charlot & Longhetti (2001)  to describe the emission from stars and the interstellar gas.
Star Formation Rate and Metallicity SFRs and metallicities were determined using the host galaxy spectra. The data at z > 1 come from the VLT/X-Shooter spectrograph  (Vernet et al. 2011) , and the spectra have already been presented in  Krühler et al. (2015)  and  Vergani et al. (2017). The large wavelength coverage (3000 to 25000 Å) and sensitivity of X-Shooter allow us to detect the strongest rest-frame optical emission lines up to z = 2, ensuring a homogeneous methodology for the determination of star formation rates and metallicities. We performed a new data reduction and analysis of the data, with the standard Esoreflex pipeline  (version 2. 7. 3, Modigliani et al. 2010)  using the nodding recipe. The spatial width of the 2D to 1D spectrum extraction was scaled according to the spatial width of the detected emission lines to maximise the signal to noise ratio.
Bayesian framework Our calculations rely on the assumption that the probability distribution function (PDF) for our data can be reasonably well described by an asymmetric Gaussian distribution for which the scale parameter is given by the asymmetric errors and the location parameter is given by the value quoted in our table. For example, the PDF of a quantity µ +σ p -σ m is given by: PDF(x) = A          exp(-(x-µ) 2 2 σ 2 p ) if x ≥ µ, exp(-(x-µ) 2 2 σ 2 m ) if x < µ, ( 1 ) where A is the normalisation given by: 1 = +∞ -∞ PDF(x) dx In the event of upper limits on the stellar mass of our galaxies, we use a uniform distribution (uninformative prior) between log(M * /M ) = 7 and the upper limit for the comparison of BAT6 sample at different redshifts; when comparing with the COS-MOS2015UD and MOSDEF surveys, the lower stellar mass limit is set to the mass completeness of the survey 5.
For lower limits on the SFR or the specific SFR (sSFR) (objects for which no extinction could be derived), we use a uniform distribution between the limit and a maximum SFR calculated by assuming an A V of 4. We then estimate the median and 95% confidence bounds on our cumulative distribution functions (CDFs), by computing 10000 Monte Carlo realisations of our data sampling from the aforementioned PDFs, this confidence interval is represented as a shaded area in the figures showing CDFs. In a similar fashion, we computed 10000 realisations of the K-S test for each individual CDF when comparing with the MOSDEF and COSMOS2015UD samples 6.
Acknowledgements This work is part of the  BEaPro  project (P. I. : SDV), funded by the  French National Research Agency (ANR)  under contract  ANR-16-CE31-0003. JTP, SDV, RS, SB and ELF acknowledge the  ANR  support. This work benefits also of the support of the  Programme National de Cosmologie et Galaxies (PNCG). JTP wishes to thank  J. K Krogager  and  T. Charnock  for fruitful discussions and  C. Laigle  for providing the mass completeness of the COSMOS2015 Ultra Deep survey. SDV thanks  Fabrice Martins ,  George Meynet  and  Fabian Schneider  for very useful discussions. We thank  T. Krühler  for the data reduction of GROND observations and more in general for his openness in collaborating. R. L. S. was supported by a  UCLA Graduate Division Dissertation Year Fellowship , and also acknowledges a  NASA  contract supporting the " WFIRST Extragalactic Potential Observations (EXPO) Science Investigation Team " ( 15-WFIRST15-0004 ), administered by  GSFC.
JJ acknowledges support from  NOVA  and  NWO-FAPESP  grant for advanced instrumentation in astronomy. AVG acknowledges support from the  ERC  via two Advanced Grants under grant agreements number  321323-NEOGAL  and number 742719-MIST. DC acknowledges support by the  Centre National d'Etudes Spatiales  and support by the  Région Provence-Alpes-Côte d'Azur  for the funding of his PhD. This work is based in part on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. Support for this work was provided by  NASA  through an award issued by  JPL/Caltech. Part of the funding for  GROND  (both hardware as well as personnel) was generously granted from  the Leibniz-Prize  to  Prof.
Detector commissioning phases The Belle II experiment is scheduled to begin its first "physics" run in 2019. As a prelude to this, two commissioning periods known as "Phase 1" (February 2016-June 2016) and "Phase 2" (Februray 2018 -July 2018) were scheduled where a collection of detectors, known as BEAST 2 (Beam Exorcism for A Stable Belle II Experiment) were deployed for measuring background rates and operating conditions. During Phase 1, the solenoid was not active, and no collisions took place. However, for Phase 2 all subsystems except for the full vertex detectors were employed, for colliding beams to produce useful physics and calibration events. Given the expected luminosity profile, it will likely take until at least mid-2019 for Belle II to collect an Υ (4S) dataset large enough to equal that of the B-factory experiments.
22 GeV for Belle II and 0. 28 GeV for Belle. The signal efficiencies and expected signal and background yields in two E ECL windows are reported in Table  39  for this analysis and compared to the Belle MC from hadronic tag measurement  [234]. Belle II MC, L = 1 ab (c) Maximum likelihood fit to pseudo-data E ECL distribution sampled from simulation. The red and blue histograms represent the signal and background fit functions (templates from simulation). The events correspond to an integrated luminosity of 1 ab -1. Fig. 64 : E ECL distributions for signal and background in the analysis of B → τ ν. In order to estimate the expected precision of the B → τ ν τ branching fraction measurement, a toy MC study has been performed generating a high-statistics sample of pseudo-experiments. For each experiment, a pseudo-dataset has been generated according to the signal and background MC expectations, and a binned maximum likelihood fit is performed using a two-component parameterised function where the E ECL distributions for signal and background events are taken from simulation.
In Fig. 64(c ) an illustrative plot of the fit to one pseudo-dataset is shown. Assuming a branching fraction of 0. 82×10 -4 (December 2016 result from the CKMfitter group  [91] ) the mean uncertainty is found to be ∼29%, with 1 ab -1 of equivalent integrated luminosity. A large number of pseudo-experiments has been generated to estimate the expected significance of the branching fraction measurement, according to the following procedure: a likelihood ratio test statistic Q has been defined and evaluated on pseudo-datasets sampled from signal plus background (S+B) and background only E ECL distributions. Then the p-value of the background null hypothesis is evaluated as the ratio between the number of pseudo-experiments which give a value of Q lower than the expected test statistics 158/688.
L. ) for photons above 1 GeV with the full Υ (4S) dataset of 711 fb -1  [247]  (see  [248]  for an updated Belle result). In preparation for Belle II, a new analysis has been prepared with Belle MC Fig. 68 : Dependence of the theoretical prediction for the partial branching fraction ∆B(B + → + ν γ) on the value of the QCD factorisation parameter λ B for two signal photon selection criteria: the threshold with lower theoretical uncertainties E γ > 1. 7 GeV (lower, dashed); and the threshold E γ > 1. 0 GeV which yields a significantly higher efficiency  [246]. Table  45 : Expected signal yields determined with Belle MC for the new analysis using the signal-specific FEI in basf2 (N New ). The values are compared to the expected yields in the published Belle analysis (N Published )  [247]. Both MC studies assume a partial branching fraction of ∆B(B + → + ν γ) = 5.
0 × 10 -6 , to enable a comparison of the expected yields with the different analysis frameworks. in the basf2 framework, where the signal-specific FEI is employed (Sec. 6. 6). To enable a comparison of the two analysis methods, the expected yield (determined from MC with a partial branching fraction of ∆B(B + → + ν γ) = 5. 0 × 10 -6 ) for both methods is displayed in Table  45. The new tagging algorithm results in three times the expected signal yield with the same dataset. The yield is extracted from a simultaneous fit to the squared missing mass distributions of the electron and muon channels. The results for the improved analysis are shown in Fig. 69. Further constraints on the energy of the neutrino would enable the experimental examination of the difference between the axial and vector form factor, and thus the impact of the power-suppressed contributions to the decay width  [223].
However, the selection required for this study-the neutrino has to receive the majority of the B meson's energy-reduces the statistics significantly, rendering it unfeasible with the Belle dataset. However, with the large Belle II dataset, this aspect of the decay will also be addressed. 45. Table  46 : Expected statistical error in 10 -6 for Belle and Belle II for a simulated partial branching fraction of ∆B(B + → + ν γ) = 5. 0 × 10 -6.
Belle Belle II Belle II New analysis 5 ab -1 50 ab -1 +1. 48 +0. 56 +0. 18 -1. 39 -0. 53 -0. 17 In addition, a toy MC study is used to estimate the expected statistical error with 5 ab -1 and 50 ab -1 of Belle II data. The statistical errors are expected to be significantly reduced with the increased dataset. The results can be found in Table  46   The decays B → D ( * ) τ ν are described at the quark level as b → cτ ν tree-level transitions that proceed in the SM through the exchange of a virtual W boson. The ratios, defined as R D ( * ) = Br(B → D ( * ) τ ν τ ) Br(B → D ( * ) ν ) , (136) are useful observables to test for new physics (NP) as theoretical uncertainties in form factors and |V cb | largely cancel out. First measured by Belle  [249] , these ratios have since been more precisely measured by BaBar  [250] , Belle  [251] , and LHCb  [252].
Belle also reported the first measurement of the semi-inclusive branching fractions B(B s → D s X ν) and B(B s → D * s X ν) using its entire 121 fb -1 Υ (5S) dataset. These measurements were limited by production rate uncertainties. In Belle II, B s tagging methods on data samples in excess of 1 ab -1 will circumvent B s normalisation limitations and mitigate background from B mesons. With a sample of about 5 ab -1 we should expect to reach a statistical precision of 2% and systematic precision of about 4% using a hadronic tag. Other methods may be of greater use with smaller data sets, such as a D s and/or lepton tag, but ultimately the hadronic tag is most effective in accurate absolute branching ratio measurements. The inclusive values vary depending on the kinematic fiducial region, which may be due to differences in theory treatment in these regions, or to experimental signal and background modelling imperfections.
In the Uspin limit, the B → J/ψ K 0 S amplitude in eq. (  305 ) is related to the B s → J/ψ K 0 S amplitude, denoted A , via  [663] [664] [665]   A f = λ d c T f + λ d u P f. (309) Since B s → J/ψ K 0 S constitutes a b → ccd process, the λ d u P f term is no longer CKMsuppressed. Therefore both ∆S J/ψ K 0 S and C J/ψ K 0 S can be sizeable and hence be more easily determined. Using external knowledge of the B s mixing phase, φ s , permits the extraction of an estimate for Re(P f /T f ) and thus ∆S J/ψ K 0 S. Similarly, B → J/ψ π 0 can be used as a partner mode  [666] [667] [668]. This mode has the advantage that it is more easily measurable than B s → J/ψ K 0 S at Belle II, at which the B s dataset will be limited. However, in this case a dynamical assumption regarding small annihilation contributions in P f is necessary in addition to U -spin to obtain the analogue of eq.
From this projection it is therefore reasonable to expect that LHCb will approach the sensitivity of BaBar and Belle at the end of Run2 in the year 2018. LHCb is expected to be less competitive in the B 0 → η K 0 and ωK 0 S channels. The systematic uncertainties for B 0 → η K 0 , the channel with higher yield, have been extrapolated from Belle results using the same assumption used for B 0 → J/ψK 0. They become comparable to statistical uncertainties for an integrated luminosity of about 10 ab -1. The other b → q qs decay channels will be statistically limited up to 50 fb -1. The equivalent integrated luminosity used for these studies is 5 ab -1. With this dataset, the measurements will still be dominated by statistical uncertainties. We verified that the statistical sensitivity of the analyses scales well with 1/ √ L.
Estimate of sensitivity from pseudo-experiments studies. To estimate the sensitivity of the analysis we performed an ensemble test with 1 ab -1 and 5 ab -1 equivalent Monte Carlo (MC) samples. For the signal, SXF and non-resonant events, the full MC simulation is used and combinatorial and B B background events are generated by the parameters of each corresponding pdf determined by the fully simulated generic MC datasets. To study the sensitivity on the signal and background yields, we perform a scan varying the number of injected signal and background events, covering approximately one order of magnitude about the expected values. For each mode and each of the investigated signal/background hypotheses we performed 1000 pseudo-experiments. We confirm that the fit returns unbiased results about the signal for both scalar and vector components as well as background yields.
Estimate of sensitivity from ensemble tests studies. To estimate the statistical uncertainties expected from this analysis, we performed a set of ensemble test studies. As input parameters we used S η K 0 S = 0. 7 and A η K 0 S = 0. Using MC we performed the multivariate maximum likelihood fit and extracted pdf for the various distributions. For the time independent part we used M bc , ∆E, the cross-feed discriminating BDT variable, and the continuum suppression BDT variables, both described above. The generated ensemble MC datasets, including signal, self cross-feed, continuum background, and peaking background, correspond to integrated luminosities of 1 and 5 ab -1. The signal, as well as the signal cross feed, were obtained by extracting a random sub-sample from the full signal MC dataset.
These secondary solutions do not arise from ambiguities intrinsic on the method, but are rather artefacts that result from the limited statistics of the sample that was analysed and are expected to vanish with significantly larger datasets. This strongly motivates the repetition of the analysis at Belle II, with a data sample of at least a few ab -1. The dominant background will arise from random combinations of particles arising from continuum events and the model for the signal component will include the ρ(770), ρ  (1450) , and ρ(1700) resonances. For the reasons explained above, and due to the difficulty of realistically simulating the full Dalitz plot analysis on the Monte Carlo, we do not provide any prediction of the sensitivity attainable by Belle II. L → s R γ R is m s /m b suppressed compared to b R → s L γ L , if QCD interactions are switched off.
Deltat_residual Extrapolation of the K 0 S π 0 sensitivity. We estimate the sensitivity to the S K 0 S π 0 and A K 0 S π 0 parameters of the K 0 S π 0 mode analogously to what we have done in section 10. 3. 2. The vertex reconstruction position resolution is taken from the study of K 0 S π 0 γ presented above, and we assume a reconstruction efficiency of 30%, based on the performance of BaBar and Belle. The results are presented in Table  96. We summarise in Table  97  the expected uncertainties to the S and A CP -violating parameters in the channels sensitive to sin 2φ 1 discussed in this chapter. For the J/ψK 0 mode, we provide the estimate, dominated by systematic uncertainties, for the full 50 ab -1 dataset. For the penguin dominated modes the estimates are based on an integrated luminosity of 5 ab -1 , for which we can safely assume that all the channels will still be dominated by the statistical uncertainties and the assumptions on which the current studies are based are valid.
However, a model-dependency is not introduced by this procedure. B ± → DK ± , D → K 0 S π + π -and D → K 0 S K + K -[ 758 Instead, if the model is incorrect, then the binning selected will simply not be optimal, since the direct measurement of the parameters is still valid and does not depend on the details of how the binning was derived. 11. 4. 2. Signal reconstruction at Belle II. The dataset used is the MC5 production dataset without beam background, and corresponds to integrated luminosity of 2 ab -1. For purely hadronic modes which do not use calorimeter-based observables, like the one studied here, the beam background is expected to have little effect. The selection is not as refined as that of the Belle analysis due to the lack of multivariate continuum suppression and tuning of the K 0 S reconstruction.
However, the implementation of the whole analysis even with a non-optimal selection gives a conservative estimate of what can be achieved. From the dataset, a sample for further study is selected based on a loose final state particle requirement, using the decay chains D * ± → π ± D, B ± → Dπ ± and B ± → DK ± with D → K 0 S π + π -in all cases. The final state hadronic particles are selected with the Belle II framework's standard particle selection. The K 0 S is selected with a BDT trained on the K 0 S flight distance, mass, the minimum distance between the vertex and the electron beam direction, and vertex p-value, after an initial dipion selection satisfying 0. 477 < M (π + π -) < 0. 518 GeV. After selecting D candidates, a mass-vertex fit using RAVE is performed to improve the resolution of the Dalitz plot variables.
6 • and 1. 6 • with datasets corresponding to 10 ab -1 and 50 ab -1 , respectively. The most important systematic uncertainties are related to the inputs from charm physics, which will be discussed in Sec. 11. 5, the signal extraction fits and backgrounds from charmless B decay. The latter two sources can be controlled using the B + → Dπ + sample and sidebands of the M D distribution, respectively, so they should scale with the statistical uncertainty.
Further, there is still much scope to refine the analysis further. However, the anticipated precision based on the combination of all Belle results, including GLW and ADS as well, is not completely dominated by the GGSZ measurement alone, such that once the full combination is extrapolated the uncertainty is expected to be 1. 6 • with a 50 ab -1 dataset. A caveat is that the extrapolation is predicated on there being sufficient BESIII data collected at the ψ(3770), approximately 10 fb -1 , to determine the strong-phase difference parameters required. If such a sample does not exist there will be a few degree systematic uncertainty that will limit the impact of the GGSZ measurements on the combination. However, further improvements are possible as several B → DK modes have not been exploited at Belle.