dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and simulation samples The analysis uses pp collision data with a bunch crossing interval of 25 ns, collected in 2015 and 2016 at √ s = 13 TeV. Only events that were recorded in stable-beam conditions, when relevant detector components were functioning properly, are considered. Events were collected using a diphoton trigger requiring two reconstructed photon candidates with transverse energies (E T ) of at least 35 and 25 GeV for the E T -ordered leading and subleading photons, respectively. The trigger efficiency with respect to the offline-reconstructed photons, measured using the same method as described in Ref. [19] , is 99%. The data sample corresponds to an integrated luminosity of 36. 1 fb -1. There are on average 25 interactions in the same bunch crossing (in-time pileup) in this data sample.
The photon energy is calibrated using a multivariate regression algorithm trained with fully reconstructed MC samples and then corrections based on in situ techniques on data, as explained in Ref. [41]. The overall energy scale in data, as well as the difference in the constant term of the energy resolution between data and simulation, are estimated with a sample of Z boson decays to electrons recorded in 2015 and 2016. The photon direction is estimated either using electromagnetic (EM) calorimeter longitudinal segmentation (if unconverted) or conversion vertex position (if converted), together with constraints from the pp collision point. A "tight" photon identification requirement  [40]  is applied to reduce the misidentification of hadronic jets containing a high-p T neutral hadron (e. g.
Signal and background parameterization The signal and backgrounds are extracted by fitting analytic functions to the diphoton invariant mass distribution in each category. For the signal and the SM Higgs-boson background, the expected normalizations are obtained from their theoretical cross sections multiplied by the product of the acceptance times efficiency from the simulation. The shapes are modeled with a double-sided Crystal Ball function (as defined in Ref. [43] ). The shape parameters are determined by fitting the diphoton mass distribution in simulation for each category. Both the normalization and the shape of the non-resonant background are obtained by fitting the diphoton invariant mass distribution in data for each category. A variety of analytic functions are considered for the non-resonant background parameterization: exponential functions of different-order polynomials, Bernstein polynomials of different order, and an adapted dijet function  [59].
Summary A search for dark matter in association with a Higgs boson decaying to two photons is presented. This study is based on data collected with the ATLAS detector, corresponding to an integrated luminosity of 36. 1 fb -1 of proton-proton collisions at the LHC at a center-of-mass energy of 13 TeV in 2015 and 2016. No significant excess over the expected background is observed. For the Mono-Higgs category, a visible cross section larger than 0. 19 fb is excluded at 95% CL for BSM physics processes producing missing transverse momentum and a SM Higgs boson decaying into two photons. Upper limits at 95% CL are also set on the production cross section times branching fraction of the Higgs boson decaying into two photons in association with missing transverse momentum in three different benchmark models: a Z B model, a Z -2HDM model and a heavy scalar boson (H) model.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; SRNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ; EPLANET,  ERC ,  ERDF ,  FP7 ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Région Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF; BSF, GIF  and Minerva, Israel; BRF, Norway;  CERCA Programme Generalitat de Catalunya, Generalitat Valenciana , Spain; the  Royal Society and Leverhulme Trust ,  United Kingdom.
Using data recorded with the ANTARES telescope from 2007 to 2015, a new search for dark matter annihilation in the Milky Way has been performed. Three halo models and five annihilation channels, WIMP + WIMP → b b, W + W -, τ + τ -, µ + µ -and νν, with WIMP masses ranging from 50 GeV/c 2 to 100 TeV/c 2 , were considered. No excess over the expected background was found, and limits on the thermally averaged annihilation cross section were set.
Neutrino telescopes may play a paramount role in the search for WIMPs via their annihilation products, because of their particularly clean signals and low expected backgrounds. In this paper the results from the search for dark matter in the Milky Way using data recorded with the ANTARES neutrino telescope from 2007 to 2015, with a total live time of 2102 days are presented. Only neutrinos detected via muons produced inside or around the detector are considered. Here and in the following "neutrino" means ν µ + νµ , unless stated otherwise. In Section 2 it is presented how the neutrino flux can be derived from the annihilation of DM particles. The detector and the reconstruction method are described in Section 3, while the new analysis methodology is explained in Section 4. The results are presented in Section 5.
Simulation and reconstruction The ANTARES neutrino telescope  [14]  is installed at the bottom of the Mediterranean Sea, about 40 km from Toulon and about 2475 m below the sea surface. Being located in the Northern hemisphere (42 • 48 ′ N, 6 • 10 ′ E) allows the ANTARES detector to directly observe the centre of the Milky Way, using the Earth as a shield against the background from atmospheric muons. The integrated J-Factor, J int , for a cone-shaped region ∆Ω centred on the Galactic Centre with an opening angle Ψ. For the halo models the parameters from Table  1  are used. The calculations are done using the code CLUMPY  [13]. ANTARES consists of 12, 450-m long, detector lines that are anchored to the seabed and kept vertical by buoys. Each line comprises 25 storeys with three 10-inch photomultipliers (PMTs)  [15]  per storey.
Atmospheric neutrinos are an additional but much smaller part of the background. However, unlike atmospheric muons, this background is irreducible, although the information of the energy and correlations with the source can help to discriminate it from the signal. In order to evaluate the sensitivity of the search, Monte Carlo simulations, using a detailed detector response for each data run, have been performed  [23]. Concerning the background, atmospheric neutrinos  [24]  and muons  [25]  with energies ranging from 10 GeV/c 2 to 100 TeV/c 2 have been simulated with the standard ANTARES simulation chain  [16, 26, 27]. From this simulation the detector resolution and acceptance is calculated for all five annihilation channels and for WIMP masses ranging from 50 GeV/c 2 to 100 TeV/c 2. In this paper, data taken from 2007 to 2015, corresponding to 2102 days of live time, was used.
Conclusions The results from a new search for dark matter annihilation in the Milky Way using data from the ANTARES neutrino telescope from 2007 to 2015 show no excess above the expected background. Limits at 90% C. L. have been set for the NFW, the McMillan and the Burkert profile, five annihilation channels and WIMP masses ranging from 50 GeV/c 2 to 100 TeV/c 2. In the present version, the 90% C. L limits reported in Figs. 3 to 6 have been obtained from an integrated acceptance (eq. 8) computed more accurately. These upper limits are weaker for WIMP masses larger than 1 TeV/c 2 with respect to those computed in the previous version of this paper. They remain the most stringent for a certain region of the parameter space arising from neutrino detectors.
SSWICH-SW asymptotic The SSWICH  [13] [14] [15]  code follows a three-field approach to self-consistently model the interplay between RF waves and edge plasma DC biasing. In the SSWICH-SW version which is used in this study, the RF waves are reduced to the slow magnetosonic wave (SW) only. The simulation domain is a set of straight open magnetic field lines in the SOL of magnetized plasma in the vicinity of an IC antenna. This domain contains protruding material objects able to intersect magnetic field lines. The inclusion of the limiters allows to distinguish long field lines passing in front of the antennas from short field lines connected to the limiters or in between the limiters. The magnetic field lines are either perpendicular or parallel to the boundaries in order to be consistent with the assumption of the slow wave only  [16].
Inside this domain and as shown on figure  2 , three fields are solved for: the RF parallel electric field E , an oscillating sheath voltage V RF (both varying as as exp(-iω 0 t) at the wave pulsation ω 0 ) and the DC plasma potential V DC. Sheaths at both ends of open flux tubes are described by RF and DC sheaths boundary conditions  [17, 18]. The non-linear I-V characteristic of the sheaths couples the RF and DC quantities. More details can be found in  [13]. The excitation field map is computed in the present paper with the antenna code RAPLICASOL. The physical model of SSWICH-SW is implemented using the COMSOL Finite Element solver. In the present study, only the asymptotic part of SSWICH is considered in order to reduce the simulation time of an extensive number of simulations (the loop in figure  2  only runs once).
Direct CP violation in B + → K ⋆+ π 0 The B + → K ⋆+ π 0 amplitude can be accessed in a B + → K 0 S π + π 0 Dalitz-plot analysis, for which only a preliminary result from BABAR is available  [41]. A large, negative CP asymmetry A CP (B + → K ⋆+ π 0 ) = -0. 52 ± 0. 14±0. 04 +0. 04 -0. 02 is reported there with a 3. 4 σ significance. This CP asymmetry has also been measured by BABAR Constraint on the direct CP asymmetry parameter C(B + → K ⋆+ π 0 ) = -ACP(B + → K ⋆+ π 0 ) from BABAR data on B + → K 0 S π + π 0 (red curve) , BABAR data on B + → K + π 0 π 0 (blue curve) and the combination (green shaded curve). The constraints are obtained using the observables described in the text. through a quasi-two-body analysis of the B + → K + π 0 π 0 final state  [42] , obtaining A CP (B + → K ⋆+ π 0 ) = -0.
VI. PROSPECTS FOR LHCB AND BELLE II In this section, we study the impact of improved measurements of Kππ modes from the LHCb and Belle II experiments. During the first run of the LHC, the LHCb experiment has collected large datasets of B-hadron decays, including charmless B 0 , B + , B s meson decays into tree-body modes. LHCb is currently collecting additional data in Run-2. In particular, due to the excellent per- formances of the LHCb detector for identifying charged long-lived mesons, the experiment has the potential for producing the most accurate charmless three-body results in the B + → K + π -π + mode, owing to high-purity event samples much larger than the ones collected by BABAR and Belle. Using 3. 0 fb -1 of data recorded during the LHC Run 1, first results on this mode are already available  [68] , and a complete amplitude analysis is expected to be produced in the short-term future.
The corresponding inputs are gathered in App. C. We use the reference scenario described in Tab. XI for the central values, so that we can guarantee the self-consistency of the inputs and we avoid reducing the uncertainties artificially because of barely compatible measurements (which would occur if we used the central values of the current data and rescaled the uncertainties). The expected uncertainties, obtained from the extrapolations discussed previously, are described in Tab. XII. The blue area in Fig. 13  illustrates the potential for the first step of our prospective study (B-factories and LHCb Run1+Run2). For the input values used in the prospective, the modulus of the P +-/T +-ratio will be constrained with a relative 10% accuracy, and its complex phase will be constrained within 3 degrees (we discuss 68% C.
This result suggests in turn to search for optomechanical structures where large mechanical motion could be induced all-optically, possibly resulting in strong optomechanical coupling. As a note, the structure discussed here is not an adequate candidate for strong optomechanical coupling as its optical quality factor is lower than required  [7]. Finally, from an AO point of view, the doubly resonant structure achieves large transmission modulation although the thickness is only about one fifth of the optical wavelength. The interaction length is then several orders of magnitude smaller than in non-resonant AO devices. As a result, our study suggests ways to integrate efficient AO modulators in highly compact photonic devices. Funding. Agence Nationale de la Recherche (ANR) (ANR-11-LABX-0001-01); Labex ACTION Program.
Results We can use the expressions for the Wilson line obtained in the previous subsection to compute different components of the Wilson loop in the fundamental representation of the SU (2), Eqs. (4. 4) and (4. 5). We recall that in order to find the instanton correction to the Wilson loop we only need the top component W (16). It has the general form  (3. 11)  and  (3. 15)  and is specified by the set of coefficients f ℓ 1 ℓ 2. The calculation of (4. 4) and (4. 5) is rather lengthy and can be performed with a help of Mathematica. This yields the following result for the coefficients f ℓ 1 ℓ 2 (with ℓ 1 , ℓ 2 ≥ 1) f (F ) ℓ 1 ℓ 2 = 2 16 ×        0 0 -288 0 0 0 -144 0 288 0 -288 0 192 0 -36 0 288 0 -66 -12 0 0 -36 -12 -1        , (4. 16) where we inserted the superscript to indicate that these coefficients define W (16) F in the fundamental representation.
A state-of-the-art of equivalence models is exposed and discussed. It is shown that the existing equivalent models implemented in scenario codes, such as COSI6, are not suited to uncertainty propagation computation, for the following reasons: (i) existing analytical models neglect irradiation, which has a strong impact on the result and its uncertainty; (ii) current black-box models are not suited to cross-section perturbations management; and (iii) models based on transport and depletion codes are too time-consuming for stochastic uncertainty propagation. A new type of equivalence model based on Artificial Neural Networks (ANN) has been developed, constructed with data calculated with neutron transport and depletion codes. The model inputs are the fresh fuel isotopy, the irradiation parameters (burnup, core fractionation, etc.
), cross-sections perturbations and the equivalence criterion (for instance the core target reactivity in pcm at the end of the irradiation cycle). The model output is the fresh fuel content such that target reactivity is reached at the end of the irradiation cycle. Those models are built and then tested on databases calculated with APOLLO2 (for thermal spectra) and ERANOS (for fast spectra) reference deterministic transport codes. A short preliminary uncertainty propagation and ranking study is then performed for each equivalence models.
Taking into account the fact that a COSI6 simulation requires a high amount of RAM, the uncertainty propagation process reaches unreasonable timescales. Therefore, it is necessary to find a method to accelerate such computation. Given that depletion calculations, which are performed with the code CESAR5. 3 [8]  represent approximately 95% of the total scenario simulation time, an optimization can be done, with the development and implementation of a surrogate models library of CESAR5. 3 in COSI6. A surrogate model is an estimator of the output of a code according to its input, usually built with easy to calculate mathematical functions such as polynomials or neural networks. The different inputs of CESAR5. 3 (initial composition for dozen of nuclides, burn-up, power) are sampled by coupling CESAR5.
The complexity of this model is relatively low. Consequently, there is no reason for the choice of ANN over polynomial regressions or any other estimator except the presence in COSI6 of a previous implementation of ANN for another study  [10]  dedicated to irradiation surrogate models. First, the DOE (Design of Experiment) is divided into two subsets of equal size (300 points): a construction sample (or learning database), and a test sample (or validation database). The ANN estimators are built based on the construction sample. Table  2  summarizes a few quality criteria for each ANN, calculated on the test sample. Even the simplest ANN estimator, composed of one neuron in the hidden layer, returns a satisfying plutonium content. On the contrary, increasing too much the number of neurons reduces the overall quality of the estimator because the construction sample is relatively small.
Description of the method Method B is based on the observation that a significant part of the computation time in method A comes from the successive ERANOS calculations in the Newton's algorithm: for each point in the design of experiments, it usually takes 3 or more iterations to compute the plutonium content. Furthermore, although useful, the intermediate results (for instance core reactivity computed for a given composition and a plutonium content giving more or less than the target reactivity) are not used. These t (%)  Although they do not correspond to a core composition that will actually have to be calculated by an equivalence model, those results can be used as trends. Computing a series of r core using randomly generated {y i } and t will provide a set of data {r core , {y i }, t}.
It is worth noting that the number of neurons is case-dependent, and should generally be determined by a screening or an optimization study. Figure  8  represents the application of this estimator of the plutonium content on the same DOE as in method A (iterative computation of the Pu content with ERANOS). One can observe that this ANN is a reliable estimator of the plutonium content computed iteratively by ERANOS. The method also generates less lack of fit in the regions wherein method A introduced non-linear trends due to algorithmdependent jumps. However, as discussed earlier in this paper, those jumps do not represent physical trends, and the local linearization provided by method B appears preferable. Overall one can conclude that method B produces better estimators than method A. Furthermore, the sample construction of method B is faster because of the absence of transport iterationsin other words method B learns trends from data that are useless from the point of view of method A.
Conclusions and perspectives Equivalence model calculates the fresh fuel enrichment or fissile content as a function of the isotopic composition and other parameters. The equivalence criterion used in scenario studies with COSI6 is generally the end of cycle reactivity. A new type of equivalence model, able to perform uncertainty propagation studies, was created in this work, based on statistical estimators such as ANN. The estimators required for the creation of a dedicated learning database were constructed using {depletion + transport} computations, performed with reference codes. Those ANN surrogate-based models enable the propagation of crosssection uncertainties in scenario computations and were created in the case of SFR CFV and PWR MOX fuels. Preliminary uncertainty propagation and ranking studies were performed using these models.
Notation Different configurations of cases A and B with different sodium densities and temperatures will be used in this paper. These configurations will be referred to as follows: -A init and B init refer to the initial configuration, without perturbation. The TFM matrices are calculated with a modified version of Serpent 2. 1. 21 [7]. The modifications concern the calculation of the fission matrices, including the distinction between prompt and delayed neutrons, the fission to fission time matrix, and the correlated sampling technique to generate the locally perturbed matrices. The nuclear database used is JEFF 3. 1  [8]. The number of simulated neutrons for each calculation is one billion. The system boundary conditions for both geometries are leakage on axial boundaries. Concerning the radial boundary a reflection is used for case A and a leakage for case B.
Deterministic code ERANOS The ERANOS  [9]  calculations are based on the traditional two level lattice/core scheme and the JEFF 3. 1 nuclear database. First, self shielded cross sections are computed by the ECCO code cell, using the fundamental mode assumption for each kind of material of the 1D core description. For fissile material, a buckling search algorithm is used to obtain the critical flux for the cross section collapsing to a 33 energy group mesh. For the subcritical materials such as fertile or structural parts of the 1D sub-assembly, the process is based on source calculations using the spectrum coming from previous fissile calculations. For core calculations, the subassembly is modeled by a 1D core and a discrete ordinate S N method with n = 16 and with 33 energy groups corresponding the self shielded cross sections prepared as described above.
Introduction Gravitational waves (GWs) were predicted as a natural consequence of general relativity  [1] , but until recently only indirect evidence of their existence had been found by measuring the time evolution of orbital parameters in binary pulsars  [2, 3]. The first direct detection of GWs was achieved with the discovery of GW150914  [4]  during the first science run of the Advanced LIGO interferometer (O1). This observation was followed by the observation of several other events of different significance  [5]. The INTErnational Gamma-Ray Astrophysics Laboratory [INTEGRAL; 6] team participated in the search for electromagnetic counterparts of almost all of the LIGO/Virgo events, providing the tightest upper limits for the whole GW localization region above 75 keV. The only GW event so far during which INTEGRAL was not taking scientific data is GW151226, which happened when INTEGRAL was in the proximity of one of the perigee passages  1.