dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, in many situations the dominant kinematically allowed decay channels are the two-body decays, which can easily be computed by squaring three-point vertices and multiplying by the appropriate phase-space factors. The first main technical advance of this paper is that we present an extension of the FeynRules package that allows one to compute analytically all two-body decay rates, and thus to include their values into the output of the translation interfaces. Moreover, we have extended the Universal FeynRules Output format (UFO)  [27] , which is the standard interface from FeynRules to MadGraph5 aMC@NLO, GoSam  [28]  and Herwig++  [17, 29] , to include all these analytic formulas so that they can be used dynamically when generating a scattering process. Two-body decays might however be insufficient and three (or even more)body decays might be required for a reliable estimation of the total widths of some of the particles.
However, computing all the analytic formulae for an arbitrary mass spectrum is beyond reach. In addition, one has to deal with the double counting coming from an intermediate propagator going on-shell, which corresponds to a cascade of two-body decays. Therefore, the second main technical achievement of this paper is to introduce a new MadGraph5 aMC@NLO module, dubbed MadWidth, which determines automatically the required final state multiplicity to reach a given precision on the total width. In addition, it generates the diagrams without any double counting, estimates the contribution of each decay channel and selects those that should be integrated numerically. The inclusion of higher multiplicities may however still be insufficient in cases where loop-induced decay modes are important or in cases where threshold effect are large so that their resummation needs to be taken into account.
The paper is organized as follows: In Section 2 we give a short review of the FeynRules package and we introduce the new functions that can be used to compute two-body decay rates. In Section 3 we present the extension of the UFO format to include the information on these two-body decays. In Section 4 we describe the algorithm implemented in MadWidth to generate all numerically relevant diagrams associated with N -body decay channels and present how the code can be used inside the MadGraph5 aMC@NLO framework. Finally, in Section 5 we validate our implementations by comparing partial widths for four selected models, namely the Standard Model, the Higgs Effective Field Theory  [30, 31] , the Strongly Interacting Light Higgs model  [32]  and the Minimal Supersymmetric Standard Model. Our conclusions are presented in Section 6.
The Mathematica package FeynRules In this section we introduce one of the main actors involved in this work, the FeynRules package. We start by presenting a short review of the main functionalities of the package, and we focus on the automatic computation of decay rates within the FeynRules framework in Section 2. 2. In a nutshell, FeynRules is a Mathematica package that allows one for the automatic computation of the Feynman rules of a quantum field theory model directly from its Lagrangian. It can be used with a large variety of physics models involving fields with spin of at most two  [33, 34]  and/or superfields  [35, 36]. The only requirements consist of Lorentz and gauge invariance. In other words, and more technically speaking, all indices appearing inside a Lagrangian must be correctly contracted.
Apart from these restrictions, no further assumptions are made on the functional form of the Lagrangian, so that FeynRules can also be used to compute vertices associated with operators of dimension greater than four and in the context of any gauge choice. In addition, FeynRules also contains several translation interfaces that allow one to output the Feynman rules into a format readable by various Feynman diagram generators. Currently, dedicated interfaces exist for CalcHep/Comp-Hep  [37, 38, 15, 16] , FeynArts/FormCalc  [39, 40, 41, 42, 43] , Sherpa  [20, 44]  and Whiz-ard/O'Mega  [45, 21, 46]. Furthermore, it is also possible to output the model information in the so-called Universal FeynRules Output (UFO) format, a format for the implementation of beyond the Standard Model theories into matrix element generators that is not tight to any existing code and that does not make any a priori assumption on the structure of the interaction vertices that appear in the model.
Finally, FeynRules also comes with specific computational modules that include, e. g. , methods dedicated to superspace calculations  [35]  or mass matrix diagonalization  [47]. The implementation of a model into FeynRules does not only require to enter the Lagrangian, but also the definitions of all particles and parameters that appear inside it. In other words, all symbols that are used to write the Lagrangian in Mathematica must be properly declared before Feynman rules may be calculated by FeynRules. The syntax for the declaration of the particles and parameters is based on the original format for model files of FeynArts, extended by new options required by FeynRules. For example, the Standard Model Z-boson can be declared by including in the FeynRules model description, V[1] == { ClassName -> Z, SelfConjugate -> True, Mass -> {MZ, 91.
Functions dedicated to the computation of decay widths In this section, we describe the new functionalities of the FeynRules package related to the computation of all two-body decays of a model analytically. We start by giving a very brief review on two-body decays before presenting the new user functions implemented into FeynRules. The leading-order decay rate of a heavy particle of mass M into N particles of mass m i is given by Γ = 1 2|M |S dΦ N |M| 2 , (1) where S denotes the phase space symmetry factor, |M| 2 the averaged squared matrix element and dΦ N the usual N -body phase-space measure in four dimensions dΦ N = (2π) 4 δ (4) P - N i=1 p i N i=1 d 4 p i (2π) 3 δ + (p 2 i -m 2 i ) = (2π) 4 δ (4) P - N i=1 p i N i=1 d 3 p i 2 (2π) 3 E i. (2) In this expression P = (M, 0) stands for the four-momentum of the heavy decaying particle at rest and p i and E i are the momenta and energies of the decay products.
The restriction to two-body decays provides in general a good estimate of the width of the particles. In some cases, it is however important to include at least three-body decay contributions. We will discuss how such cases are identified and handled in the context of the MadGraph5 aMC@NLO framework in Section 4. Using the vertices provided by FeynRules, it is very easy to evaluate Eq. (  4 ) analytically and to obtain the analytic results for all two-body decay rates associated with any new physics model. In the rest of this section we describe the new functions included in the FeynRules package that allow one to perform this task on the example of the Standard Model implementation, which is included in the distribution of the package. In order to compute the partial widths of the particles, it is necessary to first compute the vertices associated with the model in the usual way and to store them in some variable, e.
0, the UFO interface automatically includes all two-body decays in the output. It is however possible to disable this feature by setting the option AddDecays of the WriteUFO[] function to False (the default being True). If included, information on the two-body decays is stored into the UFO format in the file decays. py. The content of this file contains declarations of instances of the class Decay (defined in object_library. py). Each instance of this class can be thought of as a collection of analytic formulas for the two-body partial widths of a given particle. where Γ H→ij schematically represent the analytic formulas for the partial widths of the Higgs boson. The syntax used to write these analytic formulas in Python form is identical to the syntax used in the UFO format for defining internal parameters, and we refer to Ref.
MadWidth -a MadGraph5 aMC@NLO module to compute decay widths MadGraph5 aMC@NLO is a suite of packages related to the computation of matrix elements  [19, 48, 49, 50, 51, 52]. Two of its main usages consist of cross section computation and event generation at the leading order (via the MadEvent package  [18] ) and next-to-leading order (through the MC@NLO framework  [26] ) in perturbation theory. In addition to their intrinsic accuracy, the precision of the results provided by the aforementioned packages is limited by the precision of the calculations of the numerical values of the model parameters. In particular, the way in which the widths of the particles have been obtained plays a non-negligible role. Although the total widths are in general estimated fairly well if only two-body decays are considered, it is often necessary to include decays to higher-multiplicity final states.
In principle, we can generate all N -body tree-level decay processes directly in MadGraph5 aMC-@NLO and find the (partial) widths. 5 However, in addition to the genuine N -body decay processes, this also generates cascade decays and radiation from (n < N )-body decays which can be accounted more efficiently from the parent lower-multiplicity decays. Also, often only a small subset of higher-multiplicity final states are relevant for the total width. Both factors hinder a balance between precision and efficiency using MadGraph5 aMC@NLO alone. MadWidth has been designed with the purpose to solve this issue. It is embedded as a module into MadGraph5 aMC@NLO, and allows the user to compute N -body partial decay widths for arbitrary values of N at tree-level. Assuming the narrow-width approximation  6  , the module iteratively builds diagrams from lower multiplicities while at the same time carefully removes contributions from cascade decays and radiative processes.
We stress that Mad-Width is not the first publicly available code to compute leading-order decay rates, but MadWidth has been designed to go beyond what is done by existing tools like CalcHep, Bridge or Herwig++ in several aspects. First, while CalcHep can evaluate total decay widths on the fly, it only includes contributions from three-body (four-body) decays only if there is no contribution from two-body (three-body) decays. Second, while Herwig++ and Bridge are able to avoid contributions from cascade decays, they are much more limited in the support of beyond the Standard Model theories than MadWidth, which can handle, by its very design, any theory for which a UFO implementation exists. In the remainder of this section we give a brief account of the algorithms underlying MadWidth. We first explain the algorithm for the diagram generation in Section 4.
The MadGraph5 aMC@NLO interface to MadWidth The MadWidth module is fully embedded into the MadGraph5 aMC@-NLO framework and there are currently two ways to use it. Either MadWidth is directly called from a MadGraph5 aMC@NLO or MadEvent shell, or it is instead run on the fly, at the time of event generation or cross section computation. In all situations, using MadWidth requires a valid UFO model. In the cases where the file decays. py is available (see Section 3), the analytic results for two-body decays are directly used by MadWidth. Otherwise, all partial widths are computed numerically. The first way to use MadWidth is to call it from a MadGraph5 aMC-@NLO or MadEvent command interface, respectively initiated via the commands. /bin/mg5 aMC and. /bin/madevent. This method is in particular useful for creating a valid param card.
dat file to use during the numerical evaluation. • --output=value [default: overwrite input file]. The path where to store the new param card. dat file that includes the computed widths. The second way to use MadWidth is to run it on the fly either through a MadEvent or a aMC@NLO session. Both programs start by checking the param card. dat file. If any of the widths is set to the value Auto then MadWidth is called on the fly (with the default options) to evaluate these quantities. The param card. dat file is then overwritten before any further computation. For   where the corresponding partial widths are given under the form of a comment, at the end of each line. Since the code returns not only the total widths, but also all partial widths, the output file is perfectly suitable to be passed to a parton shower program that can then further decay the unstable particles possibly present in the hard events.
Two-body decays In this section, we focus on two-body decay widths and perform various calculations in the framework of the Standard Model, the Strongly Interacting Light Higgs (SILH) model  [32]  and the SPS1a MSSM benchmark scenario  [55]. We first compute for each of these models all two-body partial widths with FeynRules and then numerically compare the results to those returned by the MadWidth module of MadGraph5 aMC@NLO. Moreover, in the case of the MSSM, we also confront the FeynRules results to the analytic formulas of Ref. [56]. Agreement has been found in all cases, which validates our implementations in particular for theories involving higher dimensional operators (cf. the SILH model) and those with Majorana fermions (cf. the MSSM). A selection of numerical results can be found in Table  4 , Table  5  and Table  6  for the Standard Model, the SILH model and the MSSM, respectively.
Conclusion In this paper, we have presented new routines of the FeynRules package dedicated to the computation of two-body partial widths, so that the latter can now be calculated automatically and analytically from the knowledge of the Lagrangian alone. The UFO format has been extended accordingly to include all the relevant information. This extension is currently supported by Mad-Graph5 aMC@NLO, which uses it to calculate particle widths at run-time. In addition, a new module of MadGraph5 aMC@NLO, named MadWidth, has been developed with the aim of computing N -body decay widths in full generality and in an efficient manner (possibly at run-time when using the matrixelement generator). The MadWidth routines automatically remove all the subprocesses that are numerically negligible, tune the final-state multiplicity to reach a given target precision on the total widths and avoid the double-counting of any channel.
Acknowledgments We want to thank  C. Degrande  and  F. Maltoni  for useful discussions on this project and  D. Goncalves Netto  and  K. Mawatari  for their constructive bug reports. O. M. want to thank the  IPPP center  for its hospitality during the time of this project. B. F. was supported in part by the  French ANR   12 JS05 002 01 BATS@LHC  and by the  Theory-LHC-France initiative  of the  CNRS/IN2P3. CHS is supported by the  DOE  under grant number  de-sc0010255. DGO is supported by  Theoretische Forschungen auf dem Gebiet der Elementarteilchen (SNF). OM is 'Chercheur scientifique logistique postdoctoral  F. R. S. -FNRS', Belgium. This work was partly supported by the  Research Executive Agency (REA)  of the  European Union  under the Grant Agreement number  PITN-GA-2010-264564  (LHCPhenoNet), by MCnetITN  FP7 Marie Curie Initial Training Network   PITN-GA-2012-315877 , by the IISN "MadGraph" convention 4.
To account for the azimuthally non-uniform response of the Inner Tracking System (ITS) in this dataset, two exclusive classes of tracks are used  [30] : tracks with Silicon Pixel Detector (SPD) hits (70% of all tracks in central Pb-Pb collisions, and 95% in pp collisions); and tracks without SPD hits but with a primary vertex constraint. The primary vertex is required to lie within 10 cm of the nominal center of the detector along the beam axis, and within 1 cm of it in the transverse plane. After offline event selection, the Pb-Pb dataset consists of 17M events in the 0 -10% centrality percentile interval. The pp collision data used to validate PYTHIA and pQCD calculations were recorded during the 2010 low-luminosity pp run at √ s = 7 TeV, using a MB trigger. The MB trigger configuration, offline event selection, and tracking are the same as described in  [31].
After event selection cuts, the pp dataset consists of 168M events. There is negligible difference in the inclusive jet cross section for events selected by the ALICE online trigger, and for a non-single diffractive event population. Simulations of pp collisions were carried out using PYTHIA 6. 425, with the Perugia 0, Perugia 2010, and Perugia 2011 tunes  [32]. Instrumental effects are calculated using the Perugia 0 and Perugia 2010 tunes for pp and Pb-Pb collisions respectively, with a detailed detector model implemented using GEANT3  [33]. In addition, a simulation based on HIJING  [34]  is used to evaluate the detector response in the high multiplicity environment of Pb-Pb collisions. Perugia 2011, which has been tuned to other LHC data, is used as an alternative to compare with the new data presented here.
4 , right panel, is therefore generated from an exclusive subset of the data, with 10% of the data used for threshold values 0. 1 and 0. 2, 20% for 0. 4, and 60% for 0. 7. Subsets of unequal size are chosen to optimize the statistical errors. Due to the limited statistical precision of the data, correction of the raw distributions in Fig. 4  via unfolding for background fluctuations and instrumental effects is not possible. In order to compare the Pb-Pb distributions with a reference distribution for pp collisions, we therefore impose the effects of instrumental response and Pb-Pb background fluctuations on the distribution calculated by PYTHIA for pp collisions at √ s = 2. 76 TeV. The instrumental response, modeled by GEANT, is dominated by tracking efficiency and momentum resolution. The effects of background fluctuations are modeled by embedding detector-level PYTHIA events into real Pb-Pb events.
Section 4. 3 presented considerations of trigger-jet energy loss in the interpretation of these measurements. A related but distinct effect is variation of R AA , the suppression of inclusive hadron yield in central Pb-Pb collisions, over the p T,trig -interval of the Signal TT bin  [13, 14]. Such a variation can generate different hard-process selection bias for the same hadron trigger cuts in pp and Pb-Pb collisions, even if trigger-jet energy loss effects in Pb-Pb are negligible. Using PYTHIA simulations, we estimate that this variation may generate an increase in ∆ recoil of 5% at p T,jet = 20 GeV/c and 15% at p T,jet = 100 GeV/c, but negligible change in the ratio of ∆ recoil in Pb-Pb with different R (see Fig. 10  and discussion below). Such effects will however be included in theoretical calculations which incorporate quenching and accurately reproduce the measured p T -dependence of inclusive hadron R AA , and we do not correct the data for them.
Comparison of the shape of the azimuthal distribution of di-jet pairs in central Pb-Pb data and embedded PYTHIA events has been reported  [22, 23] , with indication of an enhancement in the tail of the distribution for central Pb-Pb collisions  [22]. More detailed characterization of the change in the angular distribution with change in TT interval is provided by Σ (∆ϕ thresh ) (Eq. 6), which measures the yield in the tail of the distribution beyond a threshold, π/2 < ∆ϕ < π -∆ϕ thresh. Fig. 12 , left panel, shows the Σ (∆ϕ thresh ) distributions for central Pb-Pb data and the embedded PYTHIA reference. As discussed in Sect. 5. 2, for the measurement of Σ (∆ϕ thresh ) the dataset is divided into exclusive subsets, with each subset used for only one value of threshold, so that the data points in Fig.
Sequence-dependent BII propensities from simulations versus NMR A previous dataset of 323 measured δPs has established that the 16 dinucleotides (NpN steps for a single strand in a duplex context) composing B-DNA are associated with specific δP values  [28]. Since δP translates into a BII propensity it implies that the BI/BII populations are primarily controlled by the dinucleotide sequence  [28]. The additional 72 δPs considered here conform to this sequence pattern, validating the notion of dinucleotide-specific BII propensity  [27]. Thus, the sequence-dependent BII populations derived from δPs provide a rare opportunity to test the sequence-dependent behavior of DNA force-fields in solution. One notes that adjustments made to Parmbsc0 εzOLI and CHARMM36 to increase the BII populations were not tailored depending on the base sequence, in contrast with, for instance, the CMAP approach  [54].
Conformational combinations of facing phosphate and inter base pair parameters BI•BI, BI•BII|BII•BI or BII•BII are associated to different values of slide, roll and twist in X-ray structures  [15, 16, 39]. However, the requirement to select only very high resolution X-ray structures to ensure the accuracy of backbone dihedral angles  [59]  drastically limits the data for analysis. A previous study  [28]  underlined that BII conformers in such X-ray structures occur almost exclusively in CpG, CpA, TpG, GpG, and GpC; furthermore, the BII•BII combination was only observed in CpA•TpG. The improved representation of the DNA backbone with Parmbsc0 εzOLI and CHARMM36 offers the opportunity to broaden the analysis of the helicoidal parameters associated to the facing phosphate combinations for a larger variety of complementary dinucleotides than in X-ray datasets.
They reveal a general mechanical property of free DNA. Thus, BII containing complementary dinucleotides are characterized by more positive slide, more negative roll and higher twist than those in BI. As most steps have access to the BI•BII|BII•BI states (see the preceding section), the DNA deformation cost upon protein binding could be less important than expected when BII-like features are required for recognition. This can be illustrated by the TTAAA sequence in Oligo 3. This segment is considered as one of the strongest anchoring points in the nucleosome assembly  [60] [61] [62] [63] [64] , by forming multiple interactions with histones H3 and H4. In the X-ray structures of nucleosome containing the sequence 601 (PDB entries 3ZL0, 3ZL1  [63]  and 3MVD  [61] ), TTAAA•TTTAA displays rather variable but globally negative rolls (-7 ±6°).
MDs with Parmbsc0 εζOLI and CHARMM36 force fields MD simulations were performed with the Parmbsc0 εzOLI force-field  [13]  using the AMBER 14 program  [68] , or the CHARMM36 force-field  [11]  with program NAMD  [69]. Parmbsc0 εzOLI and CHARMM36 simulations were carried out following protocols as comparable as possible. Yet, with Parmbsc0 εzOLI and CHARMM36, we used the counterion parameters classically associated to the Amber  [70]  and CHARMM  [71]  force-fields, respectively. Parmbsc0 εzOLI and CHARMM36 simulations were performed at constant temperature (300K) and pressure (1bar) using the Berendsen algorithm  [72]. The integration time-step was 2fs and covalent bonds involving hydrogen were constrained using SHAKE  [73]. The non-bonded pair-list was updated heuristically. Long-range electrostatic interactions were treated using the particle mesh Ewald (PME) approach  [74].
Introduction Flow cytometry is a key technology for the characterization of the cellular component of the immune system. Flow cytometers are able to simultaneously quantify different surface markers of single cells, allowing the identification and quantification of different immune cell subpopulations. In recent years, improvements in measurement speed and experimental automation have enabled comprehensive immunoprofiling of larger cohorts  [1]. The gold standard for the analysis of raw flow cytometry data has until now remained "hand gating" (i. e. , analysis through computer-assisted procedures for the classification of cells into single cell types using software tools such as FlowJo  [2] ). Each sample is analyzed by successively separating cell types by successive "gating" in a series of one-or two-dimensional projections.