Index: contrib/contribs/RecursiveTools/trunk/README =================================================================== --- contrib/contribs/RecursiveTools/trunk/README (revision 1246) +++ contrib/contribs/RecursiveTools/trunk/README (revision 1247) @@ -1,346 +1,358 @@ ------------------------------------------------------------------------ RecursiveTools FastJet contrib ------------------------------------------------------------------------ The RecursiveTools FastJet contrib aims to provide a common contrib for a number of tools that involve recursive reclustering/declustering of a jet for tagging or grooming purposes. Currently it contains: - ModifiedMassDropTagger This corresponds to arXiv:1307.0007 by Mrinal Dasgupta, Alessandro Fregoso, Simone Marzani and Gavin P. Salam - SoftDrop This corresponds to arXiv:1402.2657 by Andrew J. Larkoski, Simone Marzani, Gregory Soyez, Jesse Thaler - RecursiveSoftDrop - BottomUpSoftDrop This corresponds to arXiv:1804.03657 by Frederic Dreyer, Lina Necib, Gregory Soyez and Jesse Thaler - IteratedSoftDrop This corresponds to arXiv:1704.06266 by Christopher Frye, Andrew J. Larkoski, Jesse Thaler, Kevin Zhou - Recluster A generic tool to recluster a given jet into subjets Note: a Recluster class is available natively in FastJet since v3.1. Users are therefore encouraged to use the FastJet version rather than this one which is mostly provided for compatibility of this contrib with older versions of FastJet. The interface for these tools is described in more detail below, with all of the available options documented in the header files. One note about nomenclature. A groomer is a procedure that takes a PseudoJet and always returns another (non-zero) PseudoJet. A tagger is a procedure that takes a PseudoJet, and either returns another PseudoJet (i.e. tags it) or returns an empty PseudoJet (i.e. doesn't tag it). ------------------------------------------------------------------------ ModifiedMassDropTagger ------------------------------------------------------------------------ The Modified Mass Drop Tagger (mMDT) recursively declusters a jet, following the largest pT subjet until a pair of subjets is found that satisfy the symmetry condition on the energy sharing z > z_cut where z_cut is a predetermined value. By default, z is calculated as the scalar pT fraction of the softest subjet. Note that larger values of z_cut correspond to a more restrictive tagging criteria. By default, mMDT will first recluster the jet using the CA clustering algorithm, which means that mMDT can be called on any jet, regardless of the original jet finding measure. A default mMDT can be created via double z_cut = 0.10; ModifiedMassDropTagger mMDT(z_cut); More options are available in the full constructor. To apply mMDT, one simply calls it on the jet of interest. PseudoJet tagged_jet = mMDT(original_jet); Note that mMDT is a tagger, such that tagged_jet will only be non-zero if the symmetry cut z > z_cut is satisfied by some branching of the clustering tree. To gain additional information about the mMDT procedure, one can use tagged_jet.structure_of() which gives access to information about the delta_R between the tagged subjets, their z value, etc. ------------------------------------------------------------------------ SoftDrop ------------------------------------------------------------------------ The SoftDrop procedure is very similar to mMDT, albeit with a generalised symmetry condition: z > z_cut * (R / R0)^beta Note that larger z_cut and smaller beta correspond to more aggressive grooming of the jet. SoftDrop is intended to be used as a groomer (instead of as a tagger), such that if the symmetry condition fails throughout the whole clustering tree, SoftDrop will still return a single particle in the end. Apart from the tagger/groomer distinction, SoftDrop with beta=0 is the same as mMDT. A default SoftDrop groomer can be created via: - double z_cut = 0.10; double beta = 2.0; + double z_cut = 0.10; double R0 = 1.0; // this is the default value SoftDrop sd(beta,z_cut,R0); and acts on a desired jet as PseudoJet groomed_jet = sd(original_jet); and additional information can be obtained via groomed_jet.structure_of() SoftDrop is typically called with beta > 0, though beta < 0 is still a viable option. Because beta < 0 is infrared-collinear unsafe in grooming mode, one probably wants to switch to tagging mode for negative beta, via set_tagging_mode(). ------------------------------------------------------------------------ RecursiveSoftDrop ------------------------------------------------------------------------ The RecursiveSoftDrop procedure applies the Soft Drop procedure N times in a jet in order to find up to N+1 prongs. N=0 makes no modification to the jet, and N=1 is equivalent to the original SoftDrop. Once one has more than one prong, one has to decide which will be declustered next. At each step of the declustering procedure, one undoes the clustering which has the largest declustering angle (amongst all the branches that are searched for substructure). [see "set_fixed_depth" below for an alternative] Compared to SoftDrop, RecursiveSoftDrop takes an extra argument N specifying the number of times the SoftDrop procedure is recursively applied. Negative N means that the procedure is applied until no further substructure is found (i.e. corresponds to taking N=infinity). - double z_cut = 0.10; double beta = 2.0; + double z_cut = 0.10; double R0 = 1.0; // this is the default value int N = -1; RecursiveSoftDrop rsd(beta, z_cut, N, R0); One then acts on a jet as PseudoJet groomed_jet = rsd(jet) and get additional information via groomed_jet.structure_of() ------------------------------------------------------------------------ IteratedSoftDrop ------------------------------------------------------------------------ Iterated Soft Drop (ISD) is a repeated variant of SoftDrop. After performing the Soft Drop procedure once, it logs the groomed symmetry factor, then recursively performs Soft Drop again on the harder branch. This procedure is repeated down to an (optional) angular cut theta_cut, yielding a set of symmetry factors from which observables can be built. An IteratedSoftDrop tool can be created as follows: double beta = -1.0; double z_cut = 0.005; double theta_cut = 0.0; double R0 = 0.5; // characteristic radius of jet algorithm IteratedSoftDrop isd(beta, z_cut, double theta_cut, R0); By default, ISD applied on a jet gives a result of type IteratedSoftDropInfo that can then be probed to obtain physical observables IteratedSoftDropInfo isd_info = isd(jet); unsigned int multiplicity = isd_info.multiplicity(); double kappa = 1.0; // changes angular scale of ISD angularity double isd_width = isd_info.angularity(kappa); vector > zg_thetags = isd_info.all_zg_thetag(); vector > zg_thetags = isd_info(); for (unsigned int i=0; i< isd_info.size(); ++i){ cout << "(zg, theta_g)_" << i << " = " << isd_info[i].first << " " << isd_info[i].second << endl; } Alternatively, one can directly get the multiplicity, angularity, and (zg,thetag) pairs from the IteratedSoftDrop class, at the expense of re-running the declustering procedure: unsigned int multiplicity = isd.multiplicity(jet); double isd_width = isd.angularity(jet, 1.0); vector > zg_thetags = isd.all_zg_thetag(jet); Note: the iterative declustering procedure is the same as what one would obtain with RecursiveSoftDrop with an (optional) angular cut and recursing only in the hardest branch [see the "Changing behaviour" section below for details], except that it returns some information about the jet instead of a modified jet as RSD does. ------------------------------------------------------------------------ BottomUpSoftDrop ------------------------------------------------------------------------ This is a bottom-up version of the RecursiveSoftDrop procedure, in a similar way as Pruning can be seen as a bottom-up version of Trimming. In practice, the jet is reclustered and at each step of the clustering one checks the SoftDrop condition z > z_cut * (R / R0)^beta If the condition is met, the pair is recombined. If the condition is not met, only the hardest of the two objects is kept for further clustering and the softest is rejected. +BottomUpSoftDrop takes the same arguments as SoftDrop, and a groomer +can be created with: + + double beta = 2.0; + double z_cut = 0.10; + double R0 = 1.0; // this is the default value + BottomUpSoftDrop busd(beta,z_cut,R0); + +One then acts on a jet as + + PseudoJet groomed_jet = busd(jet) + ------------------------------------------------------------------------ Recluster ------------------------------------------------------------------------ *** NOTE: this is provided only for backwards compatibility *** *** with FastJet <3.1. For FastJet >=3.1, the native *** *** fastjet::Recluster is used instead *** The Recluster class allows the constituents of a jet to be reclustered with a different recursive clustering algorithm. This is used internally in the mMDT/SoftDrop/RecursiveSoftDrop/IteratedSoftDrop code in order to recluster the jet using the CA algorithm. This is achieved via Recluster ca_reclusterer(cambridge_algorithm, JetDefinition::max_allowable_R); PseudoJet reclustered_jet = ca_reclusterer(original_jet); Note that reclustered_jet creates a new ClusterSequence that knows to delete_self_when_unused. ------------------------------------------------------------------------ Changing behaviour ------------------------------------------------------------------------ The behaviour of the all the tools provided here (ModifiedMassDropTagger, SoftDrop, RecursiveSoftDrop and IteratedSoftDrop) can be tweaked using the following options: SymmetryMeasure = {scalar_z, vector_z, y, theta_E, cos_theta_E} [constructor argument] : The definition of the energy sharing between subjets, with 0 corresponding to the most asymmetric. . scalar_z = min(pt1,pt2)/(pt1+pt2) [default] . vector_z = min(pt1,pt2)/pt_{1+2} . y = min(pt1^2,pt2^2)/m_{12}^2 (original y from MDT) . theta_E = min(E1,E2)/(E1+E2), with angular measure theta_{12}^2 . cos_theta_E = min(E1,E2)/(E1+E2), with angular measure 2[1-cos(theta_{12})] The last two variants are meant for use in e+e- collisions, together with the "larger_E" recursion choice (see below) RecursionChoice = {larger_pt, larger_mt, larger_m, larger_E} [constructor argument] : The path to recurse through the tree after the symmetry condition fails. Options refer to transverse momentum (pt), transverse mass (mt=sqrt(pt^2+m^2), mass (m) or energy (E). the latter is meant for use in e+e- collisions mu_cut [constructor argument] : An optional mass drop condition set_subtractor(subtractor*) [or subtractor as a constructor argument] : provide a subtractor. When a subtractor is supplied, the kinematic constraints are applied on subtracted 4-vectors. In this case, the result of the ModifiedMassDropTagger/SoftDrop is a subtracted PseudoJet, and it is assumed that ModifiedMassDropTagger/SoftDrop is applied to an unsubtracted jet. The latter default can be changed by calling set_input_jet_is_subtracted(). set_reclustering(bool, Recluster*) : An optional setting to recluster a jet with a different jet recursive jet algorithm. The code is only designed to give sensible results with the CA algorithm, but other reclustering algorithm (especially kT) may be appropriate in certain contexts. Use at your own risk. set_grooming_mode()/set_tagging_mode() : In grooming mode, the algorithm will return a single particle if the symmetry condition fails for the whole tree. In tagging mode, the algorithm will return an zero PseudoJet if no symmetry conditions passes. Note that ModifiedMassDropTagger defaults to tagging mode and SoftDrop defaults to grooming mode. set_verbose_structure(bool) : when set to true, additional information will be stored in the jet structure. This includes in particular values of symmetry, delta_R, and mu of dropped branches For the specific case of RecursiveSoftDrop, additional tweaking is possible via the following methods set_fixed_depth_mode(bool) : when this is true, RSD will recurse (N times) into all the branches found during the previous iteration [instead of recursing through the largest declustering angle until N prongs have been found]. This yields at most 2^N prong. For infinite N, the two options are equivalent. set_dynamical_R0(bool) : By default the angles in the SD condition are normalised to the parameter R0. With "dynamical R0", RSD will dynamically adjust R0 to be the angle between the two prongs found during the previous iteration. set_hardest_branch_only(bool) : When substructure is found, only recurse into the hardest of the two branches for further substructure search. This uses the class RecursionChoice. set_min_deltaR_squared(double): : set a minimal angle (squared) at which we stop the declustering procedure. This cut is ineffective for negative values of the argument. ------------------------------------------------------------------------ Technical Details ------------------------------------------------------------------------ Both ModifiedMassDropTagger and SoftDrop inherit from RecursiveSymmetryCutBase, which provides a common codebase for recursive declustering of a jet with a symmetry cut condition. A generic RecursiveSymmetryCutBase depends on the following (virtual) functions (see header file for exact full specs, including constness): double symmetry_cut_fn(PseudoJet &, PseudoJet &) : The function that defines the symmetry cut. This is what actually defines different recursive declustering schemes, and all classes that inherit from RecursiveSymmetryCutBase must define this function. string symmetry_cut_description() : the string description of the symmetry cut. ------------------------------------------------------------------------