[1f1f858] | 1 | ------------------------------------------------------------------------
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| 2 | RecursiveTools FastJet contrib
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| 3 | ------------------------------------------------------------------------
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| 4 |
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| 5 | The RecursiveTools FastJet contrib aims to provide a common contrib
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| 6 | for a number of tools that involve recursive reclustering/declustering
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| 7 | of a jet for tagging or grooming purposes.
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| 8 |
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| 9 | Currently it contains:
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| 10 |
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| 11 | - ModifiedMassDropTagger
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| 12 | This corresponds to arXiv:1307.0007 by Mrinal Dasgupta, Alessandro
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| 13 | Fregoso, Simone Marzani and Gavin P. Salam
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| 14 |
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| 15 | - SoftDrop
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| 16 | This corresponds to arXiv:1402.2657 by Andrew J. Larkoski, Simone
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| 17 | Marzani, Gregory Soyez, Jesse Thaler
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| 18 |
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[b7b836a] | 19 | - RecursiveSoftDrop
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| 20 | - BottomUpSoftDrop
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| 21 | This corresponds to arXiv:1804.03657 by Frederic Dreyer, Lina
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| 22 | Necib, Gregory Soyez and Jesse Thaler
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| 23 |
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| 24 | - IteratedSoftDrop
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| 25 | This corresponds to arXiv:1704.06266 by Christopher Frye, Andrew J.
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| 26 | Larkoski, Jesse Thaler, Kevin Zhou
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| 27 |
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[1f1f858] | 28 | - Recluster
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| 29 | A generic tool to recluster a given jet into subjets
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[b7b836a] | 30 | Note: a Recluster class is available natively in FastJet since v3.1.
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| 31 | Users are therefore encouraged to use the FastJet version
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| 32 | rather than this one which is mostly provided for
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| 33 | compatibility of this contrib with older versions of FastJet.
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[1f1f858] | 34 |
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| 35 | The interface for these tools is described in more detail below, with
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| 36 | all of the available options documented in the header files.
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| 37 |
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| 38 | One note about nomenclature. A groomer is a procedure that takes a
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| 39 | PseudoJet and always returns another (non-zero) PseudoJet. A tagger is
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| 40 | a procedure that takes a PseudoJet, and either returns another PseudoJet
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| 41 | (i.e. tags it) or returns an empty PseudoJet (i.e. doesn't tag it).
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| 42 |
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| 43 | ------------------------------------------------------------------------
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| 44 | ModifiedMassDropTagger
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| 45 | ------------------------------------------------------------------------
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| 46 |
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| 47 | The Modified Mass Drop Tagger (mMDT) recursively declusters a jet,
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| 48 | following the largest pT subjet until a pair of subjets is found that
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| 49 | satisfy the symmetry condition on the energy sharing
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| 50 |
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| 51 | z > z_cut
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| 52 |
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| 53 | where z_cut is a predetermined value. By default, z is calculated as
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| 54 | the scalar pT fraction of the softest subjet. Note that larger values
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| 55 | of z_cut correspond to a more restrictive tagging criteria.
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| 56 |
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| 57 | By default, mMDT will first recluster the jet using the CA clustering
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| 58 | algorithm, which means that mMDT can be called on any jet, regardless
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| 59 | of the original jet finding measure.
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| 60 |
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| 61 | A default mMDT can be created via
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| 62 |
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| 63 | double z_cut = 0.10;
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| 64 | ModifiedMassDropTagger mMDT(z_cut);
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| 65 |
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| 66 | More options are available in the full constructor. To apply mMDT,
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| 67 | one simply calls it on the jet of interest.
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| 68 |
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| 69 | PseudoJet tagged_jet = mMDT(original_jet);
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| 70 |
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| 71 | Note that mMDT is a tagger, such that tagged_jet will only be non-zero
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| 72 | if the symmetry cut z > z_cut is satisfied by some branching of the
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| 73 | clustering tree.
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| 74 |
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| 75 | To gain additional information about the mMDT procedure, one can use
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| 76 |
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| 77 | tagged_jet.structure_of<ModifiedMassDropTagger>()
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| 78 |
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| 79 | which gives access to information about the delta_R between the tagged
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| 80 | subjets, their z value, etc.
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| 81 |
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| 82 | ------------------------------------------------------------------------
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| 83 | SoftDrop
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| 84 | ------------------------------------------------------------------------
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| 85 |
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| 86 | The SoftDrop procedure is very similar to mMDT, albeit with a
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[b7b836a] | 87 | generalised symmetry condition:
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[1f1f858] | 88 |
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| 89 | z > z_cut * (R / R0)^beta
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| 90 |
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| 91 | Note that larger z_cut and smaller beta correspond to more aggressive
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| 92 | grooming of the jet.
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| 93 |
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| 94 | SoftDrop is intended to be used as a groomer (instead of as a tagger),
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| 95 | such that if the symmetry condition fails throughout the whole
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| 96 | clustering tree, SoftDrop will still return a single particle in the
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| 97 | end. Apart from the tagger/groomer distinction, SoftDrop with beta=0 is
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| 98 | the same as mMDT.
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| 99 |
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| 100 | A default SoftDrop groomer can be created via:
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| 101 |
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| 102 | double z_cut = 0.10;
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| 103 | double beta = 2.0;
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| 104 | double R0 = 1.0; // this is the default value
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| 105 | SoftDrop sd(z_cut,beta,R0);
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| 106 |
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| 107 | and acts on a desired jet as
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| 108 |
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| 109 | PseudoJet groomed_jet = sd(original_jet);
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| 110 |
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| 111 | and additional information can be obtained via
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| 112 |
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| 113 | groomed_jet.structure_of<SoftDrop>()
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| 114 |
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| 115 | SoftDrop is typically called with beta > 0, though beta < 0 is still a
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| 116 | viable option. Because beta < 0 is infrared-collinear unsafe in
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| 117 | grooming mode, one probably wants to switch to tagging mode for negative
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| 118 | beta, via set_tagging_mode().
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| 119 |
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[b7b836a] | 120 | ------------------------------------------------------------------------
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| 121 | RecursiveSoftDrop
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| 122 | ------------------------------------------------------------------------
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| 123 |
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| 124 | The RecursiveSoftDrop procedure applies the Soft Drop procedure N times
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| 125 | in a jet in order to find up to N+1 prongs. N=0 makes no modification
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| 126 | to the jet, and N=1 is equivalent to the original SoftDrop.
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| 127 |
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| 128 | Once one has more than one prong, one has to decide which will be
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| 129 | declustered next. At each step of the declustering procedure, one
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| 130 | undoes the clustering which has the largest declustering angle
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| 131 | (amongst all the branches that are searched for substructure). [see
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| 132 | "set_fixed_depth" below for an alternative]
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| 133 |
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| 134 | Compared to SoftDrop, RecursiveSoftDrop takes an extra argument N
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| 135 | specifying the number of times the SoftDrop procedure is recursively
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| 136 | applied. Negative N means that the procedure is applied until no
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| 137 | further substructure is found (i.e. corresponds to taking N=infinity).
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| 138 |
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| 139 | double z_cut = 0.10;
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| 140 | double beta = 2.0;
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| 141 | double R0 = 1.0; // this is the default value
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| 142 | int N = -1;
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| 143 | RecursiveSoftDrop rsd(z_cut, beta, N, R0);
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| 144 |
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| 145 | One then acts on a jet as
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| 146 |
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| 147 | PseudoJet groomed_jet = rsd(jet)
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| 148 |
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| 149 | and get additional information via
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| 150 |
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| 151 | groomed_jet.structure_of<RecursiveSoftDrop>()
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| 152 |
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| 153 | ------------------------------------------------------------------------
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| 154 | IteratedSoftDrop
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| 155 | ------------------------------------------------------------------------
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| 156 |
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| 157 | Iterated Soft Drop (ISD) is a repeated variant of SoftDrop. After
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| 158 | performing the Soft Drop procedure once, it logs the groomed symmetry
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| 159 | factor, then recursively performs Soft Drop again on the harder
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| 160 | branch. This procedure is repeated down to an (optional) angular cut
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| 161 | theta_cut, yielding a set of symmetry factors from which observables
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| 162 | can be built.
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| 163 |
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| 164 | An IteratedSoftDrop tool can be created as follows:
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| 165 |
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| 166 | double beta = -1.0;
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| 167 | double z_cut = 0.005;
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| 168 | double theta_cut = 0.0;
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| 169 | double R0 = 0.5; // characteristic radius of jet algorithm
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| 170 | IteratedSoftDrop isd(beta, z_cut, double theta_cut, R0);
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| 171 |
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| 172 | By default, ISD applied on a jet gives a result of type
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| 173 | IteratedSoftDropInfo that can then be probed to obtain physical
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| 174 | observables
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| 175 |
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| 176 | IteratedSoftDropInfo isd_info = isd(jet);
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| 177 |
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| 178 | unsigned int multiplicity = isd_info.multiplicity();
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| 179 | double kappa = 1.0; // changes angular scale of ISD angularity
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| 180 | double isd_width = isd_info.angularity(kappa);
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| 181 | vector<pair<double,double> > zg_thetags = isd_info.all_zg_thetag();
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| 182 | vector<pair<double,double> > zg_thetags = isd_info();
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| 183 | for (unsigned int i=0; i< isd_info.size(); ++i){
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| 184 | cout << "(zg, theta_g)_" << i << " = "
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| 185 | << isd_info[i].first << " " << isd_info[i].second << endl;
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| 186 | }
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| 187 |
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| 188 | Alternatively, one can directly get the multiplicity, angularity, and
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| 189 | (zg,thetag) pairs from the IteratedSoftDrop class, at the expense of
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| 190 | re-running the declustering procedure:
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| 191 |
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| 192 | unsigned int multiplicity = isd.multiplicity(jet);
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| 193 | double isd_width = isd.angularity(jet, 1.0);
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| 194 | vector<pair<double,double> > zg_thetags = isd.all_zg_thetag(jet);
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| 195 |
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| 196 |
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| 197 | Note: the iterative declustering procedure is the same as what one
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| 198 | would obtain with RecursiveSoftDrop with an (optional) angular cut
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| 199 | and recursing only in the hardest branch [see the "Changing
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| 200 | behaviour" section below for details], except that it returns some
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| 201 | information about the jet instead of a modified jet as RSD does.
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| 202 |
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| 203 |
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| 204 | ------------------------------------------------------------------------
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| 205 | BottomUpSoftDrop
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| 206 | ------------------------------------------------------------------------
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| 207 |
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| 208 | This is a bottom-up version of the RecursiveSoftDrop procedure, in a
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| 209 | similar way as Pruning can be seen as a bottom-up version of Trimming.
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| 210 |
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| 211 | In practice, the jet is reclustered and at each step of the clustering
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| 212 | one checks the SoftDrop condition
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| 213 |
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| 214 | z > z_cut * (R / R0)^beta
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| 215 |
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| 216 | If the condition is met, the pair is recombined. If the condition is
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| 217 | not met, only the hardest of the two objects is kept for further
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| 218 | clustering and the softest is rejected.
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| 219 |
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[1f1f858] | 220 | ------------------------------------------------------------------------
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| 221 | Recluster
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| 222 | ------------------------------------------------------------------------
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| 223 |
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[b7b836a] | 224 | *** NOTE: this is provided only for backwards compatibility ***
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| 225 | *** with FastJet <3.1. For FastJet >=3.1, the native ***
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| 226 | *** fastjet::Recluster is used instead ***
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| 227 |
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[1f1f858] | 228 | The Recluster class allows the constituents of a jet to be reclustered
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| 229 | with a different recursive clustering algorithm. This is used
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[b7b836a] | 230 | internally in the mMDT/SoftDrop/RecursiveSoftDrop/IteratedSoftDrop
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| 231 | code in order to recluster the jet using the CA algorithm. This is
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| 232 | achieved via
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[1f1f858] | 233 |
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| 234 | Recluster ca_reclusterer(cambridge_algorithm,
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| 235 | JetDefinition::max_allowable_R);
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| 236 | PseudoJet reclustered_jet = ca_reclusterer(original_jet);
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| 237 |
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| 238 | Note that reclustered_jet creates a new ClusterSequence that knows to
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| 239 | delete_self_when_unused.
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| 240 |
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| 241 | ------------------------------------------------------------------------
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| 242 | Changing behaviour
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| 243 | ------------------------------------------------------------------------
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| 244 |
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[b7b836a] | 245 | The behaviour of the all the tools provided here
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| 246 | (ModifiedMassDropTagger, SoftDrop, RecursiveSoftDrop and
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| 247 | IteratedSoftDrop) can be tweaked using the following options:
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[1f1f858] | 248 |
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[b7b836a] | 249 | SymmetryMeasure = {scalar_z, vector_z, y, theta_E, cos_theta_E}
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| 250 | [constructor argument]
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[1f1f858] | 251 | : The definition of the energy sharing between subjets, with 0
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[b7b836a] | 252 | corresponding to the most asymmetric.
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| 253 | . scalar_z = min(pt1,pt2)/(pt1+pt2) [default]
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| 254 | . vector_z = min(pt1,pt2)/pt_{1+2}
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| 255 | . y = min(pt1^2,pt2^2)/m_{12}^2 (original y from MDT)
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| 256 | . theta_E = min(E1,E2)/(E1+E2),
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| 257 | with angular measure theta_{12}^2
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| 258 | . cos_theta_E = min(E1,E2)/(E1+E2),
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| 259 | with angular measure 2[1-cos(theta_{12})]
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| 260 | The last two variants are meant for use in e+e- collisions,
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| 261 | together with the "larger_E" recursion choice (see below)
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| 262 |
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| 263 | RecursionChoice = {larger_pt, larger_mt, larger_m, larger_E}
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| 264 | [constructor argument]
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[1f1f858] | 265 | : The path to recurse through the tree after the symmetry condition
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[b7b836a] | 266 | fails. Options refer to transverse momentum (pt), transverse mass
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| 267 | (mt=sqrt(pt^2+m^2), mass (m) or energy (E). the latter is meant
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| 268 | for use in e+e- collisions
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[1f1f858] | 269 |
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| 270 | mu_cut [constructor argument]
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| 271 | : An optional mass drop condition
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| 272 |
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[b7b836a] | 273 | set_subtractor(subtractor*) [or subtractor as a constructor argument]
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[1f1f858] | 274 | : provide a subtractor. When a subtractor is supplied, the
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| 275 | kinematic constraints are applied on subtracted 4-vectors. In
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| 276 | this case, the result of the ModifiedMassDropTagger/SoftDrop is a
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| 277 | subtracted PseudoJet, and it is assumed that
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| 278 | ModifiedMassDropTagger/SoftDrop is applied to an unsubtracted jet.
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| 279 | The latter default can be changed by calling
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| 280 | set_input_jet_is_subtracted().
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| 281 |
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| 282 | set_reclustering(bool, Recluster*)
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| 283 | : An optional setting to recluster a jet with a different jet
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| 284 | recursive jet algorithm. The code is only designed to give sensible
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| 285 | results with the CA algorithm, but other reclustering algorithm
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| 286 | (especially kT) may be appropriate in certain contexts.
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| 287 | Use at your own risk.
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| 288 |
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| 289 | set_grooming_mode()/set_tagging_mode()
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| 290 | : In grooming mode, the algorithm will return a single particle if the
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| 291 | symmetry condition fails for the whole tree. In tagging mode, the
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| 292 | algorithm will return an zero PseudoJet if no symmetry conditions
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| 293 | passes. Note that ModifiedMassDropTagger defaults to tagging mode
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| 294 | and SoftDrop defaults to grooming mode.
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| 295 |
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[b7b836a] | 296 | set_verbose_structure(bool)
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| 297 | : when set to true, additional information will be stored in the jet
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| 298 | structure. This includes in particular values of symmetry,
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| 299 | delta_R, and mu of dropped branches
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| 300 |
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| 301 | For the specific case of RecursiveSoftDrop, additional tweaking is
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| 302 | possible via the following methods
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| 303 |
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| 304 | set_fixed_depth_mode(bool)
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| 305 | : when this is true, RSD will recurse (N times) into all the
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| 306 | branches found during the previous iteration [instead of recursing
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| 307 | through the largest declustering angle until N prongs have been
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| 308 | found]. This yields at most 2^N prong. For infinite N, the two
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| 309 | options are equivalent.
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| 310 |
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| 311 | set_dynamical_R0(bool)
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| 312 | : By default the angles in the SD condition are normalised to the
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| 313 | parameter R0. With "dynamical R0", RSD will dynamically adjust R0
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| 314 | to be the angle between the two prongs found during the previous
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| 315 | iteration.
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| 316 |
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| 317 | set_hardest_branch_only(bool)
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| 318 | : When substructure is found, only recurse into the hardest of the
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| 319 | two branches for further substructure search. This uses the class
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| 320 | RecursionChoice.
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| 321 |
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| 322 | set_min_deltaR_squared(double):
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| 323 | : set a minimal angle (squared) at which we stop the declustering
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| 324 | procedure. This cut is ineffective for negative values of the
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| 325 | argument.
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| 326 |
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[1f1f858] | 327 | ------------------------------------------------------------------------
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| 328 | Technical Details
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| 329 | ------------------------------------------------------------------------
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| 330 |
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| 331 | Both ModifiedMassDropTagger and SoftDrop inherit from
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| 332 | RecursiveSymmetryCutBase, which provides a common codebase for recursive
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| 333 | declustering of a jet with a symmetry cut condition. A generic
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| 334 | RecursiveSymmetryCutBase depends on the following (virtual) functions
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| 335 | (see header file for exact full specs, including constness):
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| 336 |
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| 337 | double symmetry_cut_fn(PseudoJet &, PseudoJet &)
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| 338 | : The function that defines the symmetry cut. This is what actually
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| 339 | defines different recursive declustering schemes, and all classes
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| 340 | that inherit from RecursiveSymmetryCutBase must define this
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| 341 | function.
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| 342 |
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| 343 | string symmetry_cut_description()
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| 344 | : the string description of the symmetry cut.
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| 345 |
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| 346 | ------------------------------------------------------------------------
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