Rivet analyses referenceCDF_2004_I647490Transverse cone and `Swiss cheese' underlying event studiesExperiment: CDF (Tevatron Run 1) Inspire ID: 647490 Status: VALIDATED Authors:
Beam energies: (315.0, 315.0); (900.0, 900.0) GeV Run details:
This analysis studies the underlying event via transverse cones of $R = 0.7$ at 90 degrees in \phi relative to the leading (highest E) jet, at $\sqrt{s} = 630$ and 1800 GeV. This is similar to the 2001 CDF UE analysis, except that cones, rather than the whole central \eta range are used. The transverse cones are categorised as `TransMIN' and `TransMAX' on an event-by-event basis, to give greater sensitivity to the UE component. `Swiss Cheese' distributions, where cones around the leading $n$ jets are excluded from the distributions, are also included for $n = 2, 3$. This analysis is useful for constraining the energy evolution of the underlying event, since it performs the same analyses at two distinct CoM energies. WARNING! The pT plots are normalised to raw number of events. The min bias data have not been reproduced by MC, and are not recommended for tuning. Beam energy must be specified (in GeV) as analysis option "ENERGY" when rivet-merging samples. Source code: CDF_2004_I647490.cc 1// -*- C++ -*-
2// "Acosta" underlying event analysis at CDF, inc. "Swiss Cheese"
3
4#include "Rivet/Analysis.hh"
5#include "Rivet/Jet.hh"
6#include "Rivet/Projections/ChargedFinalState.hh"
7#include "Rivet/Projections/FastJets.hh"
8#include "Rivet/Projections/TriggerCDFRun0Run1.hh"
9
10namespace Rivet {
11
12
13 /// @brief CDF calo jet underlying event analysis at 630 and 1800 GeV
14 ///
15 /// CDF measurement of underlying event using calorimeter jet scales and
16 /// alignment, particle flow activity in transverse cones, and the Swiss
17 /// Cheese analysis method, where cones are excluded around the 2 and 3
18 /// hardest jets.
19 ///
20 /// @author Andy Buckley
21 class CDF_2004_I647490 : public Analysis {
22 public:
23
24 RIVET_DEFAULT_ANALYSIS_CTOR(CDF_2004_I647490);
25
26
27 /// @name Analysis methods
28 /// @{
29
30 void init() {
31 // Set up projections
32 declare(TriggerCDFRun0Run1(), "Trigger");
33 const FinalState calofs(Cuts::abseta < 1.2);
34 declare(calofs, "CaloFS");
35 declare(FastJets(calofs, JetAlg::CDFJETCLU, 0.7), "Jets");
36 const ChargedFinalState trackfs(Cuts::abseta < 1.2 && Cuts::pT >= 0.4*GeV);
37 declare(trackfs, "TrackFS");
38 // Restrict tracks to |eta| < 0.7 for the min bias part.
39 const ChargedFinalState mbfs(Cuts::abseta < 0.7 && Cuts::pT >= 0.4*GeV);
40 declare(mbfs, "MBFS");
41 // Restrict tracks to |eta| < 1 for the Swiss-Cheese part.
42 const ChargedFinalState cheesefs(Cuts::abseta < 1.0 && Cuts::pT >= 0.4*GeV);
43 declare(cheesefs, "CheeseFS");
44 declare(FastJets(cheesefs, JetAlg::CDFJETCLU, 0.7), "CheeseJets");
45
46 // Book histograms
47 if (isCompatibleWithSqrtS(1800*GeV)) {
48 book(_pt90MaxAvg1800 ,1, 1, 1);
49 book(_pt90MinAvg1800 ,1, 1, 2);
50 book(_pt90Max1800 ,2, 1, 1);
51 book(_pt90Min1800 ,2, 1, 2);
52 book(_pt90Diff1800 ,2, 1, 3);
53 book(_num90Max1800 ,4, 1, 1);
54 book(_num90Min1800 ,4, 1, 2);
55 book(_pTSum1800_2Jet ,7, 1, 1);
56 book(_pTSum1800_3Jet ,7, 1, 2);
57
58 book(_pt90Dbn1800Et40 ,3, 1, 1);
59 book(_pt90Dbn1800Et80 ,3, 1, 2);
60 book(_pt90Dbn1800Et120 ,3, 1, 3);
61 book(_pt90Dbn1800Et160 ,3, 1, 4);
62 book(_pt90Dbn1800Et200 ,3, 1, 5);
63 book(_numTracksDbn1800MB ,5, 1, 1);
64 book(_ptDbn1800MB ,6, 1, 1);
65 } else if (isCompatibleWithSqrtS(630*GeV)) {
66 book(_pt90Max630 ,8, 1, 1);
67 book(_pt90Min630 ,8, 1, 2);
68 book(_pt90Diff630 ,8, 1, 3);
69 book(_pTSum630_2Jet ,9, 1, 1);
70 book(_pTSum630_3Jet ,9, 1, 2);
71
72 book(_numTracksDbn630MB ,10, 1, 1);
73 book(_ptDbn630MB ,11, 1, 1);
74 }
75 }
76
77
78 /// Do the analysis
79 void analyze(const Event& event) {
80 // Trigger
81 const bool trigger = apply<TriggerCDFRun0Run1>(event, "Trigger").minBiasDecision();
82 if (!trigger) vetoEvent;
83
84 {
85 MSG_DEBUG("Running max/min analysis");
86 Jets jets = apply<JetFinder>(event, "Jets").jets(cmpMomByE);
87 if (!jets.empty()) {
88 // Leading jet must be in central |eta| < 0.5 region
89 const Jet leadingjet = jets.front();
90 const double etaLead = leadingjet.eta();
91 // Get Et of the leading jet: used to bin histograms
92 const double ETlead = leadingjet.Et();
93 MSG_DEBUG("Leading Et = " << ETlead/GeV << " GeV");
94 if (fabs(etaLead) > 0.5 && ETlead < 15*GeV) {
95 MSG_DEBUG("Leading jet eta = " << etaLead
96 << " not in |eta| < 0.5 & pT > 15 GeV");
97 } else {
98 // Multiplicity & pT distributions for sqrt(s) = 630 GeV, 1800 GeV
99 const Particles tracks = apply<FinalState>(event, "TrackFS").particles();
100 const ConesInfo cones = _calcTransCones(leadingjet.momentum(), tracks);
101 if (isCompatibleWithSqrtS(630*GeV)) {
102 _pt90Max630->fill(ETlead/GeV, cones.ptMax/GeV);
103 _pt90Min630->fill(ETlead/GeV, cones.ptMin/GeV);
104 _pt90Diff630->fill(ETlead/GeV, cones.ptDiff/GeV);
105 } else if (isCompatibleWithSqrtS(1800*GeV)) {
106 _num90Max1800->fill(ETlead/GeV, cones.numMax);
107 _num90Min1800->fill(ETlead/GeV, cones.numMin);
108 _pt90Max1800->fill(ETlead/GeV, cones.ptMax/GeV);
109 _pt90Min1800->fill(ETlead/GeV, cones.ptMin/GeV);
110 _pt90Diff1800->fill(ETlead/GeV, cones.ptDiff/GeV);
111 _pt90MaxAvg1800->fill(ETlead/GeV, cones.ptMax/GeV); // /numMax
112 _pt90MinAvg1800->fill(ETlead/GeV, cones.ptMin/GeV); // /numMin
113 //
114 const double ptTransTotal = cones.ptMax + cones.ptMin;
115 if (inRange(ETlead/GeV, 40., 80.)) {
116 _pt90Dbn1800Et40->fill(ptTransTotal/GeV);
117 } else if (inRange(ETlead/GeV, 80., 120.)) {
118 _pt90Dbn1800Et80->fill(ptTransTotal/GeV);
119 } else if (inRange(ETlead/GeV, 120., 160.)) {
120 _pt90Dbn1800Et120->fill(ptTransTotal/GeV);
121 } else if (inRange(ETlead/GeV, 160., 200.)) {
122 _pt90Dbn1800Et160->fill(ptTransTotal/GeV);
123 } else if (inRange(ETlead/GeV, 200., 270.)) {
124 _pt90Dbn1800Et200->fill(ptTransTotal/GeV);
125 }
126 }
127
128 }
129 }
130 }
131
132
133 // Fill min bias total track multiplicity histos
134 {
135 MSG_DEBUG("Running min bias multiplicity analysis");
136 const Particles mbtracks = apply<FinalState>(event, "MBFS").particles();
137 if (isCompatibleWithSqrtS(1800*GeV)) {
138 _numTracksDbn1800MB->fill(mbtracks.size());
139 } else if (isCompatibleWithSqrtS(630*GeV)) {
140 _numTracksDbn630MB->fill(mbtracks.size());
141 }
142 // Run over all charged tracks
143 for (const Particle& t : mbtracks) {
144 FourMomentum trackMom = t.momentum();
145 const double pt = trackMom.pT();
146 // Plot total pT distribution for min bias
147 if (isCompatibleWithSqrtS(1800*GeV)) {
148 _ptDbn1800MB->fill(pt/GeV);
149 } else if (isCompatibleWithSqrtS(630*GeV)) {
150 _ptDbn630MB->fill(pt/GeV);
151 }
152 }
153 }
154
155
156
157 // Construct "Swiss Cheese" pT distributions, with pT contributions from
158 // tracks within R = 0.7 of the 1st, 2nd (and 3rd) jets being ignored. A
159 // different set of charged tracks, with |eta| < 1.0, is used here, and all
160 // the removed jets must have Et > 5 GeV.
161 {
162 MSG_DEBUG("Running Swiss Cheese analysis");
163 const Particles cheesetracks = apply<FinalState>(event, "CheeseFS").particles();
164 Jets cheesejets = apply<JetFinder>(event, "Jets").jets(cmpMomByE);
165 if (cheesejets.empty()) {
166 MSG_DEBUG("No 'cheese' jets found in event");
167 return;
168 }
169 if (cheesejets.size() > 1 &&
170 fabs(cheesejets[0].eta()) <= 0.5 &&
171 cheesejets[0].Et()/GeV > 5.0 &&
172 cheesejets[1].Et()/GeV > 5.0) {
173
174 const double cheeseETlead = cheesejets[0].Et();
175
176 const double eta1 = cheesejets[0].eta();
177 const double phi1 = cheesejets[0].phi();
178 const double eta2 = cheesejets[1].eta();
179 const double phi2 = cheesejets[1].phi();
180
181 double ptSumSub2(0), ptSumSub3(0);
182 for (const Particle& t : cheesetracks) {
183 FourMomentum trackMom = t.momentum();
184 const double pt = trackMom.pT();
185
186 // Subtracting 2 leading jets
187 const double deltaR1 = deltaR(trackMom, eta1, phi1);
188 const double deltaR2 = deltaR(trackMom, eta2, phi2);
189 MSG_TRACE("Track vs jet(1): "
190 << "|(" << trackMom.eta() << ", " << trackMom.phi() << ") - "
191 << "|(" << eta1 << ", " << phi1 << ")| = " << deltaR1);
192 MSG_TRACE("Track vs jet(2): "
193 << "|(" << trackMom.eta() << ", " << trackMom.phi() << ") - "
194 << "|(" << eta2 << ", " << phi2 << ")| = " << deltaR2);
195 if (deltaR1 > 0.7 && deltaR2 > 0.7) {
196 ptSumSub2 += pt;
197
198 // Subtracting 3rd leading jet
199 if (cheesejets.size() > 2 &&
200 cheesejets[2].Et()/GeV > 5.0) {
201 const double eta3 = cheesejets[2].eta();
202 const double phi3 = cheesejets[2].phi();
203 const double deltaR3 = deltaR(trackMom, eta3, phi3);
204 MSG_TRACE("Track vs jet(3): "
205 << "|(" << trackMom.eta() << ", " << trackMom.phi() << ") - "
206 << "|(" << eta3 << ", " << phi3 << ")| = " << deltaR3);
207 if (deltaR3 > 0.7) {
208 ptSumSub3 += pt;
209 }
210 }
211 }
212 }
213
214 // Swiss Cheese sub 2,3 jets distributions for sqrt(s) = 630 GeV, 1800 GeV
215 if (isCompatibleWithSqrtS(630*GeV)) {
216 if (!isZero(ptSumSub2)) _pTSum630_2Jet->fill(cheeseETlead/GeV, ptSumSub2/GeV);
217 if (!isZero(ptSumSub3))_pTSum630_3Jet->fill(cheeseETlead/GeV, ptSumSub3/GeV);
218 } else if (isCompatibleWithSqrtS(1800*GeV)) {
219 if (!isZero(ptSumSub2))_pTSum1800_2Jet->fill(cheeseETlead/GeV, ptSumSub2/GeV);
220 if (!isZero(ptSumSub3))_pTSum1800_3Jet->fill(cheeseETlead/GeV, ptSumSub3/GeV);
221 }
222
223 }
224 }
225
226 }
227
228
229 void finalize() {
230 /// @todo Take these normalisations from the data histo (it can't come from just the MC)
231
232 if (isCompatibleWithSqrtS(1800*GeV)) {
233 // Normalize to actual number of entries in pT dbn histos...
234 normalize(_pt90Dbn1800Et40, 1656.75); // norm OK
235 normalize(_pt90Dbn1800Et80, 4657.5); // norm OK
236 normalize(_pt90Dbn1800Et120, 5395.5); // norm OK
237 normalize(_pt90Dbn1800Et160, 7248.75); // norm OK
238 normalize(_pt90Dbn1800Et200, 2442.0); // norm OK
239 }
240
241 // ...and for min bias distributions:
242 if (isCompatibleWithSqrtS(1800*GeV)) {
243 normalize(_numTracksDbn1800MB, 309718.25); // norm OK
244 normalize(_ptDbn1800MB, 33600.0); // norm OK
245 } else if (isCompatibleWithSqrtS(630*GeV)) {
246 normalize(_numTracksDbn630MB, 1101024.0); // norm OK
247 normalize(_ptDbn630MB, 105088.0); // norm OK
248 }
249 }
250
251 /// @}
252
253
254 private:
255
256
257 /// @name Cone machinery
258 /// @{
259
260 /// @cond CONEUE_DETAIL
261
262 struct ConesInfo {
263 ConesInfo() : numMax(0), numMin(0), ptMax(0), ptMin(0), ptDiff(0) {}
264 unsigned int numMax, numMin;
265 double ptMax, ptMin, ptDiff;
266 };
267
268 /// @endcond
269
270
271 ConesInfo _calcTransCones(const double etaLead, const double phiLead,
272 const Particles& tracks) {
273 const double phiTransPlus = mapAngle0To2Pi(phiLead + PI/2.0);
274 const double phiTransMinus = mapAngle0To2Pi(phiLead - PI/2.0);
275 MSG_DEBUG("phi_lead = " << phiLead
276 << " -> trans = (" << phiTransPlus
277 << ", " << phiTransMinus << ")");
278
279 unsigned int numPlus(0), numMinus(0);
280 double ptPlus(0), ptMinus(0);
281 // Run over all charged tracks
282 for (const Particle& t : tracks) {
283 FourMomentum trackMom = t.momentum();
284 const double pt = trackMom.pT();
285
286 // Find if track mom is in either transverse cone
287 if (deltaR(trackMom, etaLead, phiTransPlus) < 0.7) {
288 ptPlus += pt;
289 numPlus += 1;
290 } else if (deltaR(trackMom, etaLead, phiTransMinus) < 0.7) {
291 ptMinus += pt;
292 numMinus += 1;
293 }
294 }
295
296 ConesInfo rtn;
297 // Assign N_{min,max} from N_{plus,minus}
298 rtn.numMax = (ptPlus >= ptMinus) ? numPlus : numMinus;
299 rtn.numMin = (ptPlus >= ptMinus) ? numMinus : numPlus;
300 // Assign pT_{min,max} from pT_{plus,minus}
301 rtn.ptMax = (ptPlus >= ptMinus) ? ptPlus : ptMinus;
302 rtn.ptMin = (ptPlus >= ptMinus) ? ptMinus : ptPlus;
303 rtn.ptDiff = fabs(rtn.ptMax - rtn.ptMin);
304
305 MSG_DEBUG("Min cone has " << rtn.numMin << " tracks -> "
306 << "pT_min = " << rtn.ptMin/GeV << " GeV");
307 MSG_DEBUG("Max cone has " << rtn.numMax << " tracks -> "
308 << "pT_max = " << rtn.ptMax/GeV << " GeV");
309
310 return rtn;
311 }
312
313
314 ConesInfo _calcTransCones(const FourMomentum& leadvec,
315 const Particles& tracks) {
316 const double etaLead = leadvec.eta();
317 const double phiLead = leadvec.phi();
318 return _calcTransCones(etaLead, phiLead, tracks);
319 }
320
321 /// @}
322
323
324 /// @name Histogram collections
325 /// @{
326
327 /// Profile histograms, binned in the \f$ E_T \f$ of the leading jet, for
328 /// the average \f$ p_T \f$ in the toward, transverse and away regions at
329 /// \f$ \sqrt{s} = 1800 \text{GeV} \f$.
330 /// Corresponds to Table 1, and HepData table 1.
331 Profile1DPtr _pt90MaxAvg1800, _pt90MinAvg1800;
332
333 /// Profile histograms, binned in the \f$ E_T \f$ of the leading jet, for
334 /// the \f$ p_T \f$ sum in the toward, transverse and away regions at
335 /// \f$ \sqrt{s} = 1800 \text{GeV} \f$.
336 /// Corresponds to figure 2/3, and HepData table 2.
337 Profile1DPtr _pt90Max1800, _pt90Min1800, _pt90Diff1800;
338
339 /// Profile histograms, binned in the \f$ E_T \f$ of the leading jet, for
340 /// the \f$ p_T \f$ sum in the toward, transverse and away regions at
341 /// at \f$ \sqrt{s} = 630 \text{GeV} \f$.
342 /// Corresponds to figure 8, and HepData table 8.
343 Profile1DPtr _pt90Max630, _pt90Min630, _pt90Diff630;
344
345 /// Profile histograms, binned in the \f$ E_T \f$ of the leading jet, for
346 /// the cone track multiplicity at \f$ \sqrt{s} = 1800 \text{GeV} \f$.
347 /// Corresponds to figure 5, and HepData table 4.
348 Profile1DPtr _num90Max1800, _num90Min1800;
349
350 /// Profile histograms, binned in the \f$ E_T \f$ of the leading jet, for
351 /// the \f$ p_T \f$ sum at \f$ \sqrt{s} = 1800 \text{GeV} \f$.
352 /// Corresponds to figure 7, and HepData table 7.
353 Profile1DPtr _pTSum1800_2Jet, _pTSum1800_3Jet;
354
355 /// Profile histograms, binned in the \f$ E_T \f$ of the leading jet, for
356 /// the \f$ p_T \f$ sum at \f$ \sqrt{s} = 630 \text{GeV} \f$.
357 /// Corresponds to figure 9, and HepData table 9.
358 Profile1DPtr _pTSum630_2Jet, _pTSum630_3Jet;
359
360 /// Histogram of \f$ p_{T\text{sum}} \f$ distribution for 5 different
361 /// \f$ E_{T1} \f$ bins.
362 /// Corresponds to figure 4, and HepData table 3.
363 Histo1DPtr _pt90Dbn1800Et40, _pt90Dbn1800Et80, _pt90Dbn1800Et120,
364 _pt90Dbn1800Et160, _pt90Dbn1800Et200;
365
366 /// Histograms of track multiplicity and \f$ p_T \f$ distributions for
367 /// minimum bias events.
368 /// Figure 6, and HepData tables 5 & 6.
369 /// Figure 10, and HepData tables 10 & 11.
370 Histo1DPtr _numTracksDbn1800MB, _ptDbn1800MB;
371 Histo1DPtr _numTracksDbn630MB, _ptDbn630MB;
372
373 /// @}
374
375 };
376
377
378
379 RIVET_DECLARE_ALIASED_PLUGIN(CDF_2004_I647490, CDF_2004_S5839831);
380
381}
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