Annotation of ttbar/p20_taujets_note/Tools.tex, revision 1.1.1.1
1.1 uid12904 1:
2: \section{Tools}
3:
4:
5: \subsection{Object ID}
6:
7:
8: \subsubsection{\label{sub:tau--ID}$\tau$ ID}
9:
10:
11: \paragraph{Tau decay modes}
12:
13: The $\tau$ lepton have several decay channels, classified by the
14: number of charged particles (tracks) associated with it \cite{PDG}
15: :
16:
17: \begin{itemize}
18: \item electron + muon ($\tau\rightarrow e\nu_{e}\nu_{\tau}$ or $\tau\rightarrow\mu\nu_{\mu}\nu_{\tau})$,
19: BR = 35\%
20: \item charged hadron ($\tau\rightarrow\pi^{-}\nu_{\tau}$), BR =12\%
21: \item charged hadron + $\geq1$ neutral particle (i.e. $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow\pi^{0}n+\pi^{-}\nu_{\tau}$)
22: , BR = 38\%
23: \item 3 charged hadrons + $\geq0$ neutral hadrons, BR = 15\% (so-called
24: {}``3-prong'' decays)
25: \end{itemize}
26:
27: \paragraph{Tau ID variables}
28:
29: At D0 $\tau$s are identified in their hadronic modes (contributing
30: to inefficiency of id) as narrow (0.3 cone) jets,isolated and matched
31: to a charged track. The (most important) discriminating variables
32: are \cite{tau ID}:
33:
34: \begin{itemize}
35: \item Profile - $\frac{E_{T}^{1}+E_{T}^{0}}{\sum_{i}E_{T}^{i}}$, where
36: $E_{T}^{i}$ is the $E_{T}$ of the ith highest $E_{T}$ tower in
37: the cluster
38: \item Isolation, defined as $\frac{E(0.5)-E(0.3)}{E(0.3)}$, where $E(R)$
39: is the energy contained in a radius of R around cal cluster centroid
40: \item Track isolation, defined as $\sum p_{T}$ of non-$\tau$ tracks in
41: cone of 0.5 around the calorimeter cluster centroid
42: \end{itemize}
43: Using these and other variables, 2 Neural Networks are trained to
44: identify 3 types of $\tau$ ($\pi$-type, $\rho$-type and 3-prong)
45:
46: The output of these NN provides a set of 3 variables (nnout 1... 3)
47: to be used to select $\tau$ in the event. The high values of NN have
48: to correspond to the physical $\tau$ leptons, while the low ones
49: should indicate fakes. For more details, see \cite{tau ID}.
50:
51:
52: \paragraph{Energy Scale}
53:
54: In \cite{ingo1} the process $Z\rightarrow\tau\tau$ had been studied.
55: In particular the Figure 4 of that note demonstrates an excellent
56: agreement between the data and $Z\rightarrow\tau\tau$ MC in distribution
57: of the invariant mass of the $\tau$ pair. Figure 15 of \cite{ingo2}
58: shows other important properties ($P_{T}$ of $\tau$ and $\not E_{T}$)
59: which also agree very well. Since no energy correction had been applied
60: to the $\tau$ in this work, one can conclude that we can take the
61: energy scale of $\tau$ ID to be 1.
62:
63:
64: \paragraph{Performance }
65:
66: The $\tau$ NN had been trained and optimized for the low jet multiplicity
67: events (i.e. $Z\rightarrow\tau\tau$). We wanted to compare its performance
68: for the high multiplicity signal (top) that we are searching for here.
69:
70: In order to evaluate the ID efficiency reliably one has to match the
71: reconstructed $\tau$ candidate with the true $\tau$ from MC. We
72: start with all the $\tau$ candidates in an event, regardless of the
73: quality. We then want to select those that can be with high confidence
74: correspond to the real $\tau$ leptons. The assumption is that those
75: $\tau$ candidates, whose energy and direction matched to a physical
76: $\tau$ are indeed representing the detector signature of this particle.
77: We can then determine how well does the $\tau$ ID identify this $\tau$
78: lepton. Figure \ref{cap:Matching-of-MC} illustrates the matching
79: procedure - the $\tau$ candidates with $\Delta R$ from a real MC
80: $\tau$ of 0.05 and $\Delta P$ of 10 GeV are deemed to be the {}``real''
81: matched $\tau$
82:
83: For such $\tau$ we plotted the NN for different $\tau$ types (Fig
84: \ref{cap:NN-for-matched}). From these one can determine the efficiency
85: of $\tau$ ID for various cuts on NN (Fig \ref{tauID}).
86:
87: %
88: \begin{figure}
89: \subfigure[$\Delta R$ between reco $\tau$ and MC $\tau$]{\includegraphics[scale=0.3]{plots_for_talk/drmin}}\subfigure[$\Delta R$ between reco $\tau$ and MC $\tau$ (low values)]{\includegraphics[scale=0.3]{plots_for_talk/drmin_zoomed}}
90:
91: \subfigure[Difference in energy between reco and MC $\tau$]{\includegraphics[scale=0.3]{plots_for_talk/dpmin}}\subfigure[Difference in energy between MC and reco $\tau$ that were matched in angle]{\includegraphics[scale=0.3]{plots_for_talk/dpmin005}}
92:
93:
94: \caption{Matching of MC $\tau$ and reco $\tau$. Black is $Z\rightarrow\tau\tau$,
95: red is $t\overline{t}\rightarrow\tau+jets$The histograms are normalized
96: to 1 to enable comparision.}
97:
98: \label{cap:Matching-of-MC}
99: \end{figure}
100:
101:
102: %
103: \begin{figure}
104: \subfigure[NN for ALL types]{\includegraphics[scale=0.3]{plots_for_talk/nnmatched}}\subfigure[NN for type 1]{\includegraphics[scale=0.3]{plots_for_talk/nn1matched}}
105:
106: \subfigure[NN type 2]{\includegraphics[scale=0.3]{plots_for_talk/nn2matched}}\subfigure[NN for type 3]{\includegraphics[scale=0.3]{plots_for_talk/nn3matched}}
107:
108:
109: \caption{NN for matched $\tau$. Black is $Z\rightarrow\tau\tau$, red is
110: $t\overline{t}\rightarrow\tau+jets$. The histograms are normalized
111: to 1 to enable comparision.}
112:
113: \label{cap:NN-for-matched}
114: \end{figure}
115:
116:
117: %
118: \begin{figure}
119: \subfigure[ALL types]{\includegraphics[scale=0.3]{plots_for_talk/eff0}}\subfigure[Type 1]{\includegraphics[scale=0.3]{plots_for_talk/eff1}}
120:
121: \subfigure[Type 2]{\includegraphics[scale=0.3]{plots_for_talk/eff2}}\subfigure[Type 3]{\includegraphics[scale=0.3]{plots_for_talk/eff3}}
122:
123:
124: \caption{$\tau$ ID Efficiencies for different types. Black is $Z\rightarrow\tau\tau$,
125: red is $t\overline{t}\rightarrow\tau+jets$}
126:
127: \label{tauID}
128: \end{figure}
129:
130:
131: In order to choose the best cut on $\tau$ NN one has to also consider
132: the fake rate (the number of fake $\tau$ candidates passing the ID
133: requirements successfully). For this purpose we had examined the $\tau$
134: candidates in the preselected ALLJET data sample (the details of preselection
135: are described in section \ref{sub:Preselection}). Since this dataset
136: is QCD dominated (no more then 0.2\% of electroweak is expected) we
137: can safely assume all $\tau$ in it to be fake (this assumption will
138: be employed again for our QCD background estimation in section \ref{sub:QCD-modeling}).
139: Figure \ref{cap:NN-for-fake} shows the distribution of NN for the
140: $\tau$. From this we can determine the fake rate dependence on NN
141: cut (Fig \ref{tauID_Fake}). We can note that type 3 has noticeably
142: higher fake rate. This is to be expected, since most jets have higher
143: track multiplicities than type 1 and 2 $\tau$ making it harder for
144: them to pass $\tau$ ID requirements.
145:
146: %
147: \begin{figure}
148: \subfigure[NN for ALL types]{\includegraphics[scale=0.3]{plots/NN0fakeNN}}\subfigure[NN for type 1]{\includegraphics[scale=0.3]{plots/NN1akeNN}}
149:
150: \subfigure[NN type 2]{\includegraphics[scale=0.3]{plots/NN2akeNN}}\subfigure[NN for type 3]{\includegraphics[scale=0.3]{plots/NN3akeNN}}
151:
152:
153: \caption{NN for fake $\tau$}
154:
155: \label{cap:NN-for-fake}
156: \end{figure}
157:
158:
159: %
160: \begin{figure}
161: \subfigure[ALL types]{\includegraphics[scale=0.3]{plots/NN0fake}}\subfigure[Type 1]{\includegraphics[scale=0.3]{plots/NN1fake}}
162:
163: \subfigure[Type 2]{\includegraphics[scale=0.3]{plots/NN2fake}}\subfigure[Type 3]{\includegraphics[scale=0.3]{plots/NN3fake}}
164:
165:
166: \caption{$\tau$ ID Fake Rate for different types}
167:
168: \label{tauID_Fake}
169: \end{figure}
170:
171:
172: On Fig \ref{tauID_Fake_Eff} we plot the fake rate vs. efficiency
173: of the $\tau$ ID for our channel. From this we can select the optimal
174: selection cut on $\tau$ NN, based on the $\tau$ID significance,
175: defined as $\frac{Number\, of\, real\, taus}{\sqrt{Number\, of\, real+Number\, fakes}}$
176: (Fig \ref{tauID_Fake_signif}). It is computed on our preselected
177: analysis data set (section \ref{sub:Preselection})
178:
179: We can conclude that D0 $\tau$ ID algorithm has efficiency for $t\bar{t}$
180: comparable with $Z\rightarrow\tau\tau$. The optimal cut on $\tau$
181: NN appears to be 0.95 for all the types.
182:
183: %
184: \begin{figure}
185: \subfigure[ALL types]{\includegraphics[scale=0.3]{plots/NN0fake_eff}}\subfigure[Type 1]{\includegraphics[scale=0.3]{plots/NN1fake_eff}}
186:
187: \subfigure[Type 2]{\includegraphics[scale=0.3]{plots/NN2fake_eff}}\subfigure[Type 3]{\includegraphics[scale=0.3]{plots/NN3fake_eff}}
188:
189:
190: \caption{$\tau$ ID Efficiency vs. the Fake rate. Type 2 is the cleanest,
191: type 3 has highest fake rate, as expected}
192:
193: \label{tauID_Fake_Eff}
194: \end{figure}
195:
196:
197: %
198: \begin{figure}
199: \subfigure[ALL types]{\includegraphics[scale=0.3]{plots/NN0fake_signif}}\subfigure[Type 1]{\includegraphics[scale=0.3]{plots/NN1fake_signif}}
200:
201: \subfigure[Type 2]{\includegraphics[scale=0.3]{plots/NN2fake_signif}}\subfigure[Type 3]{\includegraphics[scale=0.3]{plots/NN3fake_signif}}
202:
203:
204: \caption{$\tau$ ID Significance vs. the NN cut. The 0.95 cut appears to be
205: advantageous for all the types}
206:
207: \label{tauID_Fake_signif}
208: \end{figure}
209:
210:
211:
212: \subsubsection{\label{sub:B-tagging}B-tagging}
213:
214: The chosen b-tagging algorithm is Secondary Vertex Tagger (SVT) \cite{b-ID}.
215: It is characterized by high (compared to other taggers) purity, which
216: is essential for such QCD-dominated channel as ours.
217:
218: The algorithm reconstructs secondary vertices inside a jet, using
219: the jet's associated tracks. The tracks are also required to pass
220: a set of cuts outlined in Table \ref{cap:The-standard-cuts}. Then,
221: the decay length significance is computed. If the jet has this significance
222: grater then 7 (for SVT TIGHT) it is considered b-tagged.
223:
224: As can be seen from Figures \ref{cap:SVT-Signifficance} and \ref{cap:SVT-Signifficance_data},
225: the TIGHT cut is most appropriate for our signal.
226:
227: %
228: \begin{table}
229: \begin{tabular}{|l|l|l|l|}
230: \hline
231: {\large SVT}&
232: &
233: &
234: \tabularnewline
235: \hline
236: &
237: LOOSE &
238: MEDIUM &
239: TIGHT\tabularnewline
240: \hline
241: Number of SMT hits &
242: 2 &
243: 2 &
244: 2\tabularnewline
245: \hline
246: $P_{T}$ of tracks &
247: 1 GeV/c &
248: 1 GeV/c &
249: 1 GeV/c\tabularnewline
250: \hline
251: impact parameter significance of tracks &
252: 3 &
253: 3.5 &
254: 3.5\tabularnewline
255: \hline
256: track $\chi^{2}$&
257: 10 &
258: 10 &
259: 10\tabularnewline
260: \hline
261: max vertex $\chi^{2}$&
262: 100 &
263: 100 &
264: 100\tabularnewline
265: \hline
266: vertex collinearity &
267: 0.9 &
268: 0.9 &
269: 0.9\tabularnewline
270: \hline
271: max vertex decay length &
272: 2.6cm &
273: 2.6cm &
274: 2.6cm\tabularnewline
275: \hline
276: Decay Length Significance Cut &
277: 5 &
278: 6 &
279: 7\tabularnewline
280: \hline
281: \end{tabular}
282:
283:
284: \caption{The standard cuts on SVT \cite{b-ID}}
285:
286: \label{cap:The-standard-cuts}
287: \end{table}
288:
289:
290: %
291: \begin{figure}
292: \includegraphics[scale=0.5]{MS_thesis/proposal_plots/svt}
293:
294:
295: \caption{SVT Decay Length Significance for the b-jets in $t\overline{t}\rightarrow\mu+jets$
296: MC}
297:
298: \label{cap:SVT-Signifficance}
299: \end{figure}
300:
301:
302: %
303: \begin{figure}
304: \subfigure[SVT significance across the entire range]{\includegraphics[scale=0.3]{plots/svt_data}}\subfigure[SVT significance at values near 0]{\includegraphics[scale=0.3]{plots/svt_data_zoomed}}
305:
306:
307: \caption{SVT Decay Length Significance for all the jets in the ALLJET skim
308: data.}
309:
310: \label{cap:SVT-Signifficance_data}
311: \end{figure}
312:
313:
314:
315: \paragraph{Taggability}
316:
317: In order to reconstruct a secondary vertex in a jet, the jet must
318: contain at least 2 tracks. If such tracks are found and their $P_{T}$
319: is greater then 0.5 GeV the jet is called taggable. In MC it is important
320: to distinguish the taggability from the tagging efficiency, since
321: the later depends on the jet's flavor.
322:
323:
324: \paragraph{B-tagging efficiency}
325:
326: It is known that b-tagging applied directly to MC gives an overestimated
327: efficiency. In order to account for this factor SVT had been parameterized
328: on $t\overline{t}\rightarrow\mu+jets$ MC and $\mu+jets$ data to
329: compute the correction factor, which has to be applied to MC. As result
330: we obtain the MC tagging probability and data corrected one (Figure
331: \ref{bID}). It can be noted that the data corrected efficiency is
332: indeed noticeably (>30\%) lower then what we would expect by applying
333: SVT directly to MC.
334:
335:
336: \paragraph{C-tagging efficiency}
337:
338: An assumption is made that the correction factor obtained by dividing
339: the semi-leptonic b-tagging efficiency in data to the one in MC also
340: is correct for c-jets. Hence the MC-obtained inclusive c-taging efficiency
341: is multiplied by this factor (and by it's taggability too) in order
342: to estimate the c-tagging probability
343:
344:
345: \paragraph{Light jet tagging efficiency}
346:
347: The b-tag fake rate from light quarks is computed by measuring the
348: negative tag rate. It is defined by the rate of appearance of secondary
349: vertices with negative decay length significance. It is assumed that
350: the light quarks have equal chances to produce SV with positive and
351: negative decay length significance (due to finite resolution effects)
352: while the heavy flavor jets can only produce SV with positive decay
353: length significance. This however is not quite true and a special
354: scaling factor ($SF_{hf}$) is introduced to correct for the fraction
355: of heavy flavors among the jets with the negative decay length significance.
356: Another correction is for the presence of the long lived particles
357: in light jets ($SF_{ll}$). Both factors are derived from Monte Carlo.
358:
359: %
360: \begin{figure}
361: \subfigure[SVT efficiency]{\includegraphics[scale=0.3]{plots/SVTeff_pt}}\subfigure[SVT efficiency]{\includegraphics[scale=0.3]{plots/SVTeff_eta}}
362:
363: \subfigure[SVT efficiency parametrized on data]{\includegraphics[scale=0.3]{plots/SVTeff}}\subfigure[SVT efficiency parametrized on MC]{\includegraphics[scale=0.3]{plots/SVTeffMC}}
364:
365:
366: \caption{SVT Efficiency for $t\overline{t}\rightarrow\tau+jets$ MC. Red is
367: MC parameterization black is data-corrected. Flavor depancance is
368: taken into account. The lower plots show 2D parametrizations}
369:
370: \label{bID}
371: \end{figure}
372:
373:
374:
375: \paragraph{Event tagging efficiency}
376:
377: The tag rates and the taggability had been combined and used to predict
378: the probability for a jet to be b-tagged (b-tagging weight). The final
379: resulting per-event probability of having at least one such a tag
380: for the $t\overline{t}\rightarrow\tau+jets$ MC is plotted on Figure
381: \ref{cap:The-probability-to}
382:
383: %
384: \begin{figure}
385: \includegraphics[scale=0.5]{plots/ttb_eventprobtag}
386:
387:
388: \caption{The probability to tag at least one jet with SVT for $t\overline{t}\rightarrow\tau+jets$
389: MC}
390:
391: \label{cap:The-probability-to}
392: \end{figure}
393:
394:
395: Finally it has to be noted that we tried to avoid th overlap between
396: $\tau$ ID and b-tagging. That is we remove the jets, matched to a
397: 0.8 $\tau$ candidate within $\Delta R$ = 0.5
398:
399:
400: \subsection{Trigger}
401:
402:
403: \subsubsection{\label{sub:Running-trigsim}Running TRIGSIM}
404:
405: In order to search for a signal one has to collect the data with a
406: chosen set of triggers. We want to find such a combination so to maximize
407: the fraction of signal events written out.
408:
409: For that purpose the trigger simulation program was run on the MC
410: signal. The following efficiencies were obtained (for 12.30 version
411: of the global D0 trigger definition list) (Table \ref{cap:Event-overlaps-between})
412:
413: %
414: \begin{table}
415: \begin{tabular}{|l||l||}
416: \hline
417: {\large Trigger}&
418: {\large Fraction of events passing}\tabularnewline
419: \hline
420: 4JT12 &
421: 0.74$\pm$0.05\tabularnewline
422: 3J15\_2J25\_PVZ &
423: 0.73$\pm$0.05\tabularnewline
424: MHT30\_3CJT5&
425: 0.68$\pm$0.04\tabularnewline
426: MU\_JT20\_L2M0&
427: 0.30$\pm$0.01\tabularnewline
428: \hline
429: \multicolumn{1}{|l|}{MU\_JT20\_L2M0 \&\& MHT30\_3CJT5}&
430: \multicolumn{1}{||l||}{0.2$\pm$0.01}\tabularnewline
431: \hline
432: \multicolumn{1}{||l}{MHT30\_3CJT5 \&\& 4JT12}&
433: \multicolumn{1}{||l||}{0.4$\pm$0.04 }\tabularnewline
434: \hline
435: \multicolumn{1}{||l}{4JT12 \&\& 3J15\_2J25\_PVZ}&
436: \multicolumn{1}{||l||}{0.67$\pm$0.04}\tabularnewline
437: \hline
438: \end{tabular}
439:
440:
441: \caption{Trigger efficiencies and event overlaps between the most efficient
442: (for selecting $t\overline{t}\rightarrow\tau+jets$) unprescaled triggers.
443: As one can see 3J15\_2J25\_PVZ has large overlap with 4JT12, and since
444: 4JT12 is better studied it has been chosen for this analysis.}
445:
446: \label{cap:Event-overlaps-between}
447: \end{table}
448:
449:
450: Taking this into account, we are left with 3 triggers, giving altogether
451: \textasciitilde{}85\% efficiency:
452:
453: $\mathit{MHT30\_3CJT5}$ - $\not\!\! E_{T}$ trigger, requiring at
454: least 30 GeV at level 3, which leads to \textasciitilde{}30\% inefficiency,
455: since our missing $E_{T}$ peaks around 50 GeV
456:
457: $Description$: {\large L1}: At least three Calorimeter trigger towers
458: with $E_{T}$$>$5 GeV. {\large L2}: Require jet $E_{T}$$>$20. {\large L3}:
459: Vector $H_{T}$ sum $>$30 GeV. Also, one in 4000 events is recorded
460: and marked as {}``unbiased''
461:
462: $\mathit{4JT12}$ - Trigger designed for the $t\bar{t}\rightarrow jets$
463: analysis \cite{alljet}. Meets all the jet number requirements, but
464: doesn't often have high enough $\not E_{T}$
465:
466: $Description$: {\large L1}: At least three Calorimeter JET trigger
467: towers having $E_{T}$$>$5 GeV. {\large L2}: Three JET candidates
468: with $E_{T}$$>$8 GeV and HT $>$ 50 GeV. {\large L3}: Four $|$$\eta$$|$$<$3.6
469: jet candidates with $E_{T}$$>$10 GeV found using a simple cone algorithm.
470: Three of those jets must have $E_{T}$$>$15 GeV. Record one in 500
471: events marked as 'unbiased'.
472:
473: $\mathit{MU\_JT20\_L2M0}\mathbb{\,\,}$- Muon trigger with $<$20\%
474: efficiency for our signal, but it's unprescaled and has little overlap
475: with others
476:
477: $Description$: {\large L1}: A single muon trigger based on muon scintillator
478: and also requiring one Calorimeter JET trigger tower with $E_{T}$>3
479: GeV. {\large L2}: At least one muon found meeting MEDIUM quality requirements
480: but no pT or region requirement. Also require at least one jet with
481: $E_{T}$>10 GeV. {\large L3}: At least one jet with $E_{T}$>20 GeV
482: is found using a simple cone algorithm. Additionally, one in 500 of
483: all events is recorded and marked as 'unbiased'.
484:
485:
486: \subsubsection{Triggers in version 13}
487:
488: A new trigger list has recently been used for D0 data-taking. It includes
489: a number of new triggers and modifications to existing ones. Running
490: TRIGSIM on this triglist we discover that the triggers that were best
491: in v12 ($\mathit{4JT12}$ and $\mathit{MHT30\_3CJT5}$) are also most
492: efficient in v13. In fact, OR of just these two triggers gives 90$\pm$5\%
493: efficiency. The names and definitions of these triggers had changed:
494:
495: 4JT12 became JT2\_4JT12L\_HT. An additional $H_{T}$ cut of 120 GeV
496: is being applied:
497:
498: $Description$: {\large L1}: Three calorimeter trigger towers with
499: $E_{T}$>5 GeV. {\large L2}: Pass events with at least three JET candidates
500: with $E_{T}$>6 GeV and $H_{T}$, formed with jets above 6 GeV, greater
501: than 70 GeV. {\large L3}: Requires at least four jets with $E_{T}$
502: > 12 GeV and at at least three jets with $E_{T}$ > 15 GeV. Also require
503: at least two jets to have $E_{T}$ > 25 GeV . Event $H_{T}$ (calculated
504: using all jets with $E_{T}$>9 GeV) > 120 GeV .
505:
506: MHT30\_3CJT5 became JT2\_MHT25\_HT.
507:
508: $Description$:: {\large L1}: Three calorimeter trigger towers with
509: $E_{T}$>4 GeV, |$\eta$|<2.4, and two calorimeter trigger towers
510: with $E_{T}$>5 GeV. {\large L2}: Pass events with at least three
511: JET candidates with $E_{T}$>6 GeV and $H_{T}$, formed with jets
512: above 6 GeV, greater than 70 GeV {\large L3}: Vector $H_{T}$ sum
513: for the event must be above 25 GeV. Also require event scalar $H_{T}$
514: (calculated using all jets with $E_{T}$>9 GeV) > 125 GeV.
515:
516:
517: \subsubsection{Turn-on curves}
518:
519: These features are reflected in the corresponding turn-on curves (integrated
520: efficiencies of signal) . Such a curve for MHT30 and 4JT12 triggers
521: is shown on Figure \ref{trig}.%
522: \begin{figure}
523: \includegraphics[scale=0.8]{MS_thesis/proposal_plots/MET_eff_new__7}
524:
525: \includegraphics[scale=0.6]{MS_thesis/proposal_plots/4JT12jet3pt}
526:
527:
528: \caption{Integrated trigger efficiency for MHT30 and 4JT12 triggers for the
529: $t\overline{t}\rightarrow\tau+jets$ MC, obtained using TRIGSIM.}
530:
531: \begin{centering}\label{trig}\par\end{centering}
532: \end{figure}
533:
534:
535:
536: \subsubsection{Trigger simulation}
537:
538: At the time of the analysis TRIGSIM had not reached the state in which
539: it could reliably reproduce the trigger efficiency on data. Therefore,
540: the accepted practice is to parameterize the trigger turn-ons on data
541: and apply this parameterization to MC files.
542:
543: Such procedure was performed with the top\_trigger package \cite{top_trigger}.
544: On Figure \ref{cap:The-trigger-efficiency} one can see the results
545: of a test to check the validity of such approach. We used the dataset
546: collected by a single muon trigger (MU\_JT20\_L2M0). We can assume
547: that such data has little bias with respect to the 4JT10 trigger.
548: Hence, if we count the number of the 4 jet events that passed 4JT10
549: and compare it with the top\_trigger prediction for the same events
550: we can check how well does top\_trigger perform. As can be noted from
551: Figure \ref{cap:The-trigger-efficiency} the agreement is fairly good,
552: especially in the region which we use in this analysis (we require
553: jets to have $P_{T}>20$ GeV). The efficiency turn-on curve, produced
554: by top\_trigger is shown on Fig. \ref{cap:The-trigger-efficiency_MC}
555: and is in agreement with the TRIGSIM (Fig. \ref{trig}).
556:
557: %
558: \begin{figure}
559: \includegraphics[scale=0.7]{plots/4JT10_MULOOSE}
560:
561:
562: \caption{The integrated trigger efficiency closure plot. Black is the MU\_JT20\_L2M0
563: data, red is top\_trigger prediction for this data.}
564:
565: \label{cap:The-trigger-efficiency}
566: \end{figure}
567:
568:
569: %
570: \begin{figure}
571: \includegraphics[scale=0.7]{plots/4JT12_EFF_toptrig}
572:
573:
574: \caption{The integrated trigger efficiency for the $t\overline{t}\rightarrow\tau+jets$
575: MC obtained using top\_trigger. }
576:
577: \label{cap:The-trigger-efficiency_MC}
578: \end{figure}
579:
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