Annotation of ttbar/p20_taujets_note/Tools.tex, revision 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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