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| CMS-PAS-MLG-23-002 | ||
| Machine-learning techniques for model-independent searches in dijet final states | ||
| CMS Collaboration | ||
| 2025-07-12 | ||
| Abstract: Anomaly detection methods used in a recent search for new phenomena at the CMS Experiment at the LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles. The effectiveness of these approaches in enhancing sensitivity to various signals is studied and compared using data collected in proton-proton collisions at a center-of-mass energy of 13 TeV and amounting to 138 fb$ ^{-1} $. In addition, the capabilities of anomaly detection methods are illustrated by identifying boosted jets corresponding to hadronically decaying top quarks in a model-agnostic fashion. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Diagram of the $ \text{A}\to\text{BC}\to\text{2 jets} $ signal topology targeted in this work. The particle A is produced in a collision between two protons. Reproduced from Ref. [4]. |
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Figure 2:
The VAE architecture used for jet anomaly detection. |
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Figure 3:
Jet $ p_{\mathrm{T}} $ (left) and $ \eta $ (right) distributions before and after resampling jets from the 2 $ < |\Delta\eta_\text{jj}| < $ 2.5 sideband (green) to match the distribution in the signal region (blue). The distributions of reweighted jets are shown in orange. Histograms are normalized to unity; see text for details. |
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Figure 3-a:
Jet $ p_{\mathrm{T}} $ (left) and $ \eta $ (right) distributions before and after resampling jets from the 2 $ < |\Delta\eta_\text{jj}| < $ 2.5 sideband (green) to match the distribution in the signal region (blue). The distributions of reweighted jets are shown in orange. Histograms are normalized to unity; see text for details. |
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Figure 3-b:
Jet $ p_{\mathrm{T}} $ (left) and $ \eta $ (right) distributions before and after resampling jets from the 2 $ < |\Delta\eta_\text{jj}| < $ 2.5 sideband (green) to match the distribution in the signal region (blue). The distributions of reweighted jets are shown in orange. Histograms are normalized to unity; see text for details. |
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Figure 4:
Upper row: $ p_{\mathrm{T}} $ reconstruction for SM backgournd jets. Middle row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ decay. Lower row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ decay. |
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Figure 4-a:
Upper row: $ p_{\mathrm{T}} $ reconstruction for SM backgournd jets. Middle row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ decay. Lower row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ decay. |
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Figure 4-b:
Upper row: $ p_{\mathrm{T}} $ reconstruction for SM backgournd jets. Middle row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ decay. Lower row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ decay. |
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Figure 4-c:
Upper row: $ p_{\mathrm{T}} $ reconstruction for SM backgournd jets. Middle row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ decay. Lower row: $ p_{\mathrm{T}} $ reconstruction for jets from a $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ decay. |
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Figure 5:
Simulated dijet invariant mass spectrum after selecting events based on the score of the \textit{VAE-QR} method. The distributions are shown in the upper panel for the SM background (blue) and the $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal model with $ m_{\text{X}} = $ 3 TeV and $ m_{\text{Y}} =m_{\text{Y'}} = $ 170 GeV (red). The inclusive selection is compared to the three quantile ranges used in the statistical analysis. No significant difference is observed between the shapes, showing that the quantile regression performs as expected. The lower panel shows the ratio of the quantile ranges to the inclusive distribution for the SM background. |
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Figure 6:
A schematic showing the training algorithm for \textit{TNT}. The two jets in the event are randomly assigned the labels 1 and 2. The dijet invariant masses and the autoencoder scores are used to construct signal- and background-like subsets of J1 (J2). These subsets are then merged, and a single classifier is trained to distinguish between signal- and background-like jets. |
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Figure 7:
Signal selection efficiency of the weakly supervised classifier in the \textit{CATHODE} method, evaluated for the $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal at 3 TeV. The shaded region represents the total statistical and systematic uncertainty evaluated as described in the text. |
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Figure 8:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures for two different signals. The upper panel shows results for the 2-prong $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal with $ m_{\text{X}} = $ 3 TeV, $ m_{\text{Y}} = $ 170 GeV, and $ m_{\text{Y'}} = $ 170 GeV, while the lower panel shows results for the 3-prong $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ m_\mathrm{W'}= $ 3 TeV and $ m_{\mathrm{B'}}= $ 400 GeV. Significance values larger than 7$ \sigma $ are denoted with downwards facing triangles. Reproduced from Ref. [4]. |
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Figure 8-a:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures for two different signals. The upper panel shows results for the 2-prong $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal with $ m_{\text{X}} = $ 3 TeV, $ m_{\text{Y}} = $ 170 GeV, and $ m_{\text{Y'}} = $ 170 GeV, while the lower panel shows results for the 3-prong $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ m_\mathrm{W'}= $ 3 TeV and $ m_{\mathrm{B'}}= $ 400 GeV. Significance values larger than 7$ \sigma $ are denoted with downwards facing triangles. Reproduced from Ref. [4]. |
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Figure 8-b:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures for two different signals. The upper panel shows results for the 2-prong $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal with $ m_{\text{X}} = $ 3 TeV, $ m_{\text{Y}} = $ 170 GeV, and $ m_{\text{Y'}} = $ 170 GeV, while the lower panel shows results for the 3-prong $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ m_\mathrm{W'}= $ 3 TeV and $ m_{\mathrm{B'}}= $ 400 GeV. Significance values larger than 7$ \sigma $ are denoted with downwards facing triangles. Reproduced from Ref. [4]. |
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Figure 9:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures modified to use a common set of input features, for two different signals: (upper) the 2-prong $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal with $ m_\mathrm{X}= $ 3 TeV, $ m_\mathrm{Y}= $ 170 GeV, and $ M_\mathrm{Y'}= $ 170 GeV, and (lower) 3-prong $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ M_\mathrm{W'}= $ 3 TeV and $ M_\mathrm{B'}= $ 400 GeV. Significance values larger than 7$ \sigma $ are denoted with downwards facing triangles. |
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Figure 9-a:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures modified to use a common set of input features, for two different signals: (upper) the 2-prong $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal with $ m_\mathrm{X}= $ 3 TeV, $ m_\mathrm{Y}= $ 170 GeV, and $ M_\mathrm{Y'}= $ 170 GeV, and (lower) 3-prong $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ M_\mathrm{W'}= $ 3 TeV and $ M_\mathrm{B'}= $ 400 GeV. Significance values larger than 7$ \sigma $ are denoted with downwards facing triangles. |
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Figure 9-b:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures modified to use a common set of input features, for two different signals: (upper) the 2-prong $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal with $ m_\mathrm{X}= $ 3 TeV, $ m_\mathrm{Y}= $ 170 GeV, and $ M_\mathrm{Y'}= $ 170 GeV, and (lower) 3-prong $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ M_\mathrm{W'}= $ 3 TeV and $ M_\mathrm{B'}= $ 400 GeV. Significance values larger than 7$ \sigma $ are denoted with downwards facing triangles. |
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Figure 10:
Anomaly score correlations of different methods on the background sample. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 10-a:
Anomaly score correlations of different methods on the background sample. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 10-b:
Anomaly score correlations of different methods on the background sample. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 10-c:
Anomaly score correlations of different methods on the background sample. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 10-d:
Anomaly score correlations of different methods on the background sample. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 11:
Anomaly score correlations of different methods on the $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 11-a:
Anomaly score correlations of different methods on the $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 11-b:
Anomaly score correlations of different methods on the $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 11-c:
Anomaly score correlations of different methods on the $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 11-d:
Anomaly score correlations of different methods on the $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 12:
Anomaly score correlations of different methods on the $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 12-a:
Anomaly score correlations of different methods on the $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 12-b:
Anomaly score correlations of different methods on the $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 12-c:
Anomaly score correlations of different methods on the $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 12-d:
Anomaly score correlations of different methods on the $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal model. Scores are transformed to follow a normal distribution. The Pearson linear correlation coefficient and distance correlation (DisCo) are listed for each pairing. See text for details. |
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Figure 13:
Summary plots showing the Pearson correlation coefficient for each pair of anomaly detection algorithms as evaluated on events from the SM backgrounds (top), $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal (lower left), and $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal (lower right). In many cases, the correlations are weak, indicating complementarity between the different approaches. |
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Figure 13-a:
Summary plots showing the Pearson correlation coefficient for each pair of anomaly detection algorithms as evaluated on events from the SM backgrounds (top), $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal (lower left), and $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal (lower right). In many cases, the correlations are weak, indicating complementarity between the different approaches. |
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Figure 13-b:
Summary plots showing the Pearson correlation coefficient for each pair of anomaly detection algorithms as evaluated on events from the SM backgrounds (top), $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal (lower left), and $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal (lower right). In many cases, the correlations are weak, indicating complementarity between the different approaches. |
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Figure 13-c:
Summary plots showing the Pearson correlation coefficient for each pair of anomaly detection algorithms as evaluated on events from the SM backgrounds (top), $ \mathrm{X}\to \mathrm{Y} \mathrm{Y'} \to 4 \mathrm{q} $ signal (lower left), and $ \mathrm{W'} \to \mathrm{B'} \mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal (lower right). In many cases, the correlations are weak, indicating complementarity between the different approaches. |
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Figure 14:
Significance improvement obtained from supervised classifiers trained using the same inputs as the anomaly detection methods. The top (bottom) plot shows the 3 TeV (5 TeV) mass point for all signal models. The masses of intermediate decay particles are listed in GeV. |
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Figure 14-a:
Significance improvement obtained from supervised classifiers trained using the same inputs as the anomaly detection methods. The top (bottom) plot shows the 3 TeV (5 TeV) mass point for all signal models. The masses of intermediate decay particles are listed in GeV. |
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Figure 14-b:
Significance improvement obtained from supervised classifiers trained using the same inputs as the anomaly detection methods. The top (bottom) plot shows the 3 TeV (5 TeV) mass point for all signal models. The masses of intermediate decay particles are listed in GeV. |
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Figure 15:
Comparison of anomaly detection and supervised classification performance. The improvement on the limit obtained after anomaly detection is compared to the significance improvement from classifiers trained using the same inputs as the anomaly detection methods. The 3 TeV mass point is shown for all models. |
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Figure 16:
Distribution of the J2 soft-drop mass after the basic (left) and b-tagged (right) preselections. The black dots represent the data and the colored histograms correspond to simulated events. The b tagging preselection increases the relative contribution of $ \mathrm{t} \overline{\mathrm{t}} $ (red) in the sample, but in both cases the sample is dominated by the QCD multijet background (blue). Other background processes are shown in yellow. |
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Figure 16-a:
Distribution of the J2 soft-drop mass after the basic (left) and b-tagged (right) preselections. The black dots represent the data and the colored histograms correspond to simulated events. The b tagging preselection increases the relative contribution of $ \mathrm{t} \overline{\mathrm{t}} $ (red) in the sample, but in both cases the sample is dominated by the QCD multijet background (blue). Other background processes are shown in yellow. |
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Figure 16-b:
Distribution of the J2 soft-drop mass after the basic (left) and b-tagged (right) preselections. The black dots represent the data and the colored histograms correspond to simulated events. The b tagging preselection increases the relative contribution of $ \mathrm{t} \overline{\mathrm{t}} $ (red) in the sample, but in both cases the sample is dominated by the QCD multijet background (blue). Other background processes are shown in yellow. |
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Figure 17:
Post-fit plots of the fail (left) and pass (right) regions for the $ {\mathrm{t}\overline{\mathrm{t}}} $ extraction procedure performed for the 65-150 (upper), 105-225 (middle), and 145-250 GeV (lower). The data (black points with error bars) are compared to the fitted estimates of QCD multijet (blue), Z+jets (orange), W+jets (purple), and $ {\mathrm{t}\overline{\mathrm{t}}} $ (red). The lower panel shows the ratio between the observed data points and the fitted estimates. The gray shading denotes the systematic uncertainty. The contribution from $ {\mathrm{t}\overline{\mathrm{t}}} $ is clearly visible in the pass region of the 105-225 and 145-250 GeV signal windows. |
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Figure 17-a:
Post-fit plots of the fail (left) and pass (right) regions for the $ {\mathrm{t}\overline{\mathrm{t}}} $ extraction procedure performed for the 65-150 (upper), 105-225 (middle), and 145-250 GeV (lower). The data (black points with error bars) are compared to the fitted estimates of QCD multijet (blue), Z+jets (orange), W+jets (purple), and $ {\mathrm{t}\overline{\mathrm{t}}} $ (red). The lower panel shows the ratio between the observed data points and the fitted estimates. The gray shading denotes the systematic uncertainty. The contribution from $ {\mathrm{t}\overline{\mathrm{t}}} $ is clearly visible in the pass region of the 105-225 and 145-250 GeV signal windows. |
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Figure 17-b:
Post-fit plots of the fail (left) and pass (right) regions for the $ {\mathrm{t}\overline{\mathrm{t}}} $ extraction procedure performed for the 65-150 (upper), 105-225 (middle), and 145-250 GeV (lower). The data (black points with error bars) are compared to the fitted estimates of QCD multijet (blue), Z+jets (orange), W+jets (purple), and $ {\mathrm{t}\overline{\mathrm{t}}} $ (red). The lower panel shows the ratio between the observed data points and the fitted estimates. The gray shading denotes the systematic uncertainty. The contribution from $ {\mathrm{t}\overline{\mathrm{t}}} $ is clearly visible in the pass region of the 105-225 and 145-250 GeV signal windows. |
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Figure 17-c:
Post-fit plots of the fail (left) and pass (right) regions for the $ {\mathrm{t}\overline{\mathrm{t}}} $ extraction procedure performed for the 65-150 (upper), 105-225 (middle), and 145-250 GeV (lower). The data (black points with error bars) are compared to the fitted estimates of QCD multijet (blue), Z+jets (orange), W+jets (purple), and $ {\mathrm{t}\overline{\mathrm{t}}} $ (red). The lower panel shows the ratio between the observed data points and the fitted estimates. The gray shading denotes the systematic uncertainty. The contribution from $ {\mathrm{t}\overline{\mathrm{t}}} $ is clearly visible in the pass region of the 105-225 and 145-250 GeV signal windows. |
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Figure 17-d:
Post-fit plots of the fail (left) and pass (right) regions for the $ {\mathrm{t}\overline{\mathrm{t}}} $ extraction procedure performed for the 65-150 (upper), 105-225 (middle), and 145-250 GeV (lower). The data (black points with error bars) are compared to the fitted estimates of QCD multijet (blue), Z+jets (orange), W+jets (purple), and $ {\mathrm{t}\overline{\mathrm{t}}} $ (red). The lower panel shows the ratio between the observed data points and the fitted estimates. The gray shading denotes the systematic uncertainty. The contribution from $ {\mathrm{t}\overline{\mathrm{t}}} $ is clearly visible in the pass region of the 105-225 and 145-250 GeV signal windows. |
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Figure 17-e:
Post-fit plots of the fail (left) and pass (right) regions for the $ {\mathrm{t}\overline{\mathrm{t}}} $ extraction procedure performed for the 65-150 (upper), 105-225 (middle), and 145-250 GeV (lower). The data (black points with error bars) are compared to the fitted estimates of QCD multijet (blue), Z+jets (orange), W+jets (purple), and $ {\mathrm{t}\overline{\mathrm{t}}} $ (red). The lower panel shows the ratio between the observed data points and the fitted estimates. The gray shading denotes the systematic uncertainty. The contribution from $ {\mathrm{t}\overline{\mathrm{t}}} $ is clearly visible in the pass region of the 105-225 and 145-250 GeV signal windows. |
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Figure 17-f:
Post-fit plots of the fail (left) and pass (right) regions for the $ {\mathrm{t}\overline{\mathrm{t}}} $ extraction procedure performed for the 65-150 (upper), 105-225 (middle), and 145-250 GeV (lower). The data (black points with error bars) are compared to the fitted estimates of QCD multijet (blue), Z+jets (orange), W+jets (purple), and $ {\mathrm{t}\overline{\mathrm{t}}} $ (red). The lower panel shows the ratio between the observed data points and the fitted estimates. The gray shading denotes the systematic uncertainty. The contribution from $ {\mathrm{t}\overline{\mathrm{t}}} $ is clearly visible in the pass region of the 105-225 and 145-250 GeV signal windows. |
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Figure 18:
A comparison of the top identification performance of classifiers trained in different ways, evaluated in simulation. The two models trained in the 145-250 GeV mass window (red and yellow), as well as the one trained using the b tagging preselection in the in the 105-220 GeV mass window (blue), nearly match the performance of a supervised classifier (gray). The classifier trained with the baseline preselection in the 105-220 GeV mass window (purple) exhibits a smaller, yet larger than one, improvement. |
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Figure 19:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The sensitivity of the anomaly score to the different input observables is assessed to aid in the determination of the properties of the excess. The jet mass, b tagging score, and $ \tau_{32} $ are seen to be the most important observables, consistent with the properties of boosted top jets. |
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Figure 20:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The plots compare the properties of the jets with the highest anomaly score (red) to those for all jets in the region of the excess (blue). The variables shown are the soft-drop mass $ m_\mathrm{SD} $ (upper left), the number of jet constituents $ m_\mathrm{PF} $ (upper right), the DEEPCSV score (middle left), and the three subjettiness ratios $ \tau_{21} $ (middle right), $ \tau_{32} $ (lower left), and $ \tau_{43} $ (lower right). The three-pronged nature of the signal is clear from the low $ \tau_{32} $ scores, the presence of b tags from the high DEEPCSV score, and the jet mass ($ m_\mathrm{SD} $) distribution peaking at 175 GeV indicates the top quark mass. |
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Figure 20-a:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The plots compare the properties of the jets with the highest anomaly score (red) to those for all jets in the region of the excess (blue). The variables shown are the soft-drop mass $ m_\mathrm{SD} $ (upper left), the number of jet constituents $ m_\mathrm{PF} $ (upper right), the DEEPCSV score (middle left), and the three subjettiness ratios $ \tau_{21} $ (middle right), $ \tau_{32} $ (lower left), and $ \tau_{43} $ (lower right). The three-pronged nature of the signal is clear from the low $ \tau_{32} $ scores, the presence of b tags from the high DEEPCSV score, and the jet mass ($ m_\mathrm{SD} $) distribution peaking at 175 GeV indicates the top quark mass. |
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Figure 20-b:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The plots compare the properties of the jets with the highest anomaly score (red) to those for all jets in the region of the excess (blue). The variables shown are the soft-drop mass $ m_\mathrm{SD} $ (upper left), the number of jet constituents $ m_\mathrm{PF} $ (upper right), the DEEPCSV score (middle left), and the three subjettiness ratios $ \tau_{21} $ (middle right), $ \tau_{32} $ (lower left), and $ \tau_{43} $ (lower right). The three-pronged nature of the signal is clear from the low $ \tau_{32} $ scores, the presence of b tags from the high DEEPCSV score, and the jet mass ($ m_\mathrm{SD} $) distribution peaking at 175 GeV indicates the top quark mass. |
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Figure 20-c:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The plots compare the properties of the jets with the highest anomaly score (red) to those for all jets in the region of the excess (blue). The variables shown are the soft-drop mass $ m_\mathrm{SD} $ (upper left), the number of jet constituents $ m_\mathrm{PF} $ (upper right), the DEEPCSV score (middle left), and the three subjettiness ratios $ \tau_{21} $ (middle right), $ \tau_{32} $ (lower left), and $ \tau_{43} $ (lower right). The three-pronged nature of the signal is clear from the low $ \tau_{32} $ scores, the presence of b tags from the high DEEPCSV score, and the jet mass ($ m_\mathrm{SD} $) distribution peaking at 175 GeV indicates the top quark mass. |
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Figure 20-d:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The plots compare the properties of the jets with the highest anomaly score (red) to those for all jets in the region of the excess (blue). The variables shown are the soft-drop mass $ m_\mathrm{SD} $ (upper left), the number of jet constituents $ m_\mathrm{PF} $ (upper right), the DEEPCSV score (middle left), and the three subjettiness ratios $ \tau_{21} $ (middle right), $ \tau_{32} $ (lower left), and $ \tau_{43} $ (lower right). The three-pronged nature of the signal is clear from the low $ \tau_{32} $ scores, the presence of b tags from the high DEEPCSV score, and the jet mass ($ m_\mathrm{SD} $) distribution peaking at 175 GeV indicates the top quark mass. |
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Figure 20-e:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The plots compare the properties of the jets with the highest anomaly score (red) to those for all jets in the region of the excess (blue). The variables shown are the soft-drop mass $ m_\mathrm{SD} $ (upper left), the number of jet constituents $ m_\mathrm{PF} $ (upper right), the DEEPCSV score (middle left), and the three subjettiness ratios $ \tau_{21} $ (middle right), $ \tau_{32} $ (lower left), and $ \tau_{43} $ (lower right). The three-pronged nature of the signal is clear from the low $ \tau_{32} $ scores, the presence of b tags from the high DEEPCSV score, and the jet mass ($ m_\mathrm{SD} $) distribution peaking at 175 GeV indicates the top quark mass. |
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Figure 20-f:
Excess interpretation example for the weakly supervised anomaly detection strategy applied to the $ \mathrm{t} \overline{\mathrm{t}} $ region with the b tagging preselection. The plots compare the properties of the jets with the highest anomaly score (red) to those for all jets in the region of the excess (blue). The variables shown are the soft-drop mass $ m_\mathrm{SD} $ (upper left), the number of jet constituents $ m_\mathrm{PF} $ (upper right), the DEEPCSV score (middle left), and the three subjettiness ratios $ \tau_{21} $ (middle right), $ \tau_{32} $ (lower left), and $ \tau_{43} $ (lower right). The three-pronged nature of the signal is clear from the low $ \tau_{32} $ scores, the presence of b tags from the high DEEPCSV score, and the jet mass ($ m_\mathrm{SD} $) distribution peaking at 175 GeV indicates the top quark mass. |
| Tables | |
png pdf |
Table 1:
Signal processes considered in the analysis, categorized according to the number of partons produced in the decay of each jet. |
png pdf |
Table 2:
Mass regions used by the weakly supervised methods and resonance masses considered with each bin. Signal regions are required to have sideband mass regions on either side for reliable background estimation. This means only the A1-A6 and B1-B6 regions are used to seek signals, with the A0, B0, A7, and B7 regions used solely as sideband control regions. |
png pdf |
Table 3:
Signal models used to train the six signal normalizing flows used in the \textit{QUAK} method. Each flow uses signals with specific masses for the daughter particles B and C. For signals decaying to an SM and a BSM particle, the BSM particle is always the heaviest of the two. |
| Summary |
| This note presents a detailed description of the five anomaly detection methods that were used to search for new particles decaying to two anomalous jets in Ref. [4], using data collected by the CMS Experiment between 2016 and 2018. Approaches based on weakly supervised, unsupervised, and semi-supervised training paradigms were explored. All methods were successfully able to identify some classes of anomalous jets as distinct from standard model backgrounds, and were therefore able to enhance the sensitivity to the signatures of new particles in CMS data in a model-agnostic fashion. The sensitivity of the methods to the presence of an anomalous signal in the data has been shown to be higher than an inclusive search or simple selections based on jet substructure. The performance of the five methods has been found to depend on the considered signal model, with no single method outperforming all others. Further investigation has shown that correlations between the anomaly scores are low, indicating that methods detect anomalous signal events in different ways. Furthermore, the impact of differences in the input features has been explored and shown to explain performance differences between signal models, but not between methods. In the third part of this note, a weakly supervised anomaly detection method has been used to separate top quark jets from jets produced in other standard model processes. The achieved separation power matches that of a fully supervised classifier trained with the same input variables. This constitutes a validation of resonant anomaly detection in collider data. In addition, interpretation techniques have been used to successfully retrieve some of the main properties of the top quark. This would enable further study and confirmation of the signal in the event of a positive result from an anomaly search. By presenting and comparing advanced and sophisticated machine-learning methods utilized for anomaly detection on collider data, this note lays the groundwork for future model-agnostic exploration of collider data. |
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