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| CMS-PAS-EXO-22-026 | ||
| Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $ \sqrt{s} $ = 13 TeV | ||
| CMS Collaboration | ||
| 25 March 2024 | ||
| Abstract: This note introduces a model-agnostic search for new physics in the dijet final state. Other than the requirement of a narrow dijet resonance with a mass in the range of 1800-6000 GeV, minimal additional assumptions are placed on the signal hypothesis. Search regions are obtained by utilizing multivariate machine learning methods to select jets with anomalous substructure. A collection of complementary anomaly detection methods -- based on unsupervised, weakly-supervised and semi-supervised algorithms -- are used in order to maximize the sensitivity to unknown new physics signatures. These algorithms are applied to data corresponding to an integrated luminosity of 138 fb$ ^{-1} $, recorded in the years 2016 to 2018 by the CMS experiment at the LHC, at a centre-of-mass energy of 13 TeV. No significant excesses above background expectation are seen, and exclusion limits are derived on the production cross section of benchmark signal models varying in resonance mass, jet mass and jet substructure. Many of these signatures have not previously been searched for at the LHC, making the limits reported on the corresponding benchmark models the first ever and the most stringent to date. | ||
| Links: CDS record (PDF) ; Physics Briefing ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Production in pp collisions of a dijet resonance, A, which decays to two resonances B and C, that in turn each decay to a jet with anomalous substructure arising from multiple subjets. |
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Figure 2:
P-values as a function of the injected signal cross sections for the different analysis procedures on for two different signals: (left) the 2-pronged $ \mathrm{X} \to \mathrm{YY'} \to 4 \mathrm{q} $ signal with $ M_{\mathrm{X}}= $ 3 TeV, $ M_{\mathrm{Y}}= $ 170 GeV, and $ M_{\mathrm{Y}'}= $ 170 GeV and (right) 3-pronged $ \mathrm{W^{'}} \to \mathrm{B'}\mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ M_{W'}= $ 3 TeV and $ M_{B'}= $ 400 GeV. Significances are restricted to a maximum of 7$ \sigma $, to reflect limitations of the asymptotic formula used. Values larger than this are denoted with a downwards facing triangle. |
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Figure 2-a:
P-values as a function of the injected signal cross sections for the different analysis procedures on for two different signals: (left) the 2-pronged $ \mathrm{X} \to \mathrm{YY'} \to 4 \mathrm{q} $ signal with $ M_{\mathrm{X}}= $ 3 TeV, $ M_{\mathrm{Y}}= $ 170 GeV, and $ M_{\mathrm{Y}'}= $ 170 GeV and (right) 3-pronged $ \mathrm{W^{'}} \to \mathrm{B'}\mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ M_{W'}= $ 3 TeV and $ M_{B'}= $ 400 GeV. Significances are restricted to a maximum of 7$ \sigma $, to reflect limitations of the asymptotic formula used. Values larger than this are denoted with a downwards facing triangle. |
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Figure 2-b:
P-values as a function of the injected signal cross sections for the different analysis procedures on for two different signals: (left) the 2-pronged $ \mathrm{X} \to \mathrm{YY'} \to 4 \mathrm{q} $ signal with $ M_{\mathrm{X}}= $ 3 TeV, $ M_{\mathrm{Y}}= $ 170 GeV, and $ M_{\mathrm{Y}'}= $ 170 GeV and (right) 3-pronged $ \mathrm{W^{'}} \to \mathrm{B'}\mathrm{t} \to \mathrm{b} \mathrm{Z} \mathrm{t} $ signal with $ M_{W'}= $ 3 TeV and $ M_{B'}= $ 400 GeV. Significances are restricted to a maximum of 7$ \sigma $, to reflect limitations of the asymptotic formula used. Values larger than this are denoted with a downwards facing triangle. |
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Figure 3:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (top left) CWoLa Hunting (top middle), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). The shapes of example signals are shown along with the VAE-QR $ m_\mathrm{jj} $ distribution. Though not shown, these shapes are consistent for the other methods as well. For all methods besides the VAE-QR, separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the A signal regions are shown for the weakly supervised and QUAK methods. |
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Figure 3-a:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (top left) CWoLa Hunting (top middle), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). The shapes of example signals are shown along with the VAE-QR $ m_\mathrm{jj} $ distribution. Though not shown, these shapes are consistent for the other methods as well. For all methods besides the VAE-QR, separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the A signal regions are shown for the weakly supervised and QUAK methods. |
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Figure 3-b:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (top left) CWoLa Hunting (top middle), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). The shapes of example signals are shown along with the VAE-QR $ m_\mathrm{jj} $ distribution. Though not shown, these shapes are consistent for the other methods as well. For all methods besides the VAE-QR, separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the A signal regions are shown for the weakly supervised and QUAK methods. |
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Figure 3-c:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (top left) CWoLa Hunting (top middle), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). The shapes of example signals are shown along with the VAE-QR $ m_\mathrm{jj} $ distribution. Though not shown, these shapes are consistent for the other methods as well. For all methods besides the VAE-QR, separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the A signal regions are shown for the weakly supervised and QUAK methods. |
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Figure 3-d:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (top left) CWoLa Hunting (top middle), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). The shapes of example signals are shown along with the VAE-QR $ m_\mathrm{jj} $ distribution. Though not shown, these shapes are consistent for the other methods as well. For all methods besides the VAE-QR, separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the A signal regions are shown for the weakly supervised and QUAK methods. |
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Figure 3-e:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (top left) CWoLa Hunting (top middle), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). The shapes of example signals are shown along with the VAE-QR $ m_\mathrm{jj} $ distribution. Though not shown, these shapes are consistent for the other methods as well. For all methods besides the VAE-QR, separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the A signal regions are shown for the weakly supervised and QUAK methods. |
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Figure 3-f:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (top left) CWoLa Hunting (top middle), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). The shapes of example signals are shown along with the VAE-QR $ m_\mathrm{jj} $ distribution. Though not shown, these shapes are consistent for the other methods as well. For all methods besides the VAE-QR, separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the A signal regions are shown for the weakly supervised and QUAK methods. |
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Figure 4:
The dijet invariant mass spectrum and resulting background fit to the data for CWoLa Hunting (top lef), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). Separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the B signal regions are shown. The CATHODE and CATHODE-b methods are not used in the highest mass window of the B signal regions due to limited statistics. They therefore have one fewer signal region shown than the other methods. |
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Figure 4-a:
The dijet invariant mass spectrum and resulting background fit to the data for CWoLa Hunting (top lef), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). Separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the B signal regions are shown. The CATHODE and CATHODE-b methods are not used in the highest mass window of the B signal regions due to limited statistics. They therefore have one fewer signal region shown than the other methods. |
png pdf |
Figure 4-b:
The dijet invariant mass spectrum and resulting background fit to the data for CWoLa Hunting (top lef), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). Separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the B signal regions are shown. The CATHODE and CATHODE-b methods are not used in the highest mass window of the B signal regions due to limited statistics. They therefore have one fewer signal region shown than the other methods. |
png pdf |
Figure 4-c:
The dijet invariant mass spectrum and resulting background fit to the data for CWoLa Hunting (top lef), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). Separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the B signal regions are shown. The CATHODE and CATHODE-b methods are not used in the highest mass window of the B signal regions due to limited statistics. They therefore have one fewer signal region shown than the other methods. |
png pdf |
Figure 4-d:
The dijet invariant mass spectrum and resulting background fit to the data for CWoLa Hunting (top lef), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). Separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the B signal regions are shown. The CATHODE and CATHODE-b methods are not used in the highest mass window of the B signal regions due to limited statistics. They therefore have one fewer signal region shown than the other methods. |
png pdf |
Figure 4-e:
The dijet invariant mass spectrum and resulting background fit to the data for CWoLa Hunting (top lef), TNT (top right), CATHODE (bottom left), CATHODE-b (bottom middle) and QUAK (bottom right). Separate selections were applied for different signal mass hypotheses and the resulting mass spectra were fit separately. The figures therefore show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The results in the B signal regions are shown. The CATHODE and CATHODE-b methods are not used in the highest mass window of the B signal regions due to limited statistics. They therefore have one fewer signal region shown than the other methods. |
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Figure 5:
The discovery sensitivity for the process A $ \to $ BC, using the anomaly detection methods, and a comparison to sensitivity of the inclusive search. In all signal processes, the mass of the heavy resonance was set to $ m(\mathrm{A}) = $ 3 TeV. For the BSM daughter particles, the masses of the $ \mathrm{Y} $ and $ \mathrm{Y'} $ were set to 170 GeV while the masses of the $ \mathrm{B'} $, $ \mathrm{R} $ and H were set to 400 GeV. In the top panel, for each method, the cross section which would have led to an expected 3$ \sigma $ (5$ \sigma $) excess is shown as a cross (square) marker. Sensitivities from six anomaly detection methods (six colors) are compared to an inclusive dijet search in which no substructure selection is made (black) and traditional substructure cuts targeting two-pronged (dark brown) or three-pronged decays (tan). The expected 95% confidence level upper limits from the inclusive search are also shown in the top panel as a dashed line. For all signal models at least one anomaly detection method is able to achieve an expected 5 $ \sigma $ significance at a cross section at or below the upper limit of the inclusive search. In the bottom panel is the ratio of the cross section sensitivity from the inclusive search to the corresponding sensitivity for each method. |
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Figure 6:
The upper limit at 95% confidence level on the cross section for the process A$ \rightarrow $BC, is shown for each search method applied to a variety of signal models. For a resonance mass $m(\mathrm{A}) = $ 3 TeV (left) and $m(\mathrm{A}) = $ 5 TeV (right), we show for each signal model (columns), and search method (all colors), the observed limits (Xs), expected limits (squares), and their one standard deviation expected variation (error bars). For the BSM daughter particles, the masses of the $ \mathrm{Y} $ and $ \mathrm{Y'} $ were set to 170 GeV while the masses of the $ \mathrm{B'} $, $ \mathrm{R} $ and H were set to 400 GeV. Limits from the anomaly detection methods (six colors) are compared to those from an inclusive dijet search in which no substructure selection is made (black markers and horizontal lines), traditional substructure cuts targeting two-pronged (dark brown) or three-pronged decays (tan), and the observed limit from a previous CMS search for the $ W_{\mathrm{KK}} $ model in the all-hadronic channel [45] (gray). |
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Figure 6-a:
The upper limit at 95% confidence level on the cross section for the process A$ \rightarrow $BC, is shown for each search method applied to a variety of signal models. For a resonance mass $m(\mathrm{A}) = $ 3 TeV (left) and $m(\mathrm{A}) = $ 5 TeV (right), we show for each signal model (columns), and search method (all colors), the observed limits (Xs), expected limits (squares), and their one standard deviation expected variation (error bars). For the BSM daughter particles, the masses of the $ \mathrm{Y} $ and $ \mathrm{Y'} $ were set to 170 GeV while the masses of the $ \mathrm{B'} $, $ \mathrm{R} $ and H were set to 400 GeV. Limits from the anomaly detection methods (six colors) are compared to those from an inclusive dijet search in which no substructure selection is made (black markers and horizontal lines), traditional substructure cuts targeting two-pronged (dark brown) or three-pronged decays (tan), and the observed limit from a previous CMS search for the $ W_{\mathrm{KK}} $ model in the all-hadronic channel [45] (gray). |
png pdf |
Figure 6-b:
The upper limit at 95% confidence level on the cross section for the process A$ \rightarrow $BC, is shown for each search method applied to a variety of signal models. For a resonance mass $m(\mathrm{A}) = $ 3 TeV (left) and $m(\mathrm{A}) = $ 5 TeV (right), we show for each signal model (columns), and search method (all colors), the observed limits (Xs), expected limits (squares), and their one standard deviation expected variation (error bars). For the BSM daughter particles, the masses of the $ \mathrm{Y} $ and $ \mathrm{Y'} $ were set to 170 GeV while the masses of the $ \mathrm{B'} $, $ \mathrm{R} $ and H were set to 400 GeV. Limits from the anomaly detection methods (six colors) are compared to those from an inclusive dijet search in which no substructure selection is made (black markers and horizontal lines), traditional substructure cuts targeting two-pronged (dark brown) or three-pronged decays (tan), and the observed limit from a previous CMS search for the $ W_{\mathrm{KK}} $ model in the all-hadronic channel [45] (gray). |
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Figure 7:
A flow chart outlining how the samples for the weakly supervised training are constructed in the TNT method. See text for details. |
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Figure 8:
Diagram of the limit setting procedure for the $ \mathrm{X} \to \mathrm{YY'} \to 4 \mathrm{q} $ signal at 3 TeV with the CATHODE method. The top panel shows the estimated signal acceptance and efficiency as a function of the cross section injected in data. The shaded region shows the total statistical and systematic uncertainty on the efficiency. The resulting $ N_\text{sig}(\sigma) $ curve is shown in blue in the lower panel. The expected and observed limits on the number of signal events are shown as a horizontal solid black line and green dashed lines, respectively, and connected to the corresponding limits on the cross section (vertical lines). The 68% band around the expected limit is displayed similarly. |
| Tables | |
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Table 1:
Limits on additional signal models and daughter mass combinations for 3 TeV resonance masses. For each signal, the expected and observed 95% CL upper limit on the signal cross section from the best performing anomaly detection method is reported. The expected limit from the anomaly detection method is also compared to the expected limit of the inclusive search to quantify improvement. For some signals the anomaly detection methods do not improve with respect to the inclusive search. This is indicated by a improvement factor less than one. |
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Table 2:
Limits on additional signal models and daughter mass combinations for 5 TeV resonance masses. For each signal, the expected and observed 95% CL upper limit on the signal cross section from the best performing anomaly detection method is reported. The expected limit from the anomaly detection method is also compared to the expected limit of the inclusive search to quantify improvement. For some signals the anomaly detection methods do not improve with respect to the inclusive search. This is indicated by a improvement factor less than one. |
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Table 3:
For the A $ \to $ BC searches, the sensitivity improvement of the anomaly detection methods over the best-performing comparison method. The considered comparison methods are the inclusive search, two-prong targeted selection and three-prong targeted selection. The fourth and fifth columns list, for each signal model, the improvement on the exclusion limit for the best performing anomaly detection method for signals at masses of $ m_{\mathrm{A}} = $ 3 and 5 TeV respectively. This is quantified as the ratio of the expected upper limit on the production cross section obtained by the anomaly detection method as compared that of the inclusive search. The sixth column lists the improvement on the 5 $ \sigma $ discovery potential for the best performing anomaly detection method for each signal at $ m_{\mathrm{A}} = $ 3 TeV. This is quantified as the ratio of the cross section which would have led to a 5 $ \sigma $ excess for the anomaly detection method as compared to that of the comparison method. |
| Summary |
| In conclusion, this analysis presents a model-agnostic search for new resonances in the dijet final state. Five separate anomaly detection methods are employed to improve sensitivity to signals which would produce jets with substructure distinct from that of standard QCD multijet events. No significant excesses were observed by any of the methods. The performance of the anomaly detection techniques was illustrated on a set of benchmark narrow resonance signals covering a wide range of substructure signatures. It was found that the anomaly detection methods improved the discovery sensitivity and expected limits on the benchmark signals. The anomaly detection methods were shown to enhance sensitivity by larger factors, and on a much wider class of models, than traditional cut-based substructure selections, but fell short of the sensitivity of a dedicated model-specific search. The performance of the anomaly detection methods on a diverse set of benchmark models demonstrates the sensitivity of the employed techniques to a wide class of dijet resonances which have substructure and fall within the considered mass range. By construction, these approaches have potential sensitivity to an even broader class of models than the specific benchmarks which were studied. The anomaly detection methods employed in this search represent a significant step forward in the search for new particles at the LHC in a model-agnostic fashion. Further development and deployment of these techniques will play a crucial role in maximizing the discovery potential of LHC data. |
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