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| CMS-EXO-22-026 ; CERN-EP-2024-291 | ||
| Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $ \sqrt{s} $ = 13 TeV | ||
| CMS Collaboration | ||
| 4 December 2024 | ||
| Submitted to Reports on Progress in Physics | ||
| Abstract: This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8-6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. 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 semisupervised 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 by the CMS experiment at the LHC, at a center-of-mass energy of 13 TeV. No significant excesses above background expectations are seen. 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 been previously sought, making several of the limits reported on the corresponding benchmark models the first ever. When compared to benchmark inclusive and substructure-based search strategies, the anomaly detection methods are found to significantly enhance the sensitivity to a variety of models. | ||
| Links: e-print arXiv:2412.03747 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Production of a dijet resonance, A, in a proton-proton collision. The A resonance decays to two resonances B and C, which in turn each decay to a jet with anomalous substructure arising from multiple subjets. |
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Figure 2:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures 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 \PBpr\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 2-a:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures 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 \PBpr\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 2-b:
The $ p $-values as a function of the injected signal cross sections for the different analysis procedures 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 \PBpr\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 3:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (upper left), CWoLa Hunting (upper right), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and QUAK (lower right). The shapes of two benchmark signals are shown for the VAE-QR method; the signal shapes for the other methods are similar. For all methods besides the VAE-QR, separate selections are applied for different signal mass hypotheses and the resulting mass spectra are fit separately. The figures show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The spectra in the $ \alpha $ signal regions (indicated by the vertical dotted lines) are shown for the weakly supervised methods and a similar selection of signal regions are shown for the QUAK method. |
png pdf |
Figure 3-a:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (upper left), CWoLa Hunting (upper right), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and QUAK (lower right). The shapes of two benchmark signals are shown for the VAE-QR method; the signal shapes for the other methods are similar. For all methods besides the VAE-QR, separate selections are applied for different signal mass hypotheses and the resulting mass spectra are fit separately. The figures show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The spectra in the $ \alpha $ signal regions (indicated by the vertical dotted lines) are shown for the weakly supervised methods and a similar selection of signal regions are shown for the QUAK method. |
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Figure 3-b:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (upper left), CWoLa Hunting (upper right), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and QUAK (lower right). The shapes of two benchmark signals are shown for the VAE-QR method; the signal shapes for the other methods are similar. For all methods besides the VAE-QR, separate selections are applied for different signal mass hypotheses and the resulting mass spectra are fit separately. The figures show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The spectra in the $ \alpha $ signal regions (indicated by the vertical dotted lines) are shown for the weakly supervised methods and a similar selection of signal regions are shown for the QUAK method. |
png pdf |
Figure 3-c:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (upper left), CWoLa Hunting (upper right), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and QUAK (lower right). The shapes of two benchmark signals are shown for the VAE-QR method; the signal shapes for the other methods are similar. For all methods besides the VAE-QR, separate selections are applied for different signal mass hypotheses and the resulting mass spectra are fit separately. The figures show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The spectra in the $ \alpha $ signal regions (indicated by the vertical dotted lines) are shown for the weakly supervised methods and a similar selection of signal regions are shown for the QUAK method. |
png pdf |
Figure 3-d:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (upper left), CWoLa Hunting (upper right), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and QUAK (lower right). The shapes of two benchmark signals are shown for the VAE-QR method; the signal shapes for the other methods are similar. For all methods besides the VAE-QR, separate selections are applied for different signal mass hypotheses and the resulting mass spectra are fit separately. The figures show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The spectra in the $ \alpha $ signal regions (indicated by the vertical dotted lines) are shown for the weakly supervised methods and a similar selection of signal regions are shown for the QUAK method. |
png pdf |
Figure 3-e:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (upper left), CWoLa Hunting (upper right), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and QUAK (lower right). The shapes of two benchmark signals are shown for the VAE-QR method; the signal shapes for the other methods are similar. For all methods besides the VAE-QR, separate selections are applied for different signal mass hypotheses and the resulting mass spectra are fit separately. The figures show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The spectra in the $ \alpha $ signal regions (indicated by the vertical dotted lines) are shown for the weakly supervised methods and a similar selection of signal regions are shown for the QUAK method. |
png pdf |
Figure 3-f:
The dijet invariant mass spectrum and resulting background fit to the data for VAE-QR (upper left), CWoLa Hunting (upper right), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and QUAK (lower right). The shapes of two benchmark signals are shown for the VAE-QR method; the signal shapes for the other methods are similar. For all methods besides the VAE-QR, separate selections are applied for different signal mass hypotheses and the resulting mass spectra are fit separately. The figures show the fitted and observed dijet mass distribution in the signal window of each selection, which results in a discontinuous distribution. The spectra in the $ \alpha $ signal regions (indicated by the vertical dotted lines) are shown for the weakly supervised methods and a similar selection of signal regions are shown for the QUAK method. |
png pdf |
Figure 4:
The dijet invariant mass spectrum and resulting background fit to the data for the $ \beta $ signal regions (indicated by the vertical dotted lines) of CWoLa Hunting (upper), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and a similar selection of signal regions of QUAK (lower right). Separate selections are applied for different signal mass hypotheses and the resulting mass spectra are 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 CATHODE and CATHODE-b methods are not used in the highest mass window of the $ \beta $ signal regions due to the limited number of data events. They therefore have one fewer signal region shown than the other methods. |
png pdf |
Figure 4-a:
The dijet invariant mass spectrum and resulting background fit to the data for the $ \beta $ signal regions (indicated by the vertical dotted lines) of CWoLa Hunting (upper), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and a similar selection of signal regions of QUAK (lower right). Separate selections are applied for different signal mass hypotheses and the resulting mass spectra are 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 CATHODE and CATHODE-b methods are not used in the highest mass window of the $ \beta $ signal regions due to the limited number of data events. 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 the $ \beta $ signal regions (indicated by the vertical dotted lines) of CWoLa Hunting (upper), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and a similar selection of signal regions of QUAK (lower right). Separate selections are applied for different signal mass hypotheses and the resulting mass spectra are 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 CATHODE and CATHODE-b methods are not used in the highest mass window of the $ \beta $ signal regions due to the limited number of data events. 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 the $ \beta $ signal regions (indicated by the vertical dotted lines) of CWoLa Hunting (upper), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and a similar selection of signal regions of QUAK (lower right). Separate selections are applied for different signal mass hypotheses and the resulting mass spectra are 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 CATHODE and CATHODE-b methods are not used in the highest mass window of the $ \beta $ signal regions due to the limited number of data events. 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 the $ \beta $ signal regions (indicated by the vertical dotted lines) of CWoLa Hunting (upper), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and a similar selection of signal regions of QUAK (lower right). Separate selections are applied for different signal mass hypotheses and the resulting mass spectra are 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 CATHODE and CATHODE-b methods are not used in the highest mass window of the $ \beta $ signal regions due to the limited number of data events. 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 the $ \beta $ signal regions (indicated by the vertical dotted lines) of CWoLa Hunting (upper), TNT (middle left), CATHODE (middle right), CATHODE-b (lower left), and a similar selection of signal regions of QUAK (lower right). Separate selections are applied for different signal mass hypotheses and the resulting mass spectra are 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 CATHODE and CATHODE-b methods are not used in the highest mass window of the $ \beta $ signal regions due to the limited number of data events. They therefore have one fewer signal region shown than the other methods. |
png pdf |
Figure 5:
The discovery sensitivity for the process $ \mathrm{A} \to \mathrm{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 is set to $ m_\mathrm{A}= $ 3 TeV. For the BSM daughter particles, the masses of the Y and Y' are set to 170 GeV, while the masses of the \PBpr, $ \mathrm{R} $, and H are set to 400 GeV. In the upper 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 selections targeting 2-prong (dark brown) or 3-prong (tan) decays. The expected 95% confidence level upper limits from the inclusive search are also shown in the upper 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. Shown in the lower panel is the ratio of the cross section sensitivity from the inclusive search to the corresponding sensitivity for each method. |
png pdf |
Figure 6:
The upper limit at 95% confidence level on the cross section for the process $ \mathrm{A} \to \mathrm{BC} $, is shown for each search method applied to a variety of signal models. For a resonance mass $ m_\mathrm{A}= $ 3 TeV (upper) and $ m_\mathrm{A}= $ 5 TeV (lower), we show for each signal model (columns), and search method (all colors), the observed limits (crosses), expected limits (squares), and their 68% expected central intervals (error bars). For the BSM daughter particles, the masses of the Y and Y' are set to 170 GeV, while the masses of the \PBpr, $ \mathrm{R} $, and H are 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 selections targeting 2-prong (dark brown) or 3-prong decays (tan), and the observed limit from a previous CMS search [50] for the $ \mathrm{W}_\mathrm{KK} \to \mathrm{R}\mathrm{W} \to 3 \mathrm{W} $ model in the all-hadronic channel (gray). |
png pdf |
Figure 6-a:
The upper limit at 95% confidence level on the cross section for the process $ \mathrm{A} \to \mathrm{BC} $, is shown for each search method applied to a variety of signal models. For a resonance mass $ m_\mathrm{A}= $ 3 TeV (upper) and $ m_\mathrm{A}= $ 5 TeV (lower), we show for each signal model (columns), and search method (all colors), the observed limits (crosses), expected limits (squares), and their 68% expected central intervals (error bars). For the BSM daughter particles, the masses of the Y and Y' are set to 170 GeV, while the masses of the \PBpr, $ \mathrm{R} $, and H are 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 selections targeting 2-prong (dark brown) or 3-prong decays (tan), and the observed limit from a previous CMS search [50] for the $ \mathrm{W}_\mathrm{KK} \to \mathrm{R}\mathrm{W} \to 3 \mathrm{W} $ model in the all-hadronic channel (gray). |
png pdf |
Figure 6-b:
The upper limit at 95% confidence level on the cross section for the process $ \mathrm{A} \to \mathrm{BC} $, is shown for each search method applied to a variety of signal models. For a resonance mass $ m_\mathrm{A}= $ 3 TeV (upper) and $ m_\mathrm{A}= $ 5 TeV (lower), we show for each signal model (columns), and search method (all colors), the observed limits (crosses), expected limits (squares), and their 68% expected central intervals (error bars). For the BSM daughter particles, the masses of the Y and Y' are set to 170 GeV, while the masses of the \PBpr, $ \mathrm{R} $, and H are 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 selections targeting 2-prong (dark brown) or 3-prong decays (tan), and the observed limit from a previous CMS search [50] for the $ \mathrm{W}_\mathrm{KK} \to \mathrm{R}\mathrm{W} \to 3 \mathrm{W} $ model in the all-hadronic channel (gray). |
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Figure A1:
A flowchart outlining how the samples for the weakly supervised training were constructed in the TNT method. The two jets in the dijet candidate were randomly assigned labels J1 and J2. For each event, the J1 (J2) jet is placed into either a signal-like or background-like sample based on the autoencoder scores evaluated on the J2 (J1) jet. The samples of signal-like (background-like) J1's and signal-like (background-like) J2's were merged together to construct a single sample of signal-like (background-like) jets. The TNT classifier is then trained to distinguish between these two samples. |
png pdf |
Figure B1:
Diagram of the limit-setting procedure for the $ \mathrm{X}\to \mathrm{Y}\mathrm{Y'} \to 4 \mathrm{q} $ signal at 3 TeV with the CATHODE method. The upper panel shows the estimated signal acceptance times efficiency as a function of the cross section injected in data. The shaded region in the upper panel shows the total statistical and systematic uncertainty in the efficiency. The resulting $ N_\text{sig}(\sigma_\text{sig}) $ curve and its corresponding uncertainty band from the efficiency are 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% confidence level 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 a 3 TeV resonance mass. For each signal, the expected and observed 95% CL upper limits on the signal cross section from the best performing anomaly detection method are reported. The expected limit from the anomaly detection method is also compared to the expected limit of the inclusive search to quantify the improvement. For some signals the anomaly detection methods do not improve with respect to the inclusive search. This is indicated by an improvement factor less than one. |
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Table 2:
Limits on additional signal models and daughter mass combinations for a 5 TeV resonance mass. For each signal, the expected and observed 95% CL upper limits on the signal cross section from the best performing anomaly detection method are reported. The expected limit from the anomaly detection method is also compared to the expected limit of the inclusive search to quantify the improvement. For some signals the anomaly detection methods do not improve with respect to the inclusive search. This is indicated by an improvement factor less than one. |
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Table 3:
For the $ \mathrm{A} \to \mathrm{BC} $ searches, the sensitivity improvement of the anomaly detection methods with respect to the best performing comparison method. The considered comparison methods are the inclusive search, 2-prong targeted selection, and 3-prong targeted selection. The fourth and fifth columns list, for each signal model, improvement factors 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 to that of the inclusive search. The sixth column lists the improvement factor 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 that would have led to a 5$ \sigma $ excess for the comparison method as compared to that of the anomaly detection method. |
| Summary |
| To summarize, we have presented a model-agnostic search for new resonances in the dijet final state. The search is based on 138 fb$ ^{-1} $ of data collected at $ \sqrt{s} = $ 13 TeV by the CMS experiment. Five separate anomaly detection methods were employed to improve sensitivity to signals that produce jets with substructure distinct from that of QCD multijet events. No significant excesses of events 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 the sensitivity by larger factors, and on a much wider class of models, than traditional cutoff-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 that have substructure and fall within the considered mass range. By construction, these approaches have sensitivity to an even broader class of models than the specific benchmarks 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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