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| CMS-B2G-24-015 ; CERN-EP-2025-190 | ||
| Search for resonances decaying to an anomalous jet and a Higgs boson in proton-proton collisions at $ \sqrt{s}= $ 13 TeV | ||
| CMS Collaboration | ||
| 16 September 2025 | ||
| Submitted to Eur. Phys. J. C | ||
| Abstract: This paper presents a search for new physics through the process where a massive particle, X, decays into a Higgs boson and a second particle, Y. The Higgs boson subsequently decays into a bottom quark-antiquark pair, which is reconstructed as a single large-radius jet. The decay products of Y are also assumed to produce a single large-radius jet. The identification of the Y particle is enhanced by computing the anomaly score of its candidate jet using an autoencoder, which measures deviations from typical quark- or gluon-induced jets. This allows a simultaneous search for multiple Y decay scenarios within a single analysis. In the main benchmark process, Y is a scalar particle that decays into a W boson pair. Two other scalar Y decay processes are also considered as benchmarks: decays to a light quark-antiquark pair, and decays to a top quark-antiquark pair. A fourth benchmark process considers Y as a hadronically decaying top quark, arising from the decay of a vector-like quark into a top quark and a Higgs boson. Data recorded by the CMS experiment at a center-of-mass energy of 13 TeV in 2016-2018, corresponding to an integrated luminosity of 138 fb$^{-1}$, are analyzed. No significant excess above the standard model background expectation is observed. The most stringent upper limits to date are placed on benchmark signal cross sections for various masses of X and Y particles. | ||
| Links: e-print arXiv:2509.13635 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
png pdf |
Figure 1:
An illustration showing the signal targeted by this analysis. The final state consists of a large-radius jet originating from H decaying to $ \mathrm{b}\overline{\mathrm{b}} $ and another large-radius jet originating from the decay of a second particle, Y. |
png pdf |
Figure 2:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the CR. Pass (upper) and Fail (lower) categories are shown. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 2-a:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the CR. Pass (upper) and Fail (lower) categories are shown. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 2-b:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the CR. Pass (upper) and Fail (lower) categories are shown. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 2-c:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the CR. Pass (upper) and Fail (lower) categories are shown. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 2-d:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the CR. Pass (upper) and Fail (lower) categories are shown. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 3:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass (upper) and Fail (lower) categories are shown. The expected contribution from the signal benchmark with $ M_{\mathrm{X}} = $ 2200 GeV and $ M_{\mathrm{Y}} = $ 250 GeV is overlaid in the MR Pass, assuming a production cross section of 5\unitfb. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 3-a:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass (upper) and Fail (lower) categories are shown. The expected contribution from the signal benchmark with $ M_{\mathrm{X}} = $ 2200 GeV and $ M_{\mathrm{Y}} = $ 250 GeV is overlaid in the MR Pass, assuming a production cross section of 5\unitfb. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 3-b:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass (upper) and Fail (lower) categories are shown. The expected contribution from the signal benchmark with $ M_{\mathrm{X}} = $ 2200 GeV and $ M_{\mathrm{Y}} = $ 250 GeV is overlaid in the MR Pass, assuming a production cross section of 5\unitfb. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 3-c:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass (upper) and Fail (lower) categories are shown. The expected contribution from the signal benchmark with $ M_{\mathrm{X}} = $ 2200 GeV and $ M_{\mathrm{Y}} = $ 250 GeV is overlaid in the MR Pass, assuming a production cross section of 5\unitfb. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 3-d:
The $ M_\mathrm{jj} $ (left) and $ M_\mathrm{j}^{\mathrm{Y}} $ (right) projections showing the number of observed events per GeV (black markers) compared with the backgrounds estimated in the fit to the data (filled histograms) in the MR. Pass (upper) and Fail (lower) categories are shown. The expected contribution from the signal benchmark with $ M_{\mathrm{X}} = $ 2200 GeV and $ M_{\mathrm{Y}} = $ 250 GeV is overlaid in the MR Pass, assuming a production cross section of 5\unitfb. The high level of agreement between the model and the data in the Fail region is due to the nature of the background estimate. The lower panels show the ``Pull'' defined as (observed events}-\text{expected events) $ /\sqrt{\smash[b]{\sigma_{\text{obs}}^{2} + \sigma_{\text{bkg}}^{2}}} $, where $ \sigma_{\text{obs}} $ and $ \sigma_{\text{bkg}} $ are the total uncertainties in the observation and the background estimation, respectively. |
png pdf |
Figure 4:
The expected (upper) and observed (lower) 95% confidence level upper limits on $ \sigma(\mathrm{p} \to \mathrm{X} \to \mathrm{H} \mathrm{Y})\mathcal{B}(\mathrm{H}\to\mathrm{b}\overline{\mathrm{b}})\mathcal{B}(\mathrm{Y}\to\mathrm{W}\mathrm{W}\to2\mathrm{q} 2\overline{\mathrm{q}}^\prime) $ for different values of $ M_{\mathrm{X}} $ and $ M_{\mathrm{Y}} $. The limits have been evaluated in discrete steps corresponding to the centers of the boxes. The numbers in the boxes are given in fb. |
png pdf |
Figure 4-a:
The expected (upper) and observed (lower) 95% confidence level upper limits on $ \sigma(\mathrm{p} \to \mathrm{X} \to \mathrm{H} \mathrm{Y})\mathcal{B}(\mathrm{H}\to\mathrm{b}\overline{\mathrm{b}})\mathcal{B}(\mathrm{Y}\to\mathrm{W}\mathrm{W}\to2\mathrm{q} 2\overline{\mathrm{q}}^\prime) $ for different values of $ M_{\mathrm{X}} $ and $ M_{\mathrm{Y}} $. The limits have been evaluated in discrete steps corresponding to the centers of the boxes. The numbers in the boxes are given in fb. |
png pdf |
Figure 4-b:
The expected (upper) and observed (lower) 95% confidence level upper limits on $ \sigma(\mathrm{p} \to \mathrm{X} \to \mathrm{H} \mathrm{Y})\mathcal{B}(\mathrm{H}\to\mathrm{b}\overline{\mathrm{b}})\mathcal{B}(\mathrm{Y}\to\mathrm{W}\mathrm{W}\to2\mathrm{q} 2\overline{\mathrm{q}}^\prime) $ for different values of $ M_{\mathrm{X}} $ and $ M_{\mathrm{Y}} $. The limits have been evaluated in discrete steps corresponding to the centers of the boxes. The numbers in the boxes are given in fb. |
png pdf |
Figure 5:
The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $ M_{\mathrm{X}} $. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. |
png pdf |
Figure 5-a:
The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $ M_{\mathrm{X}} $. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. |
png pdf |
Figure 5-b:
The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $ M_{\mathrm{X}} $. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. |
png pdf |
Figure 5-c:
The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $ M_{\mathrm{X}} $. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. |
png pdf |
Figure 5-d:
The median expected (dashed line) and observed (solid line) 95% confidence level upper limits on the main and three alternative signal scenarios as a function of $ M_{\mathrm{X}} $. The inner (green) band and outer (yellow) band represent the regions containing 68% and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. |
| Summary |
| A search for beyond the standard model physics through the process where a resonant particle decays into a Higgs boson, H, and an additional particle Y has been presented. The H subsequently decays into a bottom quark-antiquark pair, $ \mathrm{b}\overline{\mathrm{b}} $, reconstructed as a large-radius jet in the Lorentz-boosted regime. The H candidate jets are tagged using the ParticleNet algorithm that is designed to recognize jets originating from a decay of a massive particle into $ \mathrm{b}\overline{\mathrm{b}} $. The identification of the second particle, Y, is performed by computing the anomaly score of its candidate jet using an autoencoder, allowing the simultaneous search for multiple Y decay modes within a single analysis. This approach combines the targeted identification of H decays with model-independent anomaly detection technique, enabling a broad search for new physics. By combining the strong discrimination power of the H decay to $ \mathrm{b}\overline{\mathrm{b}} $ with a model-independent anomaly detection for the second particle, this analysis achieves both enhanced sensitivity and broad applicability to diverse new-physics scenarios. The analysis considers four benchmark models. The main benchmark assumes $ \mathrm{Y} \to \mathrm{W^+}\mathrm{W^-} $, with further hadronic decays of the W bosons. It is simulated for $ M_{\mathrm{X}} $ values within 1400-3000 GeV and $ M_{\mathrm{Y}} $ values within 90-400 GeV, covering 42 signal hypotheses. No significant excess above the standard model background expectations is observed. Upper limits on benchmark signal cross sections at 95% confidence level are set on the main benchmark model in the 0.3-15.8\unitfb range. Additionally, exclusion limits are calculated for three alternative benchmark models, assuming $ \mathrm{Y} \to \mathrm{u} \overline{\mathrm{u}} $, $ \mathrm{Y} \to {\mathrm{t}\overline{\mathrm{t}}} $, or $ \mathrm{Y} \to \mathrm{b} \mathrm{q} \overline{\mathrm{q}}^\prime $ decay modes, for which the most stringent limits to date are achieved. |
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