Learning to detect new top-quark interactions

4 October 2021

A report from the CMS experiment

Figure 1

Ever since its discovery in 1995 at the Tevatron, the top quark has been considered to be a highly effective probe of new physics. A key reason is that the last fundamental fermion predicted by the Standard Model (SM) has a remarkably high mass, just a sliver under the Higgs vacuum expectation value divided by the square root of two, implying a Yukawa coupling close to unity. This has far-reaching implications: the top quark impacts the electroweak sector significantly through loop corrections, and may couple preferentially to new massive states. But while the top quark may represent a window into new physics, we cannot know a priori whether new massive particles could ever be produced at the LHC, and direct searches have so far been inconclusive. Model-inde­pendent measurements carried out within the framework of effective field theory (EFT) are therefore becoming increasingly important as a means to make the most of the wealth of precision measurements at the LHC. This approach makes it possible to systematically correlate sparse deviations observed in different measurements, in order to pinpoint any anomalies in top-quark couplings that might arise from unknown massive particles.

The top quark impacts the electroweak sector significantly through loop corrections

A new CMS analysis searches for anomalies in top-quark interactions with the Z boson using an EFT framework. The cross-section measurements of the rare associated production of either one (tZ) or two (ttZ) top quarks with a Z boson were statistically limited until recently. These interactions are among the least constrained by the available data in the top-quark sector, despite being modified in numerous beyond-SM models, such as composite Higgs models and minimal supersymmetry. Using the full LHC Run-2 data set, this study targets high-purity final states with multiple electrons and muons. It sets some of the tightest constraints to date on five generic types of EFT interactions that could substantially modify the characteristics of associated top-Z production, while having negligible or no effect on background processes.

Machine learning

In contrast to the more usual reinterpretations of SM measurements that require assumptions on the nature of new physics, this analysis considers EFT effects on observables at the detector level and constrains them directly from the data using a strategy that combines observables specifically selected for their sensitivity to EFT. The key feature of this work is its heavy use of multivariate-analysis techniques based on machine learning, which improve its sensitivity to new interactions. First, to define regions enriched in the processes of interest, a multiclass neural network is trained to discriminate between different SM processes. Subsequently, several binary neural networks learn to separate events generated according to the SM from events that include EFT effects arising from one or more types of anomalous interactions. For the first time in an analysis using LHC data, these classifiers were trained on the full physical amplitudes, including the interference between SM and EFT components.

The binary classifiers are used to construct powerful discriminant variables out of high-dimensional input data. Their distributions are fitted to data to constrain up to five types of EFT couplings simultaneously. The widths of the corresponding confidence intervals are significantly reduced thanks to the combination of the available kinematic information that was specifically chosen to be sensitive to EFT in the top quark sector. All results are consistent with the SM, which indicates either the absence of new effects in the targeted interactions or that the mass scale of new physics is too high to be probed with the current sensitivity. This result is an important step towards the more widespread use of machine learning to target EFT effects, to efficiently explore the enormous volume of LHC data more globally and comprehensively.

Further reading

CMS Collab. 2021 CMS-PAS-TOP-21-001.

bright-rec iop pub iop-science physcis connect