Research Projects

This project will introduce the optimal observable technique for accelerator-based new physics (NP) searches beyond the Standard Model (SM) as we know the SM is inherently an incomplete theory. A NP theory generally contains a set of unknown parameters. Our aim is to extract as much information on those parameters as possible by analyzing the data. For that, we rely on some observables which are sensitive to those parameters. One can enhance sensitivity by constructing intelligent observables. But, theoretically, it is not possible to measure a parameter with arbitrarily high precision. We shall employ optimal observables constructed from the maximum information stored in complex scattering processes to push the sensitivity of NP searches to their limits. The Cramer-Rao bound in statistics says that there exists a lower bound on the statistical uncertainty associated with every parameter we measure. We shall design optimal observables associated with this bound to obtain the highest possible precision on unknown theory parameters in the context of the SM effective field theory (SMEFT) or dark matter interactions with nucleons. So far, the optimal observable method has been applied to lepton colliders. We shall focus on important processes at the Large Hadron Collider involving Higgs and electroweak gauge bosons and systematically identify the set of operators, from a large number of the SMEFT operators, that can significantly affect various observables. New sensitive observables like angular correlations, asymmetries, etc. will be proposed. To enhance the sensitivity, we shall use a multivariate analysis technique in a machine learning framework with binned and unbinned data. We shall extend our SMEFT approach by considering new particles e.g. a right-handed neutrino, in addition to the SM field, as the degrees of freedom. This project has the potential to give the best search strategies for discoveries or the best exclusion limits for NP and guide the construction of new theories for the unknown high-energy domain. If NP exists within our experimental reach, it will most likely show up first in the measurements of optimal observables. One can recast any null results using our method to set the most stringent limits on the unknown model parameters. This method is very generic in nature and can be adapted to any other NP searches as well. Using concepts of Riemannian geometry, the optimal observable technique will be extended to biased estimators and nonlinear parameters. The three-year plan includes the development of user-friendly public codes to obtain optimal observables for multidimensional nonlinear parameters, that we often encounter in NP theories, incorporating systematic uncertainties of experiments. Using this, we shall derive bounds and estimate the projected reach and sensitivity of model parameters. Altogether, the purpose of this project fits very well with the current particle physics research and the global quest for NP.