Students will “discover” a new particle by characterising a resonant bump in the invariant mass distribution from a set of simulated particle collisions.
Topics: data processing (raw data → histograms), curve fitting, regression Relevant packages: NumPy, SciPy, Matplotlib Students will be provided with a simulated dataset coming from an X → abc decay (i.e. a list
of particle 4-momenta). They will plot the invariant mass of the mother particle and observe by eye a resonant “bump” corresponding to the mother particle.
Students will then write their own regression-based curve-fitting code to fit the background and resonant distributions, thus determining the mother particle mass and decay width. For a more particle physics-specific tangent, students might look into common curve-fit functions (i.e. Gaussian, Landau, Crystal Ball) and understand when a given functional form is most useful. Once students are happy with the performance of their curve-fitting tool, they will redo the analysis using scipy.optimize (which is a useful module for them to know for future research analyses).
Students will “discover” a new particle by characterising a resonant bump in the invariant mass distribution from a set of simulated particle collisions. Topics: data processing (raw data → histograms), curve fitting, regression Relevant packages: NumPy, SciPy, Matplotlib Students will be provided with a simulated dataset coming from an X → abc decay (i.e. a list of particle 4-momenta). They will plot the invariant mass of the mother particle and observe by eye a resonant “bump” corresponding to the mother particle.
Students will then write their own regression-based curve-fitting code to fit the background and resonant distributions, thus determining the mother particle mass and decay width. For a more particle physics-specific tangent, students might look into common curve-fit functions (i.e. Gaussian, Landau, Crystal Ball) and understand when a given functional form is most useful. Once students are happy with the performance of their curve-fitting tool, they will redo the analysis using scipy.optimize (which is a useful module for them to know for future research analyses).