Cutflow

Cutflow refers to a sequential application of selection criteria, known as cuts, to a dataset. This process is commonly employed in particle physics and data analysis to isolate specific events or data points of interest from a larger, more general dataset. Each cut progressively reduces the number of events that satisfy the preceding conditions, thereby refining the sample.

The purpose of a cutflow is to enhance the signal-to-background ratio. By applying cuts based on observable quantities, researchers can remove events that are unlikely to originate from the phenomenon they are studying, while retaining those that are more characteristic of the signal. This process is crucial for identifying rare events or subtle effects that might otherwise be obscured by background noise.

A typical cutflow is documented step-by-step, showing the number of events remaining after each cut is applied. This documentation allows for transparency and reproducibility of the analysis. It also helps in understanding the efficiency of each cut and the overall efficiency of the selection process. The order of cuts can be important, as some sequences might be more effective than others in reducing background while preserving signal.

The final sample, after all cuts have been applied, is then used for further analysis, such as measuring properties of the selected particles or phenomena, or searching for new physics. The performance of a cutflow is often evaluated by its signal efficiency and background rejection power.