Adaptive reshaping of objects in (multiparameter) Hilbert space for enhanced detection and classification: an application of receiver operating curve statistics to laser-based mass spectroscopy.

We propose a new approach to the classical detection problem of discrimination of a true signal of interest from an interferent signal, which may be applied to the area of chemical sensing. We show that the detection performance, as quantified by the receiver operating curve (ROC), can be substantially improved when the signal is represented by a multicomponent data set that is actively manipulated by means of a shaped laser probe pulse. In this case, the signal sought (agent) and the interfering signal (interferent) are visualized by vectors in a multidimensional detection space. Separation of these vectors can be achieved by adaptive modification of a probing laser pulse to actively manipulate the Hamiltonian of the agent and interferent. We demonstrate one implementation of the concept of adaptive rotation of signal vectors to chemical agent detection by means of strong-field time-of-flight mass spectrometry.