  Nanomechanical resonant sensors are used in mass spectrometry via detection
of resonance frequency jumps. There is a fundamental trade-off between
detection speed and accuracy. Temporal and size resolution are limited by the
resonator characteristics and noise. A Kalman filtering technique, augmented
with maximum-likelihood estimation, was recently proposed as a Pareto optimal
solution. We present enhancements and robust realizations for this technique,
including a confidence boosted thresholding approach as well as machine
learning for event detection. We describe learning techniques that are based on
neural networks and boosted decision trees for temporal location and event size
estimation. In the pure learning based approach that discards the Kalman
filter, the raw data from the sensor are used in training a model for both
location and size prediction. In the alternative approach that augments a
Kalman filter, the event likelihood history is used in a binary classifier for
event occurrence. Locations and sizes are predicted using maximum-likelihood,
followed by a Kalman filter that continually improves the size estimate. We
present detailed comparisons of the learning based schemes and the confidence
boosted thresholding approach, and demonstrate robust performance for a
practical realization.
