Semi-supervised anomaly detection – towards model-independent searches of new physics

• Published in: Journal of physics. Conference series Vol. 368; no. 1; pp. 12032 - 9
• Main Authors: Kuusela, Mikael, Vatanen, Tommi, Malmi, Eric, Raiko, Tapani, Aaltonen, Timo, Nagai, Yoshikazu
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.01.2012
• Subjects: Algorithms; Anomalies; Classification; Deviation; Gaussian; Machine learning; Neural networks; Pattern recognition; Physics; Probabilistic models; Searching; Signal classification; Systematic errors; Training

Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for exampl...