High energy nuclear collision experiments produce large amounts of data which are often challenging to handle. We present machine learning algorithms using unsupervised learning to find so-called outlier events which might occur e.g. due to detector malfunction or imperfect centrality determination. Their detection can be crucial for the correct determination of sensitive observables. We...
Matrix inversion problems are often encountered in experimental physics, and in particular in high-energy particle physics, under the name of unfolding. The true spectrum of a physical quantity is deformed by the presence of a detector, resulting in an observed spectrum. If we discretize both the true and observed spectra into histograms, we can model the detector response via a matrix....
Advanced machine learning methods are increasingly used in CMS physics analyses to maximize the sensitivity of a wide range of measurements. The landscape is diverse in terms of both methods and applications. Deep learning methods, from recurrent LSTM architectures for classification tasks to deep autoencoders for data quality monitoring, have greatly improved the physics results delivered...
The Stromboli Volcano erupted on July 3, 2019 and August 28, 2019 after a period of modest activity. The paroxysm on July 3rd was not predicted by the monitored parameters that characterize the activity of the volcano. This motivates the study of alternative seismic parameters in the eruption period in order to identify parameters that have potential to predict paroxysms in the future using...
