0000000001048240
AUTHOR
Aleko Khukhunaishvili
Search forBs0→μ+μ−andB0→μ+μ−Decays with CDF II
A search has been performed for B{sub s}{sup 0} {yields} {mu}{sup +}{mu}{sup -} and B{sup 0} {yields} {mu}{sup +}{mu}{sup -} decays using 7 fb{sup -1} of integrated luminosity collected by the CDF II detector at the Fermilab Tevatron collider. The observed number of B{sup 0} candidates is consistent with background-only expectations and yields an upper limit on the branching fraction of {Beta}(B{sup 0} {yields} {mu}{sup +}{mu}{sup -}) < 6.0 x 10{sup -9} at 95% confidence level. We observe an excess of B{sub s}{sup 0} candidates. The probability that the background processes alone could produce such an excess or larger is 0.27%. The probability that the combination of background and the expe…
Observation of the rare B(s)(0) + decay from the combined analysis of CMS and LHCb data.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported licence.-- et al.
LONG HORIZON ANOMALY PREDICTION IN MULTIVARIATE TIME SERIES WITH CAUSAL AUTOENCODERS
Predictive maintenance is essential for complex industrial systems to foresee anomalies before major system faults or ultimate breakdown. However, the existing efforts on Industry 4.0 predictive monitoring are directed at semi-supervised anomaly detection with limited robustness for large systems, which are often accompanied by uncleaned and unlabeled data. We address the challenge of predicting anomalies through data-driven end-to-end deep learning models using early warning symptoms on multivariate time series sensor data. We introduce AnoP, a long multi-timestep anomaly prediction system based on unsupervised attention-based causal residual networks, to raise alerts for anomaly preventio…