The NeuroBayes neural network package Feindt, M.; Kerzel, U.
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
04/2006, Letnik:
559, Številka:
1
Journal Article
Recenzirano
Detailed analysis of correlated data plays a vital role in modern analyses. We present a sophisticated neural network package based on Bayesian statistics which can be used for both classification ...and event-by-event prediction of the complete probability density distribution for continuous quantities. The network provides numerous possibilities to automatically preprocess the input variables and uses advanced regularisation and pruning techniques to essentially eliminate the risk of overtraining. Examples from physics and industry are given.
The LHCb RICH detectors Kerzel, U.
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
05/2010, Letnik:
617, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The LHCb experiment at the Large Hadron Collider has been optimised for high precision measurements of the charm and beauty quark sector. The different particle species produced in the high-energy ...collision are identified using two Ring-Imaging Cherenkov detectors.
The LHCb RICH online monitor and data-quality Kerzel, U.
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
05/2010, Letnik:
617, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The LHCb experiment at the Large Hadron Collider has been optimised for high precision measurements of the charm and beauty quark sector. Efficient particle identification at high purities over a ...wide momentum range from around 1 to 100
GeV/
c is vital to many LHCb analyses. Central to the LHCb particle identification strategy are two Ring Imaging CHerenkov (RICH) detectors. Rigorous monitoring of the RICH sub-detector is being developed to insure that high-quality data is recorded which meets the stringent demands from physics analyses.
The LHCb RICH detectors Kerzel, U
Journal of physics. Conference series,
05/2008, Letnik:
110, Številka:
9
Journal Article
Recenzirano
Odprti dostop
The LHCb experiment at the Large Hadron Collider has been optimised for high precision measurements of the charm and beauty quark sector. The different particle species produced in the high-energy ...reaction are identified using two Ring-Imaging Cherenkov detectors.
Fast integration using quasi-random numbers Bossert, J.; Feindt, M.; Kerzel, U.
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
04/2006, Letnik:
559, Številka:
1
Journal Article
Recenzirano
Quasi-random numbers are specially constructed series of numbers optimised to evenly sample a given
s-dimensional volume. Using quasi-random numbers in numerical integration converges faster with a ...higher accuracy compared to the case of pseudo-random numbers. The basic properties of quasi-random numbers are introduced, various generators are discussed and the achieved gain is illustrated by examples.
The LHCb experiment at the CERN Large Hadron Collider (LHC) utilises two Ring Imaging CHerenkov (RICH) detectors for particle identification. To verify that the RICH assembly will perform as expected ...prior to installation, an array of 48 production Hybrid Photon Detectors and their readout have been tested under realistic running conditions in a 25
ns-structured charged particle beam provided by the SPS facility at CERN. This system test is an important milestone in the overall commissioning of the LHCb detector and demonstrates that all aspects meet the stringent physics requirements of the LHCb experiment.
Artificial Intelligence (AI) and Machine Learning have enormous potential to transform businesses and disrupt entire industry sectors. However, companies wishing to integrate algorithmic decisions ...into their face multiple challenges: They have to identify use-cases in which artificial intelligence can create value, as well as decisions that can be supported or executed automatically. Furthermore, the organization will need to be transformed to be able to integrate AI based systems into their human work-force. Furthermore, the more technical aspects of the underlying machine learning model have to be discussed in terms of how they impact the various units of a business: Where do the relevant data come from, which constraints have to be considered, how is the quality of the data and the prediction evaluated? The Enterprise AI canvas is designed to bring Data Scientist and business expert together to discuss and define all relevant aspects which need to be clarified in order to integrate AI based systems into a digital enterprise. It consists of two parts where part one focuses on the business view and organizational aspects, whereas part two focuses on the underlying machine learning model and the data it uses.
Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input for subsequent decision making like ordering processes. In contrast to point ...estimates, such as the conditional mean of the underlying probability distribution, or confidence intervals, forecasting complete probability density functions allows to investigate the impact on operational metrics, which are important to define the business strategy, over the full range of the expected demand. Whereas metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare, and this work proposes new techniques for both qualitative and quantitative evaluation methods. Using the supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted such that each prediction is fully explainable. This is of particular importance for practitioners, as it allows to avoid "black-box" models and understand the contributing factors for each individual prediction. Another crucial aspect in terms of both explainability and generalizability of demand forecasting methods is the limitation of the influence of temporal confounding, which is prevalent in most state of the art approaches.
The X(3872) at the Tevatron Kerzel, Ulrich
Nuclear physics. Section B, Proceedings supplement,
06/2006, Letnik:
156, Številka:
1
Journal Article
The discovery of the
X(3872) by Belle S.K. Choi, et al., Belle Collaboration, Phys. Rev. Lett 91, 262001 (2003) has stimulated intensive activities in hadron spectroscopy. Many of the particle's ...properties have been determined since the first observation, however its nature is still unknown. The article summarises the latest results from CDF and D0 and interprets the results in the context of theoretical models.
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