Deep learning for inferring cause of data anomaliesDaily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the…V. Azzolini, M. Borisyak, G. Cerminara et al.·Nov 19, 2017SaveLearn
A Blade Tip-Timing method based on Periodic Nonuniform Sampling of order 2Vibrations are among main causes of fatigue and damages leading to destruction of rotating blades. Consequently, motions of blades have to be carefully studied, and in particular, periodic…Bernard Lacaze·Nov 14, 2017SaveLearn
Modeling long correlation times using additive binary Markov chains: applications to wind generation time seriesWind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but…Juliane Weber, Christopher Zachow, Dirk Witthaut·Nov 9, 2017SaveLearn
A Paradox about Likelihood Ratios?We consider whether the asymptotic distributions for the log-likelihood ratio test statistic are expected to be Gaussian or chi-squared. Two straightforward examples provide insight on the difference.Louis Lyons·Nov 2, 2017SaveLearn
Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino DetectorWe provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work…Chang-Wei Loh, Zhi-Qiang Qian, You-Hang Liu et al.·Nov 2, 2017SaveLearn
Exploiting Apache Spark platform for CMS computing analyticsThe CERN IT provides a set of Hadoop clusters featuring more than 5 PBytes of raw storage with different open-source, user-level tools available for analytical purposes. The CMS experiment started…Marco Meoni, Valentin Kuznetsov, Luca Menichetti et al.·Nov 1, 2017SaveLearn
Azimuthal Anisotropy in High Energy Nuclear Collision - An Approach based on Complex Network AnalysisRecently, a complex network based method of Visibility Graph has been applied to confirm the scale-freeness and presence of fractal properties in the process of multiplicity fluctuation. Analysis of…Susmita Bhaduri, Dr. Dipak Ghosh·Oct 27, 2017SaveLearn
A Statistical Distance Derived From The Kolmogorov-Smirnov Test: specification, reference measures (benchmarks) and example usesStatistical distances quantifies the difference between two statistical constructs. In this article, we describe reference values for a distance between samples derived from the Kolmogorov-Smirnov…Renato Fabbri, Fernando Gularte De León·Oct 24, 2017SaveLearn
Normal behaviour models for wind turbine vibrations: An alternative approachThe identification of abnormal behaviour in mechanical systems is key to anticipate and avoid their potential failure. Thus wind turbine health is commonly assessed monitoring series of 10-minute…Pedro G. Lind, Luis Vera-Tudela, Matthias Wächter et al.·Oct 24, 2017SaveLearn
Skewed distributions as limits of a formal evolutionary processTime series of observables measured from complex systems do often exhibit non-normal statistics, their statistical distributions (PDF's) are not gaussian and often skewed, with roughly exponential…F. Sattin·Oct 19, 2017SaveLearn
Towards uncovering the structure of power fluctuations of wind farmsThe structure of the turbulence-driven power fluctuations in a wind farm is fundamentally described from basic concepts. A derived tuning-free model, supported with experiments, reveals the…Huiwen Liu, Yaqing Jin, Nicloas Tobin et al.·Oct 17, 2017SaveLearn
Web interface for reflectivity fittingThe Liquids Reflectometer at Oak Ridge National Laboratory provides neutron reflectivity capability for an average of about 30 experiments each year. In recent years, there has been a large effort to…Mathieu Doucet, Ricardo Miguel Ferraz Leal, Tanner C. Hobson·Oct 13, 2017SaveLearn
A combinatorial framework to quantify peak/pit asymmetries in complex dynamicsWe explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We firstly showcase its performance by applying it to…Uri Hasson, Jacopo Iacovacci, Ben Davis et al.·Oct 13, 2017SaveLearn
High Dimensional Cluster Analysis Using Path LengthsA hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimension (N_D>3). The data set is first reduced to a smaller set of partitions…Kevin McIlhany, Stephen Wiggins·Oct 13, 2017SaveLearn
Causality Testing: A Data Compression FrameworkCausality testing, the act of determining cause and effect from measurements, is widely used in physics, climatology, neuroscience, econometrics and other disciplines. As a result, a large number of…Aditi Kathpalia, Nithin Nagaraj·Oct 11, 2017SaveLearn
Functions to map photoelectron distributions in a variety of setups in angle-resolved photoemission spectroscopyThe distribution of photoelectrons acquired in angle-resolved photoemission spectroscopy can be mapped onto energy-momentum space of the Bloch electrons in the crystal. The explicit forms of the…Y. Ishida, S. Shin·Oct 10, 2017SaveLearn
A New Correlator to Detect and Characterize the Chiral Magnetic EffectA charge-sensitive in-event correlator is proposed and tested for its efficacy to detect and characterize charge separation associated with the Chiral Magnetic Effect (CME) in heavy ion collisions.…Niseem Magdy, Shuzhe Shi, Jinfeng Liao et al.·Oct 4, 2017SaveLearn
γ-ray Spectroscopy using a Binned Likelihood ApproachThe measurement of a reaction cross section from a pulse height spectrum is a ubiquitous problem in experimental nuclear physics. In γ-ray spectroscopy, this is accomplished frequently by measuring…J. R. Dermigny, C. Iliadis, M. Q. Buckner et al.·Sep 30, 2017SaveLearn
Fluorescence decay data analysis correcting for detector pulse pile-up at very high count ratesUsing Time-Correlated Single Photon Counting (TCSPC) for the purpose of fluorescence lifetime measurements is usually limited in speed due to pile-up. With modern instrumentation this limitation can…Matthias Patting, Paja Reisch, Marcus Sackrow et al.·Sep 29, 2017SaveLearn
From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillationA network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac…Stefania Scarsoglio, Fabio Cazzato, Luca Ridolfi·Sep 26, 2017SaveLearn
Direct Measurement of Fast Transients by Using Boot-strapped Waveform AveragingAn approximation to coherent sampling, also known as boot-strapped waveform averaging, is presented. The method uses digital cavities to determine the condition for coherent sampling. It can be used…Mattias Olsson, Fredrik Edman, Khadga Jung Karki·Sep 25, 2017SaveLearn
Towards automation of data quality system for CERN CMS experimentDaily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken. We describe an approach that uses Machine…Maxim Borisyak, Fedor Ratnikov, Denis Derkach et al.·Sep 25, 2017SaveLearn
Machine and deep learning techniques in heavy-ion collisions with ALICEOver the last years, machine learning tools have been successfully applied to a wealth of problems in high-energy physics. A typical example is the classification of physics objects. Supervised…Rüdiger Haake·Sep 22, 2017SaveLearn
A new "3D Calorimetry" of hot nucleiIn the domain of Fermi energy, it is extremely complex to isolate experimentally fragments and particles issued from the cooling of a hot nucleus produced during a heavy ion collision. This paper…E. Vient, L. Manduci, E. Legouée et al.·Sep 21, 2017SaveLearn
Sigmoid-Based Refined Composite Multiscale Fuzzy Entropy and t-Distributed Stochastic Neighbor Embedding Based Fault Diagnosis of Rolling BearingMultiscale fuzzy entropy (MFE) has been a prevalent tool to quantify the complexity of time series. However, it is extremely sensitive to the predetermined parameters and length of time series and it…Zhanwei Jiang, Jinde Zheng, Haiyang Pan et al.·Sep 17, 2017SaveLearn