Forward-backward multiplicity and momentum correlations in pp and pPb collisions at the LHC energies

Correlations and fluctuations between produced particles in an ultra-relativistic nuclear collision remain one of the successor to understand the basics of the particle production mechanism. More differential tools like Forward-Backward (FB) correlations between particles from two different phase-space further strengthened our cognizance. We have studied the strength of FB correlations in terms of charged particle multiplicity and summed transverse momentum for proton-proton ($pp$) and proton-lead ($pPb$) collisions at the centre-of-mass energies $\sqrt{s}$ = 13 TeV and $\sqrt{s_{\rm NN}}$ = 5.02 TeV respectively for the EPOS3 simulated events with hydrodynamical evolution of produced particles. Furthermore, the correlation strengths are separately obtained for the particles coming from the core and the corona. FB correlation strengths are examined as a function of psedorapidity gap ($\eta_{gap}$), psedorapidity window-width ($\delta\eta$), centre-of-mass energy ($\sqrt{s}$), minimum transverse momentum ($p_{Tmin}$) and different multiplicity classes following standard kinematical cuts used by the ALICE and the ATLAS experiments at the LHC for all three EPOS3 event samples. EPOS3 model shows a similar trend of FB multiplicity and momentum correlation strengths for both $pp$ \& $pPb$ systems, though the correlation strengths are found to be larger for $pPb$ system than $pp$ system. Moreover, $\delta\eta$-weighted average of FB correlation strengths as a function of different center-of-mass energies for $pp$ collisions delineates a tendency of saturation at very high energies.Comment: 10 Pages, 13 Figures, Accepted in Physical Review

Remote diagnosis server

A network-based diagnosis server for monitoring and diagnosing a system, the server being remote from the system it is observing, comprises a sensor for generating signals indicative of a characteristic of a component of the system, a network-interfaced sensor agent coupled to the sensor for receiving signals therefrom, a broker module coupled to the network for sending signals to and receiving signals from the sensor agent, a handler application connected to the broker module for transmitting signals to and receiving signals therefrom, a reasoner application in communication with the handler application for processing, and responding to signals received from the handler application, wherein the sensor agent, broker module, handler application, and reasoner applications operate simultaneously relative to each other, such that the present invention diagnosis server performs continuous monitoring and diagnosing of said components of the system in real time. The diagnosis server is readily adaptable to various different systems

An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data

This paper discusses a mixed method that combines unsupervised learning methods and human expert input for analyzing telemetry data from long-duration robotic space missions. Our goal is to develop more automated methods for detecting anomalies in time series data. Once anomalies are identified using unsupervised learning methods we use feature selection methods followed by expert input to derive the knowledge required for building on-line detectors. These detectors can be used in later phases of the current mission and in future missions for improving operations and overall safety of the mission. Whereas the primary focus in this paper is on developing data-driven anomaly detection methods, we also present a computational platform for data mining and analytics that can operate on historical data offline, as well as incoming telemetry data on-line

Multisensor-multitarget data association for tracking

In this dissertation we develop an algorithm to solve the multisensor multi-target state estimation problem. The problem is to associate the measurements from one or more, possibly heterogeneous, sensors to identify the real targets, and to estimate their positions at any given time. Such problems arise in surveillance and tracking systems estimating the states (target positions and velocities) of an unknown number of targets. The sensors could be active (3D radars measuring full target position; or 2D radars measuring target azimuth and slant range) or passive (Electro-optic sensors measuring the azimuth and elevation angles of the source; or high frequency direction finders measuring only the azimuth angles of the source). The sensors could be stationary or mounted on a moving platform, such as a low orbiting satellite. The targets may be in motion, but are assumed to be non-maneuvering. The source of a detection can be either a real target, in which case the measurement is the true observed variable of the target plus measurement noise, or a spurious one, i.e., a false alarm. In addition, the sensors may have nonunity detection probabilities.^ The central problem in a multisensor-multitarget state estimation problem is that of data association--the problem of determining from which target, if any, a particular measurement originated. The data-association problem is formulated as a generalized S-dimensional (S-D) assignment problem, which is NP-hard for 3 or more sensor scans (S $\ge$ 3). In this dissertation, we present an efficient and recursive generalized S-D assignment algorithm (S $\ge$ 3) employing a successive Lagrangian relaxation technique, with application to a wide variety of scenarios. An important feature of this algorithm is that it improves its solution iteratively, but all the intermediate solutions are feasible. Thus, one may execute this algorithm up to some pre-specified deadline and obtain the best solution at termination. A second useful feature is that it also provides a measure of how close the current solution is from the (perhaps unknowable) optimal solution. A sliding window version of the algorithm is also presented, with application to a simple multisensor multitarget tracking example.

Condition Monitoring for Helicopter Data

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately