Geant4 Hadronic Cascade Models and CMS Data Analysis : Computational Challenges in the LHC era

This work belongs to the field of computational high-energy physics (HEP). The key methods used in this thesis work to meet the challenges raised by the Large Hadron Collider (LHC) era experiments are object-orientation with software engineering, Monte Carlo simulation, the computer technology of clusters, and artificial neural networks. The first aspect discussed is the development of hadronic cascade models, used for the accurate simulation of medium-energy hadron-nucleus reactions, up to 10 GeV. These models are typically needed in hadronic calorimeter studies and in the estimation of radiation backgrounds. Various applications outside HEP include the medical field (such as hadron treatment simulations), space science (satellite shielding), and nuclear physics (spallation studies). Validation results are presented for several significant improvements released in Geant4 simulation tool, and the significance of the new models for computing in the Large Hadron Collider era is estimated. In particular, we estimate the ability of the Bertini cascade to simulate Compact Muon Solenoid (CMS) hadron calorimeter HCAL. LHC test beam activity has a tightly coupled cycle of simulation-to-data analysis. Typically, a Geant4 computer experiment is used to understand test beam measurements. Thus an another aspect of this thesis is a description of studies related to developing new CMS H2 test beam data analysis tools and performing data analysis on the basis of CMS Monte Carlo events. These events have been simulated in detail using Geant4 physics models, full CMS detector description, and event reconstruction. Using the ROOT data analysis framework we have developed an offline ANN-based approach to tag b-jets associated with heavy neutral Higgs particles, and we show that this kind of NN methodology can be successfully used to separate the Higgs signal from the background in the CMS experiment.

A fast algorithm for finding frequent episodes in event streams

Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the frequency of an episode is some suitable measure of how often the episode occurs in the data sequence. Recently,we proposed a new frequency measure for episodes based on the notion of non-overlapped occurrences of episodes in the event sequence, and showed that, such a definition, in addition to yielding computationally efficient algorithms, has some important theoretical properties in connecting frequent episode discovery with HMM learning. This paper presents some new algorithms for frequent episode discovery under this non-overlapped occurrences-based frequency definition. The algorithms presented here are better (by a factor of N, where N denotes the size of episodes being discovered) in terms of both time and space complexities when compared to existing methods for frequent episode discovery. We show through some simulation experiments, that our algorithms are very efficient. The new algorithms presented here have arguably the least possible orders of spaceand time complexities for the task of frequent episode discovery.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

Generalized frequent episodes in Event Sequences

Discovering patterns in temporal data is an important task in Data Mining. A successful method for this was proposed by Mannila et al. [1] in 1997. In their framework, mining for temporal patterns in a database of sequences of events is done by discovering the so called frequent episodes. These episodes characterize interesting collections of events occurring relatively close to each other in some partial order. However, in this framework(and in many others for finding patterns in event sequences), the ordering of events in an event sequence is the only allowed temporal information. But there are many applications where the events are not instantaneous; they have time durations. Interesting episodesthat we want to discover may need to contain information regarding event durations etc. In this paper we extend Mannila et al.’s framework to tackle such issues. In our generalized formulation, episodes are defined so that much more temporal information about events can be incorporated into the structure of an episode. This significantly enhances the expressive capability of the rules that can be discovered in the frequent episode framework. We also present algorithms for discovering such generalized frequent episodes.

Data acquisition and processing at ocean bottom for a Tsunami warning system

The design and development of a Bottom Pressure Recorder for a Tsunami Early Warning System is described here. The special requirements that it should satisfy for the specific application of deployment at ocean bed and pressure monitoring of the water column above are dealt with. A high-resolution data digitization and low circuit power consumption are typical ones. The implementation details of the data sensing and acquisition part to meet these are also brought out. The data processing part typically encompasses a Tsunami detection algorithm that should detect an event of significance in the background of a variety of periodic and aperiodic noise signals. Such an algorithm and its simulation are presented. Further, the results of sea trials carried out on the system off the Chennai coast are presented. The high quality and fidelity of the data prove that the system design is robust despite its low cost and with suitable augmentations, is ready for a full-fledged deployment at ocean bed. (C) 2013 Elsevier Ltd. All rights reserved.

Event-triggered sampling using signal extrema for instantaneous amplitude and instantaneous frequency estimation

Event-triggered sampling (ETS) is a new approach towards efficient signal analysis. The goal of ETS need not be only signal reconstruction, but also direct estimation of desired information in the signal by skillful design of event. We show a promise of ETS approach towards better analysis of oscillatory non-stationary signals modeled by a time-varying sinusoid, when compared to existing uniform Nyquist-rate sampling based signal processing. We examine samples drawn using ETS, with events as zero-crossing (ZC), level-crossing (LC), and extrema, for additive in-band noise and jitter in detection instant. We find that extrema samples are robust, and also facilitate instantaneous amplitude (IA), and instantaneous frequency (IF) estimation in a time-varying sinusoid. The estimation is proposed solely using extrema samples, and a local polynomial regression based least-squares fitting approach. The proposed approach shows improvement, for noisy signals, over widely used analytic signal, energy separation, and ZC based approaches (which are based on uniform Nyquist-rate sampling based data-acquisition and processing). Further, extrema based ETS in general gives a sub-sampled representation (relative to Nyquistrate) of a time-varying sinusoid. For the same data-set size captured with extrema based ETS, and uniform sampling, the former gives much better IA and IF estimation. (C) 2015 Elsevier B.V. All rights reserved.

Event-triggered Sampling and Reconstruction of Sparse Real-valued Trigonometric Polynomials

We propose data acquisition from continuous-time signals belonging to the class of real-valued trigonometric polynomials using an event-triggered sampling paradigm. The sampling schemes proposed are: level crossing (LC), close to extrema LC, and extrema sampling. Analysis of robustness of these schemes to jitter, and bandpass additive gaussian noise is presented. In general these sampling schemes will result in non-uniformly spaced sample instants. We address the issue of signal reconstruction from the acquired data-set by imposing structure of sparsity on the signal model to circumvent the problem of gap and density constraints. The recovery performance is contrasted amongst the various schemes and with random sampling scheme. In the proposed approach, both sampling and reconstruction are non-linear operations, and in contrast to random sampling methodologies proposed in compressive sensing these techniques may be implemented in practice with low-power circuitry.

Discovering compressing serial episodes from event sequences

Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevant subset of patterns is a challenging problem of current interest. In this paper, we address this problem in the context of discovering frequent episodes from symbolic time-series data. Motivated by the Minimum Description Length principle, we formulate the problem of selecting relevant subset of patterns as one of searching for a subset of patterns that achieves best data compression. We present algorithms for discovering small sets of relevant non-redundant episodes that achieve good data compression. The algorithms employ a novel encoding scheme and use serial episodes with inter-event constraints as the patterns. We present extensive simulation studies with both synthetic and real data, comparing our method with the existing schemes such as GoKrimp and SQS. We also demonstrate the effectiveness of these algorithms on event sequences from a composable conveyor system; this system represents a new application area where use of frequent patterns for compressing the event sequence is likely to be important for decision support and control.

Proceedings of the 1998 Science Board Symposium on The Impacts of the 1997/98 El Niño Event on the North Pacific Ocean and Its Marginal Seas

Preface [pdf, 0.01 Mb] James J. O'Brien The big picture - The ENSO of 1997-98 [pdf, 0.01 Mb] James E. Overland, Nicholas A. Bond & Jennifer Miletta Adams Atmospheric anomalies in 1997: Links to ENSO? [pdf, 0.54 Mb] Vladimir I. Ponomarev, Olga Trusenkova, Serge Trousenkov, Dmitry Kaplunenko, Elena Ustinova & Antonina Polyakova The ENSO signal in the northwest Pacific [pdf, 0.47 Mb] Robert L. Smith, A. Huyer, P.M. Kosro & J.A. Barth Observations of El Niño off Oregon: July 1997 to present (October 1998) [pdf, 1.31 Mb] Patrica A. Wheeler & Jon Hill Biological effects of the 1997-1998 El Niño event off Oregon: Nutrient and chlorophyll distributions [pdf, 1.13 Mb] William T. Peterson Hydrography and zooplankton off the central Oregon coast during the 1997-1998 El Niño event [pdf, 0.26 Mb] William Crawford, Josef Cherniawsky, Michael Foreman & Peter Chandler El Niño sea level signal along the west coast of Canada [pdf, 1.25 Mb] Howard J. Freeland & Rick Thomson The El Niño signal along the west coast of Canada - temperature, salinity and velocity [pdf, 0.49 Mb] Frank A. Whitney, David L. Mackas, David W. Welch & Marie Robert Impact of the 1990s El Niños on nutrient supply and productivity of Gulf of Alaska waters [pdf, 0.06 Mb] Craig McNeil, David Farmer & Mark Trevorrow Dissolved gas measurements at Stn. P4 during the 97-98 El Niño [pdf, 0.13 Mb] Kristen L.D. Milligan, Colin D. Levings & Robert E. DeWreede Data compilation and preliminary time series analysis of abundance of a dominant intertidal kelp species in relation to the 1997/1998 El Niño event [pdf, 0.05 Mb] S.M. McKinnell, C.C. Wood, M. Lapointe, J.C. Woodey, K.E. Kostow, J. Nelson & K.D. Hyatt Reviewing the evidence that adult sockeye salmon strayed from the Fraser River and spawned in other rivers in 1997 [pdf,0.03 Mb] G.A. McFarlane & R.J. Beamish Sardines return to British Columbia waters [pdf, 0.34 Mb] Ken H. Morgan Impact of the 1997/98 El Niño on seabirds of the northeast Pacific [pdf, 0.06 Mb] Thomas C. Royer & Thomas Weingartner Coastal hydrographic responses in the northern Gulf of Alaska to the 1997-98 ENSO event [pdf, 0.76 Mb] John F. Piatt, Gary Drew, Thomas Van Pelt, Alisa Abookire, April Nielsen, Mike Shultz & Alexander Kitaysky Biological effects of the 1997/98 ENSO in Cook Inlet, Alaska [pdf, 0.22 Mb] H.J. Niebauer The 1997-98 El Niño in the Bering Sea as compared with previous ENSO events and the "regime shift" of the late 1970s [pdf, 0.10 Mb] A.S. Krovnin, G.P. Nanyushin, M.Yu. Kruzhalov, G.V. Khen, M.A. Bogdanov, E.I. Ustinova, V.V. Maslennikov, A.M. Orlov, B.N. Kotenev, V.V. Bulanov & G.P. Muriy The state of the Far East seas during the 1997/98 El Niño event [pdf, 0.15 Mb] Stacy Smith & Susan Henrichs Phytoplankton collected by a time-series sediment trap deployed in the southeast Bering Sea during 1997 [pdf, 0.21 Mb] Cynthia T. Tynan Redistributions of cetaceans in the southeast Bering Sea relative to anomalous oceanographic conditions during the 1997 El Niño [pdf, 0.02 Mb] Akihiko Yatsu, Junta Mori, Hiroyuki Tanaka, Tomowo Watanabe, Kazuya Nagasawa, Yikimasa Ishida, Toshimi Meguro, Yoshihiko Kamei & Yasunori Sakurai Stock abundance and size compositions of the neon flying squid in the central North Pacific Ocean during 1979-1998 [pdf, 0.11 Mb] O.B. Feschenko A new point of view concerning the El Niño mechanism [pdf, 0.01 Mb] Nathan Mantua 97/98 Ocean climate variability in the northeast Pacific: How much blame does El Niño deserve? [pdf, 0.01 Mb] Vadim P. Pavlychev Sharp changes of hydrometeorological conditions in the northwestern Pacific during the 1997/1998 El Niño event [pdf, 0.01 Mb] Jingyi Wang Predictability and forecast verification of El Niño events [pdf, 0.01 Mb] (Document contains 110 pages)

Spatio-Temporal Scanning and Statistical Test of the Accelerating Moment Release (AMR) Model Using Australian Earthquake Data

The Accelerating Moment Release (AMR) preceding earthquakes with magnitude above 5 in Australia that occurred during the last 20 years was analyzed to test the Critical Point Hypothesis. Twelve earthquakes in the catalog were chosen based on a criterion for the number of nearby events. Results show that seven sequences with numerous events recorded leading up to the main earthquake exhibited accelerating moment release. Two occurred near in time and space to other earthquakes preceded by AM R. The remaining three sequences had very few events in the catalog so the lack of AMR detected in the analysis may be related to catalog incompleteness. Spatio-temporal scanning of AMR parameters shows that 80% of the areas in which AMR occurred experienced large events. In areas of similar background seismicity with no large events, 10 out of 12 cases exhibit no AMR, and two others are false alarms where AMR was observed but no large event followed. The relationship between AMR and Load-Unload Response Ratio (LURR) was studied. Both methods predict similar critical region sizes, however, the critical point time using AMR is slightly earlier than the time of the critical point LURR anomaly.

Bibliographic Data Roadmap Group Meeting 1

The first meeting of a Jisc convened group to discuss action in the area of bibliographic data.

Bibliographic Data Roadmap Group Meeting 2

Notes from the second meeting of the Bibliographic Data Roadmap Group to discuss action on bibliographic data for libraries

Digging into Data 3 Progress Meeting

On Wednesday 17th June, the UK projects funded under round 3 of the Digging into Data challenge gathered together at Paddington for the mid-term progress meeting. This workshop provided projects with the opportunity to present, not just on progress to their plan, but on highlights, issues, challenges and share this information with the funders and other projects.

Large Hadron Collider (LHC) large scale high volume data transport – end-to-end performance from the scientific application viewpoint

Presentation slides as part of the Janet network end-to-end performance initiative

Enabling high-throughput data transfers for climate and earth observation science

Supporting presentation slides from the Janet network end to end performance initiative