Doctorate in Applied and Engineering Physics  

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Thesis Proposal for the

Doctorate Program in Applied and Engineering Physics (DAEPHYS)

Starting in the Academic Year 2016/2017


Proposal 030


Title:Data processing and Human Machine Interface for the monitoring and control system of LZ dark matter experiment
 
Objectives (recommended length: 2000-3000 char):
LUX-ZEPLIN (LZ) is the future dark matter search experiment with unprecedented sensitivity to be deployed at 1500 meters underground in the Sanford Underground Research Facility (SURF), South Dakota, USA. The project is currently fully funded and is planned to begin acquisition of science data in 2019.

The increased scale of modern dark matter search experiments requires a dedicated and fairly complex system for continuous monitoring and control of a large number of parameters. These parameters can be related to the detector with its support systems (temperature, pressure, gas flow, etc.) as well as to the electronics and data acquisition systems (data rate, disk free space, etc.). In the case of the LZ experiment, the estimated number of the channels to monitor and control is approaching 2500. Additionally, the control system design must be focused on protection of the expensive detector hardware and, especially, very expensive xenon supply.

The complexity of the system and high potential cost of its malfunction poses a problem of optimal control and early detection of irregularities in its operation. Currently, standard approach is to define allowed ranges for important parameters and raise an alarm if a sensor goes out of its range. While this keeps logic simple, the diagnosis of more complex failure situations demands a significant amount of work and expertise from the operators to check and correlate the responses from multiple sensors. To remedy this situation two solutions are proposed:
*Apply machine learning techniques (e.g. outlier detection or cluster analysis) to analysis of monitoring data.
*Develop an advanced Human Machine Interface (HMI) that will facilitate analysis of detector monitoring data for both experts and non-experts.

The main objectives of the project are as follows:
*Quick integration of the student in the collaboration by on-site work. This will include visits to SLAC National Accelerator Laboratory (California, USA) were test system for the LZ project is currently installed and to SURF where the LZ detector will be deployed. The student will become familiar with all aspects of the control and monitoring system and will learn to work with large arrays of data.
*Research on detection of irregularities in the detector operation by analysis of monitoring data with machine learning algorithms (outlier detection, cluster analysis, etc). The large database of monitoring information from the LUX experiment (50 GB corresponding to 5 years of detector operation) will be used as the training dataset.
*Development of an intelligent monitoring system based on the above research that will work in real time to help system operators to diagnose complex failure scenarios
*Development of a HMI that will integrate the intelligent monitoring system with the alarm and scripting subsystems in order to quickly analyze and easily display the status of the experiment to the operator.
 
Framework (recommended length: 500-2000 char):
According to the modern cosmological models, the dark matter accounts for about 85% of the matter of the universe while its nature is one of the major current scientific questions. Among approximately 20 dark matter direct search experiments worldwide, that are looking for the evidence of galactic dark matter in the form of Weakly Interacting Massive Particles (WIMPs), the LUX (Large Underground Xenon) experiment is the most sensitive up to date. The detector is currently running 1500 meters underground at Sanford Underground Research Facility (SURF), South Dakota, USA. It is installed in the place formerly occupied by the Davis experiment that discovered solar neutrinos back in 1970s.

The successor of LUX, the LZ experiment, is expected to improve the current limit on WIMP-nucleon cross section by at least two orders of magnitude. LZ is currently in the design stage, which will be followed by the construction, assembly and deployment (2016-2018), commissioning is scheduled for 2017, with the science run occurring in 2019-2021.

LZ collaboration includes more than 200 scientists from USA, UK, Portugal, Russia and Korea. Among the member institutions are several national laboratories (SLAC, Fermilab, Lawrence Livermore, Lawrence Berkeley and RAL) as well as world-renowned Universities (UC Berkeley, UC Davis, Texas A&M, Imperial College London, Oxford University, Edinburgh University, etc.)

The LIP-Coimbra team has been working in the field of direct dark matter search since 2002. It is currently a member of LUX and LZ collaborations and leads development of the monitoring and control system for LZ. The PhD student will be integrated in the LIP-Coimbra team and will work in a close cooperation with our colleagues from SLAC and Fermilab National Laboratories (USA) including visits to both these facilities as well to SURF for on-site work.
 
Tasks (recommended length: 1000-3000 char):
The selected PhD student will work in close cooperation with the other team members and take part in the development of the control and monitoring system for the LZ experiment. The work will include strong innovative component aiming to employ machine learning algorithms such as outlier detection, cluster analysis, pattern recognition and possibly artificial neural networks in the development of the intelligent human machine interface and early warning system.

The following tasks are foreseen:
1. Participation in the work on the test system for the LZ project that is being developed at SLAC National Accelerator Laboratory. This system will help us to test several advanced engineering solutions and will require its own SCADA system which is expected to serve as a prototype for the telemetry and control system of the LZ experiment.
2. Participation in the work on the experiment control system installation, integration and commissioning in the Homestake mine.
3. Research on detection of irregularities in the detector operation using supervised and unsupervised machine learning algorithms.
4. Development of an intelligent monitoring system based on machine learning techniques.
5. Development of the advanced HMI integrating the intelligent monitoring system with the alarm and scripting subsystems.
 
Research centre/lab or R&D unit hosting the thesis project:
LIP-Coimbra
 
University to which the thesis project will be presented:
UC - Universidade de Coimbra
 
DAEPHYS Scientific Domain in which the project fits:
Instrumentation
 
Relation of the project to the Scientific Domains of DAEPHYS:
This project aims at the development of supervisory control and monitoring platform for an underground dark matter detector, which clearly places it in the instrumentation domain of the DAEPHYS program. The skills and knowledge about SCADA systems, machine learning and analysis of large data arrays (“big data”) that the student will acquire in the course of the project can be directly applied in scientific research or industrial applications.
 
Candidate profile:
Master degree in Physics, Physics Engineering, Electronic Engineering or adjacent fields, with interests in instrumentation, data acquisition, industrial control and automation and machine learning. The student is expected to travel to the USA for periods of up to 1 month to work on site, either in SLAC National Accelerator Laboratory (Menlo Park, California) or in the Sanford Underground Laboratory (Lead, South Dakota). Good knowledge of English (both spoken and written) is required.
 
Does this proposal involve more than one University?:
no
 
Synergies between the two Universities participating in the proposal:
DAEPHYS strongly encourages the presentation of thesis projects in co-supervision by researchers from two of the universities participating in the Program. In this field, explain the benefits resulting from the proposed co-supervision and the involvement of elements from the two universities, e.g. building critical mass teams, profiting from existing infrastructures or advanced equipments, profiting from expert technical know-how, etc. If the proposal involves only one University, write n/a.
(recommended length: 500-1000 char)
:
 
Does this proposal involve a company?:
no
 
Proposals involving a company:
DAEPHYS strongly encourages the presentation of thesis projects involving a company, preferably a high-tech company. These proposals have to: 1) be centered on a technological problem in which the partner company has been (or plan / would like to be) involved; 2) have a co-supervisor on the enterprise; 3) include part of the project to be carried out in the company.
(recommended length: 500-1000 char)
:

 

Supervisor

Name:Vladimir Solovov
Institution:LIP-Coimbra
email:solovov@coimbra.lip.pt
 
link to CV or indication of ORCID ID:
http://orcid.org/0000-0002-0659-7034

 

Co-Supervisor

Name:Cláudio Frederico Pascoal da Silva
Institution:Universidade de Coimbra
email:claudio@coimbra.lip.pt
 
link to CV or indication of ORCID ID:
http://orcid.org/0000-0002-1771-1517

 

Uploaded PDF document: proposal-030.pdf