ICALEPCS2019 - List of Keywords (scattering)

Paper	Title	Other Keywords	Page
MOMPR007	Scalable High Demand Analytics Environments with Heterogeneous Clouds	data-analysis, experiment, operation, software	171
	K. Woods, R.J. Clegg, R. Millward Tessella, Abingdon, United Kingdom F. Barnsely, C. Jones STFC/RAL, Chilton, Didcot, Oxon, United Kingdom
	Funding: UK Research and Innovation - Science & Technology Facilities Council (UK SBS IT18160) The Ada Lovelace Centre (ALC) at STFC provides on-demand, data analysis, interpretation and analytics services to scientists using UK research facilities. ALC and Tessella have built software systems to scale analysis environments to handle peaks and troughs in demand as well as to reduce latency by provision environments closer to scientists around the world. The systems can automatically provision infrastructure and supporting systems within compute resources around the world and in different cloud types (including commercial providers). The system then uses analytics to dynamically provision and configure virtual machines in various locations ahead of demand so that users experience as little delay as possible. In this poster, we report on the architecture and complex software engineering used to automatically scale analysis environments to heterogeneous clouds, make them secure and easy to use. We then discuss how analytics was used to create intelligent systems in order to allow a relatively small team to focus on innovation rather than operations.
	Poster MOMPR007 [1.650 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOMPR007
About •	paper received ※ 30 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUCPR05	UX Focused Development Work During Recent ORNL EPICS-Based Instrument Control System Upgrade Projects	controls, experiment, neutron, detector	818
	X. Yao, R.D. Gregory, G.S. Guyotte, S.M. Hartman, K.-U. Kasemir, C.A. Lionberger, M.R. Pearson ORNL, Oak Ridge, Tennessee, USA
	Funding: Oak Ridge National Laboratory is managed by UT-Battelle LLC for the US Department of Energy The importance of usability and easy-to-use user interfaces (UI) have been recognized across many domains. However, the user-friendliness of scientific experiment control systems often lags behind industry standards in the flourishing user experience (UX) field. Scientific control systems can certainly benefit from these new UX research methods and approaches. Recent instrument control system upgrade projects at the SNS and HFIR facilities at Oak Ridge National Laboratory demonstrate the effectiveness of UX focused development work, and further reveal the need for more utilization of such techniques coming from the UX field. The ongoing control system upgrades are targeting the key facility-level priority of higher scientific productivity, and UX is one of the important tools to help us achieve this priority. We will highlight research methods and practices, introduce our findings and deliverables, and share challenges and lessons learned in applying UX methods to scientific control systems.
	Slides TUCPR05 [7.242 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPR05
About •	paper received ※ 03 October 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THBPP03	Deep Learning Methods on Neutron Scattering Data	experiment, neutron, network, detector	1580
	P. Mutti, F. Cecillon, Y. Le Goc, G. Song ILL, Grenoble, France
	Recently, by using deep learning methods, computers are able to surpass or come close to matching human performance on image analysis and pattern recognition. This advanced method could also help interpreting data from neutron scattering experiments. Those data contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties. We applied deep learning techniques to scientific neutron scattering data. This is a complex problem due to the multi-parameter space we have to deal with. We have used a convolutional neural network-based model to evaluate the quality of experimental neutron scattering images, which can be influenced by instrument configuration, sample and sample environment parameters. Sample structure can be deduced during data collection that can be therefore optimized. The neural network model can predict the experimental parameters to properly setup the instrument and derive the best measurement strategy. This results in a higher quality of data obtained in a shorter time, facilitating data analysis and interpretation.
	Slides THBPP03 [11.877 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THBPP03
About •	paper received ※ 04 October 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOMPR007

Scalable High Demand Analytics Environments with Heterogeneous Clouds

data-analysis, experiment, operation, software

171

K. Woods, R.J. Clegg, R. Millward
Tessella, Abingdon, United Kingdom
F. Barnsely, C. Jones
STFC/RAL, Chilton, Didcot, Oxon, United Kingdom

Funding: UK Research and Innovation - Science & Technology Facilities Council (UK SBS IT18160)
The Ada Lovelace Centre (ALC) at STFC provides on-demand, data analysis, interpretation and analytics services to scientists using UK research facilities. ALC and Tessella have built software systems to scale analysis environments to handle peaks and troughs in demand as well as to reduce latency by provision environments closer to scientists around the world. The systems can automatically provision infrastructure and supporting systems within compute resources around the world and in different cloud types (including commercial providers). The system then uses analytics to dynamically provision and configure virtual machines in various locations ahead of demand so that users experience as little delay as possible. In this poster, we report on the architecture and complex software engineering used to automatically scale analysis environments to heterogeneous clouds, make them secure and easy to use. We then discuss how analytics was used to create intelligent systems in order to allow a relatively small team to focus on innovation rather than operations.

Poster MOMPR007 [1.650 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOMPR007

About •

paper received ※ 30 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUCPR05

UX Focused Development Work During Recent ORNL EPICS-Based Instrument Control System Upgrade Projects

controls, experiment, neutron, detector

818

X. Yao, R.D. Gregory, G.S. Guyotte, S.M. Hartman, K.-U. Kasemir, C.A. Lionberger, M.R. Pearson
ORNL, Oak Ridge, Tennessee, USA

Funding: Oak Ridge National Laboratory is managed by UT-Battelle LLC for the US Department of Energy
The importance of usability and easy-to-use user interfaces (UI) have been recognized across many domains. However, the user-friendliness of scientific experiment control systems often lags behind industry standards in the flourishing user experience (UX) field. Scientific control systems can certainly benefit from these new UX research methods and approaches. Recent instrument control system upgrade projects at the SNS and HFIR facilities at Oak Ridge National Laboratory demonstrate the effectiveness of UX focused development work, and further reveal the need for more utilization of such techniques coming from the UX field. The ongoing control system upgrades are targeting the key facility-level priority of higher scientific productivity, and UX is one of the important tools to help us achieve this priority. We will highlight research methods and practices, introduce our findings and deliverables, and share challenges and lessons learned in applying UX methods to scientific control systems.

Slides TUCPR05 [7.242 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPR05

About •

paper received ※ 03 October 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THBPP03

Deep Learning Methods on Neutron Scattering Data

experiment, neutron, network, detector

1580

P. Mutti, F. Cecillon, Y. Le Goc, G. Song
ILL, Grenoble, France

Recently, by using deep learning methods, computers are able to surpass or come close to matching human performance on image analysis and pattern recognition. This advanced method could also help interpreting data from neutron scattering experiments. Those data contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties. We applied deep learning techniques to scientific neutron scattering data. This is a complex problem due to the multi-parameter space we have to deal with. We have used a convolutional neural network-based model to evaluate the quality of experimental neutron scattering images, which can be influenced by instrument configuration, sample and sample environment parameters. Sample structure can be deduced during data collection that can be therefore optimized. The neural network model can predict the experimental parameters to properly setup the instrument and derive the best measurement strategy. This results in a higher quality of data obtained in a shorter time, facilitating data analysis and interpretation.

Slides THBPP03 [11.877 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THBPP03

About •

paper received ※ 04 October 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)