INDRA-ASTRA

Session

INDRA-ASTRA Meeting

Jan 13, 2021, 10:00 AM

Conveners

INDRA-ASTRA Meeting: Planning

• Markus Diefenthaler (Jefferson Lab)

INDRA-ASTRA Meeting: Multiscale Algorithm

• There are no conveners in this block

INDRA-ASTRA Meeting: GEM Prototype

• There are no conveners in this block

INDRA-ASTRA Meeting: Online Mode

• There are no conveners in this block

INDRA-ASTRA Meeting: Online Mode

• There are no conveners in this block

INDRA-ASTRA Meeting: Introduction, GEM Data, Online Mode

• There are no conveners in this block

INDRA-ASTRA Meeting: GEM Data, Multiscale Algorithm, Online Mode

• There are no conveners in this block

INDRA-ASTRA Meeting: Improved Algorithms

• There are no conveners in this block

INDRA-ASTRA Meeting: Finding changes in the pedestals, pedestal correction

• There are no conveners in this block

INDRA-ASTRA Meeting: Motivation, Publication

• There are no conveners in this block

INDRA-ASTRA Meeting: Detector Calibration

• There are no conveners in this block

INDRA-ASTRA Meeting: Preparations for GAP analysis

• There are no conveners in this block

INDRA-ASTRA Meeting: Continuation from June 24

• There are no conveners in this block

1. INDRA-ASTRA: Introduction

Markus Diefenthaler (Jefferson Lab)

1/13/21, 10:00 AM

Streaming readout gives opportunity to streamline workflows and to take advantage of other emerging technologies, e.g., artificial intelligence (AI) or machine learning (ML). In the INDRA-ASTRA project, we have explored the possibility for automated calibrations using AI / ML which would allow a rapid turnaround from data taking to physics results.

2. Automated Data Quality Monitoring with ADWIN2

Abdullah Farhat (ODU)

1/13/21, 10:15 AM

We introduce the ADWIN algorithm for detecting changes in streaming data and present an example implementation of ADWIN to detect sudden and gradual changes in a sample of ZEUS MC data. We compare the algorithm between one-dimensional and two-dimensional cases and give an example of integrating a simple, near-real time calibration method with ADWIN.

3. Change Detection Based on Multiscale Basis

Ronglong Fang (ODU)

1/13/21, 10:45 AM

We have used the Multiscale basis to detect the sudden change. For the sudden change, the mean information of data is enough to detect the change. In the next step, we plan to detect the gradual change, this requires us to explore the higher moment information of the data. The corresponding multiscale basis also has a higher vanishing moment property.

4. Discussion: Next steps (Further Evaluation, Tests with detector data)

1/13/21, 11:15 AM

5. Update on Multiscale Algorithm

Ronglong Fang (ODU)

5/14/21, 3:00 PM

6. Multiscale Algorithm for Streamed Data

Abdullah Farhat (ODU)

5/14/21, 3:30 PM

7. Test Data from the INDRA Lab

Holly Szumila-Vance (JLab)

5/14/21, 3:45 PM

8. Working with the Detector Data

Holly Szumila-Vance (JLab), Ronglong Fang (ODU)

6/4/21, 4:00 PM

12. Report from AIEC and INDRA-ASTRA meeting

Markus Diefenthaler (Jefferson Lab)

6/11/21, 4:00 PM

13. Working with the Detector Data

Abdullah Farhat (ODU), Holly Szumila-Vance (JLab), Ronglong Fang (ODU)

6/11/21, 4:15 PM

14. Performance of Online Mode

Abdullah Farhat (ODU), Markus Diefenthaler (Jefferson Lab), Ronglong Fang (ODU), Yuesheng Xu (ODU)

6/18/21, 4:00 PM

15. Introduction Srijith Rajamohan

Markus Diefenthaler (Jefferson Lab)

6/24/21, 4:00 PM

17. Online performance of ADWIN2 and Multiscale Methods

Ronglong Fang (ODU)

6/24/21, 4:05 PM

18. Discussion: Next Steps

6/24/21, 4:35 PM

20. Priority I: Detector tests

7/23/21, 4:00 PM

Status of test runs
Status of test run analysis

21. Priority II: Developing the Multiscale Method

7/23/21, 4:10 PM

Singlescale Shift Online Method: Is shifting by +1 sufficient?

How can we show that the method gives the correct results beyond detector tests?

19. Priority III: Online Mode

7/23/21, 4:30 PM

ERSAP Update
Status of compiling the code

22. Preparation for AI Town Hall

7/23/21, 4:40 PM

23. Updates

Ronglong Fang (ODU)

8/13/21, 4:00 PM

24. Update

Ronglong Fang (ODU)

9/17/21, 4:00 PM

25. Discussion

9/17/21, 4:30 PM

26. Motivation

Markus Diefenthaler (Jefferson Lab)

9/24/21, 4:00 PM

27. Discussion

9/24/21, 4:15 PM