INDRA-ASTRA

Name: INDRA-ASTRA
Start: 2021-01-13T10:00:00-05:00
End: 2021-12-31T12:00:00-05:00
Location: No location set

January 13, 2021 to December 31, 2021

US/Eastern timezone

Session

INDRA-ASTRA Meeting

Jan 13, 2021, 10:00 AM

INDRA-ASTRA Meeting: Planning

Markus Diefenthaler (Jefferson Lab)

INDRA-ASTRA Meeting: Multiscale Algorithm

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INDRA-ASTRA Meeting: GEM Prototype

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INDRA-ASTRA Meeting: Online Mode

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INDRA-ASTRA Meeting: Online Mode

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

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

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INDRA-ASTRA Meeting: Improved Algorithms

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

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INDRA-ASTRA Meeting: Motivation, Publication

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INDRA-ASTRA Meeting: Detector Calibration

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INDRA-ASTRA Meeting: Preparations for GAP analysis

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INDRA-ASTRA Meeting: Continuation from June 24

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There are no materials yet.

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