Difference between revisions of "AIOP Jun. 24, 2024"
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=== Minutes === | === Minutes === | ||
− | <!-- Attendees: Torri | + | <!-- Attendees: Torri Jeske, Thomas Britton, David Lawrence, Armen Kasparian, Naomi Jarvis, Patrick Moran, Chris Keith, James Maxwell --> |
+ | <ul> | ||
+ | <li>Prior Announcements | ||
+ | <ul> | ||
+ | <li>Patrick will write an abstract and send around for Polarization workshop</li> | ||
+ | <li>Torri will present 50% AIEC/AIOP in Texas</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | <li>Project Progress | ||
+ | <ul> | ||
+ | <li>Data mining | ||
+ | <ul> | ||
+ | <li>Torri | ||
+ | <ul> | ||
+ | <li>Showed a notebook with distributions of fit params and inputs.</li> | ||
+ | <li>Starting to see some correlations in some subsets | ||
+ | <ul> | ||
+ | <li>Looking at Fridge_F (flow) and fit params</li> | ||
+ | <li>Move x–> (x-x0)</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | <li>Looked at other variables | ||
+ | <ul> | ||
+ | <li>Chris gave explanation for some groupings seen in the data</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | <li>Armen | ||
+ | <ul> | ||
+ | <li>Proton data fits without CC | ||
+ | <ul> | ||
+ | <li>Does slightly better (?) | ||
+ | <ul> | ||
+ | <li>Same data</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | <li>Speculate that it may be collapsing to the average of a value that doesn’t change much.</li> | ||
+ | <li>Discussed approaches with/without cc</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | <li>Trying to train with both positive and negative values doesn’t work | ||
+ | <ul> | ||
+ | <li>Split data pos: 22k neg: 13k | ||
+ | <ul> | ||
+ | <li>Some confusion on counts post split</li> | ||
+ | <li>Thomas noticed the split meant 0s end up in both</li> | ||
+ | <li>Torri/Chris discussed the kinds of data and what restrictions they have (defined to be positive)</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | <li>Can train well if pos and neg are trained apart. Not together</li> | ||
+ | <li>Discussion of better engineered features than mean, stdev | ||
+ | <ul> | ||
+ | <li>Use area?</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | <li>Todo | ||
+ | <ul> | ||
+ | <li>Armen to use area (which he already has) to retrain pos/neg data</li> | ||
+ | </ul> | ||
+ | </li> | ||
+ | </ul> | ||
==== Action Items ==== | ==== Action Items ==== |
Latest revision as of 18:22, 25 June 2024
The current weekly meeting time is every other Monday at 13:00 US/Eastern
Connection Info:
You can connect using ZOOM Video conferencing (ID: 160 515 9491). (Click "Expand" to the right for details -->):
One tap mobile: US: +16692545252,,1602484178# or +16468287666,,1602484178#
Meeting URL: https://jlab-org.zoomgov.com/j/1605159491?pwd=ZnB0Vkp0SCtYZmRPWG1hRGVnQVprUT09&from=addon
Meeting ID: 160 515 9491
Passcode: 680042
Join by Telephone
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Meeting ID: 160 515 9491
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Join from an H.323/SIP room system
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Meeting ID: 160 515 9491
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Passcode: 680042
One tap mobile: US: +16692545252,,1601987443# or +16468287666,,1601987443#
Meeting URL: https://jlab-org.zoomgov.com/j/1605159491?pwd=ZnB0Vkp0SCtYZmRPWG1hRGVnQVprUT09&from=addon
Meeting ID: 160 515 9491
Passcode: 680042
AIOP - Polarized Target
Agenda:
- Previous Meeting
- Announcements
- Conferences/Workshops
- CAARI-SNEAP (July 21-26) Torri
- PSTDP2024 (Sept. 22-27)
- Project Progress
- Data mining/preparation
- GitHub AIOP Project (AIOP-Photon issues)
- Milestones: PDF(GitHub AIOP Project AIOP-Photon issues FTE Profiles)
- PIER Activities
- Middle School Data Science Hackathon Workshop
- HUGS mini-Workshop - COMPLETED!
- AOT
Minutes
- Prior Announcements
- Patrick will write an abstract and send around for Polarization workshop
- Torri will present 50% AIEC/AIOP in Texas
- Project Progress
- Data mining
- Torri
- Showed a notebook with distributions of fit params and inputs.
- Starting to see some correlations in some subsets
- Looking at Fridge_F (flow) and fit params
- Move x–> (x-x0)
- Looked at other variables
- Chris gave explanation for some groupings seen in the data
- Armen
- Proton data fits without CC
- Does slightly better (?)
- Same data
- Speculate that it may be collapsing to the average of a value that doesn’t change much.
- Discussed approaches with/without cc
- Does slightly better (?)
- Trying to train with both positive and negative values doesn’t work
- Split data pos: 22k neg: 13k
- Some confusion on counts post split
- Thomas noticed the split meant 0s end up in both
- Torri/Chris discussed the kinds of data and what restrictions they have (defined to be positive)
- Can train well if pos and neg are trained apart. Not together
- Discussion of better engineered features than mean, stdev
- Use area?
- Split data pos: 22k neg: 13k
- Proton data fits without CC
- Torri
- Data mining
- Todo
- Armen to use area (which he already has) to retrain pos/neg data