4 ms·

Seems a bit outside their wheelhouse, no?

by pmdulaney 2y ago

amelius 2y ago
You mean like the web was also?
- pmdulaney 2y ago
  Good point!
serf 2y ago
model fitting and extrapolation from large data sets seems like it would be exactly their wheelhouse.
- Kye 2y ago
  I always assumed ML was used in processing data from the LHC. I don't see any other way to deal with so much.
  - superluserdo 2y ago
    It is and has been for a while, but most of the more flashy and exciting developments in ML and AI don't have very much applicability to LHC event processing. To be able to state any kind of finding about some aspect of physics based on the scattering of particles in the accelerator and their decays in the detector, you need to take the background of all events and make multivariate discriminants on the data in order to enrich your signal as much as possible while throwing as little as possible away. This requires you to have a rigorous and verifiable statistical "paper trail" from start to finish, so you can say with confidence intervals how much signal and background you ought to have, vs how much you measure in your data after processing it. An overly broad black box doesn't really work for this kind of introspection.
  - magicalhippo 2y ago
    Things like boosted decision trees have been used in several instances, like this[1] or this[2]. Surely other methods as well. [1]: https://indico.bnl.gov/event/10699/contributions/53933/attachments/37015/60968/AI_EIC_ALICE_MachineLearning_PID_v6.pdf https://indico.bnl.gov/event/10699/contributions/53933/attac... [2]: https://inspirehep.net/literature/1665070 https://inspirehep.net/literature/1665070
dan_linder 2y ago
Considering LLMs are getting larger each generation, CERN is a great place to investigate their applications. The data captured, stored, processed, and evaluated after just one of their more notable experiments generate petabytes of data. They know a thing or two that directly apply.
deleted 2y ago
[deleted]

Seems a bit outside their wheelhouse, no?

amelius 2y ago

You mean like the web was also?

pmdulaney 2y ago
Good point!

serf 2y ago

model fitting and extrapolation from large data sets seems like it would be exactly their wheelhouse.

Kye 2y ago
I always assumed ML was used in processing data from the LHC. I don't see any other way to deal with so much.
- superluserdo 2y ago
  It is and has been for a while, but most of the more flashy and exciting developments in ML and AI don't have very much applicability to LHC event processing. To be able to state any kind of finding about some aspect of physics based on the scattering of particles in the accelerator and their decays in the detector, you need to take the background of all events and make multivariate discriminants on the data in order to enrich your signal as much as possible while throwing as little as possible away. This requires you to have a rigorous and verifiable statistical "paper trail" from start to finish, so you can say with confidence intervals how much signal and background you ought to have, vs how much you measure in your data after processing it. An overly broad black box doesn't really work for this kind of introspection.
- magicalhippo 2y ago
  Things like boosted decision trees have been used in several instances, like this[1] or this[2]. Surely other methods as well. [1]: https://indico.bnl.gov/event/10699/contributions/53933/attachments/37015/60968/AI_EIC_ALICE_MachineLearning_PID_v6.pdf https://indico.bnl.gov/event/10699/contributions/53933/attac... [2]: https://inspirehep.net/literature/1665070 https://inspirehep.net/literature/1665070

dan_linder 2y ago

Considering LLMs are getting larger each generation, CERN is a great place to investigate their applications. The data captured, stored, processed, and evaluated after just one of their more notable experiments generate petabytes of data. They know a thing or two that directly apply.

deleted 2y ago

[deleted]