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Case in point : LHC Higgs results - how many detection's vs how many events? How were the detection's determined... The answer is with a large booster [1] I po

by sgt101 8y ago

Case in point : LHC Higgs results - how many detection's vs how many events? How were the detection's determined... The answer is with a large booster [1] I postulate that out of 12 billion random events it would be remarkable if a booster didn't extract 100 or so items that looked similar to a Higgs detection. Well, let's give it 20 years and a new generation of PI's who aren't invested in this and have grad students who are keen to find something different in the data. But ohh.. all the data has been thrown aways... oh! [2] [1] https://indico.cern.ch/event/705941/contributions/2897000/attachments/1605280/2546655/mlhepAthens-Feb22-2018.pdf https://indico.cern.ch/event/705941/contributions/2897000/at... [2] https://www.forbes.com/sites/startswithabang/2018/09/13/has-the-large-hadron-collider-accidentally-thrown-away-the-evidence-for-new-physics/#d1c86469270a https://www.forbes.com/sites/startswithabang/2018/09/13/has-...

ppod 8y ago
" all the data has been thrown aways... oh" That's a ridiculously disingenuous summary of the article, did you read past the Forbes headline?
nxpnsv 8y ago
You clearly have no idea what you are talking about. Did you try reading any of the papers we wrote? There are many analyses and multiple channels.
dguest 8y ago
90% of the work in LHC physics is estimating the amplitude of backgrounds that look almost exactly like your signal process. Coming up with the "large booster" for classification is only a small part of it. So yes, machine learning is used, but no, we don't use it blindly like you imply. As for throwing all the data away, the article you link to actually does a good job of explaining how this is done: we look at every collision with thousands of sensors before deciding whether to keep it. At this stage there is absolutely no machine learning anyway (just physics knowledge), so be careful blaming machine learning for any missed discoveries.

by sgt101 8y ago

ppod 8y ago

" all the data has been thrown aways... oh" That's a ridiculously disingenuous summary of the article, did you read past the Forbes headline?

nxpnsv 8y ago

You clearly have no idea what you are talking about. Did you try reading any of the papers we wrote? There are many analyses and multiple channels.

dguest 8y ago

90% of the work in LHC physics is estimating the amplitude of backgrounds that look almost exactly like your signal process. Coming up with the "large booster" for classification is only a small part of it. So yes, machine learning is used, but no, we don't use it blindly like you imply. As for throwing all the data away, the article you link to actually does a good job of explaining how this is done: we look at every collision with thousands of sensors before deciding whether to keep it. At this stage there is absolutely no machine learning anyway (just physics knowledge), so be careful blaming machine learning for any missed discoveries.