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Two points: 1. If you're using a data driven background, you're background model isn't the Standard Model but that there isn't something special about your sig

by beojan 8y ago

Two points: 1. If you're using a data driven background, you're background model isn't the Standard Model but that there isn't something special about your signal region. 2. If you are using the SM as your background, then yes you do believe it to be 100% true, since what you are looking for is evidence that there exists new physics not described by the SM. In other words, the theory under test is the SM, and it can pass very easily. If it fails despite this, that's evidence that it's incomplete. If it passes, the limit setting stage tests whatever new physics we were looking for, and gives an upper bound on how sure we are that it does not exist.

nonbel 8y ago
> "1. If you're using a data driven background, you're background model isn't the Standard Model but that there isn't something special about your signal region." Sure, it means you assume that during the signal detection everything was working exactly as it was during background data collection. There are going to be other implementation specific assumptions like what thresholds to use regarding environment noise triggers, accounting for sensor drift, etc. These data-driven models are approximations, I doubt the people actually coming up with them really believe they are 100% true. If you give a real life example I will point out exactly where there are issues. > "If you are using the SM as your background, then yes you do believe it to be 100% true" I don't know what this is supposed to mean. Of course you are assuming your background is 100% true. Do particle physicists actually believe this though? Not from my reading: "So although the Standard Model accurately describes the phenomena within its domain, it is still incomplete." https://home.cern/about/physics/standard-model https://home.cern/about/physics/standard-model
- beojan 8y ago
  Yes, I already said the background estimation can be wrong. We can check it by defining validation regions outside the signal region and making sure it correctly describes the data there (where there is no signal) and assign a conservative systematic uncertainty to cover any differences, but yes it can be wrong. This is a background estimation problem though, not a flaw with hypothesis testing. By the way, the signal region data and background data are collected simultaneously, so there can be no systematic difference in experiment conditions between the two. As for the SM, we are looking for those places where it is incomplete, and to do this we must assume it is complete and look for strong evidence that we are wrong. To date, it doesn't seem like we see though, as far as collider physics is concerned.

by beojan 8y ago

nonbel 8y ago

> "1. If you're using a data driven background, you're background model isn't the Standard Model but that there isn't something special about your signal region." Sure, it means you assume that during the signal detection everything was working exactly as it was during background data collection. There are going to be other implementation specific assumptions like what thresholds to use regarding environment noise triggers, accounting for sensor drift, etc. These data-driven models are approximations, I doubt the people actually coming up with them really believe they are 100% true. If you give a real life example I will point out exactly where there are issues. > "If you are using the SM as your background, then yes you do believe it to be 100% true" I don't know what this is supposed to mean. Of course you are assuming your background is 100% true. Do particle physicists actually believe this though? Not from my reading: "So although the Standard Model accurately describes the phenomena within its domain, it is still incomplete." https://home.cern/about/physics/standard-model https://home.cern/about/physics/standard-model

beojan 8y ago
Yes, I already said the background estimation can be wrong. We can check it by defining validation regions outside the signal region and making sure it correctly describes the data there (where there is no signal) and assign a conservative systematic uncertainty to cover any differences, but yes it can be wrong. This is a background estimation problem though, not a flaw with hypothesis testing. By the way, the signal region data and background data are collected simultaneously, so there can be no systematic difference in experiment conditions between the two. As for the SM, we are looking for those places where it is incomplete, and to do this we must assume it is complete and look for strong evidence that we are wrong. To date, it doesn't seem like we see though, as far as collider physics is concerned.