- #1
Dhananjay
- 2
- 0
which machine learning model to use to detect bottom quark, and on what basis the segregation should be done
Which machine learning model is best for detecting bottom quarks?
Click For Summary
Discussion Overview
The discussion centers on the selection of machine learning models for detecting bottom quarks, specifically in the context of identifying B mesons using data from the Large Hadron Collider (LHC). Participants explore the characteristics of bottom quarks and the parameters necessary for effective segregation from other particles.
Discussion Character
- Exploratory
- Technical explanation
- Debate/contested
Main Points Raised
- One participant inquires about the appropriate machine learning model for detecting bottom quarks and the basis for segregation.
- Another participant requests clarification on the initial question, indicating that more information is needed.
- A participant describes their project involving machine learning for identifying B mesons and mentions the use of LHC data generated through Pythia, expressing uncertainty about which model to use and the characteristics of bottom quarks.
- It is suggested that the choice of machine learning model depends on many specific details of the experiment, and that input parameters for the model may differ from the choice of algorithm.
- Participants note that the bottom quark is heavier and shorter-lived than the charm quark, which is often a significant background, implying that decay energies may be relevant variables for detection.
- There is a recommendation to consult previous publications and seek guidance from supervisors or contacts involved in the experiment.
Areas of Agreement / Disagreement
Participants express varying levels of uncertainty regarding the best machine learning model and the characteristics of bottom quarks. There is no consensus on a specific model or approach, and multiple viewpoints regarding the selection criteria and parameters remain present.
Contextual Notes
The discussion highlights limitations related to the lack of specific details about the experimental setup and the characteristics of bottom quarks that may affect model selection. There are unresolved aspects regarding the input parameters and the overall approach to the problem.
- #2
- 29,514
- 21,271
It looks like you've only posted a fragment of the question you want to ask. Can you provide more information on what you are asking?
- #3
Dhananjay
- 2
- 0
I am working on a project:-
Machine learning for identifying B measons
I have LHC data for processing (data generated through pythia)
I am not able to understand which machine learning model to use
and since I am new to this, I don't know the characteristic of the bottom quark, through which I can separate the bottom quark from other particles
Machine learning for identifying B measons
I have LHC data for processing (data generated through pythia)
I am not able to understand which machine learning model to use
and since I am new to this, I don't know the characteristic of the bottom quark, through which I can separate the bottom quark from other particles
- #4
mfb
Mentor
- 37,406
- 14,247
There are tons of options. Which one is best will depend on too many details to tell in general.
The bottom quark is heavier but shorter-living than charm, which is typically the largest background, so variables will often focus on decay energies in one way or another.
Check previous publications and ask your supervisor or other contact in the experiment you work for.
Which input parameters to use is different from the question which machine learning algorithm to use. It depends on the experiment and the specific study you want to do.Dhananjay said:
The bottom quark is heavier but shorter-living than charm, which is typically the largest background, so variables will often focus on decay energies in one way or another.
Check previous publications and ask your supervisor or other contact in the experiment you work for.
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