- #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
SUMMARY
The discussion centers on selecting the appropriate machine learning model for detecting bottom quarks, specifically in the context of identifying B mesons using data generated from Pythia at the LHC. Key considerations include understanding the characteristics of bottom quarks, which are heavier and shorter-lived than charm quarks, and the importance of choosing relevant input parameters based on the specific experimental setup. Participants emphasize the need for tailored approaches and suggest consulting previous publications and supervisors for guidance.
PREREQUISITES- Familiarity with machine learning algorithms and their applications in particle physics
- Understanding of bottom quark characteristics and decay processes
- Experience with LHC data analysis and tools like Pythia
- Knowledge of input parameter selection for machine learning models
- Research specific machine learning models suitable for particle detection, such as decision trees or neural networks
- Study the characteristics and decay patterns of bottom quarks in detail
- Explore data preprocessing techniques for LHC data generated by Pythia
- Review relevant publications on B meson identification and machine learning applications in particle physics
Particle physicists, machine learning practitioners in experimental physics, and researchers focused on quark detection and B meson identification will benefit from this discussion.
- #2
- 29,457
- 21,201
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
- 37,399
- 14,228
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.
Similar threads
- · Replies 9 ·
- · Replies 4 ·
Graduate
Quenched & Unquenched Quark Model
- · Replies 2 ·
- · Replies 6 ·
- · Replies 6 ·
- · Replies 7 ·
- · Replies 5 ·
- · Replies 2 ·
- · Replies 4 ·
- · Replies 4 ·