I Which machine learning model is best for detecting bottom quarks?

  • I
  • Thread starter Thread starter Dhananjay
  • Start date Start date
  • Tags Tags
    Detection Quark
Dhananjay
Messages
2
Reaction score
0
which machine learning model to use to detect bottom quark, and on what basis the segregation should be done
 
Physics news on Phys.org
:welcome:

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?
 
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
 
There are tons of options. Which one is best will depend on too many details to tell in general.
Dhananjay said:
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
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.

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.
 
Toponium is a hadron which is the bound state of a valance top quark and a valance antitop quark. Oversimplified presentations often state that top quarks don't form hadrons, because they decay to bottom quarks extremely rapidly after they are created, leaving no time to form a hadron. And, the vast majority of the time, this is true. But, the lifetime of a top quark is only an average lifetime. Sometimes it decays faster and sometimes it decays slower. In the highly improbable case that...
I'm following this paper by Kitaev on SL(2,R) representations and I'm having a problem in the normalization of the continuous eigenfunctions (eqs. (67)-(70)), which satisfy \langle f_s | f_{s'} \rangle = \int_{0}^{1} \frac{2}{(1-u)^2} f_s(u)^* f_{s'}(u) \, du. \tag{67} The singular contribution of the integral arises at the endpoint u=1 of the integral, and in the limit u \to 1, the function f_s(u) takes on the form f_s(u) \approx a_s (1-u)^{1/2 + i s} + a_s^* (1-u)^{1/2 - i s}. \tag{70}...

Similar threads

Replies
20
Views
2K
Replies
7
Views
2K
Replies
4
Views
2K
Replies
6
Views
2K
Replies
3
Views
2K
Replies
5
Views
2K
Replies
10
Views
3K
Back
Top