Higgs boson re-discovery from a CERN dataset (for a project)

In summary, the speaker is working on a seminar project about elementary particles and is trying to rediscover the Higgs boson from a dataset obtained from CERN. They are struggling to understand how to identify the gap in the invariant mass diagram around the Higgs boson mass of 125 GeV. The speaker has watched CERN's official video on the Higgs discovery and has looked at code implementations, but is still unsure how to see the gap around 125 GeV. They have also plotted histograms of Z boson invariant masses, but have not seen anything unusual. They are asking for more information on the dataset and the analysis methods used. They also mention that one Z boson is off shell, so it cannot be included in
  • #1
omerel
1
0
Homework Statement
Seminar Project
Relevant Equations
H -> ZZ -> 4l (muons)
Hi everyone!
I'm working on a seminar project on elementary particles, and I'm supposed to introduce the LHC and rediscover the Higgs boson from a dataset I got from CERN open source.
I don't understand how am I supposed to discover the gap (in the invariant mass diagram) around the Higgs boson mass (125 GeV).
I've watched CERN's official video on the Higgs discovery (Higgs boson decay to two photons), and several code implementations relevant to my problem (H->ZZ->4l) and couldn't understand the idea of how you actually see the gap around 125 GeV?
When I plot the histogram of Z boson invariant masses, I do not see anything unusual.
For your convenience, I attached two of my histograms- one for Z boson pairs invariant mass (i.e came from the same decay) and the second is the invariant mass of a single Z boson invariant mass. The invariant mass is in GeV units. pairs.PNG
 

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  • #2
What is this gap you are talking about?

What is the dataset you are looking at? Is it big enough to see the Higgs? The Z peak seems awfully small.
 
  • #3
Can you provide more information on how you analyzed the data? E.g. cuts used and so on. Also provide a link to the dataset

One Z is off shell so you can not write "invariant mass of two Z" here
 

FAQ: Higgs boson re-discovery from a CERN dataset (for a project)

What is the Higgs boson and why is its discovery important?

The Higgs boson is a fundamental particle associated with the Higgs field, which gives mass to other particles. Its discovery is crucial because it confirms the existence of the Higgs field, a key component of the Standard Model of particle physics, helping us understand how particles acquire mass.

Why is re-discovering the Higgs boson from a CERN dataset significant for a project?

Re-discovering the Higgs boson from a CERN dataset is significant because it allows researchers to validate the robustness of their data analysis techniques, improve their understanding of particle physics, and potentially uncover new physics beyond the Standard Model by analyzing different aspects of the data.

What tools and software are typically used to analyze CERN datasets for Higgs boson discovery?

Common tools and software include ROOT (a data analysis framework developed by CERN), Python with libraries such as NumPy and Pandas for data manipulation, and machine learning libraries like TensorFlow or Scikit-learn for advanced data analysis techniques. High-performance computing resources are often required due to the large size of the datasets.

How do scientists differentiate the Higgs boson signal from background noise in the dataset?

Scientists use sophisticated statistical methods and machine learning algorithms to differentiate the Higgs boson signal from background noise. They look for specific decay patterns and signatures that match the predicted behavior of the Higgs boson and apply various filters and cuts to reduce the background noise, enhancing the signal's visibility.

What are the challenges faced in re-discovering the Higgs boson from a dataset?

Challenges include handling the vast amount of data, distinguishing the Higgs boson signal from significant background noise, ensuring the accuracy and reliability of data analysis methods, and dealing with computational limitations. Additionally, researchers must constantly update their techniques to incorporate new findings and improve the precision of their results.

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