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Update 01-jets101.md
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garvitaa authored Aug 17, 2024
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Expand Up @@ -76,6 +76,10 @@ These algorithms follow this recipe:
> ## How the different jet algorithms look like in our events?
> <img src="../fig/episode1/JHEP04_2008_063.jpg" alt="" style="width: 600px;"/>
> Comparison of jet areas for four different jet algorithms, from "The anti-kt Clustering Algorithm" by Cacciari, Salam, and Soyez [JHEP04, 063 (2008), arXiv:0802.1189].
>
> In the figure, you can observe the high irregularity in the kt algorithm's area, the conical shape of the anti-kt jets illustrating this algorithm's preference for hard radiation, and the smaller effective radius of the SIScone jets. This is due to the split-merge procedure, which results in smaller jet areas and two jets being resolved where there is just one grey jet.
>
> Despite their differences, all these algorithms share a crucial feature — infrared and collinear safety. This ensures that the presence of a hard particle or a softly radiated gluon does not alter the number or contents of the jet. This condition is essential for making meaningful comparisons between theoretical predictions and experimental data.
{: .callout}

Some excellent references about jet algorithms can be found here:
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> ## Note
> Even though the stored jets are corrected in MiniAOD and NanoAOD, these corrections should be removed, and the latest version recommended by the JME should be applied. This is required by the JME POG.
>
> There are several advanced tools on the market which allow you to do sophisticated analysis using nanoAOD format, including [RDataFrame](https://root.cern/doc/master/classROOT_1_1RDataFrame.html), [NanoAOD-tools](https://github.com/cms-nanoAOD/nanoAOD-tools), or [Coffea](https://github.com/CoffeaTeam/coffea). We encourage you to look at them and use the one you like the most. However, we are going to use coffea for this tutorial.
{: .callout}
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{% include links.md %}

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