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In previous posts, you learned how to build 1D and 2D histograms in ROOT.Now we will learn a very important skill used in almost every experiment:plotting data points, with or without error bars. ROOT provides two common classes for this: TGraph — draw X–Y data points TGraphErrors — draw points with error bars These are

In the previous tutorials, you learned how to create 1D histograms and perform Gaussian fits using PyROOT.Now we move one step ahead and explore 2D histograms, which are extremely useful in physicsfor visualizing correlations between two variables — for example: Energy vs Time X vs Y detector positions Two correlated detector signals Calibration maps In

In the previous post, we created and visualized histograms in CERN ROOT using PyROOT.Now we’ll take the next step: fit a function to our data and extract useful quantities likethe mean, RMS, and fit parameters with uncertainties.This is a key skill in nuclear and high-energy physics, where we often model peaks (e.g., energy lines) withGaussian

In the previous tutorial, we learned how to install and test CERN ROOT on Windows.In this post, we’ll take our first practical step in data analysis — creating histogramsand visualizing data using PyROOT. 🧠 What Is a Histogram in ROOT? A histogram is a graphical representation of data distribution.In nuclear and particle physics, histograms are

If you are a student or researcher in nuclear or high-energy physics, you will eventually need to use CERN ROOT — the powerful open-source data analysis framework developed at the European Organization for Nuclear Research (CERN). ROOT is used for histogramming, curve fitting, event analysis, detector simulation, and visualization of experimental data. 🧠 What You’ll