How to Install and Uninstall root-splot.x86_64 Package on Red Hat Enterprise Linux 9 (RHEL 9)

Last updated: November 25,2024

1. Install "root-splot.x86_64" package

This tutorial shows how to install root-splot.x86_64 on Red Hat Enterprise Linux 9 (RHEL 9)

$ sudo dnf update $ sudo dnf install root-splot.x86_64

2. Uninstall "root-splot.x86_64" package

In this section, we are going to explain the necessary steps to uninstall root-splot.x86_64 on Red Hat Enterprise Linux 9 (RHEL 9):

$ sudo dnf remove root-splot.x86_64 $ sudo dnf autoremove

3. Information about the root-splot.x86_64 package on Red Hat Enterprise Linux 9 (RHEL 9)

Last metadata expiration check: 2:30:53 ago on Mon Feb 26 07:04:30 2024.
Available Packages
Name : root-splot
Version : 6.30.04
Release : 1.el9
Architecture : x86_64
Size : 48 k
Source : root-6.30.04-1.el9.src.rpm
Repository : epel
Summary : Splot library for ROOT
URL : https://root.cern/
License : LGPL-2.1-or-later
Description : A common method used in High Energy Physics to perform measurements
: is the maximum Likelihood method, exploiting discriminating variables
: to disentangle signal from background. The crucial point for such an
: analysis to be reliable is to use an exhaustive list of sources of
: events combined with an accurate description of all the Probability
: Density Functions (PDF).
:
: To assess the validity of the fit, a convincing quality check is to
: explore further the data sample by examining the distributions of
: control variables. A control variable can be obtained for instance by
: removing one of the discriminating variables before performing again
: the maximum Likelihood fit: this removed variable is a control
: variable. The expected distribution of this control variable, for
: signal, is to be compared to the one extracted, for signal, from the
: data sample. In order to be able to do so, one must be able to unfold
: from the distribution of the whole data sample.
:
: The SPlot method allows to reconstruct the distributions for the
: control variable, independently for each of the various sources of
: events, without making use of any a priori knowledge on this
: variable. The aim is thus to use the knowledge available for the
: discriminating variables to infer the behavior of the individual
: sources of events with respect to the control variable.
:
: SPlot is optimal if the control variable is uncorrelated with the
: discriminating variables.