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@wiso
wiso / plot_stack.py
Last active August 29, 2015 14:17
Just plot some histograms
import ROOT
if __name__ == "__main__":
common_path = "/users2/mratti/MonoPhotonSL6/Run/looseSR/data-output/"
samples = ({"filenames": common_path + "user.mgenest.slim.mc12_8TeV.126744.Sherpa_CT10_munugammaPt80.vCOMMON1.0.root",
"treename": "physics",
"color": ROOT.kOrange - 9,
"fill": True,
@wiso
wiso / background_ruggero
Last active August 29, 2015 14:18
Background
Many shape analyses have the problem of parametrizing the background. Many approach have been used in the past and new ideas should be investigated, trying to define common recipe(s) taking into account that different analyses can have different needs.
It would be good to extend the discussion to ATLAS, for example inside the statistical forum, but the concern is that such approach, as experimented in the past, may not converge. So the idea is to start the discussion with few people working in some similar analyses ($\gamma\gamma$, $jj$, $\gamma j$, diboson, ...), to list the various possibilities and to evaluate the pro and the cons.
These analyses share a similar background distribution of the invariant mass (smooth, decreasing) with a large number of events and the search for a resonant signal.
Some functional forms have theoretical motivation, but the detector effects are not negligible, so in general it is not mandatory to use functional form that comes from theoretical arguments.
The main topics of
@wiso
wiso / background_ruggero
Last active August 29, 2015 14:18
Background
Many shape analyses have the problem of parametrizing the background. Many approach have been used in the past and new ideas should be investigated, trying to define common recipe(s) taking into account that different analyses can have different needs.
It would be good to extend the discussion to ATLAS, for example inside the statistical forum, but the concern is that such approach, as experimented in the past, may not converge. So the idea is to start the discussion with few people working in some similar analyses ($\gamma\gamma$, $jj$, $\gamma j$, diboson, ...), to list the various possibilities and to evaluate the pro and the cons.
These analyses share a similar background distribution of the invariant mass (smooth, decreasing) with a large number of events and the search for a resonant signal.
Some functional forms have theoretical motivation, but the detector effects are not negligible, so in general it is not mandatory to use functional form that comes from theoretical arguments.
The main topics of
@wiso
wiso / localgroupdisk
Created April 14, 2015 22:03
Analysis of usage of localgroupdisk
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@wiso
wiso / chi2_problem_root
Created April 15, 2015 22:36
chi2 problem root
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@wiso
wiso / chi2_problem
Created April 15, 2015 23:03
chi2 problem root_bis
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@wiso
wiso / ftest
Last active August 29, 2015 14:19
ftest
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See as example https://indico.cern.ch/event/147827/material/slides/1?contribId=9, slide 16"
]
},
{
@wiso
wiso / diago1
Created April 23, 2015 00:07
diago1
This file has been truncated, but you can view the full file.
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Diagonalization example"
]
},
{
@wiso
wiso / gist:a7bdc5768ed6f8fce170
Created May 18, 2015 21:13
discrete profiling
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@wiso
wiso / lartemperature
Created May 27, 2015 14:32
Propagation of temperature difference
## Propagation of temperature difference
$$E^\text{(corr)}_{\text{year}} = E_{\text{year}} / (1 + \alpha_{\text{year}})$$
we want
$$E^\text{(corr)}_{\text{2012}} = E^\text{(corr)}_{\text{2015}} $$
and we know (temperature effect):