This page is far from being complete. While I give an introduction to my students, I try to write down a few notes, hoping that it can serve as a small introduction and help future students.

Analysis Steps

To analyse data, you should be familiar with the following steps:

callisto

what is done?

calculation of calibration constants, extraction of signal, calibration of data

macro

Mars/fact/analysis/callisto.C

input

sequence file, rawdata (files found with information in sequence file

output

YYYYMMDD_SSS-calibration.root (plots), YYYYMMDD_FFF_C.root (one per run)

star

what is done?

image cleaning, calculation of image parameter

macro

Mars/fact/analysis/star.C

input

sequence file, callisto output

output

YYYYMMDD_SSS-images.root (plots), YYYYMMDD_FFF_I.root (one per run)

merpp

what is done?

merge auxiliary information to data

macro

Mars/fact/analysis/merpp.C

input

sequence file, YYYYMMDD_FFF_I.root

output

YYYYMMDD_FFF_I.root (one per run)

ganymed

what is done?

background suppression, calculation of excess(, energy reconstruction)

macro

Mars/fact/analysis/ganymed.C

input

dataset file, star output (_I.root files)

output

name-analysis.root (result, eventlist after cuts, plots), name-summary.root (event list before cuts)

lightcurve

what is done?

calculation of excess rate

macro

Mars/fact/analysis/lightcurve.C

input

several datasets or information from DB

additional information

sequence

a sequence of runs, consisting of: pedestal run, light pulser run, one or more data runs

dataset

a collection of data runs

YYYYMMDD

night (date of sunset is used)

FFF

file number (starts with 0 every night

SSS

sequence number (number of first run in sequence)

dataset file

a dataset file is a simple text file containing one or more lines as e.g.

/path_to_imageparameter_file YYYYMMDD_FFF_I.root

be aware that there is a space between the path and the file name

in the filename wildcards can be used

technical information

how to start a macro

go to Mars directory
start root
.x path/macro.C ("with maybe some variable", 2)

set ROOT environment

source /path_to_ROOT/bin/thisroot.sh

rawdata in wü

/fact/raw/YYYY/MM/DD/YYYYMMDD_FFF.fits.fz

callisto in Wü

/scratch/local/dorner/data.2013.05.11/callisto/YYYY/MM/DD/ (temporary path, will be changed)

star in Wü

/scratch/local/dorner/data.2013.05.11/star/YYYY/MM/DD/ (temporary path, will be changed)

sequences in Wü

/scratch/local/dorner/sequences/YYYY/MM/DD/ (temporary path, will be changed)

exercises A

1. run callisto for a sequence (check the macro to find out how to call the macro)
2. run star for the same sequence
3. run merpp for the same sequence
4. write a dataset file
5. run ganymed for this dataset file

Data Check

Understanding the data and selecting only data with good quality is very important.

Available Information

what information does exist to check the data quality?

• information from the rawdata
• information from the auxiliary data
• callisto output
• star output
• ganymed output

List of Tools

• FACT++/viewer
• topcat
• FACT++/fitsdump
• ​https://www.fact-project.org/viewer/
• ​http://www.astro.uni-wuerzburg.de/wikineu/index.php/How_to_plot_image_parameters
• Mars/showplot
• ​https://www.fact-project.org/run_db/db/fact_runinfo.php
• ​https://www.fact-project.org/run_db/db/run_comment.php
• ​https://www.fact-project.org/datacheck/dch.php (needs login)
• ​https://www.fact-project.org/logbook/

exercises B

1. search for some runs affected by lidar
2. compare star output plots for data affected by lidar with data not affected by lidar
3. plot image parameters for data affected and data not affected by lidar, as e.g. in ​https://www.fact-project.org/logbook/showthread.php?tid=1291
4. redo exercise A5 after selecting only runs with good quality