All Threads - CIAO 4.1

Introduction

Beginners should start here. The Introductory threads provide an overview of the main components (GUI applications, parameter files) and concepts (the Data Model, filtering) in the CIAO data analysis software.

Getting Started:
- Installing CIAO using the ciao-install tool
  (16 Jun 2009)
- Installing CIAO 4.1
  (20 Apr 2009)
- Patching CIAO 4.1 to CIAO 4.1.1
- Patching CIAO 4.1 or 4.1.1 to CIAO 4.1.2
  (20 Apr 2009)
- Introduction to CIAO
- Starting CIAO
  (15 Jun 2006)
Chandra Data:
General:
GUIs:
A Complete List of CIAO tools can be found at the Introduction to Tools page.
If you are looking for a general topic, try the CXC Search at the top of the page.

Data Preparation

When Chandra data goes through Standard Data Processing (SDP), the most recently available calibration is applied to it. Since this calibration is continuously being improved, one should check whether there are newer files available. Similarly, some science decisions are made during SDP; every user has the option to reprocess the data with different parameters.

See the current Calibration Status Report
Analysis Guides:
- ACIS Data Preparation
- HRC Data Preparation
General:
- Setting the Observation-specific Bad Pixel Files
  Uses: the acis_set_ardlib script
- Filtering Data (S-Lang or Python)
- Filtering Lightcurves (S-Lang or Python)
  Uses: the lightcurves.sl S-Lang script; the lightcurves.py script
Coordinates, Astrometry, & Spatial Filtering:
ACIS:
- Check the ACIS Data Caveats
- Why topic: -110 C Observations
- Why topic: Continuous Clocking Mode Data
- Removing Cosmic-Ray Afterglows:
- acis_process_events:
  - Reprocessing Data to Create a New Level=2 Event File thread: apply the newest calibration (CTI, TGAIN); includes CC mode times of arrival calculation.
  - Clean ACIS Background in VFAINT Mode
- The ACIS "Blank-Sky" Background Files (S-Lang or Python)
  Uses: the acis_bkgrnd_lookup script; the lightcurves.sl S-Lang script; the lightcurves.py script
- Remove the ACIS Readout Streak
HRC:
- New Observation-Specific HRC Bad Pixel File
- Reprocessing Data to Create a New Level=2 Event File thread: applies the newest calibration (degap, AMP_SF/tapringing).
- Computing Average HRC Dead Time Corrections (S-Lang or Python)

Imaging

The Imaging threads cover a wide range of topics that include source detection, creating exposure maps and normalized images, and calculating image statistics. How to create color images for publication is addressed, as well as merging data from multiple observations.

Analysis Guides:
- HRC Imaging
- Extended Sources
General:
- The ACIS "Blank-Sky" Background Files (S-Lang or Python)
  Uses: the acis_bkgrnd_lookup script; the lightcurves.sl S-Lang script; the lightcurves.py script
- Match the Binning of an Image
  Uses: the get_sky_limits script
- An Estimated Background Image
  (13 Jul 2009)
- True Color Images
- True Color Images in ds9
- An Image of Diffuse Emission
  Uses: the mkBgReg.pl script; the mkSubBgReg.pl script
- Using Data Cubes
Reprojecting Data:
- Merging Data from Multiple Imaging Observations
  Uses: the merge_all script
  (25 Jun 2009)
- Overview: Reprojecting Files
- Correcting Absolute Astrometry with reproject_aspect
- Reprojecting Images: Making an Exposure-corrected Mosaic
- Reprojecting Coordinates of a Solar System Object
- Object-centered Aspect Solution and Exposure Map
Coordinates, Astrometry, & Spatial Filtering:
Source Characteristics:
- Estimate Source Counts in an Image
- Obtain and Fit a Radial Profile (S-Lang or Python)
- Compute Net Counts, Rate, or Flux for Point Sources
- Computing the Intensity Upper Limit for an Unresolved Source
  (24 Jul 2009)
- Calculate the Flux for a Position
- Measuring Source Extent
  (20 Apr 2009)
- Search for Variability in a Source (S-Lang or Python)
  (07 Jul 2009)
PSFs:
- PSFs with ChaRT: the Chandra Ray Tracer
- Create a PSF (S-Lang or Python)
Detect:
- Overview: Detecting Sources in Imaging Observations
- An Estimated Background Image
  (13 Jul 2009)
- Running celldetect
- Running vtpdetect
- Running wavdetect
- Using the Output of Detect Tools (S-Lang or Python)
Exposure Maps:
- Using merge_all to Compute ACIS Exposure Maps and Fluxed Images
  Uses: the merge_all script
  (25 Jun 2009)
- Calculating Spectral Weights for mkinstmap (S-Lang or Python)
  (3 Aug 2009)
- Single Chip ACIS Exposure Map and Fluxed Image Step-by-Step
  Uses: the get_sky_limits script
  (3 Aug 2009)
- Multiple Chip ACIS Exposure Map and Fluxed Image Step-by-Step
  Uses: the get_sky_limits script
  (3 Aug 2009)
- HRC-I Exposure Map and Fluxed Image
  Uses: the get_sky_limits script
  (6 May 2009)
- HRC-S Exposure Map and Fluxed Image
  Uses: the get_sky_limits script
  (6 May 2009)
- Match the Binning of an Image
  Uses: the get_sky_limits script
Modeling & Fitting Imaging Data with Sherpa
(from the Sherpa analysis threads):
- Fitting FITS Image Data (S-Lang or Python)
- Using an Exposure Map in Fitting Image Data (S-Lang or Python)
- Using a PSF Image as the Convolution Kernel (S-Lang or Python)
- See also: the Obtain and Fit a Radial Profile CIAO thread (S-Lang or Python)

Imaging Spectroscopy

After extracting source and background PI or PHA spectra from an imaging observation, the appropriate response files (ARF, RMF) are created so that the data may be modeled and fit. In the case of multiple or extended sources, a weighted ARF and RMF are built for the spectral analysis.

Extracting Spectra & Creating Response Files:

These threads use CIAO contributed scripts to automate the spectral extraction and the creation of ARF and RMF response files.

specextract should be used for the analysis of extended sources only. Users working with point sources should instead use the psextract script. The mkwarf tool, which is used for ARF generation by specextract, may not produce accurate results for point sources. The mkarf tool, used by psextract, is preferred for point source extraction.

In certain cases, the mkacisrmf tool will need to be run separately to create the best possible RMFs for ACIS observations; see the thread for details.
- The ACIS "Blank-Sky" Background Files (S-Lang or Python)
  Uses: the acis_bkgrnd_lookup script; the lightcurves.sl S-Lang script; the lightcurves.py script
- Using specextract to Extract ACIS Spectra and Response Files for Extended Sources
- Using psextract to Extract ACIS Spectra and Response Files for Pointlike Sources
  Uses: the psextract script; the acis_fef_lookup script
- Coadding Spectra and Weighted Responses
  Uses: the acisspec script
  (6 May 2009)
- Creating ACIS RMFs with mkacisrmf
Step-by-step Analyses:

These threads give step-by-step instructions for creating spectra and response files.
- The ACIS "Blank-Sky" Background Files (S-Lang or Python)
  Uses: the acis_bkgrnd_lookup script; the lightcurves.sl S-Lang script; the lightcurves.py script
- Weighting ARFs and RMFs: multiple sources
- Step-by-Step Guide to Creating ACIS Spectra for Pointlike Sources
  Uses: the acis_fef_lookup script
- Extract a Spectrum from the ACIS Readout Streak
  Uses: the acis_fef_lookup script
  (17 Jul 2009)
- Extracting a Spectrum of a Solar System Object
  Uses: the acis_fef_lookup script
  (24 Jul 2009)
- A Note on HRC Spectra
Modeling & Fitting Spectral Data with Sherpa
(from the Sherpa analysis threads):
- Introduction to Fitting PHA Spectra (S-Lang or Python)
- Changing the grouping scheme of a data set within Sherpa (S-Lang or Python)
- Introduction to Fitting ASCII Data with Errors: Single-Component Source Models (S-Lang or Python)
- Simultaneously Fitting Two Data Sets (S-Lang or Python)
- Simulating 1-D Data: the Sherpa FAKE_PHA Command (S-Lang or Python)
- Simulating Chandra ACIS-S Spectra with Sherpa (S-Lang or Python)
- Fitting PHA Data with Multi-Component Source Models (S-Lang or Python)
- Independent Background Responses (S-Lang or Python)
- Using A Pileup Model (S-Lang or Python)

Grating Spectroscopy

If new calibration has been applied to the event file, the grating spectrum should be re-extracted as well. It is then possible to build grating response files (gARF, gRMF) in order to model and fit the data.

Chandra Grating Analysis Page
WebGUIDE: Interactive GUIDE for ATOMDB version 1.3
Chandra Grating Data Archive and Catalog (TGCat): a browsable interface to analysis-quality spectral products.
General:
- Examining Grating Spectra and Regions: PHA2 files (S-Lang or Python)
- Create a Color Spectrum
Problems with the Zero Order:
- Correcting a Misplaced Zero-order Source Position
- Source Position for Grating Data with a Piled or Blocked Zero Order
HETG/ACIS:
- HETG/ACIS-S Grating Spectra
- Grating Spectra for Multiple Sources - ACIS
- ACIS-S Grating RMFs
- HETG/ACIS-S Grating ARFs
  Uses: the fullgarf script
- Grouping a Grating Spectrum
LETG/ACIS:
- LETG/ACIS Grating Spectra
- Grating Spectra for Multiple Sources - ACIS
- ACIS-S Grating RMFs
- LETG/ACIS-S Grating ARFs
  Uses: the fullgarf script
- Grouping a Grating Spectrum
LETG/HRC-S:
- LETG/HRC-S Grating Spectra
- Applying Customized Background Regions to LETG/HRC-S Observations (S-Lang or Python)
  (20 Apr 2009)
- Grating Spectra for Multiple Sources - HRC
- Higher-order Responses for HRC-S/LETG Spectra
- HRC Grating RMFs
- LETG/HRC-S Grating ARFs
- Grouping a Grating Spectrum
LETG/HRC-I:
Combining Spectra & Preparation for Fitting:
- Extract Coadded and Grouped Nth-Order Source & Background Spectra and ARFs
  Uses: the add_grating_orders script
- Add Grating Spectra and Average ARFs
  Uses: the add_grating_spectra script
- PHA Background File for XSPEC
  Uses: the tg_bkg script
Modeling & Fitting Grating Data with Sherpa
(from the Sherpa analysis threads):
- Fitting Grating Data (S-Lang or Python)
- Fitting Multiple Orders of HRC-S/LETG Data (S-Lang or Python)
- Measuring Line Parameters with an HETG/ACIS-S Spectrum (S-Lang or Python)
- Measuring Line Parameters with an LETG/ACIS-S Spectrum (S-Lang or Python)

Timing Analysis

In order to perform absolute timing analysis on a dataset, a barycenter correction must first be applied to the data. One may then create lightcurves and phase-binned spectra to look for variability in the source. These threads also provide information on working with data taken in the ACIS continuous clocking (CC) mode.

General:
- Why topic: Continuous Clocking Mode
- Apply Barycenter Correction
- Phase-binning a Spectrum (S-Lang or Python)
Analyzing Lightcurves:
- Why topic: Timing Analysis with Lightcurves
- Basic Lightcurves (S-Lang or Python)
- Filtering Lightcurves (S-Lang or Python)
  Uses: the lightcurves.sl S-Lang script; the lightcurves.py script
- Search for Variability in a Source (S-Lang or Python)
  (07 Jul 2009)
See the S-lang/ISIS Timing Analysis Routines (SITAR) package which provides a set of functions and subroutines for timing analysis within ISIS.

Analysis Notes

Matching CIAO and CALDB Versions

News

Science Threads for CIAO 4.1

Introduction

Getting Started:

Chandra Data:

General:

GUIs:

Data Preparation

Analysis Guides:

General:

Coordinates, Astrometry, & Spatial Filtering:

ACIS:

Removing Cosmic-Ray Afterglows:

acis_process_events:

HRC:

Imaging

Analysis Guides:

General:

Reprojecting Data:

Coordinates, Astrometry, & Spatial Filtering:

Source Characteristics:

PSFs:

Detect:

Exposure Maps:

Modeling & Fitting Imaging Data with Sherpa (from the Sherpa analysis threads):

Imaging Spectroscopy

Extracting Spectra & Creating Response Files:

Step-by-step Analyses:

Modeling & Fitting Spectral Data with Sherpa (from the Sherpa analysis threads):

Grating Spectroscopy

General:

Problems with the Zero Order:

HETG/ACIS:

LETG/ACIS:

LETG/HRC-S:

LETG/HRC-I:

Combining Spectra & Preparation for Fitting:

Modeling & Fitting Grating Data with Sherpa (from the Sherpa analysis threads):

Timing Analysis

General:

Analyzing Lightcurves:

Data Used in Threads

Modeling & Fitting Imaging Data with Sherpa
(from the Sherpa analysis threads):

Modeling & Fitting Spectral Data with Sherpa
(from the Sherpa analysis threads):

Modeling & Fitting Grating Data with Sherpa
(from the Sherpa analysis threads):