All Threads - Sherpa 4.1

Introduction

Beginners should start here. The Introductory threads explain how to start Sherpa and provide an overview of using the application.

Getting Started:
- Starting Sherpa
- Obtaining data used in Sherpa threads
- Running Scripts in Sherpa (S-Lang or Python)
- ChIPS commands are used from within Sherpa to customize plots and create hardcopy output (PS, PNG, JPG). Refer to the Introduction to ChIPS thread (S-Lang or Python) for an overview of using that program.
Introduction to Fitting ASCII Data with Errors: Single-Component Source Models (S-Lang or Python)
Introduction to Fitting PHA Spectra (S-Lang or Python)

Sherpa provides extensive facilities for modeling and fitting data. The topics here range from basic fits using source spectra and responses to more advanced areas, such as simultaneous fits to multiple data sets, accounting for the effects of pileup, and fitting spatial and grating data.

Before fitting ACIS spectral data sets with limited pulse-height ranges, please read the CIAO caveat "Spectral analyses of ACIS data with a limited pulse-height range."

Spectral (1-D) Data
- 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)
- Calculating Uncertainties by Simulating Flux Distributions (S-Lang or Python)
- Independent Background Responses (S-Lang or Python)
- Using A Pileup Model (S-Lang or Python)
- Sherpa User Models (S-Lang or Python)
- Sherpa Table Models (S-Lang or Python)
Spatial (2-D) Data
- Fitting FITS Image Data (S-Lang or Python)
- Radial and elliptical profiles of Image Data (S-Lang or Python)
- Using an Exposure Map in Fitting Image Data (S-Lang or Python)
- Accounting for PSF Effects in 2D Image Fitting (S-Lang or Python)
- See also: the Obtain and Fit a Radial Profile (S-Lang or Python) CIAO thread
- Sherpa User Models (S-Lang or Python)
Grating Data

Users interested in conducting wavelength analysis are encouraged to learn the limitations associated with the set_analysis command (S-Lang or Python help) before doing so with the information contained in these 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)
Generic Data

These threads involve data analysis which is not specific to X-ray spectral or spatial modeling, therefore they can be followed using simple ASCII files as input, or other non-X-ray data.
- Introduction to Fitting ASCII Data with Errors: Single-Component Source Models (S-Lang or Python)
- CIAO thread: Obtain and Fit a Radial Profile (S-Lang or Python)
- Sherpa User Models (S-Lang or Python)
- Sherpa Table Models (S-Lang or Python)

Plotting

Sherpa allows the user to plot data, fits, statistics, ARFs, contours, and more. These threads describe the basics of plotting as well as various methods for customizing plots.

Plotting in Sherpa Using Common Options (S-Lang or Python)

Statistics

Sherpa provides numerous tools for determining goodness of fit, errors in parameter values, confidence intervals, and other statistical measures of a model's validity. These threads describe how to use these tools in your analysis.

Step-by-Step Guide to Estimating Errors and Confidence Levels (S-Lang or Python)
Calculating Uncertainties by Simulating Flux Distributions (S-Lang or Python)
Visit the The California-Harvard Astrostatistics Collaboration page

Simulations

The Sherpa fake_pha (S-Lang or Python help) command is available for simulating a Chandra PHA data set with an input instrument response and source model expression. These threads describe how to produce simulated data appropriate for your analysis.

Simulating Chandra ACIS-S Spectra with Sherpa (S-Lang or Python)
Simulating Chandra ACIS-S LETG Spectra with Sherpa (S-Lang or Python)
Simulating 1-D Data: the Sherpa FAKE_PHA Command (S-Lang or Python)

Sherpa Threads for CIAO 4.1

Introduction

Getting Started:

Fitting

Spectral (1-D) Data

Spatial (2-D) Data

Grating Data

Generic Data

Plotting

Statistics

Simulations