Last modified: 15 April 2020


Per-Observation Detections MLE Draws Files (draws3.fits)

The per-observation MLE draws FITS file is named: 〈i〉〈s〉〈obs〉_〈obi〉N〈v〉[_〈c〉]_〈r〉〈b〉_draws3.fits

where 〈i〉 is the instrument designation, 〈s〉 is the data source; 〈obs〉 is the observation identification; 〈obi〉 is the observation interval identification; 〈v〉 is the data product version number; 〈c〉 is the cycle; 〈r〉 is the region ID, formatted as "rnnnn", where nnnn is the 4-digit region number, formatted with leading zeros; and 〈b〉 is the energy band designation. The optional discriminator identified in square brackets is included only for ACIS alternating exposure (interleaved) mode observations.

MCMC Samples

MLE applies a Bayesian algorithm to sample the posterior probability of the 2-D source model parameters. The posterior samples (draws) are used to calculate error ellipses for the source positions.

MLE applies the Sherpa implementation of pyBLoCXS (van Dyk et al. 2001), a Markov chain Monte Carlo (MCMC) based algorithm, which samples the posterior probability distribution of the parameters starting from the maximum likelihood step in the MLE optimization. The Sherpa function get_draws is used to run MCMC chains using the 2-D model information for a specific dataset, the selected sampler, the defined priors, and the specified number of iterations. This function returns an array of statistic values, an array of acceptance Booleans, and an array of sampled parameter values, i.e. draws.

Sampler Selection

The multivariate \(t\)-distribution is the sampling distribution used for the MCMC. This distribution is defined by the multivariate normal (for the model parameter values and the covariance matrix), and \(\chi^{2}\) distribution for a given degrees of freedom. The algorithm uses a mixture jumping rule of Metropolis (symmetric) and Metropolis-Hastings (asymmetric) for selection of the proposed parameters. MLE assumes 0.5 probability for the jumps from the best-fit (Metropolis rule) or from the current sampled (Metropolis-Hastings) parameter values (see Gelman et al. 2013).

The algorithm uses a multivariate normal distribution which requires parameter values and the corresponding covariance matrix. The MLE assumes the diagonal covariance matrix based on Sherpa's int_unc function.

An additional scale parameter allows to adjust the scale size of the multivariate normal in the definition of the \(t\)-distribution. This is to improve the efficiency of the sampler and obtain an acceptance of about 35%, based on tests the scale = 10 was assumed.

Model Parameters and Priors

The Sherpa 2-D Gaussian model sigmagauss2d is selected as a 2-D source model with the following parameters:

  • x, y — center of the gaussian
  • sigma_a, sigma_b — the \(\sigma\) of the Gaussian along the major- and minor-axis
  • theta — the angle of the major axis, in radians, measured counter-clockwise from the x-axis (i.e. the line \(y=0\))
  • ampl — the maximum peak value of the Gaussian model.

When fitting with a point source model, only the center of the Gaussian and the amplitude of the model are fit, while the other parameters are kept frozen at default values (sigma_a, sigma_b are set to the size of 1 image pixel; theta is set to 0), and the full set of parameters is fit for extended sources.

Bounded, uniform, flat priors for the center and \(\sigma\) parameters (x, y, sigma_a, sigma_b) are used. This is accomplished with box2d Sherpa model with the limits based on the minimum and maximum allowed parameter values, i.e. the limits of the source image. The Sherpa default flat priors are used for theta and ampl parameters within the default parameter limits. Note that the MLE sets the ampl limits for the maximum likelihood fit using a specific guess_amplitude function which depends on a source count rate, so the flat prior for the ampl has the soft minimum and maximum limit based on the specific value for each source.

Number of Iterations

get_draws samples the posterior probability density starting from the model parameters at the maximum likelihood given by the MLE optimization. Simulations are used to set the number of iterations which is based on the quality of the draws and the runtimes. The number of iterations in the pipeline is set to 5000 for a point source (3 parameters) and 15000 for an extended source (6 parameters).

The quality of the draws is assessed with the rhat parameter (Gelman & Rubin, 1992), the standard convergence diagnostic test. To calculate rhat, the MCMC chain for each parameter was divided into two parts, each containing 2500 draws. The second half was then divided into 10 equal sections of 250 draws and then variances were calculated for both within-chain sections and between-chain sections. These two variances are used in the determination of the rhat parameter, as explained in Gelman & Rubin. Values of rhat that are much larger than 1 indicate that the chain has not converged.

Output Files

The FITS table file contains the output of get_draws, with each row specifying a draw and columns containing statistics (cash in Sherpa), acceptance flag, and parameter values. The first row in the file lists the initial parameter values (best-fit from MLE optimization). Two separate blocks are used to store the draws for the point source model (SRCDRAWS) and the extended source model (EXTDRAWS), if the extended source model was also fit in the MLE.

Acceptance fraction, and rhat values for each parameter are given in the header of the FITS file.

Examples of the draws files for 3C273 observations in the CSC2:

Point source:

$ dmlist acisf01711_001N021_r0052b_draws3.fits.gz blocks
Dataset: acisf01711_001N021_r0052b_draws3.fits.gz
     Block Name                          Type         Dimensions
Block    1: PRIMARY                        Null        
Block    2: SRCDRAWS                       Table         6 cols x 5001     rows

$ dmlist acisf01711_001N021_r0052b_draws3.fits.gz header |more
Header keys for block SRCDRAWS
0001 STAT_TYP             CASH                           String       Fitting statistic
0002 ACC_FRAC                 0.33080                    Real8        MCMC draws acceptance fraction
0003 RHATAMPL                     1.0104938294           Real8        Amplitude draws convergence criterion
0004 RHAT_X                       1.0277521659           Real8        X position draws convergence criterion
0005 RHAT_Y                       1.0123222439           Real8        Y position draws convergence criterion

Extended source:

$ dmlist acisf01711_001N021_r0096b_draws3.fits.gz blocks
Dataset: acisf01711_001N021_r0096b_draws3.fits.gz
     Block Name                          Type         Dimensions
Block    1: PRIMARY                        Null        
Block    2: SRCDRAWS                       Table         6 cols x 5001     rows
Block    3: EXTDRAWS                       Table        12 cols x 15001    rows

$ dmlist acisf01711_001N021_r0096b_draws3.fits.gz[EXTDRAWS] header |more
Header keys for block EXTDRAWS
0001 STAT_TYP             CASH                           String       Fitting statistic
0002 ACC_FRAC                 0.36880                    Real8        MCMC draws acceptance fraction
0003 RHATSIGA                     1.4093168273           Real8        Sigma_a draws convergence criterion
0004 RHATSIGB                     1.1257415111           Real8        Sigma_b draws convergence criterion
0005 RHATAMPL                     1.1456318595           Real8        Amplitude draws convergence criterion
0006 RHATROTA                     1.0124760525           Real8        Rotang_a draws convergence criterion
0007 RHAT_X                       1.0174783236           Real8        X position draws convergence criterion
0008 RHAT_Y                       1.0149884518           Real8        Y position draws convergence criterion

Related Science Documents

  • Andrew Gelman and Donald B Rubin. Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 7(4):457-511, 1992.
  • Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. Bayesian Data Analysis, third edition. CRC Press, 2013.
  • van Dyk, D.A., Connors, A., Kashyap, V.L., & Siemiginowska, A. 2001, Ap.J., 548, 224, Analysis of Energy Spectra with Low Photon Counts via Bayesian Posterior Simulation.