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Welcome to David van Dyk's web site photo: dvd


  1. Cal-Concordance: Calibration Concordance for Astronomical Instruments. (Wang, X., Chen, Y., van Dyk, D. A., Meng, X.~L., Marshall, H., and Kashyap. V.) Available on GitHub: (Download Software)

  2. timedelay: R package for Time Delay Estimation in Stochastic Time Series of Gravitationally Lensed Quasars. (Tak, H., Mandel, K., van Dyk, D. A. Kashyap, V. L., Meng, X.-L., and Siemiginowska, A.) Available on Cran: (Download Software)

  3. Automark: Automatic Marking of Marked Poisson Process in Astronomical High-Dimensional Datasets. (Wong, R. K. W., Kashyap, V. L., Lee, T. C. M., and van Dyk, D. A.) Available on GitHub: (Download Software)

  4. BASE9: Bayesian Analysis for Stellar Evolution with 9 Parameters. (von Hippel, T., Robinson, E., Jeffery, E., Wagner-Kaiser, R., DeGennaro, S., Stein, N., Stenning, D., Jeffrey, W., and van Dyk, D.) Available on GitHub: (Download Software) (Download Users Manual)

  5. LIRA: Low-counts Image Reconstruction and Analysis (formerly EMC2). This package is designed for Multi-Scale Non-parametric Image analysis for use in High-Energy Astrophysics. The code implements an MCMC sampler that simultaneously fits the image and the necessary tuning/smoothing parameters in the model. The model-based approach allows for quantification of the standard error of the fitted image. (Connors, A., Esch, D., Kashyap, V., Stein, N., Siemiginowska, A., and van Dyk, D. A.)

    The code is available on github: (Download Software).

    Related papers are available.
    (Download orginal LIRA paper.),
    (Preliminary paper on testing.), and
    (Download paper on testing for unspecified structure.)

  6. BEHR: Bayesian Estimation of Hardness Ratios. This code uses Poisson models for principled Bayesian estimation of hardness ratios in ultra low-resolution spectral analysis for use in high-energy astrophysics. (Park, T., van Dyk, D. A. and Kashyap, V.)

    The computer code and a paper describing and illustrating the method are available. (Download Software). (Download related ApJ paper.).

  7. BLoCXS on pyBLoCXS: (python) Bayesian Low-Count X-ray Spectral Analysis. These package implement fully Bayesian X-ray spectral analysis, including model checking and can be used to account for calibraiton uncertainty. (Kramer, J., van Dyk, D. A., Connors, A., Kashyap, V., Refsdal, B., and Siemiginowska, A.)

    The code is available via
    (python version of BLoCXS, optimized for use in Sherpa)
    (latest python version), and
    (legacy code).

    Related papers are available:
    (Download paper on spectral analysis.)
    (Download paper on p-values.)
    (Download 2011 paper on calibration uncertainty.)
    (Download 2014 paper on calibration uncertainty.)

  8. BASCS: Bayesian Separation of Close Sources. This code fits the Bayesian model of Jones et al. (2015) that combines spectral and spatial data to probabalistically separate overlapping X-ray sources. (Jones, D. E., Kashyap, V. L., and van Dyk, D. A.)

    The code is available on github: (Download Software).

    Related papers are available.
    (Download the BASCS paper.)