(Chair: Vinay Kashyap)
A forum for the discussion of statistical, methodological, and algorithmic issues that affect the calibration of astronomical instruments, of how calibration data are used in data analysis, and how the analysis results are interpreted.
Mailing list: iachec-calstat@cfa.harvard.edu
Current members: List of members
Talks/Meetings | Library
Talks
- 2022 May 24: Herman Marshall on Progress in Calibration Concordance at IACHEC Spring Workshop
- 2021 Dec 1: Sam Sweere on AI-Assisted Super-Resolution and De-Noising for XMM-Newton EPIC-pn
- 2021 Jun 7: Yang Chen on Systematic Uncertainties in Multi-Telescope Observations at AAS 238
- 2020 Dec 8: Diab Jerius on Doing the Hokey-Pokey, or Deriving Statistical errors for Measurements of the Chandra X-ray Observatory PSF at CHASC
- 2020 Sep 8:Herman Marshall & Yang Chen on Concordance: In-flight Calibration of X-ray Telescopes without Absolute References at CHASC
- 2020 Aug 6: Kristin Madsen X-ray data and its many challenges at JSM 2020
- Wednesday, 2021 December 1
6-7am PST / 9-10am EST / 2-3pm GMT / 3-4pm CET / 7:30-8:30pm IST / 11pm-midnight JST - AI-Assisted Super-Resolution and De-Noising for XMM-Newton EPIC-pn
Sam Sweere (XMM/SOC, ESAC) - Abstract: The field of AI image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. My research looks at applying these techniques to enhance XMM-Newton X-ray images. Specifically, having an AI-model increase the resolution and denoise the images with the goal of increasing their scientific value. During the talk, I will touch on the processes, initial results, and challenges of super-resolution AI models for XMM-Newton.
- Presentation slides [.pdf]
- Presentation video — https://smithsonian.zoom.us/rec/share/4k-ko9F5rJFQi4eVKTuSfKjtCrfb_tOHEdrinwvn4BzI8PbOqfuSdKDI8oTmP_VW.7JwN2Y10kK0aV_j1 [zoom]
Recent Working Group Meetings
2023 Apr 25 during IACHEC XV
CalStats Part I 9-10:30am CEST
- IXPE data fitting, by Herman Marshall
- Systematic uncertainties in IXPE, by Stefano Silvestri
- Possible ways to improve the energy calibration process of future X-ray missions, by Benjamin Schneider
- AI-assisted super-resolution and denoising for XMM-Newton images, by Ivan Valtchanov
CalStats Part II 4:10-6pm CEST
- NICER SCORPEON background, by Craig Markwardt
- Weak lines and high background at high resolution, by Vinay Kashyap
- Panel 1: Concordance; panelists Yang Chen and Herman Marshall
- Panel 2: C-stat and systematics; panelists Yang Chen and Max Bonamente
Summary Reports Apr 27 9am-Noon CEST
- CalStats Working Group Report, by Vinay Kashyap
2022 Jul 1
6-8am PDT / 9-11am EDT / 2-4pm BST / 3-5pm CEST / 8:30-10:30pm IST / 11pm-1am(+1d) CT / Midnight-2am(+1d) JST
iachec.org/calibration-statistics/#2022jul01
Agenda:
To discuss polarization statistics (led by H.Marshall) and probabilistic background removal (led by S.Ehlert)
Slides:
- Statistics in X-ray Polarimetry: Overview of some statistics in use or development for X-ray Polarimetry by Herman Marshall
- A Probabilistic Method of NXB Removal for X-ray Astronomy by Steven Ehlert
2021 Nov 1
iachec.org/calibration-statistics/#2021nov1
Agenda:
— Pandemic report [arXiv]
— Concordance update
— Coordination with Background, HiRes, Timing WG
2020 Dec 1
6-8am PST / 9-11am EST / 2-4pm GMT / 3-5pm CET / 7:30-9:30pm IST / 10pm-Midnight CT / 11pm-1am(+1d) JST
iachec.org/calibration-statistics/#2020dec1
A follow-up to the IACHEC 2020 Online Symposium, this two-hour meeting is designed to give more opportunity for detailed discussions. Each presenter will have approximately 15 min, including discussion time. The goal here is not to have a parade of speakers, but to allow time for Q&A and discussions.
Agenda
Moderator: Terry Gaetz (CfA)
Video [.mp4]
Discussion chat log [.txt]
- Statistical Topics in X-ray Polarimetry: Herman Marshall (MIT)
Abstract: I’ll be talking about 3 topics: 1) spectral fitting of I,Q,U data as f(E) (summarizing a paper by Strohmayer), 2) machine learning as applied to measuring tracks in the IXPE gas pixel detectors, and 3) general maximum likelihood considerations when the polarimeter modulation factor depends upon energy. - Handling model uncertainties by means of comparison densities: Sara Algeri (Minnesota)
- Modeling the background: a case study with Suzaku XIS and N132D: Eric Miller (MIT)
- Likelihood selection: Guillaume Belanger (ESA)
References: On Detecting Transient Phenomena, Belanger 2013 ; On More Sensitive Periodogram Statistics, Belanger 2017 - Using the Kaastra cstat goodness-of-fit in Sherpa: Vinay Kashyap (CfA)
tar file including .py script and .ipynb - Concordance Q&A: Yang Chen (Michigan) and Herman Marshall (MIT)
Slides from Symposium - Incorporating Calibration Uncertainties: Tabled by Jelle Kaastra (SRON)
(via Jukka Navaleinen) our latest caibration showed there is a typical 1-2 % small-scale systematic uncertainty in the derived flux level of any source (likely caused by things such as minor effective area uncertainties, flickering pixels etc.)
Now in a fit using chi**2 statistics, one may simply add this 1-2 % error in quadrature to the statistical errors in the process of spectral fitting. However, we all know that we have to use C-statistics, but of course additional 1-2% Gaussian uncertainties superimposed on the Poissonian data make no sense.
What is the best way to combine systematic uncertainties with C-stat fitting?
(Ed note: see pyBLoCXS)
A Curated Library
- Atomic Data Uncertainty
- Background
- Best Practices
- Calibration Uncertainty and Concordance
- Polarization Statistics
- Miscellaneous
Atomic Data Uncertainty
to understand the nature of the uncertainties present in compilations and measurements of atomic lines, emissivities, and other spectral features, and determine what effects they have and how to incorporate them during analyses.
- Foster, A., Smith, R.K., Brickhouse, N.S., Kallman, T.R., and Witthoeft, M.C., 2010, Space Sci.Rev. 157, 135, The Challenges of Plasma Modeling: Current Status and Future Plans
- Yu, X., Del Zanna, G., Stenning, D.C., Cisewski-Kehe, J., Kashyap, V.L., Stein, N., van Dyk, D.A., Warren, H.P., and Weber, M.A., 2018, ApJ 866, 146, Incorporating Uncertainties in Atomic Data into the Analysis of Solar and Stellar Observations: A Case Study in Fe XIII
- Heuer, K., Foster, A.R., and Smith, R., 2020, arXiv:2011.08230, Spectral Implications of Atomic Uncertainties in Optically-thin Hot Plasmas
- Leahy, D., Foster, A., and Seitenzahl, I., 2023, arXiv:2311.11181, On the Interpretation of XSPEC Abundances and Emission Measures
Background
to compile models of instrument background and provide scripts and recipes for how to use them in spectral, spatial, and timing analyses.
A compilation of studies, models, packages, and scripts dealing with the non X-ray backgrounds from various instruments is maintained at the wiki page.
Statistics Best Practices
to describe and develop principled methods to handle the analysis of data such that biases and improper or imprecise interpretations are minimized, and to provide tutoorials and guides.
- Protassov, R., van Dyk, D.A., Connors, A., Kashyap, V.L., & Siemiginowska, A., 2002, ApJ 571, 545, Statistics, Handle with Care: Detecting Multiple Model Components with the Likelihood Ratio Test
- Park, T., Kashyap, V.L., Siemiginowska, A., van Dyk, D.A., Zezas, A., Heinke, C., & Wargelin, B.J., 2006, ApJ 652, 610, Bayesian Estimation of Hardness Ratios: Modeling and Computations
— BEHR Code download - Kashyap, V.L., van Dyk, D.A., Connors, A., Freeman, P.E., Siemiginowska, A., Xu, J., & Zezas, A., 2010, ApJ 719, 900, On Computing Upper Limits to Source Intensities
- Belanger, G., 2016, ApJ 822, 14, On More Sensitive Periodogram Statistics
- Belanger, G., 2017, ApJ 773, 66, On Detecting Transient Phenomena
- Kaastra, J.S., 2017, A&A 605, A51, On the use of C-stat in testing models for X-ray spectra
- Kashyap, V., 2020, Tutorial Guide to X-ray and Gamma-ray Astronomy, Ed. C. Bambi, Ch.6, Basics of Astrostatistics
- Feigelson, E., et al., 2022, Time domain methods for Xray and gamma-ray astronomy, In: Bambi, C., Santangelo, A. (eds) Handbook of X-ray and Gamma-ray Astrophysics. Springer, Singapore. doi:10.1007/978-981-16-4544-0_135-1
Calibration Uncertainty, Systematics, and Concordance
to establish the magnitudes of the statistical and systematic instrument uncertainties, and develop methods to incorporate them or correct for them in analyses.
- Drake, J.J., Ratzlaff, P., Kashyap, V., Edgar, R., Izem, R., Jerius, D., Siemiginowska, A., and Vikhlinin, A., 2006, Proc.SPIE 6270, 62701I, Monte Carlo processes for including Chandra instrument resopnse uncertainties in paramater estimation studies
- Kashyap, V.L., Lee, H., Siemiginowska, A., McDowell, J., Rots, A., Drake, J., Ratzlaff, P., Zezas, A., Izem, R., Connors, A., van Dyk, D., and Park, T., 2008, Proc.SPIE 7016, 70160P, How to handle calibration uncertainties in high-energy astrophysics
- Lee, H., Kashyap, V.L., van Dyk, D.A., Connors, A., Drake, J.J., Izem, R., Meng, X.-L., Min, S., Park, T., Ratzlaff, P., Siemiginowska, A., and Zezas, A., 2011, ApJ 731, 126, Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting
- Kashyap, V., 2014-may-12, IACHEC 2014, pyBLoCXS: Strategy to deal with known errors in effective areas
- Xu, J., van Dyk, D.A., Kashyap, V.L., Siemiginowska, A., Connors, A., Drake, J., Meng, X.-L., Ratzlaff, P., and Yu, Y., 2014, ApJ 794, 97, A Fully Bayesian Method for Jointly Fitting Instrumental Calibration and X-ray Spectral Models
- Chen, Y., Meng, X.-L., Wang, X., van Dyk, D.A., Marshall, H.L., and Kashyap, V.L., 2019, Journal of the American Statistical Association, 114:527, 1018, Calibration Concordance for Astronomical Instruments via Multiplicative Shrinkage
- Marshall, H., Yang, C., Drake, J.J., Guainazzi, M., Kashyap, V.L., Meng, X.-L., Plucinsky, P.P., Ratzlaff, P., van Dyk, D.A., and Wang, X., 2021, AJ, 162, 254, arXiv:2108.13476, Concordance: In-flight Calibration of X-ray Telescopes without Absolute References
- Bonamente, M., 2023, MNRAS, arXiv:2302.04011, Systematic errors in the maximum likelihood regression of Poisson count data: introducing the overdispersed chi-square distribution
- Silvestri, S., 2023, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 1048, 2023, 167938, Accounting for systematic uncertainties in the Imaging X-ray Polarimetry Explorer (IXPE) detector response
- Nevalainen, J. and Molendi, S., 2023, arXiv:2305.04785, Studying X-ray instrumentation with galaxy clusters
Polarization Statistics
- Marshall (2022+, in prep) (Poisson likelihood with background, nonuniform exposure angle, mRMF)
- Gonzales-Caniulef et al. 2022 (likelihood method for pulsars)
- Di Marco et al. 2022 (event weights using IXPE track ellipticities)
- Marshall 2022 (likelihood method, modeling modulation facor)
- Marshall 2021
- Peirson et al. 2021 (ML to get better modulation factor)
- Strohmayer 2017 (fitting IQU spectra in XSPEC, mRMF)
- Kislat et al. 2015 (unbinned analysis, event weighting)
- Elsner, O’Dell & Weisskopf, 2012 (Gaussian statistics, background)
Miscellaneous
a catch-all category to handle other items of interest that do not fit in one of the above categories.
- Feldman, G.J. and Cousins, R.D., 1998, Phys.Rev.D 57, 3873, Unified approach to the classical statistical analysis of small signals
- Roe, B.P. and Woodroofe, M.B., 2002, Phys.Rev.D 60, 053009, Improved probability method for estimating signal in the presence of background
- Mandelkern, M., 2002, Statistical Science 17, 149, Setting Confidence Intervals for Bounded Parameters
- Schellenberger, G, Reiprich, T.H., Lovisari, L., Nevalainen, J., and David, L., 2014, A&A 575, 30, XMM-Newton and Chandra cross-calibration using HIFLUGCS galaxy clusters . Systematic temperature differences and cosmological impact
- Kaastra, J.S. and Bleeker, J.A.M., 2016, A&A 587, A151, Optimal binning of X-ray spectra and response matrix design
- Bonamente, M., 2019, J.Applied Statistics 47, 2044, Distribution of the C statistic with applications to the sample mean of Poisson data
- Algeri, S., 2020, Phys.Rev.D 101, 015003, Detecting new signals under background mismodeling
- Turner, D.J., Giles, P.A., Romer, A.K., Wilkinson, R., Upsdell, E.W., Bhargava, S., Collins, C.A., Hilton, M., Mann, R.G., Sahl, M., Stott, J.P., and Viana, P.T.P., 2021, arXiv:2109.11807, The XMM Cluster Survey: An independent demonstration of the fidelity of the eFEDS galaxy cluster data products and implications for future studies