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Fun with DEMs: Understanding and Analyzing Emission Data in Astrophysics

Learn about Differential Emission Measure (DEM) in astrophysics, analyze data, and run Monte Carlo simulations using xrt_dem_iterative. Explore error estimation, temperature range adjustments, and iterative fitting techniques. Increase sensitivity with multiple iterations for accurate DEM modeling.

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Fun with DEMs: Understanding and Analyzing Emission Data in Astrophysics

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  1. Fun with DEMs (courtesy M. Weber Analysis, Inc.)

  2. So what is this DEM thing anyway? ~ The amount of “stuff” emitting in a given volume per unit area per unit temperature. DEM ne2 dVol Units: cm-5K-1 (Huh?) Like so….  (cm-3)2 d(cm3) /(cm2)/T  cm-5K-1 no problemo… Your Observations: Datafilter_i = DEM(T) Rfilter_I(T) dT

  3. The Magical Code of Mark xrt_dem_iterative Simplest mode: xrt_dem_iterative, filter_list, count_rates, logt, dem Eg, filter_list = [‘Al-mesh’,’Be-thin’,’Al-poly/Ti-poly’, …] Count_rates = [3.2, 2.1, 2.7, …] Useful keyword options: Obs_err = err (default is 0.03*count_rate) Min_T = Tmin, Max_T = Tmax, dT = dt (all in logT) (defaults are 5.5, 8.0, 0.1) (it seems we are not very sensitive above logT ~ 7.5 or so.) Maxiter=itmax (maximum number of iterations to allow) Chisq=chisq (chisq of fit)

  4. Monte Carlo DEMs xrt_dem_iterative, filter_list, count_rates, logt, dem, $ max_t=7.6,obs_err=sqrt(60*count_rates),mc_iter=n This allows one to see what errors your DEM has by running the code n times with a Gaussian distribution of random errors (sigma given by obs_err) With mc_iter, dem is now [ntemp,mc_iter+1], dem(*,0) is “best fit”, others are MC results Other keywords are redimensioned similarly (eg, chisq goes from constant to size [mc_iter+1] Mc_iter ~ 100 or so for a “quick look”, ~1000 or more for “publication”

  5. Example: Full Disk DEM with Monte Carlo plot,logt,dem(*,0),psym=10 [green] oplot,logt,median(dem(*,1:*)) [yellow] for i=1,99 do oplot,logt,dem(*,i),psym=10,lines=1 [blue]

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