METHOD OF DEM ESTIMATION 1.Generating “true” model observations. We start with the XRT temperature response functions R c (T) and a coronal emission model P(,T) as given. We select a “true” DEM model (in this poster, we use isothermal and active region models) Q(T), which we try to reconstruct. The “true” observations I c are generated from these functions according to the equations in the Figure 1 box. 2.Forward-fitting solution to DEM reconstruction. Given the “true” observations I c, solve the inverse problem to make an estimate Q’(T) of the DEM. Our approach is to guess a DEM, calculate the observables, and compare them against the “true” data. The search method is simplex, and reduces the least-squares error in the observables. Convergence to a solution gives the DEM estimate Q’(T). FURTHER COMMENTS Our initial DEM guess is always a positive flat DEM. The search method does not enforce positivity in the solution, but negative components in the solution are rarely encountered. Because the number of unknowns (i.e., temperature bins in Q(T)) is typically greater than the number of channels, the inversion problem is under- constrained --- methods of direct-inversion will often produce negative components. DEM reconstruction is crucial for making one of XRT’s data products: temperature maps of the corona. This poster only addresses XRT’s stand-alone temperature diagnostic capabilities. In practice, XRT and EIS will complement each other well and regularly in temperature studies. FIGURES 4: For observing “cool” x-ray emitting plasmas, 4 filters work almost as well as 9 filters do. For observing cooler plasmas (log T = ), a selection of four non- redundant thin filters {thin-Al-poly, C-poly, thin-Be, and med-Be} is still capable of temperature analysis. The four-filter set (dashed red) is nearly as accurate across the low end of XRT’s temperature sensitivity as is the full nine-filter set (dashed blue). No thick filter is required to constrain the DEM reconstruction at temperature bins above the isothermal temperature since all of the x-ray filters have a high-T response. FIGURES 3: For observing hot plasmas, 4 filters work almost as well as 9 filters do. For observing hot plasmas (e.g., flares, active regions), a selection of the three thickest filters {med-Al, thick-Al, and thick-Be} and one thin filter {C-poly} is still capable of temperature analysis. The four-filter set (dashed red) is nearly as accurate across hot temperatures (log T = ) as is the full nine-filter set (dashed blue). Why one thin filter? To constrain the DEM reconstruction at temperature bins cooler than the isothermal temperature. The three thickest filters have negligible response at the cool end of this T range, so do not provide much constraint there. FIGURES 2: Because of redundancies, 6 filters work almost as well as 9 filters do. There are three pairs of nearly redundant filters: {thin-Al-mesh, thin-Al- poly}, {C-poly, Ti-poly}, and {med-Be, med-Al}. For a set of isothermal DEM models, at a variety of temperatures, the DEM reconstructions using 6 x-ray filters (dashed red) are nearly as accurate as using all 9 filters (dashed blue). Temperature Analysis with the XRT M. Weber* for the XRT Team * Smithsonian Astrophysical Observatory, 60 Garden St, MS-58, Cambridge, MA 02138, USA ABSTRACT The X-Ray Telescope on-board Solar-B has an array of nine diagnostic filters with which to analyze the temperature and differential emission measure of coronal plasma, which is spatially resolved with 1 arcsec pixels. We illustrate the XRT’s capabilities to discriminate temperatures and to recover differential emission measures, with both full and partial sets of the diagnostic filters. — P 09 — THE X-RAY TELESCOPE Wolter I grazing-incidence telescope (imaging) Bandwidth = 0.2 to 1.2 keV Plate-scale = ~ 1 arcsec / pixel Mirror PSF = ~ 1 arcsec (FWHM) FOV = > 34 arcmin (full Sun when centered) Focal-plane filters = 9 x-ray filters for attenuation of visible light and for temperature diagnostics + 1 G-band for the optical telescope FIGURE 1: Temperature Responses The XRT’s temperature responses R c (T) for the 9 x-ray channels, as identified by the associated focal-plane filters. The response R c (T) is a combination of the channel’s effective area A c ( ) with a model of the coronal spectral emission, P(,T), such that R c (T) = ∫ A c ( ) P(,T) d. The signal I c observed in a channel is a combination of R c (T) with a differential emission measure (“DEM”, Q(T)), such that I c = ∫ R c (T) Q(T) dT. FIGURES 5: Application to a more realistic active region DEM. We perform DEM estimation on an active region DEM model provided in CHIANTI, which is based upon OSO-6 observations. First plot: Comparing the results for a non-redundant six-filter set {thin-Al-poly, C-poly, thin-Be, med-Al, thick-Al, and thick- Be} (in dashed red) versus the full nine-filter set (dashed blue). Both give excellent results. Second plot: Performing “cool” DEM estimation across the temperature range log T = 6.0 to 7.0, using four thin filters {thin-Al-poly, C-poly, thin-Be, and med- Be}. The result shows some indication of the peak in emission measure at log T = 6.8, but does not successfully recover the “dip”. Third plot: Performing hot DEM estimation across the temperature range log T = 6.5 to 8.0, using the thicker filters {C-poly, med-Al, thick-Al, and thick-Be}. An excellent reconstruction is achieved with only 4 filters. DOING TEMPERATURE ANALYSIS WITH THE XRT The temperature (T) and differential emission measure (Q(T)) of a coronal plasma may both be constrained by observations in channels with some independence in temperature sensitivity. Previous coronal imagers (e.g., Yohkoh/SXT and TRACE) typically only took observations in two or three EUV/x-ray passbands during their nominal duty cycle. As more channels are used in simultaneous observations, the ability to constrain T and Q(T) is increased. The XRT has 9 such coronal imaging channels available. There are some good reasons to not always observe in the full complement of channels, but the primary consideration is that a larger set of channels implies a slower cadence. Given the time lags associated with exposures, channel switching, CCD readout, etc., a set of 4 or 5 channels may easily take over a minute for an imager. This cadence would be insufficient to study many interesting sorts of dynamic phenomenae in the solar atmosphere. Observing programs for XRT must balance these considerations in order to make the best use of the instrument’s capabilities. With this in mind, this poster illustrates the comparative value of 4 filters versus 9 filters for temperature analysis with the XRT. Our conclusion is that observing programs of 4 appropriately chosen filters will be adequate for many studies.