WARM AND FUZZY: TEMPERATURE AND DENSITY ... - IOPscience

The Astrophysical Journal, 738:146 (7pp), 2011 September 10 C 2011.

doi:10.1088/0004-637X/738/2/146

The American Astronomical Society. All rights reserved. Printed in the U.S.A.

WARM AND FUZZY: TEMPERATURE AND DENSITY ANALYSIS OF AN Fe xv EUV IMAGING SPECTROMETER LOOP J. T. Schmelz1,2 , L. A. Rightmire1,3 , S. H. Saar2 , J. A. Kimble1 , B. T. Worley1 , and S. Pathak1 1

Physics Department, University of Memphis, Memphis, TN 38152, USA; [email protected] 2 Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA 3 Physics Department, University of Alabama, Huntsville, AL 35899, USA Received 2011 May 12; accepted 2011 June 13; published 2011 August 19

ABSTRACT The Hinode EUV Imaging Spectrometer (EIS) and X-Ray Telescope (XRT) were designed in part to work together. They have the same spatial resolution and cover different but overlapping coronal temperature ranges. These properties make a combined data set ideal for multithermal analysis, where EIS provides the best information on the cooler corona (log T < 6.5) and XRT provides the best information on the hotter corona (log T > 6.5). Here, we analyze a warm non-flaring loop detected in images made in a strong EIS Fe xv emission line with a wavelength of 284.16 Å and peak formation temperature of log T = 6.3. We perform differential emission measure (DEM) analysis in three pixels at different heights above the footpoint and find multithermal results with the bulk of the emission measure in the range 6.0 < log T < 6.6. Analysis with the EIS lines alone gave a DEM with huge amounts of emission measure at very high temperatures (log T >7.2); analysis with XRT data alone resulted in a DEM that was missing most of the cooler emission measure required to produce many of the EIS lines. Thus, both results were misleading and unphysical. It was only by combining the EIS and XRT data that we were able to produce a reasonable result, one without ad hoc assumptions on the shape and range of the DEM itself. Key words: Sun: UV radiation – Sun: X-rays, gamma rays Online-only material: color figures

et al. 2007) and X-Ray Telescope (XRT; Golub et al. 2007) data to construct a DEM for a coronal loop on the disk. The biggest challenge in these analyses, however, was to find the appropriate instrument cross-calibration factor. Solar physicists continue to debate the thermal properties of coronal loops, including the cross-field (i.e., perpendicular to the loop axis) temperature distribution (see, e.g., Kano & Tsuneta 1996; Neupert et al. 1998; Lenz et al. 1999; Aschwanden et al. 1999; Schmelz et al. 2001; Schmelz & Martens 2006). Here, a loop is a discernable structure in an observation and a strand is a fundamental flux tube where the temperature and density are constant perpendicular to the magnetic field. A loop could be composed of a single strand or many unresolved strands. Recent results indicate that there does not appear to be a simple answer to the question of whether loops were isothermal or multithermal. For example, Schmelz et al. (2007) analyzed two loops observed with SOHO CDS. Their analysis showed that the cross-field temperature was isothermal for the first loop and multithermal for the second. The analysis of Hinode EIS data by Warren et al. (2008) revealed several loops that had a narrow (but not isothermal) temperature distribution. DEM analysis of Hinode XRT and EIS loop data by Schmelz et al. (2010b) implied that the temperature distribution was neither narrow nor extremely broad, with the bulk of the emission measure 6.1 < log T < 6.6. In this paper, we analyze coronal loop data observed with EIS and XRT, two of the instruments on the Japanese/USA/UK Hinode spacecraft, which was launched in 2006 September. The region of interest was selected based on the presence of warm X-ray/EUV loops observed by both instruments. The observation and analysis are particularly challenging because of the image fuzziness (see, e.g., Fe xv 284 Å images from TRACE and SOHO-EIT). The fuzzy nature of these warm coronal images is due to the large number of unresolved structures along the

1. INTRODUCTION Multithermal analysis of coronal loops is particularly challenging since most instruments are sensitive to either the lower- or the higher-temperature plasma, but not both. Most modern EUV imagers and spectrometers (e.g., Transition Region and Coronal Explorer (TRACE), Solar and Heliospheric Observatory (SOHO) EIT, SOHO CDS, Hinode EIS) have excellent diagnostics for the cooler end of active region temperatures, but the highest-temperature (strong, unblended) lines observable are Fe xv or Fe xvi, with peak formation temperature of log T = 6.3 and 6.4, respectively. This is not high enough to constrain effectively the warmer active region plasma. X-ray imagers and spectrometers (e.g., Hinode XRT, Yohkoh soft X-ray telescope (SXT), Solar Maximum Mission (SMM) flat crystal spectrometer, SMM bent crystal spectrometer) have the opposite problem: they have great high-temperature diagnostics, but the coolest lines are usually Ovii or Oviii, with peak formation temperature of log T = 6.1 and 6.2, respectively. This is not cool enough to constrain effectively the cooler active region plasma (see, e.g., Schmelz et al. 1996). One way researchers have gotten around this problem is to apply ad hoc constraints. For example, the range of the multithermal analysis is artificially limited, and the argument is made that we should not calculate the differential emission measure (DEM) at temperatures where there are no data to constrain it. This, however, is the wrong way to look at the problem. A better way is that we should not calculate the DEM unless there are data to constrain it across the full temperature range of interest. Another approach has been to use data from two different instruments. Schmelz et al. (2001) used coordinated SOHO CDS and Yohkoh SXT observations to construct a DEM for a coronal loop on the limb. Schmelz et al. (2010b) used EUV Imaging Spectrometer (EIS; Culhane 1

The Astrophysical Journal, 738:146 (7pp), 2011 September 10

Schmelz et al. Table 1 EIS Data from 2007 December 9 Ion

λ (Å)

Log T

Fe x Fe xi Fe xii Fe xii Fe xii Fe xiii Fe xiii Fe xiv Fe xiv Fe xv Fe xvi

184.54 188.23 186.88 193.51 195.12 202.04 203.83 264.78 274.20 284.16 262.98

6.0 6.1 6.1 6.1 6.1 6.2 6.2 6.3 6.3 6.3 6.4

Table 2 XRT Data from 2007 December 8 Filter Al_poly C_poly Ti_poly Al_poly-Ti Figure 1. XRT observation of AR 10978. The EIS field of view is marked by the black box on the left side of the Sun. (A color version of this figure is available in the online journal.)

Time (UT)

Exposure (s)

23:17:35 23:20:54 23:17:59 23:17:41

1.44 s 0.18 s 2.90 s 0.51 s

The region of interest was selected based on the presence of warm non-flaring loops seen in EIS images made in the Fe xv emission line with a peak formation temperature of log T = 6.3. AR 10978 was observed at a position of S09E43 at 00:12:26 UT on 2007 December 9 by EIS using its 1 slit. Figure 1 shows the XRT full disk image at that time with the EIS field of view indicated by the box on the bottom left. The data from the various EIS spectral lines and XRT filters were manually co-aligned. Standard SolarSoft programs were used to calibrate the data from both instruments. EIS lines were fit using eis_auto_fit with intensities in erg cm−2 s sr. XRT responses for 2007 December 8 were modeled using make_xrt_temp_resp; XRT intensities are in Data Numbers (DNs)s−1 pixel−1 . Atomic physics data were obtained from the CHIANTI version 6.2 (Dere et al. 1997, 2009) data base. The standard XRT response functions assume CHIANTI coronal abundances and the ionization balance calculations of Mazzotta et al. (1998). The contribution functions for the EIS lines were calculated with the same assumptions. The EIS data set of AR 10978 contains several strong Fe lines. Table 1 lists these ions, the wavelengths in Angstroms, and the log of their peak formation temperatures. The corresponding XRT data are listed in Table 2, with the filter, observing time, and exposure time. The details of the region selected for analysis are shown in Figure 2 in the EIS Fe xv 284.16 Å line. The structure of interest is the nearly vertical loop segment marked on the right panel of the figure. The background intensities were determined by taking cuts across the loop and then creating linear fits to these cuts. Cuts were made at y = 113, y = 123, and y = 133 on the loop (the dashed lines in Figure 2). Background intensities were then subtracted from each loop pixel to obtain the isolated loop intensity. Figure 3 demonstrates this technique using a cut across the loop at y = 113 for the Fe xv 284.16 Å line. After background subtraction, this loop had a peak intensity along the vertical line corresponding to x = 180 for all three cuts. Three pixels along the loop were selected for analysis: pixels (180, 113), (180, 123), and (180, 133). As shown in Figure 2, all three pixels are located on the leg of the loop with pixel (180, 133) being closest to the footpoint. These pixels will henceforth

line of sight at this temperature (Brickhouse & Schmelz 2006). The loop in this analysis was located in AR 10978, which was monitored by Hinode in 2007 December. 2. OBSERVATIONS EIS is a spectrometer with two CCDs designed for viewing light in the EUV ranges of 170−210 Å and 250−290 Å. At these wavelengths, EIS is ideal for imaging the top of the transition region and the solar corona. Photons enter the EIS instrument through an Al filter, which limits the amount of light from optical wavelengths and are reflected off the primary mirror. They pass through a slit (with an aperture of 1, 2, 40, or 266 arcsec), a second Al filter, onto a grating, and finally onto the two CCDs. EIS specifications include a temperature range of 6.0 < log T < 7.3, a spatial resolution of 2 arcsec, and a field of view of 6 arcmin by 8.5 arcmin. Its spatial resolution provides a significant improvement over its predecessor, SOHO CDS, and its spectral resolution allows it to produce images of active regions for multiple strong emission lines simultaneously. XRT is a grazing incidence telescope designed for viewing light in the X-ray wavelengths. The most significant constraints to its design were a temperature range of 6.1 < log T < 7.5 and a temperature resolution of log T = 0.2. Additional requirements included a spatial resolution of 2 arcsec and a field of view of more than 30 arcmin. These specifications make XRT ideal for imaging features such as flares, jets, coronal mass ejections, coronal loops, and coronal holes. Light that enters XRT begins by passing through a filter designed to limit the amount of light from optical wavelengths. Two additional filter wheels have been placed in the optical path, further limiting the wavelengths that can pass through to the CCD. Thicker filters only allow higher temperature wavelengths to pass through and thus are better for viewing high energy events such as solar flares. XRT is a broadband instrument similar to Yohkoh SXT, but with better spatial resolution, sensitivity, and temperature coverage. 2


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Figure 2. EIS Fe xv spectral line image (left) which has been zoomed in (right) to show the nearly vertical loop segment that was analyzed. The horizontal lines mark the pixels where background subtraction was done. (A color version of this figure is available in the online journal.) Table 3 Densities for Loop Pixels Ion

λ (Å)

λ (Å)

Ratio

Density

Log Density

Pixel 113

Fe xii Fe xii Fe xii Fe xiv

186.88 186.88 203.83 264.78

195.12 193.51 202.04 274.2

0.4484 0.6239 2.6590 0.7391

1.5E+10 1.2E+10 1.1E+10 5.8E+08

10.16 10.06 10.04 8.76

Pixel 123

Fe xii Fe xii Fe xiii Fe xiv

186.88 186.88 203.83 264.78

195.12 193.51 202.04 274.2

0.4739 0.7440 2.8572 0.7644

1.7E+10 2.1E+10 1.6E+10 6.9E+08

10.24 10.32 10.20 8.84

Pixel 133

Fe xii Fe xii Fe xiii Fe xiv

186.88 186.88 203.83 264.78

195.12 193.51 202.04 274.2

0.3998 0.6016 2.9805 0.9590

9.6E+09 1.0E+10 2.1E+10 1.7E+09

9.98 10.00 10.32 9.24

3. ANALYSIS The EIS data set included multiple density sensitive pairs of iron lines: Fe xii 186.88-to-195.12 Å and 186.88-to-193.51 Å, Fe xiii 203.83-to-202.04 Å, and Fe xiv 264.78-to-274.2 Å. The initial density analysis of this loop was done by examining these density-sensitive pairs. The measured ratio of line intensities for each pair was compared to the predicted ratio of intensities given by the CHIANTI atomic physics data base. Electron densities for each of the three pixels were obtained for two Fe xii pairs, an Fe xiii pair, and an Fe xiv pair. The intensity ratios and corresponding densities are shown in Table 3. These results show that the density determined from the Fe xiv line ratio is significantly lower than the value calculated for the Fe xii and Fe xiii pairs, a result also seen by Doschek et al. (2007). CHIANTI indicates that the 252.20/264.78 branching ratio should have a fixed value of 0.242, but the observed value from EIS is 0.07 (P. Young 2011, private communication). The 264.78 Å line might be blended, but the unidentified blended line would have to be 3× stronger than the main line, which seems unlikely. These results suggest that the atomic data for the Fe xiv 264.78 Å line may need to be improved and that we should rely on the densities obtained from the Fe xii and Fe xiii ratios. As a result, the Fe xiv 264.78 Å line will not be used in the remainder of our analysis. If the plasma along the line of sight were isothermal, then the intensity for each spectral line observed by EIS is proportional to G(T ) × EM, where G(T ) is the contribution function

Figure 3. Intensity as a function of pixel number in the EIS Fe xv 284.16 Å image. The upper panel shows a cut taken across the loop at y = 113. The measured intensities are plotted and the background is determined by creating a linear fit to the edges of the cut where the loop is not present. This background is then subtracted to obtain the isolated loop intensity, which is shown in the lower panel. (A color version of this figure is available in the online journal.)

be referred to as pixels 113, 123, and 133. Finally, background subtraction also revealed that pixel 113 did not have significant emission from the Fe x 184.54 and Fe xi 188.23 Å lines, so these lines can only be used as upper limits to help constrain the cool (log T < 6.0) coronal material present in the loop at this location. 3


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Figure 4. EM Loci analysis. For all three pixels, the lines intersect over a range of temperatures. The solid curves represent the EIS lines and the dashed curves show the XRT filter data. The dot-dashed line in the top panel represents an upper limit. (A color version of this figure is available in the online journal.)

Figure 5. DEM analysis of EIS data from pixel 113 using DEM_manual. Left column shows three different DEM models (a) isothermal, (b) intermediate, and (c) broad. The right column shows the corresponding predicted-to-observed intensity ratios for the lines in Table 1. The Fe x, Fe xi, and Fe xiv 264.78 Å lines are not included in the analysis (see the text). The reduced χ 2 is printed in the upper right corner showing the bad fits for models (a) and (b) and the good fit for model (c). (A color version of this figure is available in the online journal.)

(erg cm3 s−1 ) from CHIANTI and EM is the plasma emission measure (cm−5 ). For XRT, the intensity for each filter is proportional to Resp(T ) × EM, where Resp(T ) is the response function (DN s−1 pixel−1 per unit emission measure) provided by the instrument team and available in SolarSoft. Note: the EIS and XRT units do not have to be converted to the same units, as long as the proper contribution and response functions are used. EM loci plots were created from the combined EIS−XRT data for each of the three loop pixels. These plots of log EM versus log T are shown in Figure 4 for pixels 113, 123, and 133. The solid curves represent the EIS lines and the dashed curves show the XRT filter data, where the values have been scaled to account for the different pixel sizes and the instrument cross-calibration factor (Testa et al. 2011). If the observed background-subtracted loop plasma were isothermal, all the curves should intersect at a single point, within uncertainties, with coordinates of the plasma temperature and emission measure. This, however, is not the case for any of the plots seen in Figure 4. In all three cases the curves cover a broad range of temperatures but do not intersect, indicating that the isothermal approximation does not hold. The plasma is likely to be multithermal and a DEM analysis is required. If the plasma is multithermal, then the observed intensity for each EIS spectral line is proportional to G(T ) × DEM(T ) ΔT , and for each XRT filter, Resp(T ) × DEM(T ) ΔT . This paper uses two different and complementary DEM analysis techniques, each with its own strengths and weaknesses. The first, DEM_manual (Schmelz et al. 2010a, 2011) is

a forward folding technique with a manual manipulation of the DEM. The best fit is determined from a χ 2 minimization of the differences between the observed and predicted loop fluxes. The main advantages are that no smoothing is required beyond that imposed by the temperature resolution of the spectral line contribution functions or the XRT instrument response functions. Also, no a priori shape (Gaussian or double Gaussian, for example) is imposed on the final DEM curve. The main disadvantages are that the method is slow and it is not usually possible to explore a broad χ 2 parameter space, so some families of solutions might be missed. Figure 5 shows a series of results from DEM_manual using the EIS spectral lines from pixel 113. The top panels show the results for an isothermal (spike) DEM as well as the predictedto-observed intensity ratios for each of the EIS emission lines. The reduced χ 2 for the isothermal model is 59.8, too high for an acceptable fit. The middle panel shows a slightly broader DEM and the corresponding flux ratios. The fit is better, but still not quite good enough. The bottom panel shows the best fit, with a reduced χ 2 = 1.3. We did a similar analysis with the EIS data from pixels 123 and 133. These results all agree with the EM 4


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(a)

(a)

(b)

(b)

(c)

(c)

Figure 7. DEM results for pixel 113 from xrt_dem_iterative2 for (a) EIS lines, (b) XRT filters, and (c) the combined EIS−XRT data set. The dotted histograms represent the Monte Carlo solutions, where the vertical range indicates how well constrained the DEM solution is in a particular temperature bin.

Figure 6. Predicted-to-observed intensity ratio for each EIS spectral line from pixel 123 for a density of log ne = (a) 9.0, (b) 10.0, and (c) 11.0. The value of the reduced χ 2 is in the upper right corner of each panel. Note that some of the data points (e.g., the second and third Fe xii lines) are too high in the top panel and too low in the bottom panel, while other lines (e.g., Fe xi) are too low in the top panel and too high in the bottom panel. (A color version of this figure is available in the online journal.)

forward-fitting approach where a DEM is guessed and folded through each contribution or response function to generate predicted fluxes. The DEM function is interpolated using several spline points, which are directly manipulated by mpfit, which performs a Levenberg–Marquardt least-squares minimization. There are Ni − 1 splines, representing the degrees of freedom for Ni observations. This routine uses Monte Carlo iterations to estimate errors on the DEM solution. For each iteration, the observed flux in each filter was varied randomly with a Gaussian noise σ equal to the rms noise in the data and the program was run again with the new values. Best DEM models are found by minimizing the χ 2 between the observed and predicted intensities. Using EIS contribution functions calculated from the DEMweighted densities and the XRT response functions calculated for 2007 December 8, we can use xrt_dem_iterative2 to find DEM curves for each pixel. Figure 7 shows the results for EIS and XRT separately, which demonstrate the power of this combined EIS-XRT data set. The dotted histograms represent the Monte Carlo solutions, where the vertical range indicates how well constrained the DEM solution is in a particular temperature bin. The top panel shows the result with the EIS data alone. There is an expected component with EM in the range 6.0 < log T < 6.5, but there is also a huge component at higher temperatures. This high-temperature component results as the program attempts to produce a mathematically acceptable solution with a low value of reduced χ 2 ; it does this by adding an unphysically large amount of emission measure in these high-temperature bins where the EIS data alone cannot constrain

loci plots shown in Figure 4; the EIS data cannot be modeled successfully with an isothermal plasma. With these preliminary DEM results, we can refine the density values as follows: without changing the shape of the DEM curve, e.g., Figure 5(c), we select a series of densities between 8.0 log ne 12.0, every 0.1 in the log; we plot the ratio of the intensities predicted by these DEM curves to the intensities observed by EIS and calculate the resulting reduced χ 2 for each density. This method is illustrated in Figure 6, where the predicted-to-observed intensity ratio is plotted for each EIS spectral line for a density of log ne = (a) 9.0, (b) 10.0, and (c) 11.0. The value of the reduced χ 2 is in the upper right corner of each panel. Note that some of the data points (e.g., the second and third Fe xii lines) are too high in the top panel and too low in the bottom panel, while other lines (e.g., Fe xi) are too low in the top panel and too high in the bottom panel. The lowest value of the reduced χ 2 results in a DEM-weighted density that best fits the multithermal data, with an uncertainty of approximately 0.1 in the log. This produced DEM-weighted densities of log ne = 9.8 ± 0.1 for pixels 123 and 133 and log ne = 9.7 ± 0.1 for pixel 113. The second DEM analysis technique is xrt_dem_iterative2 (Weber et al. 2004; Schmelz et al. 2009). This routine was originally written for XRT data alone, but was modified by one of us (SHS) to work with EIS data as well. It employs a 5


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(a)

(b)

(c)

Figure 8. Best-fit results from the combined EIS-XRT data set for (a) pixel 113, (b) pixel 123, and (c) pixel 133. The dotted histograms in the left column represent the Monte Carlo solutions for the different loop pixels derived from xrt_dem_iterative2. These results for pixel 113 are essentially the same as those in Figure 7(c). The red line is the best fit from DEM_manual, and the right column shows the predicted-to-observed intensity ratio plot from this DEM. (A color version of this figure is available in the online journal.)

4. DISCUSSION

it. The middle panel shows the DEM calculated from the XRT intensities. Since there are only four data points, and therefore only three allowed splines, the curve is particularly simple. Also, since XRT is responsive primarily to warm-to-hot plasma, it completely misses the cooler portion of the DEM curve required to fit the EIS lines. The bottom panel shows the DEM from the combined data set, with a peak at log T = 6.3 and a width broad enough to model both the EIS and XRT intensities. The distribution of the Monte Carlo indicates that the fit is quite good and that the data do a respectable job of constraining the DEM; no ad hoc assumptions are required. The best-fit results from the combined EIS-XRT data set are shown in Figure 8. As with Figure 7, the dotted histograms in the left column represent the Monte Carlo solutions for the different loop pixels derived from xrt_dem_iterative2. The red line is the best fit from DEM_manual, and the right column shows the predicted-to-observed intensity ratio plot from this DEM.

The fuzzy appearance of Fe xv 284 Å images such as the one analyzed here as well as those obtained by TRACE and SOHOEIT make the analysis of these warm loops particularly difficult. Brickhouse & Schmelz (2006) discussed the possible reasons for the fuzziness, which included (1) instrument scattering; (2) contamination of He ii 304 Å photons in the passband; (3) resonance scattering; and (4) the filling-factor model, which is related to the observational result that coronal structures appear fuzzy if they are not resolved by the instrument. The first three possibilities could readily be eliminated (see Brickhouse & Schmelz 2006 for details), leaving the filling factor model. Since most active region DEMs peak around 3 MK, there will be more higher temperature (284 Å) plasma along the line of sight than cooler (e.g., 171 Å) plasma, such that the piling up of structures along the line of sight appears to contribute to the fuzziness factor. 6


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As a result of the fuzzy appearance of these Fe xv images, it is particularly challenging not only to analyze the loops, but even to find appropriate candidates for DEM analysis. We need isolated loops with a simple background. This particular loop was discovered in the EIS data base and analyzed by one of us (LAR) as part of a Masters thesis project. The XRT data of the loop were added later, allowing us to improve the hightemperature constraint of the DEM results and move forward with the analysis. Adding the XRT data to our analysis better constrained the DEM at high temperatures, just as adding the EIS data to the analysis of an XRT loop better constrained the DEM at low temperatures (Schmelz et al. 2010b). The XRT and EIS instruments are on the same spacecraft, have the same spatial resolution, and cover different but overlapping areas of wavelength and temperature space. They were designed to work together, but joint analysis has been rare, partly because of the calibration and contamination issues that plagued XRT early in the mission (see, e.g., Narukage et al. 2011). A careful examination of Figure 8 will show that the peak DEM temperature shifts from log T = 6.35 for pixel 113 to log T = 6.15 for pixel 123, which is closer to the loop footpoint. The downward trend does not continue for pixel 133, but it should be noted that the background subtraction gets more difficult here because different structures are merging into a smaller area and are not as easily resolved with EIS and XRT. The trend, with hotter plasma near the loop tops, has also been observed with isothermal analysis of Yohkoh SXT data (e.g., Kano & Tsuneta 1996; Priest et al. 1998) and multithermal analysis of SOHO CDS data (Schmelz et al. 2001; Schmelz & Martens 2006). It is a shame that these particular data are not good enough to continue this DEM-type analysis for pixels closer to the loop top, but for even the limited data available, some of the results, like the absence of the cooler Fe x and Fe xi lines for pixel 113, are suggestive. Following the reasoning described by Klimchuk (2006), if the coronal loops are overdense relative to equilibrium (e.g., Winebarger et al. 2003) and have lifetimes that are longer than the radiative cooling times (e.g., Mulu-Moore et al. 2011), then loops simply cannot be modeled as a single flux tube. If the loops are multi-stranded, then they should (at least some of the time) also be multithermal. There are now a variety of results obtained with different instruments and different DEM analysis techniques that show that the cross-field temperature distribution of coronal loops is multithermal. Schmelz et al. (2001) and Schmelz & Martens (2006) used SOHO CDS and Yohkoh SXT data to construct DEM models at several positions along a coronal loop on the limb with DEM_manual. Warren et al. (2008) analyzed 20 loop segments selected in the 195 Å Fe xii images of EIS with a Gaussian fitting algorithm as well as with the Markov Chain Monte Carlo (MCMC) based reconstruction algorithm (Kashyap & Drake 1998) available in SolarSoft. The DEM distributions were narrow, but not isothermal. Schmelz et al. (2010a, 2011) looked at a variety of loops from different disk active regions observed with the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory. All the loops were selected in the 171 Å channel of AIA, which has a peak response temperature of log T = 5.8, and analyzed with both DEM_manual and DEM_interactive (Warren 2005), an automatic method built around mpfit. Most of the loops could not be modeled with a narrow (isothermal) temperature distribution. Schmelz et al. (2010b) analyzed joint XRT and EIS data of a disk loop observed on 2007 May 13 with both xrt_dem_iterative2 and MCMC. The loop was multithermal.

Now, with the results described in this paper, we have a second observation of an EIS-XRT loop that appears to agree with these multi-stranded loop models and multithermal loop observations. This is not to say that all coronal loops have a measurable, broad cross-field temperature distribution. An examination of AIA 171 Å images will reveal multiple loops and loop segments that do not appear in hotter (193 Å) or cooler (131 Å) images. Full DEM analysis using spectral lines (e.g., Schmelz et al. 2007) or images (e.g., Schmelz et al. 2011) can reveal loop segments that are consistent with an isothermal (spike) DEM, like the one shown in Figure 5(a). So the question now becomes, why are the DEMs for some loops broad and other loops narrow? This issue will be investigated in future papers. Hinode is a Japanese mission developed and launched by ISAS/JAXA, with NAOJ as domestic partner and NASA and STFC (UK) as international partners. It is operated by these agencies in co-operation with ESA and the NSC (Norway). CHIANTI is a collaborative project involving the NRL (USA), the Universities of Florence (Italy) and Cambridge (UK), and George Mason University (USA). Solar physics research at the University of Memphis is supported by a Hinode subcontract from NASA/SAO as well as NSF ATM-0402729. S.H.S. is supported by contract NNM07AB07C to NASA. We benefited greatly from discussions at four workshops on solar coronal loops: Paris (2002 November), Palermo (2004 September), Santorini (2007 June), and Florence (2009 June). REFERENCES Aschwanden, M. J., Newmark, J. S., Delaboudinie‘re, J.-P., et al. 1999, ApJ, 515, 842 Brickhouse, N. S., & Schmelz, J. T. 2006, ApJ, 636, L53 Culhane, J. L., Harra, L. K., James, A. M., et al. 2007, Sol. Phys., 243, 19 Dere, K. P., Landi, E., Mason, H. E., Monsignori Fossi, B. C., & Young, P. R. 1997, A&AS, 125, 149 Dere, K. P., Landi, E., Young, P. R., et al. 2009, A&A, 498, 915 Doschek, G. A., Mariska, J. T., Warren, H. P., et al. 2007, PASJ, 59, S707 Golub, L., Deluca, E., Austin, G., et al. 2007, Sol. Phys., 243, 63 Kano, R., & Tsuneta, S. 1996, PASJ, 48, 535 Kashyap, V., & Drake, J. J. 1998, ApJ, 503, 450 Klimchuk, J. A. 2006, Sol. Phys., 234, 41 Lenz, D. D., DeLuca, E. E., Golub, L., Rosner, R., & Bookbinder, J. A. 1999, ApJ, 517, L155 Mazzotta, P., Mazzitelli, G., Colafrancesco, S., & Vittorio, N. 1998, A&A, 133, 403 Mulu-Moore, F. M., Winebarger, A. R., Warren, H. P., & Aschwanden, M. J. 2011, ApJ, 733, 59 Narukage, N., Sakao, T., Kano, R., et al. 2011, Sol. Phys., 269, 169 Neupert, W. M., Newmark, J., Delaboudinire, J.-P., et al. 1998, Sol. Phys., 183, 305 Priest, E. R., Foley, C. R., Heyvaerts, J., et al. 1998, Nature, 393, 545 Schmelz, J. T., Jenkins, B. S., Worley, B. T., et al. 2011, ApJ, 731, 49 Schmelz, J. T., Kimble, J. A., Jenkins, B. S., et al. 2010a, ApJ, 725, L34 Schmelz, J. T., & Martens, P. C. H. 2006, ApJ, 636, L49 Schmelz, J. T., Nasraoui, K., Del Zanna, G., et al. 2007, ApJ, 658, L119 Schmelz, J. T., Saar, S. H., DeLuca, E. E., et al. 2009, ApJ, 693, L131 Schmelz, J. T., Saar, S. H., Nasraoui, K., et al. 2010b, ApJ, 723, 1180 Schmelz, J. T., Saba, J. L. R., Ghosh, D., & Strong, K. T. 1996, ApJ, 473, 519 Schmelz, J. T., Scopes, R. T., Cirtain, J. W., Winter, H. D., & Allen, J. D. 2001, ApJ, 556, 896 Testa, P., Reale, F., Landi, E., DeLuca, E., & Kashyap, V. 2011, ApJ, 728, 30 Warren, H. P. 2005, ApJS, 157, 147 Warren, H. P., Ugarte-Urra, I., Doschek, G. A., Brooks, D. H., & Williams, D. R. 2008, ApJ, 686, L131 Weber, M. A., De Luca, E. E., Golub, L., & Sette, A. L. 2004, in IAU Symp. 223, Multi-Wavelength Investigations of Solar Activity, ed. A. V. Stepanov, E. E. Benevolenskaya, & A. G. Kosovichev (Cambridge: Cambridge Univ. Press), 321 Winebarger, A. R., Warren, H. P., & Mariska, J. T. 2003, ApJ, 587, 439

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