An apparent, modest population of blue straggler stars is clearly visible in Leo IV’s CMD at 0≤(V−I)≤0.2 and 23≤V≤24. Fig. 5.16 is an (RA,Dec) plot of Leo IV’s stars.
182.5 182.6 21.8 21.7 21.6 21.5 21.4 21.3 21.2 21.1 21 20.9 20.8 200.5 200.6 202.5 202.6 237.4 237.5 238.5 238.6
Figure 5.10:The photometric variability plot for Leo IV’s variable star V1, 2007.
Radial velocity members as identified by Sim07 are plotted in magenta, RGB and HB stars not so identified are plotted in black, and the blue straggler stars are plotted as large blue points. This plot gets at two issues.
First, are the putative blue straggler stars actually blue stragglers, or members of a younger main sequence population? Central concentration, or indeed any kind of clumpy distribution, would point to relatively recent star formation, and would suggest that these
861.7 861.8 21.8 21.7 21.6 21.5 21.4 21.3 21.2 21.1 21 20.9 20.8 862.7 862.8 897.7 897.8
Figure 5.11:The photometric variability plot for Leo IV’s variable star V1, 2009.
Second, if at least some are blue stragglers, what kind of blue stragglers are they? Blue stragglers can form either by collisions or by mass transfer in a binary system. The former type would exhibit more central concentration, owing to their larger masses, while mass transfer blue stragglers would follow the spatial distribution of primordial binaries, whose spatial distribution follows that of HB and RGB stars (Mapelli et al. 2006, Mapelli et al. 2007).
182.5 182.6 21.8 21.7 21.6 21.5 21.4 21.3 21.2 21.1 21 20.9 20.8 200.5 200.6 202.5 202.6 237.4 237.5 238.5 238.6
Figure 5.12:The photometric variability plot for Leo IV’s variable star V2, 2007.
sample and spatial coverage is too small for any kind of detailed spatial distribution study. However, it is evident that the blue straggler stars show no signs of central concentration within the inner half light radius of Leo IV.
Sand et al. 2010 and Okamoto et al. 2012 both observed the blue straggler population of Leo IV with much larger fields of view than this study (240 by 240 and 400 by 300, respectively), and were thus able to observe a much larger number of these stars over a
861.7 861.8 21.8 21.7 21.6 21.5 21.4 21.3 21.2 21.1 21 20.9 20.8 862.7 862.8 897.7 897.8
Figure 5.13:The photometric variability plot for Leo IV’s variable star V2, 2009.
about twice as many as Sand et al. because of their larger field of view.) Sand et al. 2010 concluded that at least some of the blue straggler stars that they observed are young main sequence stars rather than blue stragglers. They arrive at this conclusion this by examining the ratio of the number of blue straggler stars to the number of blue horizontal branch (BHB) stars. Momany et al. 2007 found a trend in this ratio with MV for the Milky Way dwarf spheroidals. Sand et al. 2010 compared Leo IV’s blue straggler / BHB ratio with the number predicted by Momany et al. 2007’s trend and find that Leo IV’s is significantly larger. The
182.5 182.6 17.6 17.5 17.4 17.3 17.2 17.1 17 16.9 16.8 16.7 16.6 200.5 200.6 202.5 202.6 237.4 237.5 238.5 238.6
Figure 5.14:The photometric variability plot for comparison star 4383 (numbering of this study, the star ID assigned by DAOPHOT.) Note that the vertical axis is v0,SOI, V in the SOI instrumental rather than Johnson-Cousins photometric system.
861.7 861.8 17.6 17.5 17.4 17.3 17.2 17.1 17 16.9 16.8 16.7 16.6 862.7 862.8 897.7 897.8
Figure 5.15:The photometric variability plot for comparison star 4383 (numbering of this study, the star ID assigned by DAOPHOT.) Note that the vertical axis is v0,SOI, V in the SOI instrumental rather than Johnson-Cousins photometric system.
173.28 173.26 173.24 173.22 173.2 -0.56 -0.54 -0.52 -0.5 RA
Figure 5.16: (RA,Dec) plot of Leo IV’s stars. Radial velocity members as identified by Sim07 are plotted as magenta triangles, RGB and HB stars not so identified are plotted in black, and the blue straggler stars are plotted as large blue points.
excess blue straggler stars, they surmise, are not blue stragglers at all and must therefore be young main sequence stars.
This study’s CMD for Leo IV contains five stars in the blue straggler region and five in the blue horizontal branch region. This one-to-one ratio lies well below Momany et al. 2007’s trend for dSphs. However, this is a simplistic application - no background and completeness corrections were calculated for the blue straggler and BHB populations. And the field of view considered here covers Leo IV only out to approximately one r1/2.
Sand et al. 2010 also find that most of their seven high probability blue stragglers (de- fined as those blue straggler candidates found in the brighter and bluer portion of the blue straggler region of the CMD, and therefore least likely to be Galactic contaminants) are concentrated in the southern half of the galaxy, something which is somewhat evident in Fig. 5.16. Small number statistics may be responsible for this phenomenon in both studies, however.
Okamoto et al. 2012 examined only the spatial distribution of Leo IV’s blue stragglers. Their version of Figure 5.16, much like Figure 5.16, shows neither central concentration nor clumpy distribution, unlike what would be expected for a young main sequence population. They conclude on this basis that these stars are most likely blue stragglers of the mass transfer binary type.
The difference in findings concerning the spatial distribution of blue straggler stars between these two studies likely stems in large part from the fact that Sand et al. 2010 looks only at the spatial distribution of blue straggler stars which have V.23.5 in order to avoid contamination by background galaxies and foreground stars. Okamoto et al. 2012 applied no such cutoff. In calculating the blue straggler / BHB ratio, however, Sand et al. 2010 also did not apply a cutoff, resulting in a larger number than would have resulted with application of a cutoff.
5.2 Boötes II
5.2.1 Color Magnitude Diagram
A 900 s V band image of the Boötes II field is shown in Fig. 5.17. The unfiltered color magnitude diagram for Boötes II is presented in Figure 5.18. The same issues of contamination by non-stellar objects and galaxies that afflict the unfiltered CMD of Leo IV (Fig. 5.2) are present here as well. Casual inspection of the CMD shows that foreground contamination from the Milky Way is a great deal less of an issue because Boötes II is farther above the galactic plane (b = 68.9◦, as opposed to Leo IV’s declination of b = 56.5◦.) The CMD for Boötes II therefore underwent the same filtering and cleaning procedure as Leo IV’s CMD except for statistical subtraction with a control field, since that last step is designed to remove the foreground contamination from the Milky Way. Aside from that last step, all of the other steps are identical between the processing of the CMDs of Leo IV and Boötes II, with one caveat.
When filtering by sharp, some consideration has to be given to the fact that Boötes II’s final photometry in each filter was arrived at via an intensity weighted average of the photometry of the individual frames, rather by measurement of a single combined frame for each filter, as was the case with Leo IV.
Since all of the filtering steps for Boötes II are identical to those of Leo IV with the exceptions outlined above, the sections below will not repeat the justifications and minutiae for each step, as those have already been laid out in the corresponding sections for Leo IV. Instead, they will be devoted to specific numbers and plots for the V band photometry of Boötes II for the sake of illustration, as well as fleshing out the aforementioned difference in filtering bysharpbetween Leo IV and Boötes II.
Figure 5.17: A 900 s V band image of Boötes II. The SOI imager has a field of view of 5.24×5.24 arcmin2 at a pixel scale of 0.00077.
5.2.1.1 Filtering by Photometric Uncertainty
This was done in exactly the same manner as for Leo IV (§5.1.1.1) Panel a of Fig. 5.19 shows a plot of photometric uncertainty vs. raw instrumental magnitude for the V photometry of Boötes II. As with Leo IV, one can see that there is a roughly exponential main run of points, above which float outlying points.
Figure 5.18:Color magnitude diagram of Boötes II prior to the application of any filtering (by photometric uncertainty orsharp).
Panel b shows the exponential function which was fit to the main run of points in green, and in red, those points which were removed because they lie too far from the fit. For the V photometry, 1252 points out of an original 3569 were eliminated in this step, leaving 2317 data points.
(a) (b)
Figure 5.19:Same as Fig. 5.3 for Leo IV. a) Photometric error versus the raw instrumental magnitude for the V photometry of Boötes II. One can see a main run of points following an approximately exponential curve along with a “cloud” of points lying above the main body. An exponential function was fit to the data. At each given step, points greater than one RMS uncertainty away from the fit were eliminated and the fit recalculated. b) The final fit is shown in green, while the rejected points are marked in red. The multiple “sequences” are not a cause for concern. The large majority of those points would have been easily excluded based onsharpfiltering anyway (see Fig. 5.20.) Examination of those points which would not have been excluded bysharpfiltering revealed them to largely be false detections found only in single frames, often on the edges of those frames.
5.2.1.2 Filtering bysharp
This was done in largely the same manner as for Leo IV (see section 5.1.1.2 for details.) “Largely” because there is a slight subtlety in the choice of limits onsharp.
A master frame was constructed for each filter for Leo IV by combining all frames of the filter in question. That master frame then measured. Pickingsharplimits in such a case is very simple, as thesharpindex returned by Allstar for any given star is simply itssharp index in the master frame. One simply looks at the FWHM of the typical stellar image in the master frame and pickssharplimits corresponding to FWHM that are far enough above and below the frame PSF’s FWHM that they allow one to confidently eliminate the large
majority of non-stellar objects without losing any real, non-blended stars.
Boötes II’s photometry was not handled in this way, however (§4.2). No master frames were constructed. Instead, each individual frame was measured, and DAOMASTER was used to take an intensity weighted average of the photometry for the individual frames within each filter. Thesharpindex returned in this case is also a weighted average across the individual frames.
The FWHM of the Boötes II V frames range from 4.1 to 7.1 pixels (0.600to 1.0900). The sharpcriteria was set as :
−2< sharp <2.5 (5.5)
These limits, applied to any individual frame, would yield FWHM limits which safely delineate stellar from non-stellar objects. They should therefore work when applied to sharpaveraged across those same frames.
For the V photometry, 1331 of the 2317 points kept by the previous filtering step were removed by this step, leaving 986 points. These results are plotted in Figure 5.20.
5.2.1.3 Filtering by Cross Matching
This was done in exactly the same manner as for Leo IV. After filtering by photometric uncertainty andsharp, 986 points were left in the V photometry of Boötes II, and 1605 in the I photometry. Cross matching the two filters by DAOMASTER left a final tally of 375 stars.
The final, filtered CMD for Boötes II is shown in Figure 5.21. Fig. 5.22 shows the same CMD with variable star V1 (§5.2.2) marked with a red square and radial velocity members
(a) (b)
Figure 5.20:Same as Fig. 5.4 for Leo IV. Results after filtering by bothsharpand photo- metric uncertainty for the V photometry of Boötes II. Points in red were eliminated. a) Plot ofsharpvs. raw instrumental magnitude. b) Photometric uncertainty vs. raw instrumental magnitude. The multiple “sequences” are not a cause for concern (see Fig. 5.19’s caption for details.)
5.2.2 Variable Stars
A search based on DAOMASTER’s variability index revealed one variable star in Boötes II, V1, at (RA,Dec) of (13:58:07, 12:51:24) (see Fig. 5.23 for finding chart.) Its V band photometric variability plot is shown in Figure 5.24. Transformation of the instrumental magnitudes onto the standard system was accomplished via differential photometry, for reasons and through methods outlined in Section 4.9. Star 1478 (this study’s numbering, star IDs assigned by DAOPHOT) was used as the comparison star. Its photometric variability plot is shown in Figure 5.25.
The error incurred in the transformation to Johnson V by differential photometry owing to the color difference between comparison and program stars is ∆(v−i)0 (Eq. 4.8d). is −0.0649 for the night of May 17, 2007 (Table 4.1), and it can be expected to remain constant. 1478 has (v−i)0 of 0.214 (This translates into (V−I) of 0.635.) Based on this
Figure 5.21: The final CMD for Boötes II. The data for the CMD has been filtered by photometric uncertainty andsharp.
study’s photometry, V1 is estimated to have average (v−i)0 of∼0.187 (This translates into (V−I) of 0.611.) While these numbers for V1 are based on highly incomplete light curves, they will suffice for estimating the transformation error.
Based on the estimated average brightnesses, V1 has average∆(v−i)0 relative to 1478 of−0.027, leading to average∆(v−i)0 of 0.002. This is an estimate of the average error in V caused by the color difference between V1 and star 1478. Of course, RR Lyraes and
Figure 5.22:Same as Fig. 5.21 except with variable star V1 (§5.2.2) marked with a red square and radial velocity members identified by Koch et al. 2009 marked with cyan trian- gles.
Figure 5.23:Finding chart for V1. North is up and east is to the left.
their colors arevariable. Given that the variation of RRab(V−I) are typically∼0.2 about the median, the error introduced by the discrepancy in colors between V1 and star 1478 goes up to∼0.016, depending on the phase of the data point considered.
Though incomplete, V1’s photometric variability plot clearly shows it to be a variable star. Its amplitude of slightly less than a magnitude in V and its position on the HB of Boötes II’s CMD obviously suggest that it is an ab type RR Lyrae.
Given the highly incomplete nature of its measured light curve, any estimate of V1’s mean V and (V−I) must be used with caution. It is therefore of little use as a standard candle with which to determine Boötes II’s distance. This is especially the case given the lack of clear horizontal branch stars in Boötes II’s CMD against which to compare it (see Fig. 5.22.)
The logical follow-up to these results would be to measure a radial velocity and obtain a complete light curve for this star.
237.5 237.6 237.7 18.8 18.7 18.6 18.5 18.4 18.3 18.2 18.1 18 17.9 238.5 238.6 238.7 269.4 269.5 269.6
237.5 237.6 237.7 19.3 19.2 19.1 19 18.9 18.8 18.7 18.6 18.5 18.4 18.3 238.5 238.6 238.7 269.4 269.5 269.6
Figure 5.25:The photometric variability plot for comparison star 1478 (numbering of this study, the star ID assigned by DAOPHOT.) Note that the vertical axis is v0,SOI, V in the SOI instrumental rather than Johnson-Cousins photometric system.
CHAPTER 6 : ISOCHRONE FITTING & M92
This chapter does two things. First, it discusses M92 and presents the work done to obtain its age. Secondly, in the process, it discusses the methods of age determination used, methods which were also applied to Leo IV and Boötes II. This includes a detailed discussion of the Dartmouth and Victoria-Regina isochrones as well as two versions of the ΔVHBT O method of age dating. General issues involved in isochrone fitting are also discussed, including distance determination.