For the fits discussed within this paper, we have used a combination
of binning by signal-to-noise and uniform channel binning. Binning
was employed to allow the use of statistics, which are faster
to minimize (this is a non-trivial concern when using the
warmabs model, which is an extremely slow to evaluate code).
Furthermore, adding of data and binning of channels can serve to
average over systematic calibration uncertainties. However, any
non-uniform binning, e.g., a signal-to-noise criterion, can introduce
biases in line fits.
For our data, the signal-to-noise per channel rapidly varies at
energies keV, and there is no (small) set of uniform channel
binnings that achieves adequate signal-to-noise in these channels.
Thus, we choose mixed criteria (using the ISIS group
function) that ensures both a minimum signal-to-noise and a minimum
number of channels in the binned data. In practice for these
particular data, only the minimum channel criterion applies
above 1keV, as this is sufficient to ensure signal-to-noise
in all of these channels.
![]() ![]() |
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Component | ID |
![]() |
![]() |
(Å) | (Å) | (Å) | (mÅ) | (Å) | (c) | |||
4.142 | 3.832 | 0.013 | ![]() |
![]() |
11 | - | - | - |
6.173 | 5.830 | 0.002 | ![]() |
![]() |
6 | SiXIV (Ly![]() |
6.182 | ![]() |
6.793 | 6.285 | 0.014 | ![]() |
![]() |
4 | SiXIII (He![]() |
6.648 | ![]() |
8.219 | 7.603 | 0.009 | ![]() |
![]() |
21 | - | - | - |
8.613 | 7.968 | 0.026 | ![]() |
![]() |
10 | MgXII (Ly![]() |
8.421 | ![]() |
8.913 | 8.418 | 0.008 | ![]() |
![]() |
3 | - | - | - |
9.160 | 8.651 | 0.002 | ![]() |
![]() |
13 | MgXI (He![]() |
9.169 | ![]() |
10.504 | 9.920 | 0.003 | ![]() |
![]() |
20 | - | - | - |
12.089 | 11.417 | 0.003 | ![]() |
22.7 | 19 | - | - | - |
12.133 | 11.459 | 0.022 | ![]() |
![]() |
0 | NeX (Ly![]() |
12.133 | ![]() |
13.453 | 12.705 | 0.013 | ![]() |
![]() |
16 | NeIX (He![]() |
13.447 | ![]() |
13.909 | 13.136 | 0.002 | ![]() |
35.7 | 15 | - | - | - |
15.140 | 14.007 | 0.017 | ![]() |
![]() |
5 | - | - | - |
16.338 | 15.430 | 0.009 | ![]() |
![]() |
9 | - | - | - |
16.518 | 15.600 | 0.009 | ![]() |
![]() |
8 | - | - | - |
16.691 | 15.763 | 0.002 | ![]() |
115.5 | 17 | - | - | - |
17.739 | 16.753 | 1.132 | ![]() |
141.7 | 2 | - | - | - |
18.963 | 17.544 | 0.010 | ![]() |
![]() |
12 | OVII (He![]() |
18.627 | ![]() |
22.080 | 20.428 | 0.025 | ![]() |
![]() |
23 | OVII (He![]() |
21.602 | ![]() |
23.933 | 22.603 | 0.004 | ![]() |
117.5 | 22 | - | - | - |
24.431 | 23.073 | 0.600 | ![]() |
![]() |
1 | - | - | - |
24.855 | 23.473 | 0.129 | ![]() |
![]() |
14 | NVII (Ly![]() |
24.781 | ![]() |
25.584 | 24.161 | 0.130 | ![]() |
![]() |
18 | - | - | - |
29.610 | 27.964 | 0.099 | ![]() |
2631.8 | 7 | - | - | - |
Our fits indicating an outflowing wind with a velocity in the PG1211+143 frame
of 17300 kms
(
)
are primarily being driven by the
lines of Helium-like and
Hydrogen-like Ne, Mg, and Si. To ensure that these lines are not being
biased by our binning (although five of the six primary lines in our
fits are within the uniformly binned portion of the spectrum at
energies
keV), we have also performed a “blind line search” of
the data. We grid the HEG data to the MEG bins, and combine all
spectra, but otherwise do not perform any further channel binning. We
use the same continuum model as discussed above, namely an absorbed
disk plus powerlaw spectrum with exponential cutoff, and include line
emission from the Fe region. We then loop through the spectra, adding
one line at a time which is allowed to freely range between emission
and absorption. The initial line fit is constrained to a narrow range of
wavelengths (16 MEG channels, i.e.,
Å), but all
possible wavelength bins are searched. The line with the greatest
change in fit statistic is retained. After each step, all continuum
and line parameters are refit (within the constraints of the existing
wavelength region of the added line). This process is repeated (51
times in Figure 10, with the first 24 found lines listed
in Table 7). As the goal is to identify candidate
lines, we do not calculate confidence intervals for the final
lines.
The putative NeX line is our single most significant residual,
with the remaining 5 lines from H-like and He-like Ne, Mg,
and Si all falling within the 17 most significant residuals.
Additionally, there are three other residuals that might be associated
with a
outflow.
These, however, fall within a very
low signal-to-noise region of the spectrum. Several residuals are
broad, and are undoubtedly modifying the continuum fit. Several could
be spurious noise features. The rest remain unidentified. However,
this blind search highlights those features that are driving the
XSTAR and warmabs models to identify an outflowing
component in the rest frame of PG1211+143 .
As a further diagnostic of possible absorber components, we take the
1st-order Chandra -HETGS counts, and using the 9 potential H- and
He-like lines identified in Table 7, stack the data into
cosmological rest frame velocity bins. The results of this stacking
are also shown in Figure 10 (note that in this
procedure, not every bin is statistically independent from one
another, as counts can be reused for different ions). The strong
feature at a velocity in the rest frame of PG1211+143 of
17300 kms
(
)
is apparent. We also indicate the velocities of the absorber components
suggested by the analysis of Pounds et al. (2016a). There is a feature
near the
(
)
velocity found by Pounds et al. (2016a), but we
have not found any single line-like residual at such a velocity. It
is possible, however, that such an absorption component, if real, only
manifests significantly in a stacked analysis.
Ashkbiz Danehkar