Difference between revisions of "Atmospheric model verification using BICEP/Keck data"
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We then used the TOAST simulation facilities to synthesize data with the same scanning pattern and focalplane. To allow for comparison, we simulated uncorrelated instrumental noise and atmospheric noise. The resulting correlation patterns are similar to what we find in the BICEP/Keck data:
We then used the TOAST simulation facilities to synthesize data with the same scanning pattern and focalplane. To allow for comparison, we simulated uncorrelated instrumental noise and atmospheric noise. The resulting correlation patterns are similar to what we find in the BICEP/Keck data :
Revision as of 10:57, 24 September 2020
September 23, 2020 - Colin Bischoff and Reijo Keskitalo
In this post we study atmospheric noise correlations in two scansets (approximately 55 minutes) of Keck array data acquired with five co-positioned cameras observing at 100, 150 and 220 GHz.
It is expected, that as CMB-S4 SAT:s integrate down the instrumental noise across large numbers of detectors, the dominant noise component in the final analysis will have an atmospheric origin. For that reason, it is exceptionally important to forecast the extent to which the atmospheric noise in various detectors is statistically independent. The rate at which we acquire these independent measurements of the atmosphere will dictate our mapping speed.
Dataset like the BICEP/Keck data of concern here has never been used to verify the TOAST atmospheric simulation module before. Compared to earlier verification campaigns with POLARBEAR, ACT and SPT, these data uniquely offer
- detector line-of-sights separated by up to 16 degrees
- small aperture telescope with resulting near-field geometry
- multiple co-positioned cameras
Each array of detectors has a square layout of single frequency detectors. Two arrays observe at 100 GHz, one at 150GHz and two at 220GHz. The deck angle around the boresight is different between the two studied observations, 20150614C06_dk023 and 20150714B04_dk338:
Holes in the grids are left by the ad hoc data cuts implemented in this analysis.
The calibrated but unfiltered timestreams show prominent correlations across all frequencies:
Interestingly enough, the 150 and 220GHz data above seem to decouple somehow at around 2000 seconds.
The dominant features of the above plots are long time scale fluctuations that are typically filtered out early in the data processing. Here we show the same TOD after filtering each half-scan with a 2nd order polynomial and each scanset with a 10th order ground-synchronous polynomial.
We will use the filtered TOD to measure the detector-detector correlations as a function of detector separation and time lag. Two cases of detector pairs are considered: in-scan offset where the cross-scan separation of the detectors is no larger than 0.1 degrees on different cameras or 0.3 degrees on the same camera. The reason for the more relaxed definition of the pairs on the same camera is required to render a sufficient sample of in-scan separations: on the same camera the separations are very quantized. Similarly, we define cross-scan offset pairs of detectors with the same tolerances.
Here is a plot of the dimensionless correlation function (top row) between 220 GHz detectors on the same camera:
The cross-scan panel (top right) shows that detectors that are separated by less than a degree are highly correlated up to a lag of about 1 s. The azimuthal scan rate (2.8 deg/s) translated on-sky is 1.6 deg/s. Even detectors separated by more than 10 degrees exhibit measurable correlations that survive the polynomial filtering.
The in-scan panel (top left) shows that the peak correlation between detectors occurs at a lag that matches the time to scan their angular separation. Green correlation functions in the plot peak at approximately 3 seconds while the boresight travels the 6 degrees separating the detectors in 3.75 seconds. All correlation peaks are expected to tilt towards zero lags due to the correlation strength slowly weakening as the atmosphere itself changes. The atmospheric changes also explain why the height of the peak correlation drops with increasing detector separation.
If we consider pairs of detectors on different cameras, located eight feet apart on the mount, we find a very similar correlation structure:
This is a very important result for the 3-shooter design baselined for CMB-S4 SATs. Co-positioned SAT cameras on the same mount do not render independent observations of the field but rather the atmospheric noise component is highly correlated.
Correlations across frequency bands are equally prominent. Here we pair 150 and 220GHz detectors across two cameras:
While the atmospheric noise at 100GHz is notably fainter:
For completeness, here is the 220GHz correlation measured from the other scanset:
We then used the TOAST simulation facilities to synthesize data with the same scanning pattern and focalplane. To allow for comparison, we simulated uncorrelated instrumental noise and atmospheric noise. The resulting correlation patterns are similar to what we find in the BICEP/Keck data at 220GHz:
The dominant difference between real data and the simulation is the height of the in-scan offset bump. It suggests that the simulated atmosphere does not change as fast as the one observed in the two real scansets. At least three potential explanations exist: 1. Our static, wind-driven atmospheric model fails to account for turbulent evolution of the water vapor distribution in the atmosphere 2. The simulated wind speeds, based on historical distributions 10 meters above ground, are too low. 3. The assumed water vapor distribution is incorrect. If the water vapor is closer to the telescope, same wind speeds would have a larger effect.