Sign up to save your podcasts
Or
Share Tracking ALMA System Temperature with Water Vapor Data at High Frequency
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Tracking ALMA System Temperature with Water Vapor Data at High Frequency
Tracking ALMA System Temperature with Water Vapor Data at High Frequency by Hao He et al. on Thursday 24 November
The ALMA observatory is now putting more focus on high-frequency observations
(frequencies from 275-950 GHz). However, high-frequency observations often
suffer from rapid variations in atmospheric opacity that directly affect the
system temperature $T_{sys}$. Current observations perform discrete atmospheric
calibrations (Atm-cals) every few minutes, with typically 10-20 occurring per
hour for high frequency observation and each taking 30-40 seconds. In order to
obtain more accurate flux measurements and reduce the number of atmospheric
calibrations (Atm-cals), a new method to monitor $T_{sys}$ continuously is
proposed using existing data in the measurement set. In this work, we
demonstrate the viability of using water vapor radiometer (WVR) data to track
the $T_{sys}$ continuously. We find a tight linear correlation between
$T_{sys}$ measured using the traditional method and $T_{sys}$ extrapolated
based on WVR data with scatter of 0.5-3%. Although the exact form of the linear
relation varies among different data sets and spectral windows, we can use a
small number of discrete $T_{sys}$ measurements to fit the linear relation and
use this heuristic relationship to derive $T_{sys}$ every 10 seconds.
Furthermore, we successfully reproduce the observed correlation using
atmospheric transmission at microwave (ATM) modeling and demonstrate the
viability of a more general method to directly derive the $T_{sys}$ from the
modeling. We apply the semi-continuous $T_{sys}$ from heuristic fitting on a
few data sets from Band 7 to Band 10 and compare the flux measured using these
methods. We find the discrete and continuous $T_{sys}$ methods give us
consistent flux measurements with differences up to 5%. Furthermore, this
method has significantly reduced the flux uncertainty due to $T_{sys}$
variability for one dataset, which has large precipitable water vapor (PWV)
fluctuation, from 10% to 0.7%.
arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.12622v1
View all episodes
By Corentin CadiouTracking ALMA System Temperature with Water Vapor Data at High Frequency by Hao He et al. on Thursday 24 November
The ALMA observatory is now putting more focus on high-frequency observations
(frequencies from 275-950 GHz). However, high-frequency observations often
suffer from rapid variations in atmospheric opacity that directly affect the
system temperature $T_{sys}$. Current observations perform discrete atmospheric
calibrations (Atm-cals) every few minutes, with typically 10-20 occurring per
hour for high frequency observation and each taking 30-40 seconds. In order to
obtain more accurate flux measurements and reduce the number of atmospheric
calibrations (Atm-cals), a new method to monitor $T_{sys}$ continuously is
proposed using existing data in the measurement set. In this work, we
demonstrate the viability of using water vapor radiometer (WVR) data to track
the $T_{sys}$ continuously. We find a tight linear correlation between
$T_{sys}$ measured using the traditional method and $T_{sys}$ extrapolated
based on WVR data with scatter of 0.5-3%. Although the exact form of the linear
relation varies among different data sets and spectral windows, we can use a
small number of discrete $T_{sys}$ measurements to fit the linear relation and
use this heuristic relationship to derive $T_{sys}$ every 10 seconds.
Furthermore, we successfully reproduce the observed correlation using
atmospheric transmission at microwave (ATM) modeling and demonstrate the
viability of a more general method to directly derive the $T_{sys}$ from the
modeling. We apply the semi-continuous $T_{sys}$ from heuristic fitting on a
few data sets from Band 7 to Band 10 and compare the flux measured using these
methods. We find the discrete and continuous $T_{sys}$ methods give us
consistent flux measurements with differences up to 5%. Furthermore, this
method has significantly reduced the flux uncertainty due to $T_{sys}$
variability for one dataset, which has large precipitable water vapor (PWV)
fluctuation, from 10% to 0.7%.
arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.12622v1