auto_metrics Nightly Notebook

By Josh Dillon

Last Updated January 27, 2021

auto_metrics is a module in hera_qm that computes a series of statistics on night-long autocorrelation waterfalls to find antenna outliers in shape, power, or temporal structure. In general, these are assessed by collapsing each waterfall to a single (normalized) spectrum, then comparing that spectrum to the mean/median of the unflagged antennas' spectra to compute some difference metric. Those are then converted into modified Z-scores by comparing the overall distribution of good antennas and the worst antenna is flagged if it exceeds some threshold. This whole processes is repeated iteratively until no new bad antennas are identified. This proceeds in two rounds, first with more robust median-based statistics to identify the worst outliers, and then (after an RFI flagging step), a round with mean-based statistics. This notebook examines those mean-based spectra and statistics.

Statistics computed (after removing the worst offenders with a median-based metrics and then RFI flagging):

Parse Inputs and Load Data

Summary Plots and Tables

Figure 1: Antenna Positions with auto_metrics flags.

This plot shows the antenna positions of all antennas in the data. The antennas with at least one Modified Z-score for one metric on one polarization exceeding the cut are entirely flagged.

Table 1: Modified Z-Score Summary

This table displays the metrics for each antenna, highlighting which one is the worst. It is sorted by each antenna's worst metric. When one metric exceeds the threshold, auto_metrics recommends cutting that antenna. Flagged antennas and metrics exceeding the cut are shown in bold and red. Also shown is the antenna's a priori status.

Figure 2: Flagging Rationale Summary

This bar chart summarizes the number of antenna-polarizations that are statistical outliers in each metric (though often they overlap). Some of these issues occur on both polarizations, so there are fewer unique antennas flagged for each rationale than there are ant-pols flagged, as noted by the labels.

Figure 3: Flagging Rationale Correlations

This plot shows the probability that if a given ant-pol is flagged for some reason, it's also flagged for another reason.

Figure 4: Outliers in Autocorrelation Shape

This plot summarizes the spectra computed to compare to one another to find outliers in autocorrelation shape (see above for how that was computed). The mean compared to is shown as a black dashed line. Antennas in red were flagged as outliers, antennas in gray and purple were not. However, antennas in purple were flagged for some other reason, either another metric or on the other polarization. Completely flagged channels (RFI and band edges) appear as white gaps.

Figure 5: Outliers in Autocorrelation Power

This plot summarizes the spectra computed to compare to one another to find outliers in autocorrelation amplitude (see above for how that was computed). The mean compared to is shown as a black dashed line. Antennas in red were flagged as outliers, antennas in gray and purple were not. However, antennas in purple were flagged for some other reason, either another metric or on the other polarization. Completely flagged channels (RFI and band edges) appear as white gaps.

Figure 6: Outliers in Autocorrelation Temporal Variability

This plot summarizes the spectra computed to compare to one another to find outliers in autocorrelation temporal variability (as measured by a standard deviation over time; see above for how that was computed). The mean compared to is shown as a black dashed line. Antennas in red were flagged as outliers, antennas in gray and purple were not. However, antennas in purple were flagged for some other reason, either another metric or on the other polarization. Completely flagged channels (RFI and band edges) appear as white gaps.

Figure 7: Outliers in Autocorrelation Temporal Discontinuities

This plot summarizes the spectra computed to compare to one another to find outliers in autocorrelation temporal discontinuities (as measured by the average absolute integration-to-integration difference over time; see above for how that was computed). The mean compared to is shown as a black dashed line. Antennas in red were flagged as outliers, antennas in gray and purple were not. However, antennas in purple were flagged for some other reason, either another metric or on the other polarization. Completely flagged channels (RFI and band edges) appear as white gaps.

Figure 8: Average Good Autocorrelations and Flags

Here we show the waterfalls of the array-averaged autocorrelations over the night, after removing all flagged antennas. We also show the RFI mask generated between the median and mean rounds of antenna outlier detection. This is meant to show that there is little or no RFI remaining to affect the statistics.

Per-Antenna Plots

Figure 9: Per-Antenna Statistics, Spectra, and Waterfalls

Here we show the metrics for each antenna and the spectra/waterfalls that hopefully explain what led to them. The table reproduces the information from Table 1 above. The first four panels in each row clearly highlight the antenna's spectrum as it compares to the mean good antenna (black) and the distribution of good antennas (gray). Spectra in red were flagged as outliers. Spectra in purple were flagged for some other reason, either another metric or on the other polarization. Good antennas are shown in green. Completely flagged channels (RFI and band edges) appear as white gaps. In the fifth column, the waterfall of that autocorrelation is shown on a linear scale after RFI/band edge flags (white). In the sixth column, we show the log (base 10) of the same waterfall, divided by the average good antennas' waterfall of that polarization and then normalized to an average of 1.