LFI-Validation

Overview

Data validation is a step of paramount importance in the complex process of data analysis and the extraction of the final scientific goals of an experiment. The LFI approach to data validation is based upon null-tests approach and here we present the rationale behind envisaged/performed null-tests and the actual results for the present data release. Also we will provide results of the same kind of tests performed on previous release to show the overall improvements in the data quality.

Null-tests approach

In general null-tests are performed in order to highlight possible issues in the data related to instrumental systematic effect not properly accounted for within the processing pipeline and related to known events of the operational conditions (e.g. switch-over of the sorption coolers) or to intrinsic instrument properties coupled with sky signal like stray-light contamination.

Such null-tests are expected to be performed considering data on different time scales ranging from 1-minute to one year of observations, at different unit level (radiometer, horn, horn-pair, within frequency and cross-frequency both in total intensity and, when applicable, to polarisation.

This is quite demanding in terms of all possible combinations. In addition some tools are already available and can be properly used for this kind of analysis. However it may be possible that on some specific time-scale, detailed tools have to be developed in order to produce the desired null-test results. In this respect the actual half-ring jack-knives are suitable to track any effects on pointing period times scales. On time-scales between half-ring and survey there are lot of possibilities. It has to be verified if the actual code producing half-ring jack-knives (madam) can handle data producing jack-knives of larger (e.g. 1 hour) times scales.

It is fundamental that such test have to be performed on DPC data product with clear and identified properties (e.g. single $R$, $DV/V$ single fit, etc.) in order to avoid any possible mis-understanding due to usage of non homogeneous data sets.

Many of the null-tests proposed are done at map level with sometime compression of their statistical information into an angular power spectrum. However together with full-sky maps it is interesting to have a closer look on some specific sources. I would be important to compare fluxes from both polarized and un-polarized point sources with different radiometers in order to asses possible calibration mis-match and/or polarization leakage issues. Such comparison will also possibly indicate problems related to channel central frequencies. The proposed set of sources would be: M42, Tau A, Cas A and Cyg A. However other H{\sc II} regions like Perseus are valuable. One can compare directly their fluxes from different sky surveys and/or the flux of the difference map and how this is consistent with instrumental noise.

Which kind of effect is probed with a null-test on a specific time scale? Here it is a simple list. At survey time scale it is possible to underlying any side-lobes effects, while on time scales of full-mission, it is possible to have an indication of calibration problems when observing the sky with the same S/C orientation. Differences at this time scale between horns at the same frequency may also reveal central frequency and beam issues.

Total Intensity Null Tests

In order to highlight different issues, several time scales and data combinations are considered. The following table is a sort of null-test matrix to be filled with test results. It should be important to try to set a sort of pass/fail criteria for each of the tests and to be prepared to detailed actions in order to avoid and correct any failure of the tests. To assess the results an idea could be to proceed as in the nominal pipeline $i.e.$ to compare the angular power spectra of null test maps with a fiducial angular power spectrum of a white noise map. This could be made automatic and, in case the test does not pass then a more thorough investigation could be performed. This will provide an overall indication of the residuals. However structures in the residual are important as well as the overall average level and visual inspection of the data is therefore fundamental.

Concerning null-tests on various time scales a comment is in order. At large time scales (i.e. of the order of a survey or more) it is clear that the basic data set will be made of the single survey maps at radiometer/horn/frequency level that will be properly combined to obtain the null-test under consideration. For example at 6 months time scale we will analysis maps of the difference between different surveys for the radiometer/horn/frequency under test. On the other hand at 12 months time scale we will combine surveys 1 and 2 together to be compared with the same combination for surveys 3 and 4. At full-mission time scale, the analysis it is not always possible e.g. at radiometer level we have only one full-mission data set. However it would be interesting to combine odd surveys together and compare them with even surveys again combined together. On shorter time scales (i.e. less than a survey) the data products to be considered are different and will be the output of the jack-knives code when different time scales are considered: the usual half-ring JK on pointing period time scale and the new, if possible, jack-knives on 1 minute time scale. Therefore null-tests will use both surveys/full-mission maps as well as tailored jack-knives maps.

The following table reports our total intensity null-tests matrix with a $\checkmark$ where tests are possible.

Data Set	1minute	1 hour	Survey	Full Mission