In radio astronomy interferometers where the number of stations is large (in the ALMA case 66 antennas, where 8 digitizers are deployed in each antenna) tuning the digitizers parameters: thresholds and bias, is a process which needs to be repeated several times, therefore finding an algorithm that allows to speed up this process is a critical task. It is quite important to keep the digitizers properly adjusted in order to reach the maximal efficiency of the correlator, specially in a regime of coarse quantization (88% for 2 bits, 96% for 3 bits), and also is critical for avoiding signal artifacts which can degrade the collected data (DC bias or harmonics). This work presents a set of different approaches for automatically tuning the digitizers primary selected as: PID by using a proportional/integrative/derivative controller and defining a system to process a coupled MIMO system as an uncoupled SISO; Fuzzy Logic by making extensive advantage of the expert operator knowledge; and finally an hybrid scheme combining PID and Fuzzy Logic for a rapid and accurate tuning process. The aim of the present work is to evaluate the performance of each tuning method based on metrics like: required tuning time, stability and robustness under different extreme boundary conditions. In addition, we suggest the means for collecting the needed information considering an usual interferometer architecture. Furthermore, we provide an automated approach to find the best sampler's clock timing profile. The aim of this work is to provide a guideline for implementing an algorithm which allows to tune a large set of digitizers under different conditions in a fast and precise automated process. The produced report will come in handy for integration into interferometer projects comprising a large number of individual stations (ALMA, SKA, VLA, CHIME, MeerKAT).
Taking a large interferometer for radio astronomy, such as the ALMA1 telescope, where the amount of stations (50 in the
case of ALMA’s main array, which can extend to 64 antennas) produces an enormous amount of data in a short period of
time – visibilities can be produced every 16msec or total power information every 1msec (this means up to 2016
baselines). With the aforementioned into account it is becoming more difficult to detect problems in the signal produced
by each antenna in a timely manner (one antenna produces 4 x 2GHz spectral windows x 2 polarizations, which means a
16 GHz bandwidth signal which is later digitized using 3-bits samplers).
This work will present an approach based on machine learning algorithms for detecting problems in the already digitized
signal produced by the active antennas (the set of antennas which is being used in an observation). The aim of this work
is to detect unsuitable, or totally corrupted, signals. In addition, this development also provides an almost real time
warning which finally helps stop and investigate the problem in order to avoid collecting useless information.
Latest discoveries in the field of astronomy have been associated to the development of extremely sophisticated
instruments. With regards to radio-astronomy, instrumentation has evolved to higher processing data rates and a
continuous performance improvement, in the analog and digital domain. Developing, maintaining, and using such kinds
of instruments – especially in radio-astronomy – requires understanding complex processes which involve plenty of
subtle details. The above has inspired the engineering and astronomical communities to design low-cost instruments,
which can be easily replicated by the non-specialist or highly skilled personnel who possess a basic technical
background. The final goal of this work is to provide the means to build an affordable tool for teaching radiometry
sciences. In order to take a step further this way, a design of a basic interferometer (two elements) is here below
introduced, intended to turn into a handy tool for learning the basic principles behind the interferometry technique and
radiometry sciences. One of the pedagogical experiences using this tool will be the measurement of the sun’s angular
diameter. Using these two Ku band receptors, we aim to capture the solar radiation in the 11-12GHz frequency range,
the power variations at the earth spin, with a proper phase-lock of the receptors will generate a cross-correlation power
oscillation where we can obtain an approximation of the angular sun’s diameter. Variables of interest in this calculation
are the declination of the sun (which depends on the capture date and location) and the relation between maximal and
minimal power within a fringe cycle.
The ALMA telescope is composed of 66 high precision antennas, each antenna having 8 high bandwidth digitizers
(4Gsamples/Second). It is a critical task to determine the well functioning of those digitizers prior to starting a round of
observations. Since observation time is a valuable resource, it is germane that a tool be developed which can provide a
quick and reliable answer regarding the digitizer status. Currently the digitizer output statistics are measured by using
comparators and counters. This method introduced uncertainties due to the low amount of integration, in addition to
going through all the possible states for all available digitizer time which all resulted in the antennas taking a
considerable amount of time. In order to avoid the aforementioned described problems, a new method based on
correlator resources is hereby presented.
The ALMA telescope will be composed of 66 high precision antennas; each antenna producing 8 times 2GHz bandwidth signals (4 pairs or orthogonal linear polarizations signals). Detecting the root cause of a loss of coherence issue between pairs of antennas can take valuable time which could be used for scientific purposes. This work presents an approach for quickly determining, in a systematic fashion, the source of this kind of issues. Faulty sub-system can be detected using the telescope calibration software and the granularity information. In a complex instrument such as the ALMA telescope, finding the cause of a loss of coherence issue can be a cumbersome task due to the several sub-systems involved on the signal processing (Frequency down-converter, analog and digital filters, instrumental delay), the interdependencies between sub-systems can make this task even harder. A method based on the information provided by the TelCal1 sub-system (in specific the Delay Measurements) will be used to help identify either the faulty unit or the wrong configuration which is causing the loss of coherence issue. This method uses granularity information to help find the cause of the problem.