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Astronomical Data Analysis Software and Systems VI
ASP Conference Series, Vol. 125, 1997
Editors: Gareth Hunt and H. E. Payne

Non-parametric Algorithms in Data Reduction at RATAN-600

V. S. Shergin, O. V. Verkhodanov, V. N. Chernenkov, B. L. Erukhimov, and V. L. Gorokhov
Special Astrophysical observatory, Nizhnij Arkhyz, Karachaj-Cherkessia, Russia, 357147



Non-linear and non-parametric algorithms for data averaging, smoothing and clipping in the RATAN-600 flexible astronomical data processing system are proposed. Algorithms are based on robust methods and non-linear filters using an iterative approach to smoothing and clipping. Using robust procedures to detect faint sources is proposed also. This detector is based on the ratio of two statistics, characterizing the noise and signal, in the given interval. These methods allow us to accelerate the process of the data reduction and to improve the signal/noise ratio. Examples of operation of these algorithms are shown.


1. Introduction

Obtaining a reliable result on the background from different types of interferences is one of the main problems for the observational astronomy. The question ``what is useful signal and what is noise?'' is especially essential when we begin ``dumb'' data processing.

The ordinary way to obtain a good signal/noise ratio is to apply the standard average for vectors of data observed on the same sky strip. This way is the most optimal and realizes the maximum likelihood estimation with improvement. But the real observational data have a distribution far from normal. This is caused by the presence of power spikes, ``jumps,'' and slow trends. The source of this interference is human activity, atmosphere, and possible instability of the receiver. Therefore, observers prefer a manual method of data reduction, because a single bad record spoils the resulting sum when doing ``dumb'' standard averaging. To automate the users' procedure of data quality checking, special algorithms have been worked out.

The background problems are absent for ordinary average. But when we use the robust (non-parametric) average, the correct background subtraction (or smoothing) is the main problem. Therefore, the first problem to be solved is to find the correct (from the viewpoint of an observer) smoothing. Moreover, the knowledge about the background around the sources is very important for some problems in radio astronomy. The use of standard procedures (fitting with splines) very often can not help us in this situation. Thus, special non-linear algorithms for smoothing were developed.

Another type of processing where similar algorithms can be used is data compression (when we have a surplus of points per beam and can compress the data keeping useful information) and searching for sources.

2. Smoothing

The history of algorithms, based on the robust methods and non-linear filters using an iterative approach to smoothing and clipping, began twelve years ago in the data reduction at the RATAN-600. Since then they have been further developed and are used in modern computer systems in the RATAN-600 data reduction system FADPS (Verkhodanov et al. 1993; Verkhodanov 1997) and in the MIDAS (Shergin et al. 1995).

The algorithm uses several start parameters: noise dispersion, smoothing interval, iteration count, and type of smoothing curve. Practically there is no effect on a real signal in a given interval, and the background is accurately calculated.

The detailed description of the smoothing and clipping (SAC) algorithm is given in Erukhimov et al. (1990) and in Shergin et al. (1996). Briefly, the SAC algorithm consists of the following steps: (i) calculation of an input vector for -th smoothing iteration as where is a vector of weights calculated in the previous iteration. In the simplest case the function is calculated as a product of each vector component , where k is k-th component; (ii) smoothing of : , where is the smoothing operator, and is the input parameter; (iii) subtraction , where is the input data vector; (iv) calculation of a new function of weights as a non-linear transformation which is a function of input noise and number of iteration in a general case: ; and (v) transition to the next iteration. There are several possible smoothing and weighting methods. In practice we use the following methods for smoothing: simple boxcar average; convolution with Gaussian profile (see Figure 1 in Verkhodanov 1997), median average (Erukhimov et al. 1990) (see Figure 2 in Verkhodanov 1997); weighted least squares polynomial approximation of 1/15°. For calculation of weights some empirical transformations are used. They may be specialized to cut emission, absorption or both.

3. Averaging

After background subtraction the procedure of robust averaging is applied for records. Usually we do it by the Hodges-Lehmann (HL) method (Huber 1981, Erukhimov et al. 1990) with estimation of middle value:

Robust averaging with the HL method is illustrated in Figure 1.

4. Compression

Another type of data reduction procedure is data compression. The result of data compression on the base of Hodges-Lehmann (Huber 1981) estimates is illustrated in Figure 2.

A similar robust algorithm was proposed and applied in the RATAN-600 data registration system by Chernenkov (1996).

5. Detection

Based on robust procedures, a detector for faint sources (extremal-median signal detector: EMSD) in a series of synchronous scans (Verkhodanov & Gorokhov 1995). is proposed also. This detector is based on the ratio of two statistics, characterizing the noise and signal, in the given interval.

For this algorithm several statistics are calculated: , , where ; D is the matrix of m vectors, m is the number of scans, n is the number of points in a scan, r is the number of points in a signal search interval. Using these two extremal statistics the medians and the following statistics are computed in each interval:

, , where ; then their ratio is used as a test statistic:

where j=1,n. In this case the hypothesis of object detection is adapted when the quantity exceeds a certain quantity . The threshold can be either set by user or computed in the program. The example of EMSD operation is illustrated in Figure 3.

Figure: Results of EMSD operation for the different input intervals: g-interval of two beam patterns, h-one beam pattern, for real data of five synchronous scans (a-e). To compare results the scan with robust average of these data is shown (f). The antenna temperature is for the first six scans, and ratio (5) is for the two last scans plotted along the Y-axis. Original PostScript figure (68kB).


O. Verkhodanov thanks ISF-LOGOVAZ Foundation for the travel grant, the SOC for the financial aid in the living expenses and the LOC for the hospitality.


Chernenkov, V. N. 1996, Bull. of SAO, 41, 150

Erukhimov, B. L., Vitkovskij, V. V., & Shergin, V. S. 1990, Preprint SAO RAS, 50

Huber, P. J. 1981, Robust Statistics (New York: Wiley)

Shergin, V. S., Kniazev, A. Yu., & Lipovetsky, V. A. 1996, Astron. Nach., 317, 95

Verkhodanov, O. V., Erukhimov, B. L., Monosov, M. L., Chernenkov, V. N., & Shergin, V. S. 1993, Bull. of SAO, 36, 132

Verkhodanov, O. V., & Gorokhov, V. L. 1995, Bull. of SAO, 39, 155

Verkhodanov, O. V. 1997, this volume

© Copyright 1997 Astronomical Society of the Pacific, 390 Ashton Avenue, San Francisco, California 94112, USA

Next: Mapping the Jagiellonian Field of Galaxies
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Up: Algorithms
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