Data compressor designed to improve recognition of magnetic phases

Data compressors available in the web have been used to determine magnetic phases for two-dimensional (2D) systems [E. Vogel, G. Saravia, F. Bachmann, B. Fierro, J. Fischer, Phase transitions in Edwards-Anderson model by means of information theory, Physica A 388 2009 4075–4082]. In the present work, we push this line forward along four different directions. First, the compressor itself: we design a new data compressor, named wlzip, optimized for the recognition of information having physical (or scientific) information instead of the random digital information usually compressed. Second, for the first time we extend the data compression analysis to the 3D Ising ferromagnetic model using wlzip. Third, we discuss the tuning possibilities of wlzip in terms of the number of digits considered in the compression to yield maximum definition; in this way, the transition temperature of both 2D and 3D Ising ferromagnets can be reported with very good resolution. Fourth, the extension of the time window through which the data file is actually compressed is also considered to get optimum accuracy. The paper is focused on the new compressor, its algorithm in general and the way to apply it. Advantages and disadvantages of wlzip are discussed. Toward the end, we mention other possible applications of this technique to recognize stable and unstable regimes in the evolution of variables in meteorology (such as pollution content or atmospheric pressure), biology (blood pressure) and econophysics (prices of the stock market).