HERSCHEL



CuTEx

The CuTEx tool was developed to analyze images in the infrared bands and, in particular, it was designed to resolve problems concerning the study of star forming regions. The star forming process can be observed in a wide interval of wavelengths, between Near Infrared and Millimeter, in order to investigate the different aspects of this phenomenon. In the most wavelengths, such images present some problematics, due to the contributions of many sources to the global emission (more or less evolved stars, gas and dust at several temperatures, densities and distributions):
  • Crowding: stars form in clusters (Lada & Lada 2003; Faustini, et al. 2009) and their richness is proportional to the mass of the highest massive star (Testi, et al. 1997, 1998).
  • Highly spatially variable background: stars born in molecular clouds, thus they are deeply embedded in gas and dust that are non-homogeneously distributed.
  • No-psf profile: the protostars are embedded in their envelope during their accreting phase, which have not necessarily a Gaussian or spherical-like density distribution. The source profile can change depending on the stage of the accretion phase, becoming sigar-like starting from a spherical distribution.
CuTEx was designed and optimized for extracting sources in these particular conditions. The code is originally written in IDL language and it was exported in the license free GDL language. Nothing prevents to use this routine in other bands or in scientific cases different from the native case. A detail description of the method is provided in the CuTEx paper (Molinari, et al. 2011). The code is composed of two main algorithms (an algorithm for source detection and an other for flux extraction) and a lot of internal subroutines.

The CuTEx software is directly available as on-line tool from the Multi-Mission Interactive Archive web pages dedicated to the Herschel Observatory. The results of a query performed on photometric data comprise the INTERACTIVE ANALYSIS button, which allows to use the CuTEx software on the selected data.

The technique at the base of the CuTEx detection is the use of the second derivatives. Derivatives (first and second order) are essential for pointing out the presence of local maxima and flexa. The application of derivative techniques on astronomical images is not trivial, since a two-dimensional image is a discrete set and the classical methods for deriving can not be used. In CuTEx the Lagrangian methods for numerical differentiation is implemented, by extending the formula up to 5 consecutive points (a description of these interpolation and differentiation techniques can be find in Hildebrand (1956)). In the input image, the second order derivatives are calculated, point-to-point, along four directions (x, y, and the two bisectors at 45 degrees), producing 4 arrays with the same dimensions of the original image. The four derivatives arrays are considered together, simultaneously, and the result is independent by the differentiation direction. As a matter, every single pixel is surrounded by eight pixels and a change in the brightness profile is detected in any position by adopting derivatives along four directions. Differently, the use of only two differentiation directions (i.e. along x and y) implies the introduction of a preferential direction of detection and, in the case of nearby objects, the slope changes would be detected only along x and y directions.

The source list produced by the detection software is used as input for the photometry routine. The peak positions are fitted with a 2D Gaussian profile plus a plateau model. The plateou is defined by an absolute value and by an inclination angle, depending on the characteristics of diffuse component emissions in which the sources are located. This operation would be simple for isolated objects but, in star forming regions, sources tend to appear clustered in compact clouds with highly variable background emission. The photometry routine attempts to group sources by using their relative distance, obtaining several lists in which objects are classified as isolated sources or grouped of two or more sources. Every group is fitted in a different way, by taking into account the contributions of sources belonging to the same group.

Download the CuTEx TAR file: Cutex.tar

The Cutex.tar file comprises the Cutex User Guide and two further TAR files (CuTEx_GDL.tar and CuTEx_IDL.tar) for the IDL and GDL programming languages.
For installing the code, untar the package into a directory that is in IDL (or GDL) path. In the tar files you will find the directories containing the GDL-IDL procedures necessary for running CUTEX. For IDL package, an updated version of the ASTROLIB must be set in your IDL path.

Faustini, Fabiana, Sergio Molinari, Leonardo Testi, e Jan Brand, A&A, n. 503 (2009): 801.
Hillenbrand, F.B. Introduction to Numerical Analysis. 1956.
Lada, e Lada, ARA&A, n. 41 (2003): 133.
Molinari, S., et al, PASP, n. 122 (2010): 314.
Molinari, Sergio, Eugenio Schisano, Fabiana Faustini, Michele Pestalozzi, A.M. Di Giorgio, e S.J. Liu, A&A, n. 530 (2011): 133.
Testi, Leonardo, Francesco Palla, e A. Natta, A&A, n. 133, (1998): 81.
Testi, Leonardo, Francesco Palla, T. Prusti, A. Natta, e S. Maltagliati, A&A, n. 320 (1997): 159.
Traficante, A., et al. MNRAS, n. 416 (2011): 2932.