58273 - Astronomical Data Analysis Techniques

Learning outcomes

The course will provide students with the capability to analyze photometrical and spectroscopical data with software and packages commonly used in astronomy. By the end of the course, students are expected to be acquainted enough with the subjects discussed to be able to address the fundamental characteristics of an instrumental set up that they should choose, to acquire the desired astronomical quantity, and to provide an estimate of the error associated to the measure.

Course contents

Basic notions of the various types of data analyzed in astronomy (mainly images and catalogs) and of the types of softwares used for different purposes.

Basics of photometry: apparent and absolute magnitude, photometrical systems and color indices. The CCD detector, bias and flatfield. Properties of the images (PSF, noise)

DS9: opening and handling images, analyzing the header of FITS files..

Combine and/or overlay diverse images (RGB, contours). Finding objects in astronomical catalogs.

Basics on more advanced softwares for image analysis: Aladin e CARTA (optimized for radioastronomy).

Using python to display images in FITS format and perform operations with pixels.

Recall on the use of python to read and write tables, and perform basic statistical tests (histogram, fit, Spearman test).

The basics of spectroscopy instrumental set up and of the optical "dispersing" elements (prism and grating). Single spectra, multislit and multifiber spectra. Slitless spectra. Integral field spectroscopy.

An introduction to spectra extraction from 2d images. Spectral wavelength calibration and flux calibration. The effect of differential athmospheric absorption on spectra.

Spectroscopic lines: line parameters definition and measurement of emission and absorption lines, galaxy classification.

Derivation of physical properties derivation (redshift, age, metallicity, star formation rate) through full-spectral fitting technique.

Readings/Bibliography

Unfortunately a book covering all the subjects which are part of this course does not exist, For this reason, students will be provided with all the slides (pdf files) which will be shown in the lectures and with scientific papers in which they will be able to find some more information.

Teaching methods

The course has an essential part of "theory" and is mostly "built" on several practical examples on the use of the astronomical software discussed (DS9, python, etc…). The latter ones are proposed to students as an exemplification of the "theoretical concepts" and solved with them. The students will use some of the tasks presented to address practical problems related to data reduction and analysis.

Assessment methods

The exam consists in an oral test. To be admitted to the oral exam, it will be necessary to complete at least one of the proposed exercise for each module. It is not mandatory to complete all the other ones, but it is strongly suggested. The student may choose to start the exam by presenting one of the exercises done, and the first part of the exam will be centered in this argument. The remaining part will consist in one or two questions and/or exercises related to the arguments discussed during the lessons, to verify his/her knowledge and understanding of the subjects addressed in the course.

Teaching tools

Video projector and PC.

Office hours

See the website of Michele Ennio Maria Moresco

See the website of Virginia Cuciti