58273 - Astronomical Data Analysis Techniques

Academic Year 2025/2026

  • Moduli: Michele Ennio Maria Moresco (Modulo 1) Virginia Cuciti (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Bologna
  • Corso: First cycle degree programme (L) in Astronomy (cod. 8004)

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 software 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 software 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).


Basic notions on the physical principles at the origin of spectroscopic data. Use of public spectroscopic database (e.g. Sloan Digital Sky Survey) and extraction of catalogs and spectra of different samples.


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.


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


Derivation of physical properties derivation (redshift, age, metallicity).



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, starting from a topic chosen by the student from either of the two modules. They last approximately 30–40 minutes, during which the student is examined by both professor of the two modules. To be admitted to the exam, students must have completed at least one exercise for each module, agreed upon with the professor. Completing the remaining exercises is not mandatory but is strongly recommended. The codes developed for the exercises must be sent to the professors of both modules one week before the oral exam.

During the exam, each professor will ask 2 questions, covering both Modules 1 and 2, to assess the student's understanding and knowledge of the data analysis techniques discussed in class, both photometric and spectroscopic. The code developed for the exercises will also be reviewed during the exam, with potential questions on the components implemented.

The exam results in a grade out of 30 cum laude, with the following grading scale: 18–20: barely sufficient preparation and extremely limited independent analysis skills, 21–23: very limited preparation and reduced independent analysis skills, 24–26: intermediate preparation and moderate independent analysis skills, 27–29: broad but not complete preparation and good/very good independent analysis skills, 30–30L: complete preparation and excellent/outstanding independent analysis skills

Please note that the final grade can be refused a maximum of two times. Students can register for the exam sessions through the Almaesami platform.

Students with learning disabilities or temporary or permanent disabilities: please contact the relevant University office promptly (https://site.unibo.it/studenti-con-disabilita-e-dsa/it ). The office will advise students of possible adjustments, that will be submitted to the professor for approval 15 days in advance. He/she will evaluate their suitability also in relation to the academic objectives of the course.

 

Teaching tools

Video projector and PC.

Office hours

See the website of Michele Ennio Maria Moresco

See the website of Virginia Cuciti

SDGs

Quality education Gender equality

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.