NSCPHD1182 Astronomical Data Processing PhD course
Standard processing techniques of generic astronomical imaging
and spectroscopic data; for the latter data there will be a focus
on long-slit spectroscopy, thereby providing a background for
Echelle and Integral Field Spectroscopic processing. Introduction
to the fundamental issues related to the planning of data
acquisition at the telescope.
Fundamental instructions in processing of astronomical imaging and
spectroscopic data, the nature and properties of generic UV-optical
imaging and spectroscopic detectors (IR is addressed as time
permits) relevant for data processing, and in signal-to-noise
computations, noise‐contributions, photon statistics. The purpose
is to enable the student to single‐handedly process standard
imaging and (long-slit) spectral data in future research projects.
These competences lay the background for potential future expansion
of the competences to more advanced and complex data processing
techniques by the student and/or
collaborators/advisor.
Skills
To pass this course the student must:
- Be able to process both imaging and long‐slit spectral data well enough to allow the student to extract reasonable basic measurements from the data
- Demonstrate the ability to critically assess the data quality, error sources, the necessary processing and calibration tasks needed at each processing steps, and the goodness/quality of the data processing performed.
- Perform the necessary basic data processing of raw astronomical UV-optical imaging and spectral data, as presented as part of the course exercises/projects.
- Perform the necessary calibration of the scientific imaging and spectral data
- Perform basic analysis of the data such as 1‐D and 2‐D photometry, velocity widths and shifts of spectral lines, line equivalent widths, and continuum fitting.
- Calculate signal‐to‐noise ratios, generate noise images and spectra, estimate exposure times and plan the basic data taking details for new observations.
Knowledge
Upon satisfactory completion of this course the student will be
able to:
- account for the necessary steps needed to process and calibrate raw astronomical UV-optical imaging and spectral data, as obtained from the telescopes.
- Explain, justify, and assess each step in the data reduction process and how the data and their quality are evaluated.
- account for and critically assess the methods used to perform basic analyses and measurements, including accounting for errors, on the data.
- explain and justify how to calculate the signal-to-noise ratios, estimate exposure times, and can account for the important issues related to obtaining new data at the telescope
Competences
This course will provide the students with a basic background on
(a) the important aspects of the processing of UV-optical
astronomical imaging and (long-slit) spectroscopic data, (b) how to
critically assess and evaluate the data quality and sources of
error, and on (c) planning and preparing new observations. In
addition, the course will provide the students with software tools
and techniques on data processing and basic analysis. In concert,
these competences and tools can be applied during further studies
within astrophysics, for example in a M.Sc. and/or Ph. D
project.
These competences also lay the background for further expansion of the competences to more advanced and complex data processing techniques.
Lecture notes, online data-processing manuals, and
Steve B. Howell: Handbook of CCD Astronomy, 2nd Edition (Cambridge Observing Handbooks for Research Astronomers)
http://www.cambridge.org/gb/knowledge/isbn/item1157662/?site_locale=en_GB
The student may benefit from having a background equivalent to that covered by the courses ‘Statistical Physics’, ‘Electromagnetism’ (EM1, EM2), ‘Optical Physics and Laser’.
Students are required to bring their own laptops. The following terminal software is necessary:
Windows: Xming software is needed: http://sourceforge.net/projects/xming/ & http://www.straightrunning.com/XmingNotes/
Help can be obtained from SCIENCE IT: e-mail: it-support@science.ku.dk, Phone: 35 32 21 00
Linux: the X11 environment is standard and runs automatically.
MacIntosh/MACs: Starting with operating system OS 10.5 the X11 environment can be used. This can be downloaded for free from http://xquartz.macosforge.org/landing/.
For operating systems Leopard and Lion the X11 environment is a part of OS X.
- Category
- Hours
- Lectures
- 24
- Practical exercises
- 40
- Preparation
- 30
- Project work
- 112
- Total
- 206
PhD students should contact the course responsible to sign up for this course.
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentWritten assignmentTo pass the course the student must:
- be present for the majority (> 80%) of classes and actively participate in the data processing in class, and
- submit a report (minimum of 15 pages) on the data processing of both imaging and spectral data performed in the course. More specifically it should address:
All the necessary steps needed and the reasons for them and how the quality of each processing step is evaluated. The report needs to be approved by the instructors. To be approved, the report itself should be of a sufficient quality that the student or others could use it as a compendium. The report should include a sufficient amount of the material covered in the course for the reader(at the level of a starting Master’s student) to understand with ease the procedures and techniques and why they are performed. - Aid
- All aids allowed
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
More internal examiners
- Re-exam
- Same as ordinary exam. The report must be re-submitted and approved by the instructors.
Criteria for exam assesment
See Skills
Course information
- Language
- English
- Course code
- NSCPHD1182
- Credit
- 7,5 ECTS
- Level
- Ph.D.
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- A (Tues 8-12 + Thurs 8-17)
- Course capacity
- No restriction to number of participants
- Study board
- Natural Sciences PhD Committee
Contracting department
- The Niels Bohr Institute
Course responsibles
- Marianne Vestergaard (7-767f6e7c7d6e7b49776b7237747e376d74)
- Lise Christensen (25-74717b6d36706976766d366b707a717b7c6d767b6d76363839487a6d6f71777670366c73)
Lecturers
Marianne Vestergaard
Lise Christensen