ASTR 306/406 - Astronomical Techniques

Spring 2026

M/W 3:20-4:35, Sears 552

This course will focus on research techniques used by astronomers, including observational studies using data from ground- and space-based telescopes, and data mining of large on-line astronomical datasets. A significant portion of the course will be devoted to data analysis techniques and computational statistics in astronomy.

Instructor:

Chris Mihos
Sears 557
mihos@case.edu

Office Hours:

drop-in, except not M/W afternoons

Attendance Policy

Astronomical Techniques will involve a variety of activities, including traditional lectures, in-class discussions, group data analysis projects, and interactive coding exercises.

Because of the interactive nature of the course, on-time attendance is required.

Any absence must be pre-arranged and excused, or else overall grade penalties will be applied.

Computational Requirements

Python programming and data analysis will be required, including the use of the astropy package. I strongly encourage you to install the Anaconda python distribution on your computer, which also installs astropy by default.

Computer Support:

Bee Janesh
bee.janesh@case.edu
Sears 571
Charley Knox
charles.knox@case.edu
Sears 568

Assignments

There will be a variety of homework sets geared towards the development of technical skills. These assignments will often be coordinated with in-class activities. Typical assignments could include conducting a simple photometric analysis of astronomical image data, or downloading and analyzing appropriate astronomical datasets off of the web.

There will also be two writing assignments, which combine technical analysis with scientific writing themes.

Late Homework Policy: You get one "free" late assignment, which must be turned in by the "Free Late Date" (usually one week later, but some exceptions; see Assignment Table). After that, there will be a penalty of 20% per 24 hrs late, unless you have a prearranged, excused reason.

Submitting Assignments: Homework is always due at 11:59pm on the date listed. Assignments should be submitted through the Canvas course website, as pdf documents and (if requested) python code / jupyter notebooks.

Homework/Assignment Help

Data/Literature Links

Assignment Schedule
(due dates subject to change)

Assignment
Due Date
(Free Late Date)
Group

HW #1 Jan 28
(Feb 4)
HW

HW #2
M84 fits image
Notebooks:
M84 starter
Binning example
Feb 16
(Feb 23)
HW

Observing Proposal Mar 6
(Mar 13)
Writing

TAC Reports Mar 30
(Apr 2)
TAC + ClusterAGN

M101 Writeup
Apr 15
(Apr 20)
Writing

HW #3
Apr 24
(May 1)
HW

Cluster AGN Summary
May 1
(May 6)
TAC + ClusterAGN

ASTR 306 Grading Scheme

Your final grade will be weighted as follows: HW: 35%, writing: 45%, TAC + ClusterAGN: 20%.

In other words, your final course score is calculated as:

Score = 0.35*Average(HW) + 0.45*Average(Writing) + 0.2*Average(TAC+ClusterAGN)

Final letter grades will be assigned as follows:

A	90-100
B	80-89
C	70-79
D	50-69
F	< 50

Software Links

anaconda
astropy
astropy affiliated packages: photutils, astroquery
ds9 image viewer
stellarium (planetarium software)
web-based skycalc tools

ASTR 406 Information

Graduate students enrolled in ASTR 406 will have additional problems on the HW assignments as well as a class presentation on a new/upcoming astronomy research facility.

The ASTR 406 grading scheme is the same as for ASTR 306, but with finer-grained resolution to allow for plus/minus graded.

Policy on the usage of "AI" (Large Language Models)

The use of LLMs is discouraged, for two major reasons:

One of the major purposes of coursework is for you to learn how to find, use, and evaluate primary source materials: online data sites, scientific websites, published scientific literature. LLMs are not primary source material.
LLMs are often very wrong, and have a tendency to make up false information.

However, if you decide to use an LLM to help with course assignments, you must include the following in your writeup:

State explicitly that you used an LLM, say which one you used, and tell me the prompt you gave it.
Describe in detail the steps you took to verify the answers you got from it, and explain how accurate the answers were.
If it was used for code, say which pieces of the analysis used LLM coding and describe in detail how that code worked.

Course Schedule / Topic List

Class	Date	Topics	Links/Slides
1	Jan 12	The Reading of the Syllabus Coordinate Systems	Astronomer's Toolbox Coordinates 1-13
2	Jan 14	Coordinate Systems (cont) The Atmosphere	skycalc website and guide Coordinates 14-18 Atmosphere 1-5
	Jan 19	No Class: MLK Day
3	Jan 21	The Atmosphere (cont) Telescopes	Atmosphere 6-20 Telescopes 1-11
4	Jan 26	No Class: Snow Day
5	Jan 28	Telescopes Detectors	Telescopes 8-15 Detectors 1-17
6	Feb 2	ds9 DataLab Detectors (cont) Filters and Magnitudes	Detectors 18-27 DataLab worksheet Image files for DataLab Filters and Magnitudes 1-7
7	Feb 4	Filters and Magnitudes (cont) Photometry	Filters and Magnitudes 8-20 Photometry 1-9
8	Feb 9	Photometry (cont) Homework #2 Discussion	Photometry 10-26
9	Feb 11	Statistics Observing Proposal Discussion Literature Searches	Statistics 1-14 Example Proposals: Good Example Bad Example Literature Reviews 1-4
10	Feb 16	Astronomical Citations Exposure Time Calculators	Pathfinder AI Lit Search Gemini Exposure Time Calculators: GMOS, NIRI Literature Reviews 6-10
11	Feb 18	M101 Data Lab: Intro Image Reduction	M101 Data Lab M101 Lecture Notes 1-5
12	Feb 23	No class
13	Feb 25	M101 Data Lab: Sky Subtraction Photometric Calibration	M101 Data Lab M101 Lecture Notes 6-16
14	Mar 2	M101 Data Lab: Image Stacking Analysis	M101 Data Lab M101 Lecture Notes 17-27
15	Mar 4	M101 Data Lab: Analysis Writeup Discussion	M101 Data Lab M101 Lecture Notes 28-32
	Mar 9	No Class: Spring Break
	Mar 11	No Class: Spring Break
16	Mar 16	TAC Assignment Discussion Monte Carlo Error Propogation Bayesian Modeling	Statistics 16-39
17	Mar 18	Spectroscopy	Spectroscopy 1-31
18	Mar 23	Spectroscopy (continued)	Spectroscopy 32-66
19	Mar 25	Discussion of HW #3 Cluster AGN Project: Intro	Cluster AGN Intro 1-25
20	Mar 30	Cluster AGN Project: X-ray and Optical Datasets Cluster Selection Exploring SDSS Navigate	Cluster AGN: Data and Selection 1-18 Using SDSS Navigate
21	Apr 1	Cluster AGN Project: SDSS Data Analysis	Downloading SDSS data SDSS analysis notebook SDSS analysis tasks Python Tips
22	Apr 6	TAC Meeting
23	Apr 8	Special Collections @KSL
24	Apr 13	Cluster AGN Project: SDSS Data Analysis(continued)
25	Apr 15	Cluster AGN Project: Chandra X-ray Crossmatching	Chandra_crossmatch.ipynb Chandra Crossmatch tasks
26	Apr 20	Cluster AGN Project: Continued....	Object Followup tasks
27	Apr 22
28	Apr 27

Topical Slide Decks

Important: When a slide deck title is boldfaced the slide deck is final; when it is italicized it is in progress and subject to change.

Astronomer's Toolbox
Coordinate Systems
The Atmosphere
Telescopes
Detectors
Filters and Magnitudes
Photometry
Statistics
Literature Reviews: Searching and Citations
M101 Lab Lecture Notes
Spectroscopy
Cluster AGN: Project Introduction
Cluster AGN: Datasets and Cluster Selection

Learning Outcomes

After taking this course, students should be able to:

Understand the concepts of positions, coordinates, and object visibility in the night sky.
Describe the detailed effects of the Earth's atmosphere on astronomical data from ground-based telescopes.
Describe various telescope configurations and astronomical detector technologies.
Have quantitative understanding of various astronomical magnitude and filter systems.
Conduct basic data reduction and analysis of CCD imaging data.
Understand the basics of astronomical spectrographs.
Utilize online astronomical data sources to conduct multiwavelength studies of astronomical sources.
Develop quantitatively argued observing proposals.
Conduct literature searches of research journals.