ASTR610: Astronomical Instrumentation and Techniques
Spring 2022  Course Information and Outline
Class Meetings: ATL 0201, TuTh
12:302:45 pm
Instructor: Alberto Bolatto
bolatto at astro.umd.edu, 3014051521, 1158 PSC
Class Purpose
Astronomy
is an observational science – few experiments are possible, and theory serves
more to explain than to predict new phenomena. As a result, it is
important for everyone involved in gathering and interpreting data to
understand something about the instruments which filter our view of the
Universe. In this course, we cover the basic principles of
instrumentation and the associated techniques involved in recovering data from
distant objects. We will discuss physical limits on ideal instruments and some
of the real imperfections that can lead to real systematic effects. The
goal of this course is to understand instrumentation as a coherent whole,
differing only in details from one part of the spectrum to another. We
will draw heavily on examples from the radio through optical wavelengths to
illustrate the basic principles of instrumentation, but we will also discuss
other parts of the spectrum (UV, Xray, and gammaray) to show how technical
details differ in these regions. This course also includes a review of
basic statistics applied to signal and data analysis.
Expectations
My
expectation is that the students will conduct themselves as graduate students.
This means putting electronic distractions to the side during the lectures,
following the lecture material, following and reading
references after lectures, and in general demonstrating curiosity. Assignments
are individual, and should be completed by students
independently. That does not mean that students should not be talking to each
other about what they are doing (on the contrary, your objective should be to
learn!), but it means that each one should arrive at the solutions through
their own thought process and understanding (otherwise you are not learning!).
I expect students will be curious enough to go above and beyond what I ask in
the homeworks, rather than just following my script.
Instead of worrying about the grade, worry about exploring and learning and the
grade will come with that!
Class Meetings
The
class meets in room 0201 of the Atlantic Building from on Tuesdays and
Thursdays 12:301:45 pm. Whenever I travel we will
reschedule at a mutually agreeable time. Classes will consist primarily of
intensive lectures and, I hope, equally intensive questions and discussions as
we move through the course material.
Office Hours
Questions
about lecture and problem sets or other topics are strongly encouraged.
Given everyone's varied schedules, we will do this by appointment or by random
encounter. Please come ask questions early as they emerge. The hour
or two before class on the days problem sets are due seem to be popular times
for questions, but are difficult for me as I prepare
for the lecture. My email, phone, and office number are at the top of
this page.
Grading
There
will be regular homework assignments that will count for 40% of the course
grade. You should anticipate spending a considerable amount of time on
the assignments – this is, of course, where you will learn the most. A number of the homework assignments involve computer
programming. As part of the coursework, I require use of a higherlevel
language such as MATLAB, Python, or R because of their extensive data
manipulation and display capabilities. There will be two exams: a
midterm exam which counts for 25% of the course grade and a final exam that
counts for 35%. Both examinations count as Major Grading Events as
defined by the university. A good number of the problems on these exams
will be in the format of the Qualifying Examination for the Department of
Astronomy. As is usual for graduate classes, I expect that most students
will receive an A or B.
Attendance
Since we are not following a textbook for this class,
attendance and careful note taking is essential. If you must miss a
class, please arrange to get notes from one of your classmates and plan to come
talk with me about points you find unclear. If you know you will be away
for universityrelated travel or religious holidays, please let me know as soon
as possible (technically, within the first two weeks of the semester) so we can
arrange alternate scheduling of homeworks or
exams. This same timeframe holds for students who need
accommodation for documented disabilities.
Useful texts and references
There are no required
texts for this class. We
cover too much ground at too many levels for one text to suffice,
and purchasing an entire set of texts would be financially
ruinous. Here is a list of my favorite reference books for this
class. You may wish to build you library with one or two of them,
depending on your interests.
Bracewell: The Fourier Transform and its Applications,
3^{rd} Edition.
This wonderful book covers both theory and applications of the Fourier
transform.
James, A Student’s Guide to Fourier Transforms With Applications in Physics and Engineering
A very nice introduction to Fourier transforms and their uses. Paperback:
cheap!
Hecht, Optics
A particularly clearly written optics book with considerable information on
modern and classical optics.
Born and Wolf, Principles of Optics
Advanced topics in diffraction and other optical theory, optical
interferometers.
Schroeder: Astronomical Optics
This book covers astronomical optical systems, telescopes, and spectrometers in
considerable technical detail.
Thompson, Moran, Swenson: Interferometry and Synthesis
in Radio Astronomy
Detailed discussions of radio interferometers at a fundamental but advanced
level. An indispensable reference for practitioners.
Rohlfs & Wilson: Tools of Radio Astronomy
This book covers instrumentation and techniques from the radio astronomical
perspective, as well as discussing radiation mechanisms and radio astronomy in general.
Rieke: Detection of Radiation
Covers the detection of radiation from radio to gamma rays.
Howell: Handbook of CCD Astronomy
CCD operation, data processing, and instruments.
Ross: Introduction to Probability and Statistics for
Engineers and Scientists
Very clear exposition of classical elementary statistics at the upperdivision
undergraduate level.
Dalgaard: Introductory Statistics with R
Clear introduction to statistics common for data analysis and the R
language. Most of the examples are geared toward the
biological sciences, where R is very popular, but the basics are the same for
all sciences.
Lupton: Statistics in Theory and Practice
Intermediatelevel statistics book that covers more advanced topics in data
analysis with astronomical examples.
Seidelmann: Explanatory Supplement to the Astronomical Almanac
A fundamental reference for fundamental observers – coordinate systems and
transformations, time systems, precession, etc.
The aspirational plan for lectures
follows
1 
Jan 25 
Introduction. Detection of radiation
principles. 
2 
Jan 27 
Direct and heterodyne detection.

3 
Feb 1 
Semiconductor properties.
Noise sources. 
4 
Feb 3 
Calibration of CCD frames. 
5 
Feb 8 
Coherent detection. A deeper
look into heterodyne basics. 
6 
Feb 10 
Coherent detection. The
noise equation and basic measurements. 
7 
Feb 15 
Coherent detection. Basics
of radio telescopes. 
8 
Feb 17 
Conventions and units of
radio telescopes. 
9 
Feb 22 
Statistics. Basic
distributions. 
10 
Feb 24 
Hypothesis testing and
confidence limits. 
11 
Mar 1 
Errors in parameter
estimation. 
12 
Mar 3 
Geometrical optics. Basic
equations and lenses. 
13 
Mar 8 
Mirrors. Stops and pupils.
Fermat’s principle. Basic telescopes. 
14 
Mar 10 
Aberrations. Zernike
polynomials. Transfer functions. 

Mar 15 
MIDTERM 
15 
Mar 17 
Fourier transforms.
Aliasing. Filtering and windowing. 

Mar 22 
SPRING BREAK 

Mar 24 
SPRING BREAK 
16 
Mar 29 
Physical optics.
Diffraction. 
17 
Mar 31 
Two slit diffraction.
Gratings and the grating equation. 
18 
Apr 5 
Grating spectrograph. 
19 
Apr 7 
FabryPerot spectrometer. 
20 
Apr 12 
Fourier Transform
spectrometer. 
21 
Apr 14 
Interferometry basics. 
22 
Apr 19 
The twoelement
interferometer. Response and image filtering. 
23 
Apr 21 
Radio interferometer basic
design and response. 
24 
Apr 26 
Deconvolution. Closure
phases and selfcalibration. 
25 
Apr 28 
Machine Learning basics.
Regression and classification. 
26 
May 3 
Neighbors and decision trees. 
27 
May 5 
Classification and
evaluation. Over and under fitting. 
28 
May 10 
Review. 




May 17 
FINAL 