Astronomical Instrumentation (Structured MSc)

Applications and Selections

Who Teaches this Course

Requirements and Assessment

Glossary of Terms

Year 1 (90 Credits)

RequiredPH5126: Advanced Astronomical and Space Instrumentation

PH5126: Advanced Astronomical and Space Instrumentation

Semester 1 and Semester 2 | Credits: 10

In this module students will investigate the Instrumentation used to carry out Astronomy and Space Science. It will introduce students to the main instrument modalities (imaging, spectroscopic and polarimetric) across the EM spectrum. The principal aspects of Instrument design, including optical, mechanical, thermal, mass and cost will be developed, and students will have the opportunity to demonstrate design ability based on individual or group projects.
(Language of instruction: English)

Learning Outcomes

1. Demonstrate an understanding of the principles of astronomical and space instrumentation for imaging, spectroscopy, photometry and polarimetry.
2. Demonstrate an understanding of the physics and operating principles of the detectors employed in modern astronomy and space science as well as their limitations.
3. Carry out image processing and data analysis in order to measure signals, and to detect and characterise objects.
4. Design an instrument or spacecraft suitable for applications such as Earth Observation, satellite tracking, laser communication, high-resolution imaging of the ground and space and astronomical observations.

Assessments

• Continuous Assessment (60%)
• Oral, Audio Visual or Practical Assessment (40%)

Teachers

•  NICHOLAS DEVANEY 🖂
  
•  REBECCA NOLAN 🖂
  

Reading List

1. "Observational Astrophysics" by Pierre Lena, Daniel Rouan, Francois Lebrun, Francois Mignard, Didier Pelat
   ISBN: 9783642218.
   Publisher: Springer
2. "The design and construction of large optical telescopes" by Pierre Bely
   ISBN: 0387955127.
   Publisher: Springer

RequiredPH5123: Astroinformatics

PH5123: Astroinformatics

Semester 1 | Credits: 5

This is a self-contained module designed to train the student into the use of applied computing, statistical and AI techniques in the astronomical sciences. Extensive use is made of both Python and R in demonstrating and illustrating statistical and AI concepts using differing astronomical data types. This module directly addresses SDG 4: Quality Education by providing a robust foundational understanding of how quantitative methodologies can be used effectively to solve complex data problems, as well as developing intuitive skills in how best to engage and progress data science solutions that are transferable to resolving contemporary societal challenges, and so relevant to SDG 9: Industry, Innovation and Infrastructure.
(Language of instruction: English)

Learning Outcomes

1. Work confidently and effectively using the Unix-based operating system environment on diverse hardware facilities, and to use these skills to solve computational problems.
2. Develop effective programming skills using Python allowing them to preprocess and conduct exploratory data analysis for a variety of data types.
3. Process astronomical image, spectral and time-series data using contemporary image and time-series analysis algorithms, and have the capacity and confidence to develop and deploy their own solutions using the Python programming ecosystem.
4. Implement practical statistical and AI based solutions using the Python/R software ecosystems.
5. Build, test and deploy statistical/AI models for a range of differing experimental contexts, based on their understanding of the statistical properties of complex high-dimensional data

Assessments

• Department-based Assessment (100%)

Teachers

•  AARON GOLDEN 🖂
  

RequiredPH5127: Modern observational facilities

PH5127: Modern observational facilities

Semester 2 | Credits: 5

In this module we are introducing the students to large-scale modern astronomical facilities. Observational capabilities will be outlined and main science drivers discussed. The students will be acquainted with the process of obtaining competitive observation time. The students will be trained in the use of instrument simulators and/or exposure time calculators to test the feasibility of proposed observations. In-class peer review will be used to discuss ideas for observational programs. This module directly addresses SDG 4, quality education, by connecting the theoretical knowledge the students have obtained during the program with the practical aspects of fundamental research. This module also addresses SDG 17, partnerships to reach goals, by introducing the students to the global partnerships necessary to provide world class scientific facilities. and SDG 17, i.e. Quality Education and Partnership to reach goals. SDG
(Language of instruction: English)

Learning Outcomes

1. Identify flagship observational facilities and summarize some of their main science drivers
2. Recognize fundamental and practical limitations of the instruments and facilities
3. Condense the idea for a scientific experiment into the form suitable to discuss with their peers
4. Apply for observational time on international astronomical facilities to test their scientific ideas.

Assessments

• Oral, Audio Visual or Practical Assessment (100%)

Teachers

•  NICHOLAS DEVANEY 🖂
  
•  AARON GOLDEN 🖂
  
•  Christian Ginski 🖂
  

RequiredPH5109: Research Project

PH5109: Research Project

Semester 2 | Credits: 60

Learning Outcomes

1. Research Project

Assessments

• Research (100%)

OptionalGS536: Communication & Outreach

GS536: Communication & Outreach

Semester 1 and Semester 2 | Credits: 5

The student should only register for this module in the academic year that they intend to complete the module. This module aims to give students the opportunity to understand the relevance and impact of research in society and to communicate research to diverse audiences, including non-specialists. Students will be given an opportunity to broaden their understanding of the social context of research. Students are expected to engage in activities to improve their communication skills, such as workshops and training courses. A key goal of this module is to challenge the student with the task of promoting the themes of their discipline/School/College and communicating technically complex and/or advanced concepts to non-specialist audiences. Detailed learning outcomes for this module should be developed by the supervisor taking into account the suite of online training materials available and the suite of communication opportunities and outreach activities available. Students must complete a report: • describing in detail the training undertaken, • outlining their engagement in practical outreach activities , • providing evidence of their effectiveness (for example audience feedback reports) and • including any outputs, such as presentations or demonstrations.
(Language of instruction: English)

Learning Outcomes

1. Communicate complex research topics to non-specialist audiences.
2. Engage with community through active participation.
3. Appreciate the role of research in society.

Assessments

• Department-based Assessment (100%)

Teachers

•  SANDRA DONOHUE 🖂
  
•  ANNA MARIE LEONARD 🖂
  

OptionalEE352: Linear Control Systems

EE352: Linear Control Systems

Semester 1 | Credits: 5

Fundamental module on control systems, including a range of analysis techniques.
(Language of instruction: English)

Learning Outcomes

1. Use a polar plot to determine the level of stability of a closed-loop system from open-loop test/model data.
2. Use a Nichols Chart as an aid in control system design and analysis.
3. Use the Root-Locus method in the design of controllers.
4. Sketch control system step responses from closed-loop pole-zero maps.
5. Apply appropriate design strategies to meet basic performance specifications.
6. Choose appropriate controller settings to meet performance specifications.

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  MAEVE DUFFY 🖂
  

OptionalPH222: Astrophysical Concepts

PH222: Astrophysical Concepts

Semester 1 | Credits: 5

Major astrophysical concepts and processes such as radiation, gravity and cosmology are presented. These concepts are illustrated by wide ranging examples from stars, planets, nebulae and galaxies.
(Language of instruction: English)

Learning Outcomes

1. define terms and explain concepts relating to the physical principles covered by this module’s syllabus
2. describe the physical laws that connect terms and concepts covered by this module’s syllabus and, where appropriate, derive the mathematical relationships between those terms and concepts.
3. outline applications to real-world situations of the physical principles covered by this module’s syllabus
4. analyze physical situations using concepts, laws and techniques learned in this module
5. identify and apply pertinent physics concepts, and appropriate mathematical techniques, to solve physics problems related to the content of this module’s syllabus.

Assessments

• Written Assessment (80%)
• Continuous Assessment (20%)

Teachers

•  AARON GOLDEN 🖂
  
•  REBECCA NOLAN 🖂
  

Reading List

1. "Astronomy (2e)" by Openstax
   ISBN: 9781951693503.
   Publisher: Openstax

OptionalGS507: Statistical Methods for Research

GS507: Statistical Methods for Research

Semester 1 | Credits: 5

This course will introduce students to statistical concepts and thinking by providing a practical introduction to data analysis. The importance and practical usefulness of statistics in biomedical and clinical environments will be demonstrated through a large array of case studies. Students attending this course will be encouraged and equipped to apply simple statistical techniques to design, analyse and interpret studies in a wide range of disciplines. Introduction to Biostatistics Statistics can be a very important and interesting subject as it is an integral part of almost all areas of practical research both inside and outside the University. The main theme of this course for students is that they should meet and understand many of the basic statistical ideas they may meet and use in their future research. The emphasis throughout the course is on the application of Statistics and will rely heavily on a statistical computing package called MINITAB. The course concentrates on how, in any research context, to pose answerable and generalisable questions, design an experiment to answer such, carry out the appropriate statistical procedures on the resulting data from the experiment and finally to interpret and report the conclusions/answers to the questions posed on the basis of this analysis.
(Language of instruction: English)

Learning Outcomes

1. Understand the key concept of variability;
2. Understand the ideas of population, sample, parameter, statistic and probability;
3. Understand simple ideas of point estimation;
4. Recognise the additional benefits of calculating interval estimates for unknown parameters and be able to interpret interval estimates correctly;
5. Carry out a variety of commonly used hypothesis tests
6. Understand the difference between paired and independent data and be able to recognise both in practice;
7. Understand the aims and desirable features of a designed experiment;
8. Calculate the sample size needed for one and two sample problems.

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  SANDRA DONOHUE 🖂
  
•  ANNA MARIE LEONARD 🖂
  
•  JOHN NEWELL 🖂
  
•  ANDREW SIMPKIN 🖂
  

OptionalME516: Advanced Mechanics of Materials

ME516: Advanced Mechanics of Materials

Semester 2 | Credits: 5

This module is concerned with advanced mechanics of materials with a view to engineering design for structural integrity. Attention is focussed on elasticity, plasticity, creep and fracture mechanics, with application to multiaxial design against fatigue, fracture, creep, creep-fatigue interaction and plastic failure. Mini-projects will focus on applied computational mechanics of materials.
(Language of instruction: English)

Learning Outcomes

1. Derive multiaxial strain tensor from three-dimensional displacement field, including large deformation theory
2. Design for multiaxial plasticity in advanced mechanical applications
3. Undertake multiaxial creep design for high temperature applications
4. Predict multiaxial high and low cycle fatigue life
5. Develop non-linear computational mechanics models for mechanical design
6. Carry out three-dimensional transformation of stress and strain tensors for multiaxial applications

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  SEAN LEEN 🖂
  
•  DONG-FENG LI 🖂
  

Reading List

1. "Advanced Mechanics of Materials" by Boresi, AP, Schmidt, RJ, and Sidebottom, OM
   Publisher: Wiley and Sons
2. "Introduction to Computational Plasticity" by Dunne, F and Petrinic, N,
   Publisher: Oxford Univ Press
3. "Engineering Materials 1: An Introduction to Properties, Applications and Design" by Ashby, MF and Jones, DRH
   Publisher: Cambridge University Press, Elsevier
4. "Fatigue of materials" by Suresh, S
   Publisher: Cambridge Univ Press
5. "Design for Creep" by Penny, RK and Marriott, DL
   Publisher: Chapman and Hall
6. "Mechanics of Engineering Materials" by Benham, Crawford and Armstrong
   Publisher: Pearson Prentice Hall

OptionalPH504: High Performance Computing and Parallel Programming

PH504: High Performance Computing and Parallel Programming

Semester 2 | Credits: 5

Assessments

• Continuous Assessment (100%)

Teachers

•  NICHOLAS DEVANEY 🖂
  
•  SIMON WONG 🖂
  
•  REBECCA NOLAN 🖂
  

OptionalBME501: Advanced Finite Element Methods

BME501: Advanced Finite Element Methods

Semester 2 | Credits: 5

The module will educate students in the use of linear and non-linear finite element methods that are most relevant to problems and systems encountered in both fundamental and applied research in biomedical and mechanical engineering.
(Language of instruction: English)

Learning Outcomes

1. Explain the structure of a linear finite element boundary value problem solution algorithm and its implementation in a computer programme.
2. Explain the structure of non-linear finite element solution algorithms and their programming implementations, distinguishing between implicit and explicit methods.
3. Distinguish between direct and element-by-element solution methods.
4. Implement linear and non-linear constitutive laws in implicit and explicit finite element software.
5. Deal with the formulation and solution of multi-physics problems.

Assessments

• Written Assessment (50%)
• Continuous Assessment (50%)

Teachers

•  PETER MCHUGH 🖂
  
•  TED VAUGHAN 🖂
  

OptionalPH362: Stellar Astrophysics

PH362: Stellar Astrophysics

Semester 2 | Credits: 5

A comprehensive model for stellar structure and evolution is developed and used to understand star formation, evolution and destruction and the properties of extrasolar planets.
(Language of instruction: English)

Learning Outcomes

1. define terms and explain concepts relating to the physical principles covered by this module’s syllabus
2. describe the physical laws that connect terms and concepts covered by this module’s syllabus and, where appropriate, derive the mathematical relationships between those terms and concepts.
3. outline applications to real-world situations of the physical principles covered by this module’s syllabus
4. analyze physical situations using concepts, laws and techniques learned in this module
5. identify and apply pertinent physics concepts, and appropriate mathematical techniques, to solve physics problems related to the content of this module’s syllabus.

Assessments

• Written Assessment (80%)
• Continuous Assessment (20%)

Teachers

•  ALEXANDER GONCHAROV 🖂
  
•  MATTHEW PETER REDMAN 🖂
  
•  REBECCA NOLAN 🖂
  
•  CAIT FAHY 🖂
  

OptionalPH466: Astrophysics

PH466: Astrophysics

Semester 2 | Credits: 5

In this course we look at a number of astrophysical problems that have not been examined in detail in other modules in the programme. Radiation and dynamical processes are developed and applied to different example astrophysical systems such as supernovae, planetary nebulae, star formation, stellar winds, active galactic nuclei, star clusters and starburst galaxies.
(Language of instruction: English)

Learning Outcomes

1. define terms and explain concepts relating to the physical principles covered by this module’s syllabus
2. describe the physical laws that connect terms and concepts covered by this module’s syllabus and, where appropriate, derive the mathematical relationships between those terms and concepts.
3. outline applications to real-world situations of the physical principles covered by this module’s syllabus.
4. analyze physical situations using concepts, laws and techniques learned in this module
5. identify and apply pertinent physics concepts, and appropriate mathematical techniques, to solve physics problems related to the content of this module’s syllabus
6. discuss state-of-the-art applications of physical principles covered by this module’s syllabus to contemporary themes in astrophysics.

Assessments

• Written Assessment (80%)
• Continuous Assessment (20%)

Teachers

•  MATTHEW PETER REDMAN 🖂
  
•  REBECCA NOLAN 🖂
  

OptionalEOS4101: Earth Observation and Remote Sensing

EOS4101: Earth Observation and Remote Sensing

Semester 2 | Credits: 5

This module will introduce students to an array of Remote sensing techniques used in Earth Observations. It will include Satellite, Airborne (plane and drone) and Marine based technologies. Students will be introduced to the theory of electromagnetic radiation, remote sensing systems, Multispectral scanners, Radar instruments, Photogrammetry. Image processing and image interpretation will also be covered. The data provided from these methods can be used to help understand the physical, chemical, and biological processes acting on the earth’s surface. Applications include environmental monitoring climate change. Specifically geological mapping, marine and terrestrial habitat mapping, agriculture, coastal erosion, flood mapping, land use mapping and archaeology will be covered.
(Language of instruction: English)

Learning Outcomes

1. Explain the concept of electromagnetic energy (EM) including the principles of remote sensing.
2. Explore the variety of available sensors (Multispectral, Lidar, Radar) and their properties (ie spatial, spectral, radiometric, temporal resolution)
3. Summarize the principles of image acquisition from a variety of platforms. Satellite, Airborne and Drone
4. Process and integrate remotely sensed images into a GIS framework
5. Relate remote sensing technologies to successful applications of Earth observation and monitoring ((geology, atmospheric sciences, climatology, water resources, oceanography, agriculture, forestry)

Assessments

• Written Assessment (70%)
• Continuous Assessment (30%)

Teachers

•  EVE DALY 🖂
  

Reading List

1. "Introduction to Remote Sensing," by James B. Campbell
   ISBN: 978-160918176.
2. "An Introduction to Ocean Remote Sensing" by Seelye Martin
   ISBN: 978-11070193.