Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor, lecturer, lab demonstrator
Matt Owers
12 Wally's Walk, rm 510
By appointment.
Lecturer.
Andrew Hopkins
12 Wally's Walk, rm 530
By appointment.
Lecturer, lab demonstrator
Gabriella Quattropani
12 Wally's Walk, rm 507
By appointment.
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
PHYS202 or PHYS2020
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
We are in the 'golden age' of astronomy: powerful new telescopes are giving us exciting new visions of the Universe. For example, the space-based Gaia telescope has made precise measurements of the three-dimensional positions for over a billion stars, allowing an unprecedented view of the structure of our own Milky Way galaxy. However, analysing the flood of data from new instruments has been compared to 'drinking from a firehose' - impossible for individuals to do unassisted. Scientists increasingly rely on intelligent algorithms and robust statistical analysis to make new discoveries in astronomical 'big data'. In this unit, students will learn about the astrophysics of the Milky Way galaxy where new data and advanced analysis techniques are making a significant impact. Students will hone their data analysis skills during labs, where they will use machine learning, Bayesian statistics, and data-mining techniques to analyse cutting-edge astronomy data sets linked to the lecture material. The techniques learned here are broadly applicable to a wide range of problems outside of astronomy and this unit will equip students to be pioneers of the information age. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Unit workload
The 'estimated time on task' for each assessment item is an estimate of the additional time needed to complete each assessment outside of all scheduled learning activities. These estimates assume that you actively engage with all scheduled learning activities and spend an additional 12 hours of self-led study over the course of the session.
Requirements to Pass this Unit
To pass this unit you must achieve a total mark equal to or greater than 50%, and obtain a mark of at least 40% in the final exam, which is a hurdle assessment. If your mark in the final examination is between 30% and 39% inclusive, you may be a given a second and final chance to attain the required level of performance; the mark awarded for the second exam towards your final unit mark will be capped at 40%, and you will be allowed to sit the second exam only if this mark would be sufficient to pass the unit overall. The final exam is set as a hurdle task in order to ensure that Unit Learning Outcomes 1 and 2 are fulfilled by the student.
Supplementary examinations
If you receive special consideration for the final exam, a supplementary exam will be scheduled after the end of the normal exam period, typically about 3 to 4 weeks after the normal exam period. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification prior to the exam with the exact date and time of their supplementary examination.
If you are given a second opportunity to sit the final examination as a result of failing to meet the minimum mark required by the hurdle, you will be offered that chance during the same supplementary examination period and will be notified of the exact day and time after the publication of final results for the unit.
Late Assessment Policy
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern.
For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration.
Assessments where Late Submissions will be accepted
Lab Reports - YES, Standard Late Penalty applies
Problem Sets - YES, Standard Late Penalty applies
Special Consideration
The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through ask.mq.edu.au.
Name | Weighting | Hurdle | Due |
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Lab reports | 50% | No | Week 6, Week 10, Week 13 |
Problem sets | 20% | No | Week 3, 8, 12 |
Final exam | 30% | Yes | Session 1 exam period. |
Assessment Type 1: Lab report
Indicative Time on Task 2: 20 hours
Due: Week 6, Week 10, Week 13
Weighting: 50%
A report for each of the three computational projects.
Assessment Type 1: Problem set
Indicative Time on Task 2: 20 hours
Due: Week 3, 8, 12
Weighting: 20%
A series of assignments throughout the semester.
Assessment Type 1: Examination
Indicative Time on Task 2: 20 hours
Due: Session 1 exam period.
Weighting: 30%
This is a hurdle assessment task (see assessment policy for more information on hurdle assessment tasks)
Examination in the university exam period, covering all content from the unit.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Your lecturers in this course are Matt Owers, Andrew Hopkins and Gabriella Quattropani. Each week, there will be 2 hours of lectures that will be delivered on-campus. There will also be a one hour of lectorial-type demonstration delivered on-campus. The 2 hours of lectures will cover astronomy content related to the Milky Way galaxy, while the one hour lectorials will cover techniques used to extract information from datasets (Gabriella Quattropani). Both will begin in Week 1 of session, and in-person attendance is essential for success in this unit.
The lab sessions run from Week 3-12 on-campus in the Physics and Astronomy computer lab, and will involve a series of computer labs completed using Python Notebooks within the Google Colab environment. Note that labs start in Week 3. Matt Owers and Gabriella Quattropani will be your lab demonstrators. Again, in-person attendance is essential for success the labs.
Resources will be provided on iLearn. There is no required text, although the Milky Way Galaxy component will primarily draw content from the book "Galaxies in the Universe: An Introduction" 2nd Ed. by Sparke and Gallagher, supplemented by material from "An Introduction to Modern Astrophysics" 2nd Ed. by Carroll and Ostlie and "Galactic Astronomy" by Binney and Merrifield. Useful resources for the data science part of the course are the books "Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data" by Ivezic et al. and "Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow" by Geron.
Methods of Communication
Communication will be via your university email or through announcements on iLearn. General queries regarding assessments and/or lecture/lab content should be placed on the iLearn discussion board. Queries of a more personal nature can be sent to the convenor (matt.owers@mq.edu.au) from your university email address.
COVID Information
For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.
Week | Lecture material (Matt/Andrew) | Lectorial material (Gabriella) |
Computer Labs (Matt/Gabriella) |
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1 | Introduction, basic astro (Matt) | Getting started with Google Colab | N/A | |
2 | Distance measurements & introduction to GAIA (Matt) | Manipulating, visualising and cleaning data (Pandas) | N/A | |
3 | Demographics of stars and stellar populations (Matt) | Fitting a model to data. | Lab 1: Line Fitting and the Period-Luminosity relation. | |
4 | Structure and components of the Milky Way part I (Matt) | Modelling data: Bayesian reasoning and samplers (MCMC). | Lab 1: Line Fitting and the Period-Luminosity relation. | |
5 | Structure and components of the Milky Way part II | Exploring structure in data: visualisation, PCA | Lab 1: Line Fitting and the Period-Luminosity relation. | |
6 | Kinematics of the Milky Way (Matt) | Good Friday | Good Friday | |
7 | Orbital Dynamics within the Milky Way part I (Matt) | Exploring structure in data with K-means clustering. | Lab 2: Determining star cluster membership using Gaia data. | |
8 | Orbital Dynamics within the Milky Way part II (Matt) | Exploring structure in data with Gaussian Mixture Models | Lab 2: Determining star cluster membership using Gaia data. | |
8 | mid-sem | Break | ||
9 | The Milky Way's environment: the Local Group part I (Andrew) | Classification: decision trees and random forest | Lab 2: Determining star cluster membership using Gaia data. | |
10 | The Milky Way's environment: the Local Group part II (Andrew) | Artificial neural networks | Lab 3: Deep Learning to classify Galactic objects. | |
11 | Formation of the Milky Way and Local Group (Andrew) | Convolutional neural networks | Lab 3: Deep Learning to classify Galactic objects. | |
12 | Future evolution of the Milky Way and Local Group (Andrew) | Revision | Lab 3: Deep Learning to classify Galactic objects. | |
13 | Revision (Matt/Andrew) | N/A |
N.B.: This schedule is flexible and subject to change.
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via the Service Connect Portal, or contact Service Connect.
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
In response to changes in other ASTR units, we have removed the extra-solar planet material and added new content on the Milky Way's local group and evolution.
Gabriella Quattropani has been added to the teaching staff to help out with the computational lectorials and labs.
Unit information based on version 2024.03 of the Handbook