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Welcome to CAS!
Centre of Astrophysics and Supercomputing • Founded in 1998 • Currently 120+ people (including 40 PhD students, 22 faculty) • One of the top 3 astronomy research groups in the country (with ANU, USyd) • Large astronomy software group, Astronomy Data and Computing Services (20 engineers) • Strong outreach and 3D animation capability • Active in undergraduate teaching and research (including space science!)
Research • Galaxy evolution • Cosmology • Planet formation • Pulsars & transient Universe • HPC/GPU • 100+ publications in high quality journals inc. Nature or Science
Highest redshift spiral galaxy The monster galaxy that grew up too fast Yuan et al. (ApJ) Glazebrook et al. (Nature) Galaxy murder mystery Mysterious bursts of energy do come from outer space Brown et al. (MNRAS) Caleb et al. (MNRAS)
Australian Research Council Centres • We have three ARC-funded Centres of Excellence in CAS ARC Centre of Excellence for Astronomy in 3D • ~$4M p.a. in research funding from these centres!
Facilities – Telescopes!
• Access to Keck Observatory twin 10m telescopes (10 nights/yr) via Caltech is one of
Swinburne’s premier international relations
• Best/largest Optical/IR telescope in the world: transformative in areas such as Adaptive
Optics & Laser Guide stars
• Full remote operation from Hawthorn campus
• +ESO (4x8m) national access, AAT national 4m, etc.
The Keck Wide-Field Imager
Facilities – Computers!
We are a very active place! • Weekly Centre meeting (Director’s pizza lunch) • Weekly student talks, science colloquia • Weekly journal club • Cookies and code • Weekly art sessions • Many outreach opportunities • Astronomy club • Many learning/research opportunities • Honours students very welcome to get involved!
2021 CAS Honours Projects Showcase
Unveiling the nature of dark matter with galaxy-galaxy strong gravitational lensing
Dorota Bayer dbayer@swin.edu.au
• Phenomenological dark-matter models (cold vs. warm dark matter)
• Different properties of substructure in galaxies
• Strong gravitational lensing: imprints of substructure on lensed images
• Machine-learning approach
• Project aim: generate a large set of lensed images for different dark-matter models to train AITransfer learning in lens discovery
Supervisor: Dr Colin Jacobs colinjacobs@swin.edu.au
We can discover strong gravitational lenses using Simulations
deep learning. But we don’t have a big training set.
Can we make neural networks more efficient by
transferring knowledge gained from one training set
to new applications, such as a different telescope?
Explore the role of transfer learning on deep neural
networks using the Swinburne supercomputer to
making these discoveries more efficient.
Features learned by CNN Images: Space-based (Hubble) Ground based (DES)Local globular clusters as a ground truth for galaxy stellar
population analysis methods Themiya Nanayakkara
(wnanayakkara@swin.edu.au)
Astronomers compare photometric/spectroscopic
How do we know if the models and We need a sample of stellar
observations with theoretical models to determine
fitting methods give the correct solutions? populations where
the nature and conditions of the ionizing stars and
gas in galaxies. properties like the age,
metallicity etc. are
measured independently
In this project, you will use a sample of globular clusters with
spectroscopic data to generate mock observables and use
Bayesian fitting techniques to infer integrated properties to see
who is best!
+
©Prospector ©Koleva at al. 2008Dragonfly Data Herschel Data
Dust in the interstellar
medium: What is it
really like?
You will: test dust models using the
exquisite data sets taken by the
Herschel Space Observatory and
the Dragonfly Telephoto Array.
Supervisor: Dr. Jielai Zhang
jielaizhang@swin.edu.auSN2005E
Peak Lumino
Peak Lumi
SN2008ha
−14 41
Deeper, Wider, Faster: Discovering the fastest bursts in the Universe
SN2008S 10
Ca−rich PTF10acbp
Transients
NGC300OT
−12
Luminous
You will: Discover new transients in multi-wavelength data; create tools to coordinate observatories; create tools for
40
10 PTF10fqs
Red
Novae P60−M82OT−081119
383 −10 data visualisation and sonification techniques for transient discovery. M85 OT
V838 Mon
Classical Novae M31 RV
39
10
−8
Supervisor: Dr. Jielai Zhang (jielaizhang@swin.edu.au)
P60−M81OT−071213
45
10 38
10
−6
383 0
10
Transients in the local
10
1 Universe
10
2 383
log ( Characteristic Timescale [day]
[day])
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vae
10
44 10 4. Framework of Cosmic Explosions in the Year 2011 (Kasliwal
Figure Note that until 200510(Fig. 1),
2011). Supernovae
Luminous
SCP06F6 - D RDPANP
we only knew about three classes (denoted by gray bands). In the past SN2008es six years, systematic searches,
( G C A SN2005ap 31/
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et al. 2011a)Aare
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10
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43 new classes are emerging and the governing physics is being widely debated: luminous SN2007bi red novae (electron
A / RRHMH 3HIH MF
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Thermonuclear - (magnetars
- 3 , / / 1MCH
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or pair
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detonations in ultra-compact white -dwarf -
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43 43
10
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Peak Luminosity [erg s ]
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RPN R
Peak Luminosity [M ]
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Peak Luminosity [erg s ]
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apse
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(advanced LIGO, advancedCVIRGO, LCGT, INDIGO)
PTF10iuv coming AC online. DetectingAC gravitational 1
SN2005E
waves from neutron star mergers every month is expected to become routine. A basic common-
.G ) DURNM
ality 41between
−14 gravitational wave searches and theSN2008ha
electromagnetic search described above41is that
10 A
41 both are limited to the local Universe (say, < Ca−rich SN2008S 10
10 C 200 Mpc). A known challenge will be the poor sky
-99 3D DCH L
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Transients A A -
localizations of the gravitational wave signal and consequent large false positive rate of electro- / , L
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ambitious search40for an
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PTF10fqs
10
electromagnetic counterpart
A to a gravitational wave signal, it would only beRed 10
prudent to build this 9 - , - 9
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Novae 9 3 DDP31 SMR L M
40 OT −10 P60−M82OT−081119
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10
838 Mon C A V838 Mon 9
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39 39
10 10
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9
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7
log ( Characteristic Timescale [day]
[sec])Reconstructing the expansion history of the Universe
• Observations appear to suggest that the expansion of the Universe is accelerating …!
• These results assume the
“ΛCDM cosmology” – a model
we do not yet understand!
• We will use cosmological data
to reconstruct the expansion
history without assuming this
model, and directly map the
accelerating expansion
Please get in touch …! • This project will develop your
Chris Blake (cblake@swin.edu.au) skills in programming,
statistics and cosmology!Dr Michelle Cluver M82
In dust we trust! Or do we? mcluver@swin.edu.au
Galliano (2004)
NGC 1365 NGC 6946 M81
NGC 5055
M101Prof. Darren Croton di
st Project 1:
an
(dcroton@swin.edu.au) tu The distribution of gas and metals around
ni
ve
rs galaxies, from low to high redshift
e
• Galaxy formation
• supermassive black holes
• cosmological simulations
• supercomputing
• data science
Immediate group:
2 profs, 3 PhDs,
1 postdoc,
1 software engineer
lo
ca
lu
ni
ve
rs
e
Project 2:
Probing the star formation main sequence
of galaxies across cosmic timecfluke@swin.edu.au
(Peanut shell)-shaped structures from unstable stellar bars
Galaxy NGC 128
Strength of the 6th order Fourier Harmonic
term as a function of the galaxy’s radius.
Alister Graham (agraham@swin.edu.au)Galactic Candy:
quantifying the `ansae’ of galaxies
Can the 8th order cosine term capture these structures…
Alister Graham (agraham@swin.edu.au)Boxy or Discy?
morphological substructure of early-type galaxies
You will quantify the isophotal shapes of non-spiral galaxies, and search for hidden box- and disc-shaped structures.
Alister Graham (agraham@swin.edu.au)Quasar absorption lines Michael Murphy (mmurphy@swin.edu.au)
2 projects: Michael Murphy (mmurphy@swin.edu.au) Rare, heavy metals in the world’s best spectral data Possible cosmological variations in the electromagnetism
2021 CAS Honours Projects Showcase
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