DOs and DON’Ts
for
presentations
Elizabeth Tasker, Cosmosciences
Why are presentations important?
Scientists do not work alone
We work together …
… and share ideas
across the world.
Why are presentations important?
Good presentations share your ideas with the world
You and your ideas get recognition
Your work is used by others
Science success!
Career success!
Presentations are as important as research papers!
Example
Presenting a research paper to your laboratory group
?
s
d
u
Quantifying Observational
o
l
c
r
a
l
u
Projection Effects
Using
c
e
l
o
m
Molecular Cloud
Simulations
d
n
fi
s
r
e
v
r
e
s
b
o
n
a
C
Beaumont, Offner, Shetty, Glover, Goodman
ApJ, in press
arXiv:1310.1929v1
Title Slide:
Quantifying Observational
Projection Effects Using
Molecular Cloud Simulations
Paper title
Beaumont, Offner, Shetty, Glover, Goodman
ApJ, in press
journal, published?
arXiv:1310.1929v1
reference number
authors
Tempting to take text from the paper…
Introduction
Since the processes that form and sculpt molecular clouds set the initial
conditions for star formation, the spatial and kinematic structure of
molecular clouds provides clues about the star formation process.
What?
Ideally, the full six-dimensional spatial-kinematic information would be
available for studying molecular cloud structure.
Unfortunately, observations can only provide either two-dimensional
information of the intensity in the plane of the sky or three-dimensional
intensity information as a function of 2D space and line-of-sight velocity.
What is wrong?
Content is OK
But...
In a presentation, you must listen and read
and your focus is on the presenter
AND it’s in English!
This is very hard!
What is better?
Use short sentences
Quick to read!
Quick to understand!
Use pictures
1 picture = 1000 words
set context
Full sentences = not needed
This is incredibly small text that is
probably very boring and not well
written. It is really hard to
understand what is said here.
Also, it uses very very long words
such as
supercalifragilisticexpialidocious
and antidisestablishmentarianism.
Reall,y, how are these words
helpgul? They’re not. No.
=
Introduction
Stars are born inside molecular clouds
cloud properties (e.g. mass, velocity) very
important for star formation
y
z
x
To calculate properties, we’d like 3D position
and velocity : x, y, z, vx , vy , vz
v
But observers have only 2D position
and 1D velocity
Introduction
Since the processes that form and sculpt molecular clouds set the initial
conditions for star formation, the spatial and kinematic structure of
molecular clouds provides clues about the star formation process.
Ideally, the full six-dimensional spatial-kinematic information would be
available for studying molecular cloud structure.
Unfortunately, observations can only provide either two-dimensional
information of the intensity in the plane of the sky or three-dimensional
intensity information as a function of 2D space and line-of-sight velocity.
Introduction
Stars are born inside molecular clouds
cloud properties (e.g. mass, velocity) very
important for star formation
y
z
x
To calculate properties, we’d like 3D position
and velocity : x, y, z, vx , vy , vz
v
But observers have only 2D position
and 1D velocity
Introduction II
In most analyses, researchers assume (implicitly or explicitly) that intensity
features in position-position-velocity (PPV) datasets correspond more or
less cleanly to 3D (position-position-position, or PPP) density structures in
the cloud.
Molecular cloud motions are dominated by turbulence at scales above ~
0.1 pc, and have complex velocity fields. Likewise, the temperature,
excitation, and abundance conditions vary throughout the clouds by
factors of several.
How well do features in observational data relate to intrinsic structures
in the three-dimensional cloud?
II
Long sentences
Technical (unexplained) words
Hard to understand quickly
(slide time ~ 1 minute!)
y
RA
vz
D
Observation (PPV)
z
Reality (PPP)
We assume same objects are the same in PPV and PPP
But gas in clouds is complex:
turbulent velocity, not constant T or chemistry
Are these objects really the same?
x
Introduction II
In most analyses, researchers assume (implicitly or explicitly) that intensity
features in position-position-velocity (PPV) datasets correspond more or
less cleanly to 3D (position-position-position, or PPP) density structures in
the cloud.
Molecular cloud motions are dominated by turbulence at scales above ~
0.1 pc, and have complex velocity fields. Likewise, the temperature,
excitation, and abundance conditions vary throughout the clouds by
factors of several.
How well do features in observational data relate to intrinsic structures
in the three-dimensional cloud?
This Paper
Let’s look at the paper results.
Think about the slide design
Easy to read?
Quick to understand?
Main point clear?
How would you change it?
Problems with PPV
PPP
3 objects
PPV
2 objects
How to
compare?
PPV
If objects are in front / behind with ~ velocity
PPP
1 object
PPV
2 object
same object
How to
compare?
PPV
Different velocities inside the cloud
different objects
Problems with PPV
Chemistry inside cloud also
affects radiative transfer
RA
(light moving through cloud)
changes intensity (strength)
of PPV measurement
D
vz
This Paper
We cannot get PPP data from observations
But we can get both PPP and PPV data from simulations
2 simulations of a
molecular cloud
Compared clouds found in PPP and PPV data
Star-forming region
Simulation details
Simulation ‘O1’
Code name: ORION
tcross
Set-up: Constant
Resolution:
⇢ , initial turbulence
xmin = 0.006 pc
Isothermal (constant T)
After 2 tcross (to allow turbulence to mix gas), self-gravity is turned on.
When resolution becomes low, use sink particle
1
Run for t↵ , 2.3% of gas ( 700 M ) in sinks
2
(time for cloud to collapse)
Simulation details
Simulation ‘S11’
Code name: ZEUS_MP
Set-up: Constant
tcross
Resolution:
⇢ , initial turbulence
xmin = 0.078 pc
No gravity
Magnetic field: B = 5.85 µG
Non-equilibrium heating / cooling
UV radiation
Formation / destruction of CO and H2
Run for 3 tcross .
Simulation details
Simulation data
RADMC-3D
Radiative transfer program
“Post-processing”
makes “observational” data
create PPV clouds
Cloud finding
To find clouds:
use dendograms
Data
use dendograms
High density or
PPP
high intensity
PPV
Each structure = contour
(root, branch, leaf)
of constant ⇢ / intensity
Cloud finding
Can ‘prune’ denograms to remove bad structures
e.g. structures from noise, badly resolved etc
Keep ‘leaf’ if :
y
pixel is brighter than 7
neighbours in any direction.
z
Contains > 800 pixels
Max. intensity / density > 7
brighter than contour
x
Matching PPV and PPP clumps
(1a) Find PPP clumps
(1b) Find PPV clumps
(raw simulation data)
(fake observational data)
(2) Convert each PPP clump
PPV data
(3) Calculate overlap of PPP clump with each PPV clump
(4) Max overlap = match!
0<q<1
bad match
good match!
P
⇢PPV (Ri ) ⇥ I(Oj )
P
Sij = P
[ ⇢PPV (Ri )2 ⇥ I(Oj )2 ]1/2
qi = maxSij
normalisation 0 ! 1
Reminder!
A good match between PPV and PPP ( q ! 1 )
y
RA
vz
D
z
Observers are correctly finding clumps!
x
Results: PPP vs PPV
Blue = good match q ! 1
Red = bad match q ! 0
The better the PPV - PPP match
Closer the properties
(e.g. mass, size...)
1.4 - 2 factor uncertainty
in properties
What if they turn off gravity?
13
CO(J = 1
0)
Gravity collapses objects
More small objects
Less overlap
Easier to match PPP - PPV
O1 simulation: No gravity
But... change is small
Results: Boundedness
↵ measures
gravitational binding
↵ < 2 bound
But...
↵ centered ⇠ 2
scatter ⇠ 2
with PPV, very hard to tell if object is gravitationally bound!
Conclusions
PPP and PPV objects do not always correspond
Most properties (mass, size...) match to within ~40 %
But
↵
changes by a factor of 2
Because of large ↵ change, hard to tell if an object is
bound using PPV
Gravity improves PPP - PPV match, but not by much
Conclusions
Don’t worry about your English!
It doesn’t need to be perfect
Partial sentences = OK!
Don’t copy text from papers
The style is wrong for presentations
Use pictures!
Quicker to understand
Conclusions
Each slide = 1 point
What is your slide saying?
Know your audience
e.g.
Supervisor
Specialised conference
Details important!
New students
Scientists not in your field
Explain technical
terms
Non-scientists
Explain context / why it’s interesting
Conclusions
Think about presentations you liked
Why did you like them?
Can you copy the style?
Most important:
Practice!