×
Copy
Open
Link
Embed
Share
Beginning
This slide
Copy link URL
Copy link URL
Copy iframe embed code
Copy iframe embed code
Copy javascript embed code
Copy javascript embed code
Share
Tweet
Share
Tweet
Slide 1
Slide 1 text
BUILDING THE SOFTWARE INFRASTRUCTURE FOR ASTROPHYSICS by Dan Foreman-Mackey
Slide 2
Slide 2 text
who am I? / / what’ve I been up to? 1
Slide 3
Slide 3 text
7 [1] solving Hard™ data analysis problems [2] enabling and empowering astrophysicists
Slide 4
Slide 4 text
implementation.
Slide 5
Slide 5 text
data = > physics
Slide 6
Slide 6 text
open source software for astrophysics 2
Slide 7
Slide 7 text
why?
Slide 8
Slide 8 text
credit: Adrian Price-Whelan / / data: SAO/NASA ADS
Slide 9
Slide 9 text
my open source contributions 3
Slide 10
Slide 10 text
No content
Slide 11
Slide 11 text
gaussian processes 4
Slide 12
Slide 12 text
p(data|physics)
Slide 13
Slide 13 text
data ~ N(model; noise)
Slide 14
Slide 14 text
°0.6 °0.3 0.0 0.3 0.6 raw [ppt] 0 5 10 15 20 25 time [days] °0.30 °0.15 0.00 de-trended [ppt] N = 1000 reference: DFM+ (2017)
Slide 15
Slide 15 text
°0.6 °0.3 0.0 0.3 0.6 raw [ppt] 0 5 10 15 20 25 time [days] °0.30 °0.15 0.00 de-trended [ppt] N = 1000 reference: DFM+ (2017)
Slide 16
Slide 16 text
data ~ N(model; noise)
Slide 17
Slide 17 text
data ~ N(model; noise)
Slide 18
Slide 18 text
so. why not?
Slide 19
Slide 19 text
data ~ N(model; noise)
Slide 20
Slide 20 text
No content
Slide 21
Slide 21 text
reference: Ambikasaran, DFM+ (2015)
Slide 22
Slide 22 text
No content
Slide 23
Slide 23 text
reference: Ambikasaran, DFM+ (2015)
Slide 24
Slide 24 text
reference: DFM, Agol, Ambikasaran, Angus (2017); DFM (2018); DFM, Luger, et al. (2021)
Slide 25
Slide 25 text
No content
Slide 26
Slide 26 text
reference: Gordon, Agol, DFM (2020)
Slide 27
Slide 27 text
what’s next?
Slide 28
Slide 28 text
No content
Slide 29
Slide 29 text
No content
Slide 30
Slide 30 text
No content
Slide 31
Slide 31 text
credit: Quang Tran
Slide 32
Slide 32 text
reference: Luger, DFM, Hedges (2021)
Slide 33
Slide 33 text
probabilistic inference 5
Slide 34
Slide 34 text
p(data|physics)
Slide 35
Slide 35 text
have: physics = > data
Slide 36
Slide 36 text
want: data = > physics
Slide 37
Slide 37 text
integral of the form f(physics) p(physics|data) dphysics
Slide 38
Slide 38 text
No content
Slide 39
Slide 39 text
number of parameters patience required a few tenish not outrageously many reference: DFM (priv. comm.)
Slide 40
Slide 40 text
number of parameters patience required emcee a few tenish not outrageously many reference: DFM (priv. comm.)
Slide 41
Slide 41 text
number of parameters patience required emcee a few tenish not outrageously many how things should be reference: DFM (priv. comm.)
Slide 42
Slide 42 text
No content
Slide 43
Slide 43 text
No content
Slide 44
Slide 44 text
No content
Slide 45
Slide 45 text
No content
Slide 46
Slide 46 text
gradients!
Slide 47
Slide 47 text
dp(data|physics) / dphysics
Slide 48
Slide 48 text
automatic differentiation aka “backpropagation”
Slide 49
Slide 49 text
your model is just code
Slide 50
Slide 50 text
apply the chain rule
Slide 51
Slide 51 text
apply the chain rule over and over again . . .
Slide 52
Slide 52 text
sounds silly?
Slide 53
Slide 53 text
it's not! (mostly)
Slide 54
Slide 54 text
No content
Slide 55
Slide 55 text
No content
Slide 56
Slide 56 text
what’s next?
Slide 57
Slide 57 text
No content
Slide 58
Slide 58 text
jax.readthedocs.io
Slide 59
Slide 59 text
my approach to open source 6
Slide 60
Slide 60 text
No content
Slide 61
Slide 61 text
[1] don’t underestimate users [2] build libraries, not (just) scripts [3] teach by example
Slide 62
Slide 62 text
No content
Slide 63
Slide 63 text
No content
Slide 64
Slide 64 text
No content
Slide 65
Slide 65 text
bringing open source practices to research more generally
Slide 66
Slide 66 text
No content
Slide 67
Slide 67 text
No content
Slide 68
Slide 68 text
No content
Slide 69
Slide 69 text
No content
Slide 70
Slide 70 text
what’s next? 7
Slide 71
Slide 71 text
7 [1] inference with stochastic or intractable models [2] what can we do to better support open source in astrophysics
Slide 72
Slide 72 text
7
Slide 73
Slide 73 text
7 credit: Adrian Price-Whelan
Slide 74
Slide 74 text
many fundamental software packages have a shockingly small number of maintainers.
Slide 75
Slide 75 text
a selection of some* CCA-supported software: * my apologies for neglecting your favorites!
Slide 76
Slide 76 text
No content
Slide 77
Slide 77 text
BUILDING THE SOFTWARE INFRASTRUCTURE FOR ASTROPHYSICS @ CCA