Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Open Software for Astrophysics, AAS241
Search
Dan Foreman-Mackey
January 12, 2023
Science
2
540
Open Software for Astrophysics, AAS241
Slides for my plenary talk at the 241st American Astronomical Society meeting.
Dan Foreman-Mackey
January 12, 2023
Tweet
Share
More Decks by Dan Foreman-Mackey
See All by Dan Foreman-Mackey
Open software for Astronomical Data Analysis
dfm
0
150
My research talk for CCA promotion
dfm
1
780
Astronomical software
dfm
1
740
emcee-odi
dfm
1
670
Exoplanet population inference: a tutorial
dfm
3
460
Data-driven discovery in the astronomical time domain
dfm
6
720
TensorFlow for astronomers
dfm
6
820
How to find a transiting exoplanets
dfm
1
470
Long-period transiting exoplanets
dfm
1
320
Other Decks in Science
See All in Science
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1k
機械学習 - pandas入門
trycycle
PRO
0
310
MCMCのR-hatは分散分析である
moricup
0
440
機械学習 - ニューラルネットワーク入門
trycycle
PRO
0
850
Accelerated Computing for Climate forecast
inureyes
PRO
0
120
CV_3_Keypoints
hachama
0
200
白金鉱業Meetup Vol.16_数理最適化案件のはじめかた・すすめかた
brainpadpr
4
2k
データから見る勝敗の法則 / The principle of victory discovered by science (open lecture in NSSU)
konakalab
1
150
システム数理と応用分野の未来を切り拓くロードマップ・エンターテインメント(スポーツ)への応用 / Applied mathematics for sports entertainment
konakalab
1
390
NASの容量不足のお悩み解決!災害対策も兼ねた「Wasabi Cloud NAS」はここがスゴイ
climbteam
0
130
KH Coderチュートリアル(スライド版)
koichih
1
46k
知能とはなにかーヒトとAIのあいだー
tagtag
0
120
Featured
See All Featured
Speed Design
sergeychernyshev
32
1.1k
Statistics for Hackers
jakevdp
799
220k
How to train your dragon (web standard)
notwaldorf
96
6.2k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
We Have a Design System, Now What?
morganepeng
53
7.8k
The Power of CSS Pseudo Elements
geoffreycrofte
77
6k
Building an army of robots
kneath
306
46k
The Art of Programming - Codeland 2020
erikaheidi
56
13k
Measuring & Analyzing Core Web Vitals
bluesmoon
9
580
The MySQL Ecosystem @ GitHub 2015
samlambert
251
13k
StorybookのUI Testing Handbookを読んだ
zakiyama
31
6.1k
The Cost Of JavaScript in 2023
addyosmani
53
8.9k
Transcript
OPEN SOFTWARE FOR ASTROPHYSICS Dan Foreman-Mackey
None
case study: Gaussian Processes
AAS 225 / 2015 / Seattle AAS 231 / 2018
/ National Harbor
°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)
°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)
reference: Aigrain & DFM (2022)
reference: Aigrain & DFM (2022)
reference: Aigrain & DFM (2022) ignoring correlated noise accounting for
correlated noise
reference: Aigrain & DFM (2022)
a Gaussian Process is a drop - in replacement for
chi - squared
more details: Aigrain & Foreman-Mackey (2023) arXiv:2209.08940
7 [1] model building [2] computational cost
k(tn , tm ; θ) “kernel” or “covariance”
None
import george import celerite import tinygp
my f i rst try: george 1
import numpy as np def log_likelihood(params, x, diag, r) :
K = build_kernel_matrix(params, x, diag) gof = r.T @ np.linalg.solve(K, r) norm = np.linalg.slogdet(K)[1] return -0.5 * (gof + norm)
import numpy as np def log_likelihood(params, x, diag, r) :
K = build_kernel_matrix(params, x, diag) gof = r.T @ np.linalg.solve(K, r) norm = np.linalg.slogdet(K)[1] return -0.5 * (gof + norm)
k(tn , tm ; θ) “kernel” or “covariance”
from george.kernels import * k1 = 1.5 * ExpSquaredKernel(2.3) k2
= 5.5 * Matern32Kernel(0.1) kernel = 0.5 * (k1 + k2)
from george import GP gp = GP(kernel) gp.compute(x, yerr) gp.log_likelihood(y)
from george import GP gp = GP(kernel) gp.compute(x, yerr) gp.log_likelihood(y)
gp.f i t(y) ???
the astronomical Python ecosystem + MANY MORE!
* API design (library vs scripts) * don’t reinvent the
wheel
None
faster: celerite* 2 * yes, that truly is how you
pronounce it…
import numpy as np def log_likelihood(params, x, diag, r) :
K = build_kernel_matrix(params, x, diag) gof = r.T @ np.linalg.solve(K, r) norm = np.linalg.slogdet(K)[1] return -0.5 * (gof + norm)
import numpy as np def log_likelihood(params, x, diag, r) :
K = build_kernel_matrix(params, x, diag) gof = r.T @ np.linalg.solve(K, r) norm = np.linalg.slogdet(K)[1] return -0.5 * (gof + norm)
None
“semi/quasi - separable” matrices
102 103 104 105 number of data points [N] 10
5 10 4 10 3 10 2 10 1 100 computational cost [seconds] 1 2 4 8 16 32 64 128 256 direct O(N) 100 101 number o reference: DFM, Agol, Ambikasaran, Angus (2017)
102 103 104 105 number of data points [N] 10
4 10 3 10 2 10 1 100 computational cost [seconds] 1 2 4 8 16 32 64 128 256 O(N) 100 101 number o reference: DFM, Agol, Ambikasaran, Angus (2017)
None
+
+ + vs
* interdisciplinary collaboration * importance of implementation
7 [1] 1 (ish) dimensional input [2] specif i c
type of kernel restrictions:
modern infrastructure: tinygp 3
what’s missing from the astronomical Python ecosystem?
7 [1] differentiable programming [2] hardware acceleration
the broader numerical computing Python ecosystem + SO MANY MORE!
jax.readthedocs.io
import numpy as np def linear_least_squares(x, y) : A =
np.vander(x, 2) return np.linalg.lstsq(A, y)[0]
import jax.numpy as jnp def linear_least_squares(x, y) : A =
jnp.vander(x, 2) return jnp.linalg.lstsq(A, y)[0]
import jax.numpy as jnp @jax.jit def linear_least_squares(x, y) : A
= jnp.vander(x, 2) return jnp.linalg.lstsq(A, y)[0]
None
tinygp.readthedocs.io
the broader numerical computing Python ecosystem + SO MANY MORE!
* I <3 JAX * don’t reinvent the wheel
the why & how of open software in astrophysics
credit: Adrian Price-Whelan / / data: SAO/NASA ADS
None
None
None
None
takeaways
open software is foundational to astrophysics research let’s consider &
discuss interface design and user interaction leverage existing infrastructure & learn when to start fresh
get in touch! dfm.io github.com/dfm
None