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
500
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
120
My research talk for CCA promotion
dfm
1
750
Astronomical software
dfm
1
700
emcee-odi
dfm
1
630
Exoplanet population inference: a tutorial
dfm
3
430
Data-driven discovery in the astronomical time domain
dfm
6
690
TensorFlow for astronomers
dfm
6
760
How to find a transiting exoplanets
dfm
1
450
Long-period transiting exoplanets
dfm
1
300
Other Decks in Science
See All in Science
Visual Analytics for R&D Intelligence @Funding the Commons & DeSci Tokyo 2024
hayataka88
0
140
Factorized Diffusion: Perceptual Illusions by Noise Decomposition
tomoaki0705
0
340
観察研究における因果推論
nearme_tech
PRO
1
160
Celebrate UTIG: Staff and Student Awards 2024
utig
0
590
SciPyDataJapan 2025
schwalbe10
0
140
Trend Classification of InSAR Displacement Time Series Using SAE–CNN
satai
3
150
Introduction to Image Processing: 2.Frequ
hachama
0
480
07_浮世満理子_アイディア高等学院学院長_一般社団法人全国心理業連合会代表理事_紹介資料.pdf
sip3ristex
0
140
証明支援系LEANに入門しよう
unaoya
0
650
butterfly_effect/butterfly_effect_in-house
florets1
1
150
Iniciativas independentes de divulgação científica: o caso do Movimento #CiteMulheresNegras
taisso
0
980
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
3
1.4k
Featured
See All Featured
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
4
370
Agile that works and the tools we love
rasmusluckow
328
21k
Building an army of robots
kneath
303
45k
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
27
1.9k
The Straight Up "How To Draw Better" Workshop
denniskardys
232
140k
YesSQL, Process and Tooling at Scale
rocio
172
14k
Designing for humans not robots
tammielis
250
25k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
47
5.2k
Reflections from 52 weeks, 52 projects
jeffersonlam
348
20k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
12
990
Writing Fast Ruby
sferik
628
61k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
28
9.3k
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