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 Astronomical Data Analysis
Search
Dan Foreman-Mackey
February 28, 2023
Science
0
110
Open software for Astronomical Data Analysis
@ NASA Goddard
Dan Foreman-Mackey
February 28, 2023
Tweet
Share
More Decks by Dan Foreman-Mackey
See All by Dan Foreman-Mackey
Open Software for Astrophysics, AAS241
dfm
2
440
My research talk for CCA promotion
dfm
1
740
Astronomical software
dfm
1
690
emcee-odi
dfm
1
590
Exoplanet population inference: a tutorial
dfm
3
420
Data-driven discovery in the astronomical time domain
dfm
6
680
TensorFlow for astronomers
dfm
6
710
How to find a transiting exoplanets
dfm
1
440
Long-period transiting exoplanets
dfm
1
290
Other Decks in Science
See All in Science
論文紹介: PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models (WSDM 2024)
ynakano
0
110
様々な侵入者タイプに対応した適切な警備計画の策定 / Patrol route design considering various types of intrudes
konakalab
0
150
Pokemon Roughs
shoryuuken
0
550
DEIM2024 チュートリアル ~AWSで生成AIのRAGを使ったチャットボットを作ってみよう~
yamahiro
3
1.2k
第4回ナレッジグラフ勉強会 Knowledge Graph Embedding
maruru0090
0
250
多次元展開法を用いた 多値バイクラスタリング モデルの提案
kosugitti
0
170
構造設計のための3D生成AI-最新の取り組みと今後の展開-
kojinishiguchi
0
420
Lyme Disease
uni_of_nomi
0
120
Презентация программы магистратуры СПбГУ "Искусственный интеллект и наука о данных"
dscs
0
340
Non-Gaussian methods for causal discovery
sshimizu2006
0
260
240510 COGNAC LabChat
kazh
0
110
20分で分かる Human-in-the-Loop 機械学習におけるアノテーションとヒューマンコンピューターインタラクションの真髄
hurutoriya
4
2k
Featured
See All Featured
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
0
120
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
230
17k
YesSQL, Process and Tooling at Scale
rocio
167
14k
A Tale of Four Properties
chriscoyier
155
22k
Web development in the modern age
philhawksworth
205
10k
Gamification - CAS2011
davidbonilla
79
5k
Practical Orchestrator
shlominoach
185
10k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
36
2.1k
Reflections from 52 weeks, 52 projects
jeffersonlam
346
20k
The Cost Of JavaScript in 2023
addyosmani
42
5.7k
Facilitating Awesome Meetings
lara
49
5.9k
Art, The Web, and Tiny UX
lynnandtonic
294
20k
Transcript
OPEN SOFTWARE FOR ASTRONOMICAL DATA ANALYSIS by Dan Foreman-Mackey
None
open software for astrophysics 0
credit: Adrian Price-Whelan / / data: SAO/NASA ADS
7
many fundamental software packages have a shockingly small number of
maintainers.
7 credit: Adrian Price-Whelan
* astronomical software can be very high impact * we
should think about career trajectories & mechanisms for supporting this work
None
case study: gaussian processes 1
°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
None
7 [1] model building [2] computational cost
reference: Luger, DFM, Hedges (2021)
[2] computational cost
7 [1] bigger/better computers [2] exploit matrix structure [3] approximate
linear algebra [4] etc.
1 3 2
None
None
1 3 2
°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: Gordon, Agol, DFM (2020) / tinygp.readthedocs.io
* a Gaussian Process is a drop - in replacement
for chi squared * model building & computational cost are (solvable!) challenges * you should check out tinygp!
case study: probabilistic inference 2
have: physics = > data
want: data = > physics
7 [1] physical models [2] legacy code
None
number of parameters patience required a few tenish not outrageously
many reference: DFM (priv. comm.)
number of parameters patience required emcee a few tenish not
outrageously many reference: DFM (priv. comm.)
number of parameters patience required emcee a few tenish not
outrageously many how things should be reference: DFM (priv. comm.)
None
None
None
None
3.0 3.5 4.0 4.5 5.0 Wavelength [micron] 2.05 2.10 2.15
2.20 2.25 2.30 Transit Depth [%] Alderson et al. 2023 Joint Fit (N = 50) reference: Soichiro Hattori, Ruth Angus, DFM, . . . (in prep) WASP-39b / NIRSpec
reference: Soichiro Hattori, Ruth Angus, DFM, . . . (in
prep) showing 23 of the 404 parameters (8 per channel + 4 shared)
how?
d(physics = > data) / dphysics
automatic differentiation aka “backpropagation”
None
7 [1] physical models [2] legacy code
7 [1] domain - specif i c libraries [2] emulation
None
* gradient - based inference using autodiff can improve eff
i ciency * there are practical challenges with these methods in astro * of interest: domain - specif i c libraries & emulation
aside: JAX 3
None
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]
None
open research practices 4
None
None
None
None
None
None
None
open software is foundational to astrophysics research there are opportunities
at the interface of astro & applied f i elds there are ways you can participate & benef i t right away
7 I want to chat about… [1] your data analysis
problems [2] building astronomical software [3] writing documentation & tutorials
get in touch! dfm.io github.com/dfm