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Open software for Astronomical Data Analysis
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Dan Foreman-Mackey
February 28, 2023
Science
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Open software for Astronomical Data Analysis
@ NASA Goddard
Dan Foreman-Mackey
February 28, 2023
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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
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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.)
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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
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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