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03-exoplanets.jl
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### A Pluto.jl notebook ###
# v0.20.4
using Markdown
using InteractiveUtils
# ╔═╡ 0837dd0b-64b6-47cb-851f-af90948e8b2b
using PlutoUI; TableOfContents()
# ╔═╡ a480e429-6c46-44b0-a7f3-c1ef8a799a82
begin
ENV["DATADEPS_ALWAYS_ACCEPT"] = true
using Octofitter, OctofitterRadialVelocity, Distributions, Pigeons, CairoMakie, PairPlots, HypertextLiteral
end
# ╔═╡ b4e9690c-a8f2-11ef-1087-e50a286f70c4
md"""
# (Re-)Discovering the GJ876 Planets
In this tutorial, we will cover or revisit:
* Loading data from CSVs
* Basic plotting with Makie
* Creating Lomb-Scargle Periodograms
* Fitting an RV model with 1-4 planets
* Using a Bayesian model comparison to determine the number of planets supported by our data
"""
# ╔═╡ 7fe11ccb-0727-4d36-a407-4658bb8493c6
html"""<style>
main {
max-width: 1000px;
}
"""
# ╔═╡ b60ca992-b3fa-4c13-aa9b-9b4a9e6d3330
md"""
# Loading the data
In this section, we will load RV data from an online database, and plot it.
```julia
rv_data = OctofitterRadialVelocity.HARPS_RVBank_rvs("GJ876")
```
"""
# ╔═╡ dfb9700e-f379-4321-a119-5a4921279695
# ╔═╡ 9d9f5b29-e823-4cd3-8ac9-4c9fa8d97b37
md"""
We can access each column by name:
```julia
rv_data.epoch
```
"""
# ╔═╡ 8637f7d9-ac5d-40ad-a133-4ebd018cbd03
# ╔═╡ c99186b0-3991-4891-a081-0e34abf2b067
md"""
## Plotting data
We can plot the data using Makie
```julia
let
fig = Figure()
ax = Axis(fig[1,1], xlabel="epoch [MJD]", ylabel="RV [m/s]")
scatter!(ax, rv_data.epoch, rv_data.rv, rv_data.σ_rv)
fig
end
```
"""
# ╔═╡ 927b96f2-7e7a-4078-8d39-5ccaa3298dfe
# ╔═╡ 90c45bf2-9091-4540-a52f-deb95adb3398
md"""
To analyse this RV data, we start by creating a periodogram:
```julia
using LombScargle
```
"""
# ╔═╡ 3a3fea17-4e47-4192-bde1-a597065115f5
# ╔═╡ 6af1b553-aa38-4445-9b6f-0636f5caa280
md"""
```julia
pgram = lombscargle(
rv_data.epoch,
rv_data.rv,
rv_data.σ_rv # typed: \sigma + TAB)
```
"""
# ╔═╡ f3f64f48-ba56-4890-94ed-b5bc0bbca2a1
# ╔═╡ ad2a322c-80f5-4b62-ae89-12b51118b03a
md"""
```julia
let
fig = Figure()
per, pow = LombScargle.periodpower(pgram)
ax = Axis(
fig[1,1],
xlabel="period",
ylabel="power",
)
lines!(ax, per, pow, color=:black)
xlims!(ax, low=0, high=200)
# vlines!(ax, [30,61,123])
fig
end
```
"""
# ╔═╡ 564f9549-b7b3-48c5-88c7-3536adfed9d8
# ╔═╡ 410771c1-cd2c-4b16-a9a5-bea86ef0fcfd
md"""
## Orbit Model (1 planet)
We now fit a one-planet model to the data.
"""
# ╔═╡ c814f151-9dc4-4c19-a515-5592d8487d8d
md"""
Create a "radial velocity" object to hold our observations
"""
# ╔═╡ bc8ede43-3521-4241-8e36-71deac7dd8a2
# ╔═╡ 0b198ca4-d431-47fc-8c4f-44321f53c33e
md"""
```julia
rv_likelihood = MarginalizedStarAbsoluteRVLikelihood(
rv_data,
instrument_name="HARPS",
jitter=:jit1,)
```
"""
# ╔═╡ 8575a16e-f3fb-4604-9d96-efd871bb654f
# ╔═╡ bb34fff2-4a54-41a5-b351-68620d93eeed
md"""
We start by defining a probabilistic model with priors:
```julia
@planet b RadialVelocityOrbit begin
e = 0.0
ω = 0.0
# We set a prior on period in years
P ~ Uniform((61.1057 - 5)/Octofitter.julian_year, (61.1057 + 5)/Octofitter.julian_year)
a = cbrt(system.M * b.P^2)
τ ~ Uniform(0,2pi)
tp = b.τ*b.P*365.256360417 + 55000 # reference epoch for τ. Choose an MJD date near your data.
# minimum planet mass [jupiter masses]. really m*sin(i)
mass ~ LogUniform(0.001, 10)
end;
```
"""
# ╔═╡ c0a54835-9c4b-4154-8a95-1eba560c5714
# ╔═╡ 2a6f9aee-ecb1-464d-b15b-ab8bb50d265a
md"""
```julia
@system GL876_one_planet begin
# total mass [solar masses]
M ~ truncated(Normal(0.346,0.007),lower=0.1)
jit1 ~ LogUniform(0.1, 100)
end rv_likelihood b;
```
"""
# ╔═╡ 5e20862a-a040-410d-b33f-2c9904de2d9a
# ╔═╡ 5be8f2fb-a1b2-40fb-8033-45f8c3db94d4
md"""
We now compile our model to efficient machine code, with derivatives
```julia
model_one_planet = Octofitter.LogDensityModel(GL876_one_planet)
```
"""
# ╔═╡ 6cd6d70f-e7eb-46c6-9cf5-3c2cb47c9be3
# ╔═╡ d8dffb80-674a-42a5-8743-61d9562a957c
md"""
We can now sample from the model using Pigeons:
```julia
chain_one_planet,pt_one_planet = octofit_pigeons(model_one_planet,n_rounds=10)
```
"""
# ╔═╡ 7a09f779-36a3-4683-931f-dd6f88d88c32
# ╔═╡ 6618be22-76bd-49ac-a291-2bb39fea6b29
md"""
We can examine the results
```julia
display(chain_one_planet)
```
"""
# ╔═╡ a6f32590-d6a3-4317-ac56-80614b68775c
# ╔═╡ 019ab88b-2310-497e-8783-975722b84d13
md"""
```julia
Octofitter.rvpostplot(model_one_planet, chain_one_planet, show_summary=true)
```
"""
# ╔═╡ b2f496f2-792a-458d-b050-52fb0abeb5a4
# ╔═╡ de444cf7-5fc1-4c41-b06b-39ed43af4bb4
md"""
## Excercise: One Planet Model with Eccentricity
Try modifying the code above to allow non-zero eccentricity (say, between 0 and 0.5).
"""
# ╔═╡ e2bffc30-47d9-4be3-b557-8eb495399103
md"""
## Two Planet Model
There is still quite a bit of variance in that data. Maybe a second planet can explain it!
```julia
@planet c RadialVelocityOrbit begin
e ~ Uniform(0, 0.7)
ω ~ Uniform(0, 2pi)
# We set a prior on period in years
P ~ Uniform((30 - 5)/Octofitter.julian_year, (30 + 5)/Octofitter.julian_year)
a = cbrt(system.M * c.P^2) # note the equals sign.
τ ~ Uniform(0,2pi)
tp = c.τ*c.P*365.256360417 + 55000
# minimum planet mass [jupiter masses]. really m*sin(i)
mass ~ LogUniform(0.001, 10)
end;
```
"""
# ╔═╡ 0a83fb2b-5a65-4d90-ae5e-c8b1f19e2af5
# ╔═╡ 74564125-9fba-4970-8e67-860684679158
md"""
```julia
@system GL876_two_planet begin
# total mass [solar masses]
M ~ truncated(Normal(0.346,0.007),lower=0.1)
jit1 ~ LogUniform(0.1, 100)
end rv_likelihood b c;
```
"""
# ╔═╡ fcfb377a-e2b7-4b4a-ab80-fa5dd8cc0c28
# ╔═╡ 5d5b1c8f-c903-41fd-b206-18e0cf015c0b
md"""
```julia
model_two_planet = Octofitter.LogDensityModel(GL876_two_planet)
```
"""
# ╔═╡ 3da46010-85cb-401e-8311-e6f5f3b9a651
# ╔═╡ 8f55419b-b13f-4492-96e0-e342a458927a
md"""
```julia
chain_two_planet,pt_two_planet = octofit_pigeons(model_two_planet,n_rounds=10)
```
"""
# ╔═╡ 691a6191-4a46-4f42-a131-be5546b60a5d
# ╔═╡ bb259bd1-b4be-46bf-a97d-b302481d2596
md"""
```julia
display(chain_one_planet)
```
"""
# ╔═╡ af7e02c7-61ba-4df9-bfbf-6c5ed0be44e0
# ╔═╡ efba9813-2ec5-4a6a-9e88-5012772069cb
md"""
```julia
Octofitter.rvpostplot(model_two_planet, chain_two_planet, show_summary=true)
```
"""
# ╔═╡ dc6c167d-ef7d-46b8-bdb7-f16312cad088
# ╔═╡ ee5e618f-1cd8-44b3-93fe-f260498514b3
md"""
## Model Comparison
Should we prefer the one, or two planet models? We can answer this by performing a model comparison.
We will use the log Bayesian evidence, calculated directly using Pigeons. This is one of the most robust ways to compare Bayesian models, if you are able to calculate it.
```julia
stepping_stone(pt_one_planet)
```
"""
# ╔═╡ b455c009-5a9c-49ba-9a98-89a1df6c3b23
# ╔═╡ d4446e60-2a6b-4a5c-9260-b351b85b7870
md"""
```julia
stepping_stone(pt_two_planet)
```
"""
# ╔═╡ 4f3191a3-a1ec-44d2-a59d-b5d9199f25a1
# ╔═╡ ff7b5daf-eb51-4dfb-b7ab-7d25a1dc112b
md"""
```julia
stepping_stone(pt_two_planet) - stepping_stone(pt_one_planet)
```
"""
# ╔═╡ 5164f13d-8b5f-49f0-9a65-cb1dc19e48a1
# ╔═╡ 8c843f30-cb14-4af4-984c-6498b98e3af0
md"""
This difference is **overwhelming**. The additional complexity of the increased paramters is more than justified by its improved ability to model the data.
```julia
octocorner(model_one_planet,chain_one_planet, chain_two_planet,small=true)
```
"""
# ╔═╡ 2d0eb166-5d71-4ce8-9971-248d270a99ba
md"""
Finally, we can see how adding an extra planet affects the parameters of the first planet
"""
# ╔═╡ f5811f7b-b44c-443d-82f5-84368a2fbbeb
# ╔═╡ d198fa32-e492-4df2-80a2-06064be54d2f
md"""
We find that adding a second planet does significantly impact the derived parameters of planet b.
We can plot the final two planet model parameters in a corner plot, to examine the covariance.
```julia
octocorner(model_two_planet, chain_two_planet, small=true)
```
"""
# ╔═╡ 51c75ea2-b3a4-431a-b51e-f5cbb29b011d
# ╔═╡ 19b9a635-2db6-48d7-bdf1-270ff66bd86c
md"""
## Excercise! Try adding a third planet!
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
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Octofitter = "daf3887e-d01a-44a1-9d7e-98f15c5d69c9"
OctofitterRadialVelocity = "c6a353d9-c9c1-48aa-9c23-64f4679bd07d"
PairPlots = "43a3c2be-4208-490b-832a-a21dcd55d7da"
Pigeons = "0eb8d820-af6a-4919-95ae-11206f830c31"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
[compat]
CairoMakie = "~0.12.18"
Distributions = "~0.25.115"
HypertextLiteral = "~0.9.5"
Octofitter = "~5.2.1"
OctofitterRadialVelocity = "~5.1.0"
PairPlots = "~2.10.0"
Pigeons = "~0.4.8"
PlutoUI = "~0.7.60"
[extras]
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
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