Hipparcos Modelling
This tutorial explains how to model Hipparcos IAD data. The first example reproduces the catalog values of position, parallax, and proper motion. The second uses Hipparcos to constrain the mass of a directly imaged planet.
Reproduce Catalog Values
This is the so-called "Nielsen" test from Nielsen et al (2020) and available in Orbitize!.
We start by using a system with a planet with zero mass to fit the straight line motion.
using Octofitter
using Distributions
using CairoMakie
hip_like = Octofitter.HipparcosIADLikelihood(;
hip_id=21547,
renormalize=true, # default: true
)
@planet b AbsoluteVisual{KepOrbit} begin
mass = 0.
e = 0.
ω = 0.
a = 1.
i = 0
Ω = 0.
tp = 0.
end
@system c_Eri_straight_line begin
M = 1.0 # Host mass not important for this example
rv = 0.0 # system RV not significant for this example
plx ~ Uniform(0,100)
pmra ~ Uniform(-100, 100)
pmdec ~ Uniform(-100, 100)
# It is convenient to put a prior of the catalog value +- 10,000 mas on position
ra_hip_offset_mas ~ Normal(0, 10000)
dec_hip_offset_mas ~ Normal(0, 10000)
dec = $hip_like.hip_sol.dedeg + system.ra_hip_offset_mas/60/60/1000
ra = $hip_like.hip_sol.radeg + system.dec_hip_offset_mas/60/60/1000/cosd(system.dec)
ref_epoch = Octofitter.hipparcos_catalog_epoch_mjd
end hip_like b
model = Octofitter.LogDensityModel(c_Eri_straight_line)LogDensityModel for System c_Eri_straight_line of dimension 5 and 104 epochs with fields .ℓπcallback and .∇ℓπcallback
We can now sample from the model using Hamiltonian Monte Carlo. This should only take about 15 seconds.
using Pigeons
chain,pt = octofit_pigeons(model, n_rounds=6)(chain = MCMC chain (64×21×1 Array{Float64, 3}), pt = PT(checkpoint = false, ...))Plot the posterior values:
octoplot(model,chain,show_astrom=false,show_astrom_time=false)
We now visualize the model fit compared to the Hipparcos catalog values:
using LinearAlgebra, StatsBase
fig = Figure(size=(1080,720))
j = i = 1
for prop in (
(;chain=:ra, hip=:radeg, hip_err=:e_ra),
(;chain=:dec, hip=:dedeg, hip_err=:e_de),
(;chain=:plx, hip=:plx, hip_err=:e_plx),
(;chain=:pmra, hip=:pm_ra, hip_err=:e_pmra),
(;chain=:pmdec, hip=:pm_de, hip_err=:e_pmde)
)
global i, j, ax
ax = Axis(
fig[j,i],
xlabel=string(prop.chain),
)
i+=1
if i > 3
j+=1
i = 1
end
unc = hip_like.hip_sol[prop.hip_err]
if prop.chain == :ra
unc /= 60*60*1000 * cosd(hip_like.hip_sol.dedeg)
end
if prop.chain == :dec
unc /= 60*60*1000
end
if prop.hip == :zero
n = Normal(0, unc)
else
mu = hip_like.hip_sol[prop.hip]
n = Normal(mu, unc)
end
n0,n1=quantile.(n,(1e-4, 1-1e-4))
nxs = range(n0,n1,length=200)
h = fit(Histogram, chain[prop.chain][:], nbins=55)
h = normalize(h, mode=:pdf)
barplot!(ax, (h.edges[1][1:end-1] .+ h.edges[1][2:end])./2, h.weights, gap=0, color=:red, label="posterior")
lines!(ax, nxs, pdf.(n,nxs), label="Hipparcos Catalog", color=:black, linewidth=2)
end
Legend(fig[i-1,j+1],ax,tellwidth=false)
fig
Constrain Planet Mass
We now allow the planet to have a non zero mass and have free orbit. We start by specifying relative astrometry data on the planet, collated by Jason Wang and co. on whereistheplanet.com.
astrom_like1 = PlanetRelAstromLikelihood(
(;epoch=57009.1, sep=454.24, σ_sep=1.88, pa=2.98835, σ_pa=0.00401426),
(;epoch=57052.1, sep=451.81, σ_sep=2.06, pa=2.96723, σ_pa=0.00453786),
(;epoch=57053.1, sep=456.8 , σ_sep=2.57, pa=2.97038, σ_pa=0.00523599),
(;epoch=57054.3, sep=461.5 , σ_sep=23.9 ,pa=2.97404, σ_pa=0.0523599 ,),
(;epoch=57266.4, sep=455.1 , σ_sep=2.23, pa=2.91994, σ_pa=0.00453786),
(;epoch=57332.2, sep=452.88, σ_sep=5.41, pa=2.89934, σ_pa=0.00994838),
(;epoch=57374.2, sep=455.91, σ_sep=6.23, pa=2.89131, σ_pa=0.00994838),
(;epoch=57376.2, sep=455.01, σ_sep=3.03, pa=2.89184, σ_pa=0.00750492),
(;epoch=57415.0, sep=454.46, σ_sep=6.03, pa=2.8962 , σ_pa=0.00890118),
(;epoch=57649.4, sep=454.81, σ_sep=2.02, pa=2.82394, σ_pa=0.00453786),
(;epoch=57652.4, sep=451.43, σ_sep=2.67, pa=2.82272, σ_pa=0.00541052),
(;epoch=57739.1, sep=449.39, σ_sep=2.15, pa=2.79357, σ_pa=0.00471239),
(;epoch=58068.3, sep=447.54, σ_sep=3.02, pa=2.70927, σ_pa=0.00680678),
(;epoch=58442.2, sep=434.22, σ_sep=2.01, pa=2.61171, σ_pa=0.00401426),
)PlanetRelAstromLikelihood Table with 5 columns and 14 rows:
epoch sep σ_sep pa σ_pa
┌────────────────────────────────────────────
1 │ 57009.1 454.24 1.88 2.98835 0.00401426
2 │ 57052.1 451.81 2.06 2.96723 0.00453786
3 │ 57053.1 456.8 2.57 2.97038 0.00523599
4 │ 57054.3 461.5 23.9 2.97404 0.0523599
5 │ 57266.4 455.1 2.23 2.91994 0.00453786
6 │ 57332.2 452.88 5.41 2.89934 0.00994838
7 │ 57374.2 455.91 6.23 2.89131 0.00994838
8 │ 57376.2 455.01 3.03 2.89184 0.00750492
9 │ 57415.0 454.46 6.03 2.8962 0.00890118
10 │ 57649.4 454.81 2.02 2.82394 0.00453786
11 │ 57652.4 451.43 2.67 2.82272 0.00541052
12 │ 57739.1 449.39 2.15 2.79357 0.00471239
13 │ 58068.3 447.54 3.02 2.70927 0.00680678
14 │ 58442.2 434.22 2.01 2.61171 0.00401426We specify our full model:
@planet b AbsoluteVisual{KepOrbit} begin
a ~ truncated(Normal(10,1),lower=0.1)
e ~ Uniform(0,0.99)
ω ~ Uniform(0, 2pi)
i ~ Sine()
Ω ~ Uniform(0, 2pi)
θ ~ Uniform(0, 2pi)
tp = θ_at_epoch_to_tperi(system,b,58442.2)
mass = system.M_sec
end astrom_like1
@system cEri begin
M_pri ~ truncated(Normal(1.75,0.05), lower=0.03) # Msol
M_sec ~ LogUniform(0.1, 100) # MJup
M = system.M_pri + system.M_sec*Octofitter.mjup2msol # Msol
rv = 12.60e3 # m/s
plx ~ Uniform(20,40)
pmra ~ Uniform(-100, 100)
pmdec ~ Uniform(-100, 100)
# It is convenient to put a prior of the catalog value +- 1000 mas on position
ra_hip_offset_mas ~ Normal(0, 1000)
dec_hip_offset_mas ~ Normal(0, 1000)
dec = $hip_like.hip_sol.dedeg + system.ra_hip_offset_mas/60/60/1000
ra = $hip_like.hip_sol.radeg + system.dec_hip_offset_mas/60/60/1000/cos(system.dec)
ref_epoch = Octofitter.hipparcos_catalog_epoch_mjd
end hip_like b
model = Octofitter.LogDensityModel(cEri)LogDensityModel for System cEri of dimension 13 and 118 epochs with fields .ℓπcallback and .∇ℓπcallback
Now we sample:
using Pigeons
chain,pt = octofit_pigeons(model, n_rounds=8, explorer=SliceSampler())
chainChains MCMC chain (256×24×1 Array{Float64, 3}):
Iterations = 1:1:256
Number of chains = 1
Samples per chain = 256
Wall duration = 377.55 seconds
Compute duration = 377.55 seconds
parameters = M_pri, M_sec, plx, pmra, pmdec, ra_hip_offset_mas, dec_hip_offset_mas, M, rv, dec, ra, ref_epoch, b_a, b_e, b_ω, b_i, b_Ω, b_θ, b_tp, b_mass
internals = loglike, logpost, logprior, pigeons_logpotential
Summary Statistics
parameters mean std mcse ess_bulk ess_tail ⋯
Symbol Float64 Float64 Float64 Float64 Float64 ⋯
M_pri 1.7515 0.0509 0.0034 224.6770 187.7860 ⋯
M_sec 9.6336 12.2401 3.7490 8.0793 22.2639 ⋯
plx 33.9395 0.3376 0.0329 111.7684 116.3528 ⋯
pmra 44.4192 0.4373 0.0590 50.4479 155.6490 ⋯
pmdec -64.3261 0.2765 0.0218 159.0450 132.9624 ⋯
ra_hip_offset_mas -2.2463 2.8974 0.9343 17.4322 22.2639 ⋯
dec_hip_offset_mas -0.3274 0.6096 0.0491 172.1545 123.0522 ⋯
M 1.7607 0.0510 0.0035 209.3220 225.4685 ⋯
rv 12600.0000 0.0000 NaN NaN NaN ⋯
dec -2.4734 0.0000 0.0000 17.4322 22.2639 ⋯
ra 69.4004 0.0000 0.0000 172.1545 123.0522 ⋯
ref_epoch 48348.5625 0.0000 NaN NaN NaN ⋯
b_a 10.2443 0.5444 0.1756 10.6619 85.3130 ⋯
b_e 0.5975 0.0560 0.0198 10.8520 98.1379 ⋯
b_ω 2.5531 1.5358 0.2058 192.4059 88.4120 ⋯
b_i 2.4749 0.0696 0.0247 6.2835 32.1471 ⋯
b_Ω 2.3924 1.6482 0.1813 211.4972 29.7334 ⋯
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
2 columns and 3 rows omitted
Quantiles
parameters 2.5% 25.0% 50.0% 75.0% ⋯
Symbol Float64 Float64 Float64 Float64 ⋯
M_pri 1.6631 1.7143 1.7509 1.7873 ⋯
M_sec 0.1187 0.5229 4.6815 16.3014 ⋯
plx 33.3014 33.7282 33.9428 34.1771 ⋯
pmra 43.6068 44.1447 44.4122 44.6593 ⋯
pmdec -64.8232 -64.5007 -64.3496 -64.1563 - ⋯
ra_hip_offset_mas -7.3031 -3.9142 -0.9929 -0.1693 ⋯
dec_hip_offset_mas -1.7490 -0.5585 -0.1780 0.0434 ⋯
M 1.6715 1.7248 1.7605 1.7962 ⋯
rv 12600.0000 12600.0000 12600.0000 12600.0000 126 ⋯
dec -2.4734 -2.4734 -2.4734 -2.4734 ⋯
ra 69.4004 69.4004 69.4004 69.4004 ⋯
ref_epoch 48348.5625 48348.5625 48348.5625 48348.5625 483 ⋯
b_a 9.4228 9.8504 10.1217 10.5998 ⋯
b_e 0.4674 0.5646 0.6153 0.6372 ⋯
b_ω 0.9617 1.6069 1.7821 4.8210 ⋯
b_i 2.3053 2.4348 2.4910 2.5287 ⋯
b_Ω 0.2361 1.4130 1.7655 4.7276 ⋯
⋮ ⋮ ⋮ ⋮ ⋮ ⋱
1 column and 3 rows omitted
octoplot(model, chain, show_mass=true)
We see that we constrained both the orbit and the parallax. The mass is not strongly constrained by Hipparcos.