In this tutorial, we will reproduce the fits to the transiting planet in the Pi Mensae system discovered by Huang et al. (2018). The data processing and model are similar to the Putting it all together case study, but with a few extra bits like aperture selection and de-trending.
To start, we need to download the target pixel file:
[3]:
import numpy as np
import lightkurve as lk
import matplotlib.pyplot as plt
from astropy.io import fits
lc_file = lk.search_lightcurve(
"TIC 261136679", sector=1, author="SPOC"
).download(quality_bitmask="hardest", flux_column="pdcsap_flux")
lc = lc_file.remove_nans().normalize().remove_outliers()
time = lc.time.value
flux = lc.flux
m = lc.quality == 0
with fits.open(lc_file.filename) as hdu:
hdr = hdu[1].header
texp = hdr["FRAMETIM"] * hdr["NUM_FRM"]
texp /= 60.0 * 60.0 * 24.0
ref_time = 0.5 * (np.min(time) + np.max(time))
x = np.ascontiguousarray(time[m] - ref_time, dtype=np.float64)
y = np.ascontiguousarray(1e3 * (flux[m] - 1.0), dtype=np.float64)
plt.plot(x, y, ".k")
plt.xlabel("time [days]")
plt.ylabel("relative flux [ppt]")
_ = plt.xlim(x.min(), x.max())
Now, let’s use the box least squares periodogram from AstroPy (Note: you’ll need AstroPy v3.1 or more recent to use this feature) to estimate the period, phase, and depth of the transit.
[4]:
from astropy.timeseries import BoxLeastSquares
period_grid = np.exp(np.linspace(np.log(1), np.log(15), 50000))
bls = BoxLeastSquares(x, y)
bls_power = bls.power(period_grid, 0.1, oversample=20)
# Save the highest peak as the planet candidate
index = np.argmax(bls_power.power)
bls_period = bls_power.period[index]
bls_t0 = bls_power.transit_time[index]
bls_depth = bls_power.depth[index]
transit_mask = bls.transit_mask(x, bls_period, 0.2, bls_t0)
fig, axes = plt.subplots(2, 1, figsize=(10, 10))
# Plot the periodogram
ax = axes[0]
ax.axvline(np.log10(bls_period), color="C1", lw=5, alpha=0.8)
ax.plot(np.log10(bls_power.period), bls_power.power, "k")
ax.annotate(
"period = {0:.4f} d".format(bls_period),
(0, 1),
xycoords="axes fraction",
xytext=(5, -5),
textcoords="offset points",
va="top",
ha="left",
fontsize=12,
)
ax.set_ylabel("bls power")
ax.set_yticks([])
ax.set_xlim(np.log10(period_grid.min()), np.log10(period_grid.max()))
ax.set_xlabel("log10(period)")
# Plot the folded transit
ax = axes[1]
x_fold = (x - bls_t0 + 0.5 * bls_period) % bls_period - 0.5 * bls_period
m = np.abs(x_fold) < 0.4
ax.plot(x_fold[m], y[m], ".k")
# Overplot the phase binned light curve
bins = np.linspace(-0.41, 0.41, 32)
denom, _ = np.histogram(x_fold, bins)
num, _ = np.histogram(x_fold, bins, weights=y)
denom[num == 0] = 1.0
ax.plot(0.5 * (bins[1:] + bins[:-1]), num / denom, color="C1")
ax.set_xlim(-0.3, 0.3)
ax.set_ylabel("de-trended flux [ppt]")
_ = ax.set_xlabel("time since transit")
The transit model, initialization, and sampling are all nearly the same as the one in Putting it all together.
[5]:
import exoplanet as xo
import pymc3 as pm
import aesara_theano_fallback.tensor as tt
import pymc3_ext as pmx
from celerite2.theano import terms, GaussianProcess
def build_model(mask=None, start=None):
if mask is None:
mask = np.ones(len(x), dtype=bool)
with pm.Model() as model:
# Parameters for the stellar properties
mean = pm.Normal("mean", mu=0.0, sd=10.0)
u_star = xo.QuadLimbDark("u_star")
# Stellar parameters from Huang et al (2018)
M_star_huang = 1.094, 0.039
R_star_huang = 1.10, 0.023
BoundedNormal = pm.Bound(pm.Normal, lower=0, upper=3)
m_star = BoundedNormal(
"m_star", mu=M_star_huang[0], sd=M_star_huang[1]
)
r_star = BoundedNormal(
"r_star", mu=R_star_huang[0], sd=R_star_huang[1]
)
# Orbital parameters for the planets
t0 = pm.Normal("t0", mu=bls_t0, sd=1)
log_period = pm.Normal("log_period", mu=np.log(bls_period), sd=1)
log_r_pl = pm.Normal(
"log_r_pl",
sd=1.0,
mu=0.5 * np.log(1e-3 * np.array(bls_depth))
+ np.log(R_star_huang[0]),
)
period = pm.Deterministic("period", tt.exp(log_period))
r_pl = pm.Deterministic("r_pl", tt.exp(log_r_pl))
ror = pm.Deterministic("ror", r_pl / r_star)
b = xo.distributions.ImpactParameter("b", ror=ror)
ecs = pmx.UnitDisk("ecs", testval=np.array([0.01, 0.0]))
ecc = pm.Deterministic("ecc", tt.sum(ecs ** 2))
omega = pm.Deterministic("omega", tt.arctan2(ecs[1], ecs[0]))
xo.eccentricity.kipping13("ecc_prior", fixed=True, observed=ecc)
# Transit jitter & GP parameters
log_sigma_lc = pm.Normal(
"log_sigma_lc", mu=np.log(np.std(y[mask])), sd=10
)
log_rho_gp = pm.Normal("log_rho_gp", mu=0, sd=10)
log_sigma_gp = pm.Normal(
"log_sigma_gp", mu=np.log(np.std(y[mask])), sd=10
)
# Orbit model
orbit = xo.orbits.KeplerianOrbit(
r_star=r_star,
m_star=m_star,
period=period,
t0=t0,
b=b,
ecc=ecc,
omega=omega,
)
# Compute the model light curve
light_curves = pm.Deterministic(
"light_curves",
xo.LimbDarkLightCurve(u_star).get_light_curve(
orbit=orbit, r=r_pl, t=x[mask], texp=texp
)
* 1e3,
)
light_curve = tt.sum(light_curves, axis=-1) + mean
resid = y[mask] - light_curve
# GP model for the light curve
kernel = terms.SHOTerm(
sigma=tt.exp(log_sigma_gp),
rho=tt.exp(log_rho_gp),
Q=1 / np.sqrt(2),
)
gp = GaussianProcess(kernel, t=x[mask], yerr=tt.exp(log_sigma_lc))
gp.marginal("gp", observed=resid)
pm.Deterministic("gp_pred", gp.predict(resid))
# Fit for the maximum a posteriori parameters, I've found that I can get
# a better solution by trying different combinations of parameters in turn
if start is None:
start = model.test_point
map_soln = pmx.optimize(
start=start, vars=[log_sigma_lc, log_sigma_gp, log_rho_gp]
)
map_soln = pmx.optimize(start=map_soln, vars=[log_r_pl])
map_soln = pmx.optimize(start=map_soln, vars=[b])
map_soln = pmx.optimize(start=map_soln, vars=[log_period, t0])
map_soln = pmx.optimize(start=map_soln, vars=[u_star])
map_soln = pmx.optimize(start=map_soln, vars=[log_r_pl])
map_soln = pmx.optimize(start=map_soln, vars=[b])
map_soln = pmx.optimize(start=map_soln, vars=[ecs])
map_soln = pmx.optimize(start=map_soln, vars=[mean])
map_soln = pmx.optimize(
start=map_soln, vars=[log_sigma_lc, log_sigma_gp, log_rho_gp]
)
map_soln = pmx.optimize(start=map_soln)
return model, map_soln
model0, map_soln0 = build_model()
optimizing logp for variables: [log_rho_gp, log_sigma_gp, log_sigma_lc]
message: Optimization terminated successfully.
logp: 11803.534996510281 -> 12022.319792604483
optimizing logp for variables: [log_r_pl]
message: Optimization terminated successfully.
logp: 12022.319792604483 -> 12031.806646036945
optimizing logp for variables: [b, log_r_pl, r_star]
message: Optimization terminated successfully.
logp: 12031.806646036945 -> 12312.641607547512
optimizing logp for variables: [t0, log_period]
message: Optimization terminated successfully.
logp: 12312.641607547515 -> 12320.062773383888
optimizing logp for variables: [u_star]
message: Optimization terminated successfully.
logp: 12320.06277338388 -> 12323.976548867786
optimizing logp for variables: [log_r_pl]
message: Optimization terminated successfully.
logp: 12323.97654886779 -> 12324.153307400622
optimizing logp for variables: [b, log_r_pl, r_star]
message: Optimization terminated successfully.
logp: 12324.153307400622 -> 12324.272909174417
optimizing logp for variables: [ecs]
message: Optimization terminated successfully.
logp: 12324.272909174417 -> 12324.828200366446
optimizing logp for variables: [mean]
message: Optimization terminated successfully.
logp: 12324.828200366446 -> 12325.183875986868
optimizing logp for variables: [log_rho_gp, log_sigma_gp, log_sigma_lc]
message: Optimization terminated successfully.
logp: 12325.183875986868 -> 12349.80589909514
optimizing logp for variables: [log_sigma_gp, log_rho_gp, log_sigma_lc, ecs, b, log_r_pl, log_period, t0, r_star, m_star, u_star, mean]
message: Desired error not necessarily achieved due to precision loss.
logp: 12349.805899095156 -> 12410.253939054232
Here’s how we plot the initial light curve model:
[6]:
def plot_light_curve(soln, mask=None):
if mask is None:
mask = np.ones(len(x), dtype=bool)
fig, axes = plt.subplots(3, 1, figsize=(10, 7), sharex=True)
ax = axes[0]
ax.plot(x[mask], y[mask], "k", label="data")
gp_mod = soln["gp_pred"] + soln["mean"]
ax.plot(x[mask], gp_mod, color="C2", label="gp model")
ax.legend(fontsize=10)
ax.set_ylabel("relative flux [ppt]")
ax = axes[1]
ax.plot(x[mask], y[mask] - gp_mod, "k", label="de-trended data")
for i, l in enumerate("b"):
mod = soln["light_curves"][:, i]
ax.plot(x[mask], mod, label="planet {0}".format(l))
ax.legend(fontsize=10, loc=3)
ax.set_ylabel("de-trended flux [ppt]")
ax = axes[2]
mod = gp_mod + np.sum(soln["light_curves"], axis=-1)
ax.plot(x[mask], y[mask] - mod, "k")
ax.axhline(0, color="#aaaaaa", lw=1)
ax.set_ylabel("residuals [ppt]")
ax.set_xlim(x[mask].min(), x[mask].max())
ax.set_xlabel("time [days]")
return fig
_ = plot_light_curve(map_soln0)
As in Putting it all together, we can do some sigma clipping to remove significant outliers.
[7]:
mod = (
map_soln0["gp_pred"]
+ map_soln0["mean"]
+ np.sum(map_soln0["light_curves"], axis=-1)
)
resid = y - mod
rms = np.sqrt(np.median(resid ** 2))
mask = np.abs(resid) < 5 * rms
plt.figure(figsize=(10, 5))
plt.plot(x, resid, "k", label="data")
plt.plot(x[~mask], resid[~mask], "xr", label="outliers")
plt.axhline(0, color="#aaaaaa", lw=1)
plt.ylabel("residuals [ppt]")
plt.xlabel("time [days]")
plt.legend(fontsize=12, loc=3)
_ = plt.xlim(x.min(), x.max())
And then we re-build the model using the data without outliers.
[8]:
model, map_soln = build_model(mask, map_soln0)
_ = plot_light_curve(map_soln, mask)
optimizing logp for variables: [log_rho_gp, log_sigma_gp, log_sigma_lc]
message: Optimization terminated successfully.
logp: 12909.297590015649 -> 12927.002817354894
optimizing logp for variables: [log_r_pl]
message: Optimization terminated successfully.
logp: 12927.002817354894 -> 12927.026010601196
optimizing logp for variables: [b, log_r_pl, r_star]
message: Optimization terminated successfully.
logp: 12927.026010601196 -> 12927.027380355064
optimizing logp for variables: [t0, log_period]
message: Desired error not necessarily achieved due to precision loss.
logp: 12927.027380355064 -> 12927.02938955412
optimizing logp for variables: [u_star]
message: Optimization terminated successfully.
logp: 12927.029389554118 -> 12927.02995706821
optimizing logp for variables: [log_r_pl]
message: Optimization terminated successfully.
logp: 12927.02995706821 -> 12927.02995961484
optimizing logp for variables: [b, log_r_pl, r_star]
message: Optimization terminated successfully.
logp: 12927.02995961484 -> 12927.030127938131
optimizing logp for variables: [ecs]
message: Optimization terminated successfully.
logp: 12927.030127938135 -> 12927.03012832856
optimizing logp for variables: [mean]
message: Optimization terminated successfully.
logp: 12927.030128328564 -> 12927.032487170087
optimizing logp for variables: [log_rho_gp, log_sigma_gp, log_sigma_lc]
message: Optimization terminated successfully.
logp: 12927.032487170087 -> 12927.032654515917
optimizing logp for variables: [log_sigma_gp, log_rho_gp, log_sigma_lc, ecs, b, log_r_pl, log_period, t0, r_star, m_star, u_star, mean]
message: Desired error not necessarily achieved due to precision loss.
logp: 12927.032654515917 -> 12927.032851659491
Now that we have the model, we can sample:
[9]:
np.random.seed(261136679)
with model:
trace = pmx.sample(
tune=2500,
draws=2000,
start=map_soln,
cores=2,
chains=2,
initial_accept=0.8,
target_accept=0.95,
return_inferencedata=True,
)
Multiprocess sampling (2 chains in 2 jobs)
NUTS: [log_sigma_gp, log_rho_gp, log_sigma_lc, ecs, b, log_r_pl, log_period, t0, r_star, m_star, u_star, mean]
Sampling 2 chains for 2_500 tune and 2_000 draw iterations (5_000 + 4_000 draws total) took 3427 seconds.
The number of effective samples is smaller than 25% for some parameters.
[10]:
import arviz as az
[11]:
az.summary(
trace,
var_names=[
"omega",
"ecc",
"r_pl",
"b",
"t0",
"period",
"r_star",
"m_star",
"u_star",
"mean",
],
)
[11]:
mean | sd | hdi_3% | hdi_97% | mcse_mean | mcse_sd | ess_bulk | ess_tail | r_hat | |
---|---|---|---|---|---|---|---|---|---|
omega | 0.712 | 1.692 | -2.668 | 3.135 | 0.052 | 0.037 | 1246.0 | 2703.0 | 1.0 |
ecc | 0.224 | 0.145 | 0.001 | 0.499 | 0.004 | 0.004 | 1479.0 | 1037.0 | 1.0 |
r_pl | 0.018 | 0.001 | 0.017 | 0.019 | 0.000 | 0.000 | 1202.0 | 727.0 | 1.0 |
b | 0.387 | 0.220 | 0.006 | 0.731 | 0.008 | 0.007 | 652.0 | 496.0 | 1.0 |
t0 | -13.733 | 0.001 | -13.735 | -13.731 | 0.000 | 0.000 | 1771.0 | 1151.0 | 1.0 |
period | 6.268 | 0.000 | 6.267 | 6.268 | 0.000 | 0.000 | 2898.0 | 2704.0 | 1.0 |
r_star | 1.098 | 0.023 | 1.053 | 1.139 | 0.000 | 0.000 | 3850.0 | 2539.0 | 1.0 |
m_star | 1.096 | 0.038 | 1.028 | 1.167 | 0.001 | 0.000 | 4708.0 | 2999.0 | 1.0 |
u_star[0] | 0.160 | 0.147 | 0.000 | 0.435 | 0.003 | 0.003 | 2253.0 | 1959.0 | 1.0 |
u_star[1] | 0.430 | 0.257 | -0.056 | 0.901 | 0.007 | 0.005 | 1478.0 | 1813.0 | 1.0 |
mean | 0.003 | 0.006 | -0.007 | 0.014 | 0.000 | 0.000 | 3863.0 | 2420.0 | 1.0 |
After sampling, we can make the usual plots. First, let’s look at the folded light curve plot:
[12]:
flat_samps = trace.posterior.stack(sample=("chain", "draw"))
# Compute the GP prediction
gp_mod = np.median(
flat_samps["gp_pred"].values + flat_samps["mean"].values[None, :], axis=-1
)
# Get the posterior median orbital parameters
p = np.median(flat_samps["period"])
t0 = np.median(flat_samps["t0"])
# Plot the folded data
x_fold = (x[mask] - t0 + 0.5 * p) % p - 0.5 * p
plt.plot(x_fold, y[mask] - gp_mod, ".k", label="data", zorder=-1000)
# Overplot the phase binned light curve
bins = np.linspace(-0.41, 0.41, 50)
denom, _ = np.histogram(x_fold, bins)
num, _ = np.histogram(x_fold, bins, weights=y[mask])
denom[num == 0] = 1.0
plt.plot(
0.5 * (bins[1:] + bins[:-1]), num / denom, "o", color="C2", label="binned"
)
# Plot the folded model
inds = np.argsort(x_fold)
inds = inds[np.abs(x_fold)[inds] < 0.3]
pred = np.percentile(
flat_samps["light_curves"][inds, 0], [16, 50, 84], axis=-1
)
plt.plot(x_fold[inds], pred[1], color="C1", label="model")
art = plt.fill_between(
x_fold[inds], pred[0], pred[2], color="C1", alpha=0.5, zorder=1000
)
art.set_edgecolor("none")
# Annotate the plot with the planet's period
txt = "period = {0:.5f} +/- {1:.5f} d".format(
np.mean(flat_samps["period"].values), np.std(flat_samps["period"].values)
)
plt.annotate(
txt,
(0, 0),
xycoords="axes fraction",
xytext=(5, 5),
textcoords="offset points",
ha="left",
va="bottom",
fontsize=12,
)
plt.legend(fontsize=10, loc=4)
plt.xlim(-0.5 * p, 0.5 * p)
plt.xlabel("time since transit [days]")
plt.ylabel("de-trended flux")
_ = plt.xlim(-0.15, 0.15)
And a corner plot of some of the key parameters:
[13]:
import corner
import astropy.units as u
trace.posterior["r_earth"] = (
trace.posterior["r_pl"].coords,
(trace.posterior["r_pl"].values * u.R_sun).to(u.R_earth).value,
)
_ = corner.corner(
trace,
var_names=["period", "r_earth", "b", "ecc"],
labels=[
"period [days]",
"radius [Earth radii]",
"impact param",
"eccentricity",
],
)
These all seem consistent with the previously published values.
As described in the citation tutorial, we can use citations.get_citations_for_model to construct an acknowledgement and BibTeX listing that includes the relevant citations for this model.
[14]:
with model:
txt, bib = xo.citations.get_citations_for_model()
print(txt)
This research made use of \textsf{exoplanet} \citep{exoplanet} and its
dependencies \citep{celerite2:foremanmackey17, celerite2:foremanmackey18,
exoplanet:agol20, exoplanet:arviz, exoplanet:astropy13, exoplanet:astropy18,
exoplanet:kipping13, exoplanet:kipping13b, exoplanet:luger18, exoplanet:pymc3,
exoplanet:theano}.
[15]:
print("\n".join(bib.splitlines()[:10]) + "\n...")
@misc{exoplanet,
author = {Daniel Foreman-Mackey and Arjun Savel and Rodrigo Luger and
Eric Agol and Ian Czekala and Adrian Price-Whelan and
Christina Hedges and Emily Gilbert and Luke Bouma and Tom Barclay
and Timothy D. Brandt},
title = {exoplanet-dev/exoplanet v0.5.0},
month = may,
year = 2021,
doi = {10.5281/zenodo.1998447},
...