def compute_pair_separations_hill_radii(pairs):
'''
d = {'pair_inner_radius':[],
'pair_outer_radius':[],
's_mass':[],
's_met':[],
's_logage':[],
'pair_inner_sma': [],
'pair_outer_sma': [],
'n_planet_in_sys':[],
'pair_ind':[]}
'''
inner_masses, outer_masses = [], []
for inner_rad, outer_rad in zip(arr(pairs['pair_inner_radius']),
arr(pairs['pair_outer_radius'])):
inner_masses.append(_get_WM14_mass(inner_rad))
outer_masses.append(_get_WM14_mass(outer_rad))
pairs['pair_inner_mass'] = arr(inner_masses)
pairs['pair_outer_mass'] = arr(outer_masses)
m_j = arr(pairs['pair_inner_mass']) * u.Mearth
m_jp1 = arr(pairs['pair_outer_mass']) * u.Mearth
M_star = arr(pairs['s_mass']) * u.Msun
a_j = arr(pairs['pair_inner_sma']) * u.au
a_jp1 = arr(pairs['pair_outer_sma']) * u.au
R_H = 0.5 * ((m_j + m_jp1) / (3 * M_star))**(1 / 3) * (a_j + a_jp1)
sep = (a_jp1 - a_j) / R_H
pairs['R_H'] = R_H.to(u.au).value
pairs['sep_by_RH'] = sep.cgs.value
return pairs
def scatter_p2byp1_vs_p1(pairs):
# the period ratio P2/P1 tends to be larger than average when P1 is less
# than about 2-3 days
a1 = arr(pairs['pair_inner_sma'])
a2 = arr(pairs['pair_outer_sma'])
P1 = arr(pairs['pair_inner_period'])
P2 = arr(pairs['pair_outer_period'])
aByRstar1 = arr(pairs['pair_inner_abyRstar'])
aByRstar2 = arr(pairs['pair_outer_abyRstar'])
plt.close('all')
f, ax = plt.subplots(figsize=(8, 6))
ax.scatter(P1, P2 / P1, marker='o', s=5, c='#1f77b4', zorder=2)
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
ax.set_xlabel('$P_1$, inner period of planet pair [days]',
fontsize='medium')
ax.set_ylabel('$P_2/P_1$, ratio of planet pair periods', fontsize='medium')
ax.set_xscale('log')
ax.set_yscale('log')
savpath = '../results/cks_multis_vs_age/scatter_p2byp1_vs_p1.pdf'
f.tight_layout(h_pad=0, w_pad=0)
f.savefig(savpath, bbox_inches='tight')
print('made {:s}'.format(savpath))
def hill_radius_pairs_vs_age(pairs, aByRstar_cut=20):
plt.close('all')
f, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
a1 = arr(pairs['pair_inner_sma'])
a2 = arr(pairs['pair_outer_sma'])
P1 = arr(pairs['pair_inner_period'])
P2 = arr(pairs['pair_outer_period'])
aByRstar1 = arr(pairs['pair_inner_abyRstar'])
aByRstar2 = arr(pairs['pair_outer_abyRstar'])
sep_by_RH = arr(pairs['sep_by_RH'])
s_logage = arr(pairs['s_logage']) # really giso_slogage
sel = (aByRstar1 < aByRstar_cut) & (aByRstar2 < aByRstar_cut)
ax.scatter(sep_by_RH[sel], 10**(s_logage[sel]) / 1e9)
ax.set_xlabel('separation in mutual hill radii')
ax.set_ylabel('age [Gyr]')
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
#ax.yaxis.set_major_locator(MultipleLocator(10))
#ax.yaxis.set_major_formatter(FormatStrFormatter('%d'))
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
ax.grid(True, zorder=-2, which='major', alpha=0.2)
ax.set_title('{:d} pairs where both planets have '
'$a/R_\star<${:d}'.format(len(pairs[sel]), aByRstar_cut))
f.tight_layout(h_pad=0, w_pad=0)
savname = (
'../results/cks_age_plots_old_short_period/' +
'hill_radius_pairs_vs_age_aByRstar_cut_{:d}.pdf'.format(aByRstar_cut))
f.savefig(savname, bbox_inches='tight')
print('saved %s' % savname)
savname = (
'../results/cks_age_plots_old_short_period/' +
'hill_radius_pairs_vs_age_aByRstar_cut_{:d}.png'.format(aByRstar_cut))
f.savefig(savname, bbox_inches='tight', dpi=350)
print('saved %s' % savname)
def _apply_cks_VI_metallicity_study_filters(df):
'''
given df from _get_cks_data, return the boolean indices for the
subset of interest.
See Weiss+ 2018, CKS VI, table 1.
Here we reproduce the "full CKS-Gaia multis sample". We don't make the
magnitude-limited cut, because we're gonna compare multis to multis.
'''
# row zero, `r0` tuple containing stats
rows = []
rows.append(_get_Weiss18_table1_stats(df))
# not a false positive
is_fp = arr(df['cks_fp'])
sel = ~is_fp
rows.append(_get_Weiss18_table1_stats(df[sel]))
# radius correction factor <5%, logical or not given in Furlan+2017 table.
sel &= ((arr(df['fur17_rcorr_avg']) < 1.05) |
(~np.isfinite(df['fur17_rcorr_avg'])))
rows.append(_get_Weiss18_table1_stats(df[sel]))
# not grazing (b<0.9)
sel &= np.isfinite(arr(df['koi_impact']))
sel &= arr(df['koi_impact']) < 0.9
rows.append(_get_Weiss18_table1_stats(df[sel]))
# SNR>10
sel &= np.isfinite(arr(df['koi_model_snr']))
sel &= arr(df['koi_model_snr']) > 10.
rows.append(_get_Weiss18_table1_stats(df[sel]))
# Rp < 22.4 Re
sel &= np.isfinite(arr(df['giso_prad']))
sel &= arr(df['giso_prad']) < 22.4
rows.append(_get_Weiss18_table1_stats(df[sel]))
# You need finite ages, too
sel &= np.isfinite(arr(df['giso_slogage']))
rows.append(_get_Weiss18_table1_stats(df[sel]))
for row in rows:
print(row)
weiss_18_table_1 = [
(1944, 1222, 1176),
(1788, 1118, 1092),
(1700, 1060, 1042),
(1563, 990, 940),
(1495, 952, 908),
(1491, 948, 892),
]
for rowind, wrow in enumerate(weiss_18_table_1):
if wrow == rows[rowind]:
print('row {:d} matches Weiss+18 Table 1'.format(rowind))
elif np.abs(np.sum(wrow) - np.sum(rows[rowind])) < 10:
print('row {:d} is close (w/in 10 planets) of Weiss+18 Table 1'.
format(rowind))
else:
print('row {:d} is quite different from Weiss+18 Table 1'.format(
rowind))
return sel
def scatter_p2byp1_vs_p1_age_percentiles(pairs):
P1 = arr(pairs['pair_inner_period'])
P2 = arr(pairs['pair_outer_period'])
smet = arr(pairs['s_met'])
slogage = arr(pairs['s_logage'])
sep = 25
smet_pctls = [
np.percentile(smet, pct) for pct in np.arange(0, 100 + sep, sep)
]
slogage_pctls = [
np.percentile(slogage, pct) for pct in np.arange(0, 100 + 25, 25)
]
# plot pairs by stellar age percentiles
plt.close('all')
f, axs = plt.subplots(nrows=2,
ncols=2,
figsize=(8, 6),
sharex=True,
sharey=True)
axs = axs.flatten()
for ix, ax in enumerate(axs):
ax.scatter(P1, P2 / P1, marker='o', s=5, c='lightgray', zorder=1)
if ix == 0:
sel = slogage < slogage_pctls[ix + 1]
textstr = 'logage$<${:.2f}\ntot={:d},blue={:d}'.format(
slogage_pctls[ix + 1], len(pairs), int(len(pairs) * sep / 100))
elif ix == len(axs) - 1:
sel = slogage > slogage_pctls[ix]
textstr = 'logage$>${:.2f}\ntot={:d},blue={:d}'.format(
slogage_pctls[ix], len(pairs), int(len(pairs) * sep / 100))
else:
sel = slogage >= slogage_pctls[ix]
sel &= slogage < slogage_pctls[ix + 1]
textstr = 'logage=({:.2f},{:.2f})\ntot={:d},blue={:d}'.format(
slogage_pctls[ix], slogage_pctls[ix + 1], len(pairs),
int(len(pairs) * sep / 100))
ax.scatter(P1[sel],
P2[sel] / P1[sel],
marker='o',
s=3,
c='#1f77b4',
zorder=2)
ax.text(0.95,
0.95,
textstr,
horizontalalignment='right',
verticalalignment='top',
transform=ax.transAxes,
fontsize='xx-small')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim([0.4, 300])
ax.set_xlim([0.9, 102])
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
f.tight_layout(h_pad=0, w_pad=0)
# set labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none',
top=False,
bottom=False,
left=False,
right=False)
plt.grid(False)
plt.xlabel('$P_1$, inner period of planet pair [days]', fontsize='medium')
plt.ylabel('$P_2/P_1$, ratio of planet pair periods', fontsize='medium')
savpath = '../results/cks_multis_vs_age/scatter_p2byp1_vs_p1_age_percentiles.pdf'
f.tight_layout(h_pad=0, w_pad=0)
f.savefig(savpath, bbox_inches='tight')
print('made {:s}'.format(savpath))
def split_weiss18_fig1_high_low_met(df, sel):
# reproduce Weiss+18 CKS VI's Figure 1, the detected system multiplicity
# function, for high and low metallicity CKS systems.
u, u_inds, inv_inds, counts = np.unique(df[sel]['id_starname'],
return_index=True,
return_inverse=True,
return_counts=True)
multiplicity_function = counts # length: number of systems
df_multiplicity = counts[inv_inds] # length: number of transiting planets
multis = df[sel][df_multiplicity !=
1] # length: number of planets in multi systems
system_df = df[sel].iloc[
u_inds] # length: number of systems. unique stellar properties here!
# get metallicities for systems with at least two planets
smet_VII = arr(system_df['cks_smet'])[(counts >= 2)]
med_smet = np.median(smet_VII)
sel_highmet = (smet_VII > med_smet)
bins = np.arange(0.5, 7.5, 1)
n_highmet, _ = np.histogram(counts[(counts >= 2)][sel_highmet], bins)
sel_lowmet = (smet_VII <= med_smet)
n_lowmet, _ = np.histogram(counts[(counts >= 2)][sel_lowmet], bins)
N = int(np.max(counts))
plt.close('all')
f, ax = plt.subplots(figsize=(4, 3))
ind = np.arange(1, N, 1)
width = 0.35
rects1 = ax.bar(ind, n_highmet, width)
rects2 = ax.bar(ind + width, n_lowmet, width)
ax.set_xlabel('Number of Transiting Planets', fontsize='large')
ax.set_xlim([0.5, 7.5])
ax.set_ylabel('Number of Stars', fontsize='large')
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(('1', '2', '3', '4', '5', '6'))
ax.legend((rects1[0], rects2[0]), ('[Fe/H]$>${:.3f}'.format(med_smet),
'[Fe/H]$\le${:.3f}'.format(med_smet)),
fontsize='xx-small')
ax.set_title('split CKS+Gaia multis by host star metallicity',
fontsize='xx-small')
def autolabel(rects):
#Attach a text label above each bar displaying its height
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.,
1.01 * height,
'%d' % int(height),
ha='center',
va='bottom',
fontsize='xx-small')
autolabel(rects1)
autolabel(rects2)
f.tight_layout()
f.savefig(
'../results/cks_multis_vs_age/cks_gaia_multis_multiplicity_highvlowmet.pdf'
)