ApJdataFrames Rebull 2016 a & b Extra analysisTitles:
a.: Rotation in the Pleiades with K2: I. Data and First Results
b.: ROTATION IN THE PLEIADES WITH K2. II. MULTIPERIOD STARS
Authors: L. M. Rebull, J. R. Stauffer, J. Bouvier, A. M. Cody, L. A. Hillenbrand, et al.
Data is from this paper:
http://iopscience.iop.org/article/10.3847/0004-6256/152/5/114/meta
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
pd.options.display.max_columns = 150
In [2]:
%config InlineBackend.figure_format = 'retina'
In [3]:
import astropy
from astropy.table import Table
from astropy.io import ascii
import numpy as np
In [4]:
df_abc = pd.read_csv('../data/Rebull2016/Rebull_Stauffer_merge.csv')
In [5]:
df_abc.head()
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In [6]:
tab1 = pd.read_fwf('../data/Fang2016/Table_1+4_online.dat', na_values=['-99.00000000', '-9999.0', '99.000, 99.0'])
df = tab1.rename(columns={'# Object_name':'Object_name'})
In [7]:
df.head()
Out[7]:
In [8]:
df.Object_name.values[0:30]
Out[8]:
Ugh, the naming convention is non-standard in a way that is likely more work than it's worth to try to match to other catalogs. Whyyyyyyyyy.
From astropy: http://docs.astropy.org/en/stable/coordinates/matchsep.html
In [9]:
ra1 = df.RAJ2000
dec1 = df.DEJ2000
ra2 = df_abc.RAdeg
dec2 = df_abc.DEdeg
In [10]:
from astropy.coordinates import SkyCoord
from astropy import units as u
c = SkyCoord(ra=ra1*u.degree, dec=dec1*u.degree)
catalog = SkyCoord(ra=ra2*u.degree, dec=dec2*u.degree)
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
In [11]:
plt.figure(figsize=(10,10))
plt.plot(ra1, dec1, '.', alpha=0.3)
plt.plot(ra2, dec2, '.', alpha=0.3)
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In [12]:
plt.hist(d2d.to(u.arcsecond)/u.arcsecond, bins=np.arange(0,3, 0.1));
plt.yscale('log')
plt.axvline(x=0.375,color='r', linestyle='dashed')
Out[12]:
Ok, we'll accept all matches with better than 0.375 arcsecond separation.
In [13]:
boolean_matches = d2d.to(u.arcsecond).value < 0.375
How many matches are there?
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boolean_matches.sum()
Out[14]:
120 matches--- not bad. Only keep the subset of fang sources that also have K2
In [15]:
df['EPIC'] = ''
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matched_idx = idx[boolean_matches]
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matched_idx
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In [18]:
df.shape, df_abc.shape
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In [19]:
idx.shape
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In [20]:
df['EPIC'][boolean_matches] = df_abc['EPIC'].iloc[matched_idx].values
In [21]:
fang_K2 = pd.merge(df_abc, df, how='left', on='EPIC')
In [22]:
fang_K2.columns
Out[22]:
In [23]:
fang_K2[['Name_adopt', 'Object_name']][fang_K2.Object_name.notnull()].tail(10)
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In [26]:
fang_K2.to_csv('../data/Fang2016/Rebull_Fang_merge.csv', index=False)
In [27]:
fang_K2
Out[27]:
Great correspondence! Looks like there are 120 targets in both categories.
Let's spot-check if they use similar temperatures:
In [24]:
plt.figure(figsize=(7, 7))
plt.plot(fang_K2.Teff, fang_K2.Tspec, 'o')
plt.xlabel(r'$T_{\mathrm{eff}}$ Stauffer et al. 2016')
plt.ylabel(r'$T_{\mathrm{spec}}$ Fang et al. 2016')
plt.plot([3000, 6300], [3000, 6300], 'k--')
plt.ylim(3000, 6300)
plt.xlim(3000, 6300);
What's the scatter?
In [25]:
delta_Tspec = fang_K2.Teff - fang_K2.Tspec
delta_Tspec = delta_Tspec.dropna()
RMS_Tspec = np.sqrt((delta_Tspec**2.0).sum()/len(delta_Tspec))
print('{:0.0f}'.format(RMS_Tspec))
The authors disagree on temperature by about $\delta T \sim$ 100 K RMS.
Let's make the figure we really want to make: K2 Amplitude versus spectroscopically measured filling factor of starspots $f_{spot}$. We expect that the plot will be a little noisy due to differences in temperature assumptions and such, but it is absolutely fundamental.
First we need to convert the amplitude in magnitudes to a faction $\in [0,1]$. The $\Delta V$ in Stauffer et al. 2016 has negative values, so I'm not sure what it is! The Ampl from Rebull et al. 2016 is:
Amplitude, in mag, of the 10th to the 90th percentile
In [59]:
fang_K2['flux_amp'] = 1.0 - 10**(fang_K2.Ampl/-2.5)
In [60]:
plt.hist(fang_K2.flux_amp, bins=np.arange(0, 0.15, 0.005));
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plt.hist(fang_K2.fs1.dropna(), bins=np.arange(0, 0.8, 0.03));
In [62]:
sns.set_context('talk')
In [63]:
plt.figure(figsize=(7, 7))
plt.plot(fang_K2.fs1, fang_K2.flux_amp, '.')
plt.plot([0,1], [0,1], 'k--')
plt.xlim(0.0,0.2)
plt.ylim(0,0.2)
plt.xlabel('LAMOST-measured $f_{spot}$ \n Fang et al. 2016')
plt.ylabel('K2-measured spot amplitude $A \in [0,1)$ \n Rebull et al. 2016')
plt.xticks(np.arange(0, 0.21, 0.05))
plt.yticks(np.arange(0, 0.21, 0.05))
plt.savefig('K2_LAMOST_starspots_data.png', bbox_inches='tight', dpi=300);
In [64]:
plt.figure(figsize=(7, 7))
plt.plot(fang_K2.fs1, fang_K2.flux_amp, '.')
plt.plot([0,1], [0,1], 'k--')
plt.xlim(0.0,0.5)
plt.ylim(0,0.5)
plt.xlabel('LAMOST-measured $f_{spot}$ \n Fang et al. 2016')
plt.ylabel('K2-measured spot amplitude $A \in [0,1)$ \n Rebull et al. 2016')
plt.xticks(np.arange(0, 0.51, 0.1))
plt.yticks(np.arange(0, 0.51, 0.1))
plt.savefig('K2_LAMOST_starspots_wide.png', bbox_inches='tight', dpi=300);
Awesome! The location of points indicate that starspots have a large longitudinally-symmetric component that evades detection in K2 amplitudes.
What effects can cause / mimic this behavior?
In [65]:
fang_K2.columns
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In [66]:
fang_K2.beat.value_counts()
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In [67]:
plt.figure(figsize=(7, 7))
cross_tab = 'resc'
c1 = fang_K2[cross_tab] == 'yes'
c2 = fang_K2[cross_tab] == 'no'
plt.plot(fang_K2.fs1[c1], fang_K2.flux_amp[c1], 'r.', label='{} = yes'.format(cross_tab))
plt.plot(fang_K2.fs1[c2], fang_K2.flux_amp[c2], 'b.', label='{} = no'.format(cross_tab))
plt.legend(loc='best')
plt.plot([0,1], [0,1], 'k--')
plt.xlim(-0.01,0.8)
plt.ylim(0,0.15)
plt.xlabel('LAMOST-measured $f_{spot}$ \n Fang et al. 2016')
plt.ylabel('K2-measured spot amplitude $A \in [0,1)$ \n Rebull et al. 2016')
#plt.xticks(np.arange(0, 0.51, 0.1))
#plt.yticks(np.arange(0, 0.51, 0.1))
plt.savefig('K2_LAMOST_starspots_crosstab.png', bbox_inches='tight', dpi=300);
In [68]:
plt.figure(figsize=(7, 7))
cross_tab = 'Mass'
cm = plt.cm.get_cmap('Blues')
sc = plt.scatter(fang_K2.fs1, fang_K2.flux_amp, c=fang_K2[cross_tab], cmap=cm)
cb = plt.colorbar(sc)
#cb.set_label(r'$T_{spot}$ (K)')
plt.plot([0,1], [0,1], 'k--')
plt.xlim(-0.01,0.8)
plt.ylim(0,0.15)
plt.xlabel('LAMOST-measured $f_{spot}$ \n Fang et al. 2016')
plt.ylabel('K2-measured spot amplitude $A \in [0,1)$ \n Rebull et al. 2016')
#plt.xticks(np.arange(0, 0.51, 0.1))
#plt.yticks(np.arange(0, 0.51, 0.1))
plt.savefig('K2_LAMOST_starspots_cb.png', bbox_inches='tight', dpi=300);
What about inclination?
In [69]:
import astropy.units as u
In [70]:
sini = fang_K2.vsini * u.km/u.s * fang_K2.Per1* u.day /(2.0*np.pi *u.solRad)
In [71]:
vec = sini.values.to(u.dimensionless_unscaled).value
In [72]:
sns.distplot(vec[vec == vec], bins=np.arange(0,2, 0.1), kde=False)
plt.axvline(1.0, color='k', linestyle='dashed')
Out[72]:
In [73]:
inclination = np.arcsin(vec)*180.0/np.pi
In [74]:
sns.distplot(inclination[inclination == inclination], bins=np.arange(0,90.0, 5), kde=False);
In [75]:
fang_K2['sini'] = vec
In [76]:
plt.figure(figsize=(7, 7))
cross_tab = 'sini'
cm = plt.cm.get_cmap('hot')
sc = plt.scatter(fang_K2.fs1, fang_K2.flux_amp, c=fang_K2[cross_tab], cmap=cm)
cb = plt.colorbar(sc)
cb.set_label(r'$\sin{i}$')
plt.plot([0,1], [0,1], 'k--')
plt.xlim(-0.01,0.7)
plt.ylim(0,0.15)
plt.xlabel('LAMOST-measured $f_{spot}$ \n Fang et al. 2016')
plt.ylabel('K2-measured spot amplitude $A \in [0,1)$ \n Rebull et al. 2016')
#plt.xticks(np.arange(0, 0.51, 0.1))
#plt.yticks(np.arange(0, 0.51, 0.1))
plt.savefig('K2_LAMOST_starspots_cb.png', bbox_inches='tight', dpi=300);