In [1]:

#!pip install git+https://github.com/jpdeleon/chronos.git@master

In [3]:

%matplotlib inline
import chronos as cr

Created TAP+ (v1.2.1) - Connection:
	Host: gea.esac.esa.int
	Use HTTPS: True
	Port: 443
	SSL Port: 443
Created TAP+ (v1.2.1) - Connection:
	Host: geadata.esac.esa.int
	Use HTTPS: True
	Port: 443
	SSL Port: 443

run isochrones in star module¶

stellar characterization¶

In [5]:

toiid = 179
s = cr.Star(toiid=toiid)

Target: TOI 179
Searching Vizier: (44.262 -56.1919) with radius=3.0 arcsec

isochrones fitting¶

In [6]:

s.query_specs_from_tfop()

Saved: /home/jp/github/research/project/chronos/chronos/../data/tfop_sg2_spec_table.csv
There are 11 spectra in https://exofop.ipac.caltech.edu/tess/target.php?id=207141131

Out[6]:

	TIC ID	TIC	TOI	Telescope	Instrument	Spectral resolution	Wavelength coverage	SNR/resolution	SNR wavelength	Appropriate to PRV	Obs date	User	Group	Tag	Notes
1672	207141131	NaN	NaN	SMARTS (1.5 m)	CHIRON	80000.0	4500 to 8900 Angstroms	66.30	5500 angstroms	N	2019-02-18	quinn	tfopwg	2871	NaN
1673	207141131	NaN	NaN	SMARTS (1.5 m)	CHIRON	80000.0	4500 to 8900 Angstroms	53.20	5500 angstroms	N	2019-08-13	quinn	tfopwg	5197	NaN
1674	207141131	NaN	NaN	LCO (1 m)	NRES	53000.0	380 to 860 nm	45.06	550 nm	N	2018-12-19 21:51:20	rabus	NaN	6571	NRES
1675	207141131	NaN	NaN	LCO (1 m)	NRES	53000.0	380 to 860 nm	83.69	550 nm	N	2018-11-04 03:54:09	rabus	tfopwg	7575	NRES
1676	207141131	NaN	NaN	LCO (1 m)	NRES	53000.0	380 to 860 nm	69.13	550 nm	N	2018-11-05 00:08:27	rabus	tfopwg	7576	NRES
1677	207141131	NaN	NaN	LCO (1 m)	NRES	53000.0	380 to 860 nm	63.83	550 nm	N	2018-11-06 02:07:34	rabus	tfopwg	7581	NRES
1678	207141131	NaN	NaN	LCO (1 m)	NRES	53000.0	380 to 860 nm	52.31	550 nm	N	2018-11-07 04:41:30	rabus	tfopwg	7583	NRES
1679	207141131	NaN	NaN	LCO (1 m)	NRES	53000.0	380 to 860 nm	101.91	550 nm	N	2018-11-15 07:07:10	rabus	tfopwg	7592	NRES
1680	207141131	NaN	NaN	SMARTS (1.5 m)	CHIRON	80000.0	4500 to 8900 Angstroms	114.60	5150 Angstroms	N	2020-02-02 00-52-17	zhou	tfopwg	18541	NaN
1681	207141131	NaN	NaN	SMARTS (1.5 m)	CHIRON	80000.0	4500 to 8900 Angstroms	116.00	5150 Angstroms	N	2020-02-05 00-27-03	zhou	tfopwg	18544	NaN
1682	207141131	NaN	NaN	SMARTS (1.5 m)	CHIRON	80000.0	4500 to 8900 Angstroms	130.70	5150 Angstroms	N	2020-02-10 00-41-59	zhou	tfopwg	18548	NaN

In [7]:

import numpy as np

cols = "Teff logg __Fe_H_ Rad Mass vsini".split()
params={}
for col in cols:
    pars = s.query_vizier_param(col)
    mean = np.nanmean(list(pars.values()))
    err = np.nanstd(list(pars.values()))
    
    params[col] = (mean,err)
    print(f"{col}: {mean:.2f}, {err:.2f}")

Found 15 references with Teff
Teff: 5098.08, 126.84
Found 3 references with logg
logg: 4.59, 0.10
Found 4 references with __Fe_H_
__Fe_H_: -0.01, 0.00
Found 4 references with Rad
Rad: 0.78, 0.02
Found 4 references with Mass
Mass: 0.84, 0.01
Found 2 references with vsini
vsini: 4.30, 0.00

In [8]:

s.starhorse_Teff, s.starhorse_logg, s.starhorse_met

Out[8]:

(5332.04, 4.564691, -0.137198)

In [9]:

iso_params = s.get_iso_params(teff=(5100,150),
                 logg=(4,5,0.1),
                 feh=(-0.11,0.1),
                 bands="J H K".split()
                )

Querying Gaia DR2 catalog for ra,dec=(44.262 -56.1919) within 3.0 arcsec.
Querying TIC catalog for ra,dec=(44.262 -56.1919) within 3.0 arcsec.
Gaia and TIC catalog cross-match succeeded.

In [10]:

iso_model = s.init_isochrones(iso_params=iso_params)
iso_model

{'Teff': [5100.0, 150.0], 'feh': [-0.11, 0.1], 'logg': [4.0, 5.0], 'parallax': [25.901097647182596, 0.10293960586359334], 'J': [7.428, 0.018], 'H': [7.029, 0.015], 'K': [6.883, 0.02]}

Out[10]:

<isochrones.starmodel.SingleStarModel at 0x7f4a41f5add0>

In [11]:

s.iso_params_init

Out[11]:

{'EEP': 329.58,
 'log10(Age [yr])': 9.5596,
 '[Fe/H]': -0.0478,
 'ln(Distance)': 5.560681631015528,
 'Av': 0.0045}

In [12]:

iso_model.lnprior(s.iso_params0), iso_model.lnlike(s.iso_params0), iso_model.lnpost(s.iso_params0)

Out[12]:

(-14.510012104047181, -1212430.4645540293, -1212444.9745661332)

In [26]:

#set prior
#default: mass=[0.1,10] Msol and eep=[0,808] or  tip of the red giant branch (RGBTip)

# uncomment to change
# mod.set_bounds(eep=(0, 605))
# mod._priors["eep"].bounds

Out[26]:

(0, 605)

In [13]:

#set specific prior
from isochrones.priors import FlatLogPrior, FlatPrior 
import numpy as np

#age = [10, 5e3] Myr
iso_model._priors["age"] = FlatPrior(bounds=[6, np.log10(5e9)])
# mod.set_bounds(age=(6, np.log10(5e9)))
iso_model._priors["age"].bounds

Out[13]:

[6, 9.698970004336019]

In [14]:

fig = s.plot_isochrones_priors()

In [15]:

#double check input parameters
s.isochrones_model.observed_quantities

Out[15]:

['J_mag', 'H_mag', 'K_mag', 'Teff', 'feh', 'logg', 'parallax']

In [24]:

s.target_coord.distance

Out[24]:

$38.608402 \; \mathrm{pc}$

In [27]:

#run isochrone single star model
iso_model = s.run_isochrones(iso_params=iso_params, 
                       overwrite=False,
                       n_live_points=1000 #multinest parameter
                      )

Using previously initialized model.
Loading previous samples. Otherwise, try overwrite=True.
method: Nested Sampling
Done.

In [31]:

iso_model.param_names

Out[31]:

('eep', 'age', 'feh', 'distance', 'AV')

In [28]:

iso_model.map_pars

Out[28]:

array([ 2.01472425e+02,  7.70069088e+00, -8.72727155e-02,  3.86590251e+01,
        2.71707803e-01])

In [29]:

iso_model.lnpost(iso_model.map_pars)

Out[29]:

-58.09836336988235

In [30]:

#model evidence
iso_model.evidence

  analysing data from ./chains/TOI 179-mist-single-.txt

Out[30]:

(-79.40813035237709, 0.1222939812426866)

In [ ]:

# !conda install bokeh -y

In [ ]:

# import pandas as pd
# import holoviews as hv
# import hvplot.pandas
# # hv.extension('bokeh')

# def plot_samples(samples):
#     df = pd.DataFrame(samples, columns=['eep', 'age', 'feh', 'distance', 'AV'])
#     df['mass'] = mod.ic.interp_value([df.eep, df.age, df.feh], ['mass'])
#     return hv.Layout([df.hvplot.hist(c).options(width=300) for c in df.columns]).cols(3)

# #sample from priors
# priot_samples = mod.sample_from_prior(1000)
# plot_samples(priot_samples)

derived samples¶

In [32]:

s.get_isochrones_results_summary()

Out[32]:

	eep	age	feh	mass	initial_mass	radius	density	logTeff	Teff	logg	...	BP_mag	RP_mag	W1_mag	W2_mag	W3_mag	TESS_mag	Kepler_mag	parallax	distance	AV
count	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	...	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000	5718.000000
mean	250.261655	8.401845	-0.080329	0.864486	0.864495	0.771266	2.668809	3.728223	5349.873478	4.599903	...	9.191768	8.148676	6.881988	6.910748	6.853137	8.137573	8.655961	25.885925	38.631626	0.259053
std	34.448261	0.501428	0.083849	0.011461	0.011453	0.005750	0.062165	0.007980	98.229986	0.007651	...	0.007785	0.005222	0.011407	0.016831	0.014338	0.005336	0.006238	0.101433	0.151404	0.094335
min	196.833268	7.591558	-0.380536	0.809661	0.809726	0.753391	2.392821	3.704565	5065.646113	4.563189	...	9.162188	8.129349	6.840263	6.853514	6.804728	8.117729	8.632526	25.512610	38.095192	0.000658
25%	220.293851	7.965820	-0.136676	0.858386	0.858395	0.767364	2.631917	3.722787	5282.623481	4.596220	...	9.186496	8.145196	6.874261	6.899608	6.843642	8.134013	8.651806	25.817139	38.531580	0.196054
50%	246.078870	8.340973	-0.081031	0.865835	0.865840	0.771327	2.673595	3.728402	5351.041099	4.601083	...	9.191706	8.148686	6.881801	6.910520	6.852701	8.137535	8.655946	25.886869	38.629624	0.261892
75%	275.484987	8.766336	-0.023758	0.872366	0.872370	0.775029	2.712008	3.733753	5417.346597	4.605183	...	9.196935	8.152255	6.889686	6.922325	6.862623	8.141258	8.660292	25.952738	38.733959	0.326055
max	337.016430	9.684977	0.206115	0.896298	0.896300	0.792061	2.853664	3.755959	5701.678537	4.617444	...	9.218202	8.167322	6.925935	6.968122	6.904116	8.156789	8.678873	26.250031	39.196303	0.566279

8 rows × 30 columns

In [33]:

fig = s.plot_corner(posterior="observed")

In [35]:

fig = s.plot_corner(posterior='physical')

evolutionary stage¶

Based on Table II in MIST README, the star is

In [37]:

ax = s.plot_posterior_eep()

In [39]:

s.mist_eep_table

Out[39]:

	Primary EEP	EEP Number	Phase	Comment
0	1	1	pre-main sequence (PMS)	Medium Mass Type
1	2	202	zero age main sequence (ZAMS)	Medium Mass Type
2	3	353	intermediate age main sequence (IAMS )	Medium Mass Type
3	4	454	terminal age main sequence (TAMS)	Medium Mass Type
4	5	605	tip of the red giant branch (RGBTip)	Medium Mass Type
5	6	631	zero age core helium burning (ZACHeB)	Medium Mass Type
6	6	631	zero age horizontal branch (ZAHB)	Low Mass Type
7	7	707	terminal age core helium burning (TACHeB)	aka terminal age horizontal branch (TAHB)
8	8	808	thermally pulsating asymptotic giant branch (...	Low Mass Type
9	8	808	carbon burning (C-burn)	High Mass Type
10	9	1409	post asymptotic giant branch (post-AGB)	Low Mass Type
11	10	1710	white dwarf cooling sequence (WDCS)	Low Mass Type

In [38]:

ax = s.isochrones_model.derived_samples.age.apply(lambda x: (10**x)/1e6).plot(kind='kde')
ax.set_xlim(0, 1e3)

Out[38]:

(0, 1000.0)

In [ ]: