In [1]:
import numpy as np
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.table import Table
import matplotlib.pyplot as plt
from frb.frb import FRB
from frb.surveys.survey_utils import load_survey_by_name
from frb.surveys.catalog_utils import convert_mags_to_flux
from frb.analysis import cigale
WARNING: UnitsWarning: 'km/s/Mpc' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]
In [13]:
frb180924 = FRB.by_name("FRB180924")
survey = load_survey_by_name("DES",frb180924.coord,15*u.arcsec)
survey.get_catalog()
Out[13]:
Table length=2
DES_g DES_g_err DES_r DES_r_err DES_i DES_i_err DES_z DES_z_err DES_Y DES_Y_err DES_ID ra dec DES_tile WISE_W1 WISE_W1_err WISE_W2 WISE_W2_err WISE_W3 WISE_W3_err WISE_W4 WISE_W4_err float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 float64 float64 str12 float64 float64 float64 float64 float64 float64 float64 float64 25.0468 0.305723 24.5441 0.301756 23.923 0.293647 23.463 0.271636 24.7709 2.66629 209914542 326.101627 -40.899805 DES2143-4040 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 21.6222 0.0269829 20.5415 0.0156388 20.1379 0.0186293 19.8547 0.0200389 19.8083 0.0564316 209914488 326.105209 -40.900226 DES2143-4040 16.846 0.102 16.062 0.185 11.691 -999.0 8.501 -999.0
In [3]:
convert_mags_to_flux(survey.catalog,fluxunits=u.microjansky)
Out[3]:
Table length=2
DES_g DES_g_err DES_r DES_r_err DES_i DES_i_err DES_z DES_z_err DES_Y DES_Y_err DES_ID ra dec DES_tile WISE_W1 WISE_W1_err WISE_W2 WISE_W2_err WISE_W3 WISE_W3_err WISE_W4 WISE_W4_err float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 float64 float64 str12 float64 float64 float64 float64 float64 float64 float64 float64 0.34776029396353764 0.11309989577324552 0.5525352556488469 0.1770270619612232 0.9790390045854888 0.3040559413046528 1.495546753095973 0.4251305057911406 0.4483735593717958 4.7774650632357805 209914542 326.101627 -40.899805 DES2143-4040 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 8.149294155302018 0.20506524182685903 22.049563390010288 0.3198973292351718 31.97716768233088 0.5534054072117228 41.50687090621285 0.7731851361138282 43.31915677274319 2.3110707525906546 209914488 326.105209 -40.900226 DES2143-4040 56.53491951856131 5.568680812966024 64.59371982943986 11.999494472175313 667.2720168587022 -99.0 3326.3052157253505 -99.0
In [6]:
frb190608 = FRB.by_name("FRB190608")
survey = load_survey_by_name("SDSS",frb190608.coord,15*u.arcsec)
survey.get_catalog()
Out[6]:
Table length=1
ra dec SDSS_ID run rerun camcol SDSS_field type SDSS_u SDSS_g SDSS_r SDSS_i SDSS_z SDSS_u_err SDSS_g_err SDSS_r_err SDSS_i_err SDSS_z_err extinction_u extinction_g extinction_r extinction_i extinction_z photo_z photo_zerr z_spec separation arcmin float64 float64 int64 int64 int64 int64 int64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 334.020419873775 -7.89888531358588 1237652600644436162 1659 301 5 192 3 19.03135 18.06515 17.63091 17.26474 17.08397 0.04294281 0.008970303 0.007262514 0.007621691 0.01935624 0.1975715 0.1539464 0.1064994 0.07914045 0.05886595 -9999.0 -9999.0 0.1177805 0.05628329857046733
In [7]:
convert_mags_to_flux(survey.catalog)
Out[7]:
Table length=1
ra dec SDSS_ID run rerun camcol SDSS_field type SDSS_u SDSS_g SDSS_r SDSS_i SDSS_z SDSS_u_err SDSS_g_err SDSS_r_err SDSS_i_err SDSS_z_err extinction_u extinction_g extinction_r extinction_i extinction_z photo_z photo_zerr z_spec separation arcmin float64 float64 int64 int64 int64 int64 int64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 334.020419873775 -7.89888531358588 1237652600644436162 1659 301 5 192 3 0.08860535997067599 0.21574462979928918 0.32183701703197415 0.4509246681020314 0.5326132842973963 0.0035747280091641743 0.001789855963532317 0.0021599911381572647 0.0031765539944286023 0.009580444778228149 0.1975715 0.1539464 0.1064994 0.07914045 0.05886595 -9999.0 -9999.0 0.1177805 0.05628329857046733
In [2]:
survey = load_survey_by_name("DECaL",SkyCoord(1,0,unit="deg"),15*u.arcsec)
survey.get_catalog()
Out[2]:
Table length=6
DECaL_ID brick_primary DECaL_brick ra dec gaia_pointsource DECaL_g DECaL_r DECaL_z WISE_W1 WISE_W2 WISE_W3 WISE_W4 DECaL_g_err DECaL_r_err DECaL_z_err WISE_W1_err WISE_W2_err WISE_W3_err WISE_W4_err int64 int64 int64 float64 float64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 7696603045628958 1 330371 0.997871222025512 -0.003181800279689 0 25.825 24.9819 23.5421 21.0602 20.8248 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -1.19283 -1.54654 7696603045628959 1 330371 0.998236420651554 -0.0033006206513122 0 24.2206 24.3073 23.7538 25.0001 -99.0 16.9868 14.4753 -99.0 -99.0 -99.0 -99.0 -2.78168 -99.0 -99.0 7696603045694624 1 330372 1.00202546888956 -0.0009794773389576 0 24.7342 25.1195 23.9565 -99.0 -99.0 17.6525 17.2043 -99.0 -99.0 -99.0 -2.6537 -1.92639 -99.0 -99.0 7696603045629090 1 330371 0.996710980408447 0.0011405432351369 0 24.4385 23.8904 23.4821 -99.0 -99.0 18.2956 -99.0 -99.0 -99.0 -99.0 -2.69137 -0.311491 -99.0 -1.8065 7696603045629094 1 330371 0.999330927183485 0.001700881614525 0 22.9443 22.2224 21.2398 20.8062 21.169 17.6742 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -0.184622 7696603045694684 0 330372 0.999330886306945 0.0017009239077483 0 22.9455 22.2238 21.2402 20.7923 21.1589 17.5228 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -0.324041
In [5]:
convert_mags_to_flux(survey.catalog)
Out[5]:
Table length=6
DECaL_ID brick_primary DECaL_brick ra dec gaia_pointsource DECaL_g DECaL_r DECaL_z WISE_W1 WISE_W2 WISE_W3 WISE_W4 DECaL_g_err DECaL_r_err DECaL_z_err WISE_W1_err WISE_W2_err WISE_W3_err WISE_W4_err z int64 int64 int64 float64 float64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 7696603045628958 1 330371 0.997871222025512 -0.003181800279689 0 0.00016982436301503037 0.0003691815546274997 0.001390464784758612 0.001165834165004409 0.0008036561924704808 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.1 7696603045628959 1 330371 0.998236420651554 -0.0033006206513122 0 0.0007443205237248634 0.0006871950053272952 0.0011441421738770923 3.095114916252917e-05 -99.0 0.005081394490707617 0.01355945071186171 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.1 7696603045694624 1 330372 1.00202546888956 -0.0009794773389576 0 0.00046378851978996717 0.0003252370358223504 0.000949292378912325 -99.0 -99.0 0.0027523452637214325 0.0010981005189436787 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.1 7696603045629090 1 330371 0.996710980408447 0.0011405432351369 0 0.000608975743467035 0.0010088811188017934 0.001469467530477905 -99.0 -99.0 0.0015221691380578274 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.1 7696603045629094 1 330371 0.999330927183485 0.001700881614525 0 0.0024114596214222564 0.004688565572821968 0.011589908152667827 0.0014731154291372922 0.000585315864488759 0.00269788176276814 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.1 7696603045694684 0 330372 0.999330886306945 0.0017009239077483 0 0.002408795850079067 0.004682523809058658 0.011585639059153531 0.0014920960417035985 0.0005907861364100322 0.0031015794632975146 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.1
In [2]:
frb190523 = FRB.by_name("FRB190523")
survey = load_survey_by_name("Pan-STARRS",frb190523.coord,15*u.arcsec)
survey.get_catalog()
Out[2]:
Table length=10
Pan-STARRS_ID ra dec objInfoFlag qualityFlag Pan-STARRS_g Pan-STARRS_r Pan-STARRS_i Pan-STARRS_z Pan-STARRS_y Pan-STARRS_g_err Pan-STARRS_r_err Pan-STARRS_i_err Pan-STARRS_z_err Pan-STARRS_y_err separation arcmin int64 float64 float64 int64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 194962070641515671 207.06427429 72.47072443 436527104 52 22.978200912475586 22.056699752807617 21.171199798583984 20.81879997253418 20.604700088500977 0.16809199750423431 0.1003049984574318 0.058139000087976456 0.06288400292396545 0.10481999814510345 0.06154607375773434 194962070655432752 207.06557723 72.46824989 436285440 52 23.081499099731445 22.094100952148438 22.37849998474121 22.177499771118164 21.631799697875977 0.18480700254440308 0.1328050047159195 0.13767799735069275 0.23336200416088104 0.4830789864063263 0.08895380876505755 194962070589123840 207.05893867 72.46923579 444915712 53 21.392000198364258 20.566099166870117 20.111099243164062 19.763399124145508 19.794300079345703 0.0412990003824234 0.027608999982476234 0.013101000338792801 0.0261049997061491 0.05897799879312515 0.11336669396811025 194962070629201005 207.06294605 72.46679517 444674048 53 22.12299919128418 21.675800323486328 21.502899169921875 21.707399368286133 21.247299194335938 0.07845800369977951 0.0917619988322258 0.054104000329971313 0.15350599586963654 0.2990039885044098 0.1795038049154313 194962070583121611 207.05858533 72.46731982 436527104 52 22.945899963378906 21.594600677490234 21.271499633789062 21.334400177001953 20.82659912109375 0.1651419997215271 0.0853549987077713 0.037769000977277756 0.11157699674367905 0.1906830072402954 0.18498379095408174 194962070604421049 207.06062466 72.46692391 436527104 52 23.347900390625 22.50279998779297 20.951099395751953 19.76099967956543 19.138399124145508 0.2384369969367981 0.1957859992980957 0.03649500012397766 0.02630399912595749 0.04225499927997589 0.18558987558276424 194962070693028149 207.06930036 72.47280774 404226048 48 24.794200897216797 24.502300262451172 22.69540023803711 21.702800750732422 22.039600372314453 0.9575219750404358 0.9948269724845886 0.16877900063991547 0.15884999930858612 0.43328800797462463 0.2007801436413031 194962070530925676 207.05309167 72.47076983 436527248 52 20.09040069580078 19.989700317382812 19.926700592041016 19.86090087890625 19.970199584960938 0.01399100013077259 0.01583999954164028 0.009170999750494957 0.028929000720381737 0.06363300234079361 0.22419957775476 194962070764472218 207.07633366 72.46786126 1384198144 165 25.91510009765625 23.059200286865234 22.314300537109375 27.924800872802734 21.937000274658203 2.4253599643707275 0.25383898615837097 0.08086299896240234 44.88589859008789 0.44850799441337585 0.23329485719850837 194962070569838430 207.0578546 72.47301528 1343750144 160 -999.0 28.999500274658203 23.214399337768555 23.06220054626465 23.11590003967285 1.1896799802780151 59.30379867553711 0.17835399508476257 0.5811989903450012 1.0535999536514282 0.23603444468990473
In [7]:
convert_mags_to_flux(survey.catalog)
Out[7]:
Table length=10
Pan-STARRS_ID ra dec objInfoFlag qualityFlag Pan-STARRS_g Pan-STARRS_r Pan-STARRS_i Pan-STARRS_z Pan-STARRS_y Pan-STARRS_g_err Pan-STARRS_r_err Pan-STARRS_i_err Pan-STARRS_z_err Pan-STARRS_y_err separation arcmin int64 float64 float64 int64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 194962070641515671 207.06427429 72.47072443 436527104 52 0.0023373278255046587 0.005461602557007481 0.012345823812802456 0.01707969084133699 0.020802712110329864 0.00039137617942680016 0.0005286080602107476 0.0006791144932347553 0.0010184351163233295 0.0021084943100777337 0.06154607375773434 194962070655432752 207.06557723 72.46824989 436285440 52 0.002125202677523379 0.005276666099168051 0.004060691537375596 0.004886524559347609 0.008077558558724435 0.0003943483327991366 0.000686564656632898 0.0005489929338442965 0.001171693308173678 0.00452653765478498 0.08895380876505755 194962070589123840 207.05893867 72.46923579 444915712 53 0.010073952875247701 0.021555610502656475 0.03277632799851968 0.045148190061386526 0.043881348229200604 0.00039057200999074564 0.000555162579572353 0.00039789022479336114 0.0010986798971418805 0.002449597153820731 0.11336669396811025 194962070629201005 207.06294605 72.46679517 444674048 53 0.0051380735619370515 0.007756751259852961 0.009095787933875017 0.007534253820077024 0.011510132327160793 0.0003850342303793361 0.00068406923321122 0.00046474113831356575 0.0011442057649592144 0.0036492641627717056 0.1795038049154313 194962070583121611 207.05858533 72.46731982 436527104 52 0.0024079086615606065 0.008359103898910219 0.011256416154581709 0.010622822468564289 0.01695744221656394 0.000395567120829929 0.000683671055472669 0.0003984620966658142 0.0011497323714599874 0.003255686833876511 0.18498379095408174 194962070604421049 207.06062466 72.46692391 436527104 52 0.0016627993192890455 0.003621429201179749 0.015120294010286847 0.04524807654520275 0.08028609678668759 0.0004083653866674257 0.0007156204984565379 0.0005168788127853664 0.0011096065322449065 0.003186196315121414 0.18558987558276424 194962070693028149 207.06930036 72.47280774 404226048 48 0.0004388535697857522 0.000574222081760238 0.003032772958493059 0.007566232701444514 0.0055482988471717695 0.000621201495131282 0.0008613026474280501 0.000510066714819344 0.0011920648105509587 0.0027211211233817674 0.2007801436413031 194962070530925676 207.05309167 72.47076983 436527248 52 0.033407172221944476 0.0366538727118693 0.03884362542279461 0.041270491662338836 0.037318154665939435 0.00043327672652524493 0.0005386697200853883 0.0003294940755397376 0.0011144161618132158 0.0022525114671399357 0.22419957775476 194962070764472218 207.07633366 72.46786126 1384198144 165 0.00015630035169338639 0.002169301315156495 0.004308040519757273 2.4551591255144958e-05 0.0060981750172174965 0.0013028630926881155 0.0005713606582244242 0.00033310279229122443 22102382028907.805 0.003119111384398808 0.23329485719850837 194962070569838430 207.0578546 72.47301528 1343750144 160 -99.0 9.124306897865119e-06 0.0018803567748668791 0.002163315070658567 0.0020589223994881053 -197.14689714024465 4.805287257207698e+18 0.00033570497253016446 0.001531556708379542 0.003374581209827869 0.23603444468990473
In [6]:
frb180924 = FRB.by_name("FRB180924")
survey = load_survey_by_name("WISE",frb180924.coord,15*u.arcsec)
survey.get_catalog()
Out[6]:
Table length=1
source_id ra dec tmass_key WISE_W1 WISE_W2 WISE_W3 WISE_W4 WISE_W1_err WISE_W2_err WISE_W3_err WISE_W4_err ph_qual moon_lev str20 float64 float64 int64 float64 float64 float64 float64 float64 float64 int64 int64 str4 int64 3263m409_ac51-031287 326.1052734 -40.9002904 -999 16.846 16.062 11.691 8.501 0.102 0.185 -999 -999 ABUU 22
In [11]:
convert_mags_to_flux(survey.catalog)
Out[11]:
Table length=1
source_id ra dec tmass_key WISE_W1 WISE_W2 WISE_W3 WISE_W4 WISE_W1_err WISE_W2_err WISE_W3_err WISE_W4_err ph_qual moon_lev z str20 float64 float64 int64 float64 float64 float64 float64 float64 float64 int64 int64 str4 int64 float64 3263m409_ac51-031287 326.1052734 -40.9002904 -999 0.05653491951856131 0.06459371982943986 0.6672720168587022 3.3263052157253505 0.005568680812966024 0.011999494472175312 -99 -99 ABUU 22 0.1
To run CIGALE, you need photometries and redshifts of each galaxy in your catalog. In addition to this, it is recommended to have a column with unique object IDs (preferably containing "ID" in the name).
In [3]:
help(cigale.run)
Help on function run in module frb.analysis.cigale:
run(photometry_table, zcol, data_file='cigale_in.fits', config_file='pcigale.ini', wait_for_input=False, plot=True, outdir=None, compare_obs_model=False, **kwargs)
Input parameters and then run CIGALE.
Args:
photometry_table (astropy Table):
A table from some photometric catalog with magnitudes and
error measurements. Currently supports
DES, DECaLS, SDSS, Pan-STARRS and WISE
zcol (str):
Name of the column with redshift estimates.
data_file (str, optional):
Path to the photometry data file generated used as input to CIGALE
config_file (str, optional):
Path to the file where CIGALE's configuration generated
wait_for_input (bool, optional):
If true, waits for the user to finish editing the auto-generated config file
before running.
plot (bool, optional):
Plots the best fit SED if true
cores (int, optional):
Number of CPU cores to be used. Defaults
to all cores on the system.
outdir (str, optional):
Path to the many outputs of CIGALE
If not supplied, the outputs will appear in a folder named out/
compare_obs_model (bool, optional):
If True compare the input observed fluxes with the model fluxes
This writes a Table to outdir named 'photo_observed_model.dat'
kwargs: These are passed into _initialise()
sed_modules (list of 'str', optional):
A list of SED modules to be used in the
PDF analysis. If this is being input, there
should be a corresponding correct dict
for sed_modules_params.
sed_module_params (dict, optional):
A dict containing parameter values for
the input SED modules. Better not use this
unless you know exactly what you're doing.
In [2]:
frb180924 = FRB.by_name("FRB180924")
survey = load_survey_by_name("DES",frb180924.coord,15*u.arcsec)
survey.get_catalog()
cat = survey.catalog
cat['z'] = 0.1*np.ones(len(cat)) # Add redshifts
In [3]:
cat
Out[3]:
Table length=2
DES_g DES_g_err DES_r DES_r_err DES_i DES_i_err DES_z DES_z_err DES_Y DES_Y_err DES_ID ra dec DES_tile WISE_W1 WISE_W1_err WISE_W2 WISE_W2_err WISE_W3 WISE_W3_err WISE_W4 WISE_W4_err z float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 float64 float64 str12 float64 float64 float64 float64 float64 float64 float64 float64 float64 25.0468 0.305723 24.5441 0.301756 23.923 0.293647 23.463 0.271636 24.7709 2.66629 209914542 326.101627 -40.899805 DES2143-4040 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 0.1 21.6222 0.0269829 20.5415 0.0156388 20.1379 0.0186293 19.8547 0.0200389 19.8083 0.0564316 209914488 326.105209 -40.900226 DES2143-4040 16.846 0.102 16.062 0.185 11.691 -999.0 8.501 -999.0 0.1
In [4]:
cigale.run(survey.catalog,"z",compare_obs_model=True,plot=True)
No radio module found. Options are: radio.
Initialising the analysis module...
The out/ directory was renamed to 2020-01-07_12:05:12_out/
Processing block 1/1...
Computing models ...
16800/16800 computed in 19.0 seconds (884.5/s)
Estimating the physical properties ...
2/2 computed in 3.5 seconds (0.6/s)
Block processed.
Estimating physical properties on all blocks
Computing the best fit spectra
2/2 computed in 0.5 seconds (4.3/s)
1/2 computed in 0.5 seconds (2.2/s)
0.0% of the objects have chi^2_red~0 and 0.0% chi^2_red<0.5
Saving the analysis results...
Run completed!
In [5]:
ls out/
209914488_best_model.fits 209914542_SFH.fits photo_observed_model.dat
209914488_best_model.pdf observations.fits results.fits
209914488_SFH.fits observations.txt results.txt
209914542_best_model.fits pcigale.ini*
209914542_best_model.pdf pcigale.ini.spec*
In [7]:
results = Table.read("out/results.fits")
results
Out[7]:
Table length=2
id bayes.agn.fracAGN_dale2014 bayes.agn.fracAGN_dale2014_err bayes.attenuation.B_B90 bayes.attenuation.B_B90_err bayes.attenuation.E_BVs.nebular.continuum_old bayes.attenuation.E_BVs.nebular.continuum_old_err bayes.attenuation.E_BVs.nebular.continuum_young bayes.attenuation.E_BVs.nebular.continuum_young_err bayes.attenuation.E_BVs.nebular.lines_old bayes.attenuation.E_BVs.nebular.lines_old_err bayes.attenuation.E_BVs.nebular.lines_young bayes.attenuation.E_BVs.nebular.lines_young_err bayes.attenuation.E_BVs.stellar.old bayes.attenuation.E_BVs.stellar.old_err bayes.attenuation.E_BVs.stellar.young bayes.attenuation.E_BVs.stellar.young_err bayes.attenuation.FUV bayes.attenuation.FUV_err bayes.attenuation.V_B90 bayes.attenuation.V_B90_err bayes.attenuation.ebvs_old_factor bayes.attenuation.ebvs_old_factor_err bayes.attenuation.powerlaw_slope bayes.attenuation.powerlaw_slope_err bayes.attenuation.uv_bump_amplitude bayes.attenuation.uv_bump_amplitude_err bayes.attenuation.uv_bump_wavelength bayes.attenuation.uv_bump_wavelength_err bayes.attenuation.uv_bump_width bayes.attenuation.uv_bump_width_err bayes.dust.alpha bayes.dust.alpha_err bayes.nebular.f_dust bayes.nebular.f_dust_err bayes.nebular.f_esc bayes.nebular.f_esc_err bayes.nebular.lines_width bayes.nebular.lines_width_err bayes.nebular.logU bayes.nebular.logU_err bayes.param.EW(500.7/1.0) bayes.param.EW(500.7/1.0)_err bayes.param.EW(656.3/1.0) bayes.param.EW(656.3/1.0)_err bayes.param.restframe_u_prime-r_prime bayes.param.restframe_u_prime-r_prime_err bayes.sfh.age bayes.sfh.age_err bayes.sfh.age_burst bayes.sfh.age_burst_err bayes.sfh.age_main bayes.sfh.age_main_err bayes.sfh.f_burst bayes.sfh.f_burst_err bayes.sfh.tau_burst bayes.sfh.tau_burst_err bayes.sfh.tau_main bayes.sfh.tau_main_err bayes.stellar.age_m_star bayes.stellar.age_m_star_err bayes.stellar.imf bayes.stellar.imf_err bayes.stellar.metallicity bayes.stellar.metallicity_err bayes.stellar.old_young_separation_age bayes.stellar.old_young_separation_age_err bayes.universe.age bayes.universe.age_err bayes.universe.luminosity_distance bayes.universe.luminosity_distance_err bayes.universe.redshift bayes.universe.redshift_err bayes.attenuation.nebular.continuum_old bayes.attenuation.nebular.continuum_old_err bayes.attenuation.nebular.continuum_young bayes.attenuation.nebular.continuum_young_err bayes.attenuation.nebular.lines_old bayes.attenuation.nebular.lines_old_err bayes.attenuation.nebular.lines_young bayes.attenuation.nebular.lines_young_err bayes.attenuation.stellar.old bayes.attenuation.stellar.old_err bayes.attenuation.stellar.young bayes.attenuation.stellar.young_err bayes.dust.luminosity bayes.dust.luminosity_err bayes.param.restframe_Lnu(r_prime) bayes.param.restframe_Lnu(r_prime)_err bayes.param.restframe_Lnu(u_prime) bayes.param.restframe_Lnu(u_prime)_err bayes.sfh.integrated bayes.sfh.integrated_err bayes.sfh.sfr bayes.sfh.sfr_err bayes.sfh.sfr100Myrs bayes.sfh.sfr100Myrs_err bayes.sfh.sfr10Myrs bayes.sfh.sfr10Myrs_err bayes.stellar.lum bayes.stellar.lum_err bayes.stellar.lum_ly bayes.stellar.lum_ly_err bayes.stellar.lum_ly_old bayes.stellar.lum_ly_old_err bayes.stellar.lum_ly_young bayes.stellar.lum_ly_young_err bayes.stellar.lum_old bayes.stellar.lum_old_err bayes.stellar.lum_young bayes.stellar.lum_young_err bayes.stellar.m_gas bayes.stellar.m_gas_err bayes.stellar.m_gas_old bayes.stellar.m_gas_old_err bayes.stellar.m_gas_young bayes.stellar.m_gas_young_err bayes.stellar.m_star bayes.stellar.m_star_err bayes.stellar.m_star_old bayes.stellar.m_star_old_err bayes.stellar.m_star_young bayes.stellar.m_star_young_err bayes.stellar.n_ly bayes.stellar.n_ly_err bayes.stellar.n_ly_old bayes.stellar.n_ly_old_err bayes.stellar.n_ly_young bayes.stellar.n_ly_young_err bayes.DES_Y bayes.DES_Y_err bayes.DES_g bayes.DES_g_err bayes.DES_i bayes.DES_i_err bayes.DES_r bayes.DES_r_err bayes.DES_z bayes.DES_z_err bayes.WISE1 bayes.WISE1_err bayes.WISE2 bayes.WISE2_err bayes.WISE3 bayes.WISE3_err bayes.WISE4 bayes.WISE4_err best.chi_square best.reduced_chi_square best.agn.fracAGN_dale2014 best.attenuation.B_B90 best.attenuation.E_BVs.nebular.continuum_old best.attenuation.E_BVs.nebular.continuum_young best.attenuation.E_BVs.nebular.lines_old best.attenuation.E_BVs.nebular.lines_young best.attenuation.E_BVs.stellar.old best.attenuation.E_BVs.stellar.young best.attenuation.FUV best.attenuation.V_B90 best.attenuation.ebvs_old_factor best.attenuation.powerlaw_slope best.attenuation.uv_bump_amplitude best.attenuation.uv_bump_wavelength best.attenuation.uv_bump_width best.dust.alpha best.nebular.f_dust best.nebular.f_esc best.nebular.lines_width best.nebular.logU best.param.EW(500.7/1.0) best.param.EW(656.3/1.0) best.param.restframe_u_prime-r_prime best.sfh.age best.sfh.age_burst best.sfh.age_main best.sfh.f_burst best.sfh.tau_burst best.sfh.tau_main best.stellar.age_m_star best.stellar.imf best.stellar.metallicity best.stellar.old_young_separation_age best.universe.age best.universe.luminosity_distance best.universe.redshift best.attenuation.nebular.continuum_old best.attenuation.nebular.continuum_young best.attenuation.nebular.lines_old best.attenuation.nebular.lines_young best.attenuation.stellar.old best.attenuation.stellar.young best.dust.luminosity best.param.restframe_Lnu(r_prime) best.param.restframe_Lnu(u_prime) best.sfh.integrated best.sfh.sfr best.sfh.sfr100Myrs best.sfh.sfr10Myrs best.stellar.lum best.stellar.lum_ly best.stellar.lum_ly_old best.stellar.lum_ly_young best.stellar.lum_old best.stellar.lum_young best.stellar.m_gas best.stellar.m_gas_old best.stellar.m_gas_young best.stellar.m_star best.stellar.m_star_old best.stellar.m_star_young best.stellar.n_ly best.stellar.n_ly_old best.stellar.n_ly_young best.DES_g best.DES_r best.DES_i best.DES_z best.DES_Y best.WISE1 best.WISE2 best.WISE3 best.WISE4 mJy mJy mJy mJy mJy mJy mJy mJy mJy int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 209914542 0.10265319651287422 0.07558696186473438 2.3601855019348417 1.1991384422542277 0.47113336322627936 0.2400208063480193 0.47113336322627936 0.2400208063480193 0.47113336322627936 0.2400208063480193 0.47113336322627936 0.2400208063480193 0.47113336322627936 0.2400208063480193 0.47113336322627936 0.2400208063480193 4.765506260357607 2.424206426785208 1.9048457355260133 0.9682151748581753 1.0 0.0 0.0 0.0 0.0 0.0 217.49999999999994 5.684341886080802e-14 35.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 300.0 0.0 -2.0 0.0 0.864718205606048 2.3504809959980264 1.6916225922343964 3.7800988418786727 2.016250649173815 0.6295081436287228 3777.9207891521737 2718.265787781422 19.999999999999996 3.552713678800501e-15 3777.9207891521737 2718.265787781422 0.0 0.0 49.99999999999999 7.105427357601002e-15 262.2539014342263 315.7882004072654 3335.397259224269 2691.3128324705394 1.0 0.0 0.011766005663766487 0.016338026679084933 9.999999999999998 1.7763568394002505e-15 12457.063649431224 3.637978807091713e-12 1.4152197720423436e+25 0.0 0.09999999999999999 1.3877787807814457e-17 4.13426412373824e+31 5.711555748362575e+31 7.704555895124209e+32 2.248763864416448e+33 8.355266247523453e+31 1.0806330121435372e+32 1.5718443442817492e+33 4.459791766769753e+33 4.988598799521011e+34 4.381512483721603e+34 8.81070376429965e+33 2.3330200324832096e+34 6.116388699701656e+34 6.635915386981173e+34 1.6754859052657902e+19 1.0383630298357458e+18 3.0327442585447926e+18 1.63923460771547e+18 200320790.46922413 200403187.39880314 0.004617207467043068 0.01205363185212251 0.004878257512396643 0.01249546760874372 0.004639800259529555 0.012092416712201936 8.580453530723594e+34 7.068802560948031e+34 3.1474874697110587e+33 8.347740817589051e+33 1.879386050230585e+32 1.7786393664668247e+32 2.959548864688e+33 8.252235160081458e+33 7.371279871661663e+34 4.8314641737698435e+34 1.2091736590619347e+34 3.155378330758284e+34 93175917.96081553 98647570.39938815 93173296.08716904 98649102.15421802 2621.8736464920707 7081.2375448304565 107144872.50711876 102111650.19467898 107101096.37945978 102134363.10539143 43776.12765897059 114027.49933420256 8.97277440143654e+50 2.379798504395509e+51 4.5844452752761247e+49 5.543709824248255e+49 8.514329873908927e+50 2.3420233400665662e+51 0.0016876812312570282 0.0003453857000871282 0.00027386696825404985 7.140059524219485e-05 0.0009984464088849273 5.578835662735609e-05 0.0006068881306418539 5.581787086077655e-05 0.0014214280245434074 0.00021725178843803866 0.003274884415732941 0.0022733980651738405 0.002612434894435459 0.0019629505574720253 0.0140208428688698 0.014397311615078155 0.020362273455207556 0.0211718291893867 14.827479478388398 1.8534349347985497 0.2 4.311640596085016 0.86 0.86 0.86 0.86 0.86 0.86 8.739493331806761 3.4682551991753305 1.0 0.0 0.0 217.5 35.0 2.0 0.0 0.0 300.0 -2.0 0.16741457224336737 1.0634455091388613 0.9283719552240394 5994.0 20.0 5994.0 0.0 50.0 1000.0 4042.8078011627285 1.0 0.0004 10.0 12457.063649431228 1.4152197720423436e+25 0.1 1.0254262419766055e+32 1.2000148900203676e+33 1.9074696459617044e+32 2.232234639327815e+33 9.963410733140913e+34 9.316735323123057e+33 1.126763817726742e+35 1.5754060157622184e+19 6.699520454596563e+18 336535272.65539944 0.005123805293130329 0.00534082351260652 0.005143063808608143 1.3652595829323425e+35 4.54179367931597e+33 4.069398773295658e+32 4.134853801986404e+33 1.2299411821705769e+35 1.3531840076176567e+34 159815190.54415193 159812727.3010247 2463.243127235421 176720082.12974292 176671114.71628854 48967.41345441174 1.268999521607654e+51 9.990080256021887e+49 1.1690987190474353e+51 0.00033772342492053214 0.0005723457207743572 0.0009787705949424758 0.0014697231093382186 0.0017561661720113322 0.0053807610232084685 0.004914311863908245 0.028279899441657454 0.042158682692117665 209914488 0.11215194827232314 0.07506467623828443 1.7070522991750041 0.6452443466180182 0.33951113294371904 0.12678722316548507 0.33951113294371904 0.12678722316548507 0.33951113294371904 0.12678722316548507 0.33951113294371904 0.12678722316548507 0.33951113294371904 0.12678722316548507 0.33951113294371904 0.12678722316548507 3.4266720255116407 1.2795164764419253 1.374469255964486 0.5143598358218114 1.0 0.0 0.0 0.0 0.0 0.0 217.49999999999994 5.684341886080802e-14 34.99999999999999 7.105427357601002e-15 2.0 0.0 0.0 0.0 0.0 0.0 299.99999999999994 5.684341886080802e-14 -2.0 0.0 0.23221382456476306 0.7807965366187042 0.4399093860825536 1.2040027666873618 2.2602681625495475 0.3113502918154861 2854.19854671248 2190.567364684557 20.0 0.0 2854.19854671248 2190.567364684557 0.0 0.0 49.99999999999999 7.105427357601002e-15 160.3123857873963 236.07772960286454 2541.4362044964055 2046.6838810370577 1.0 0.0 0.015353400159681153 0.019589110791680586 10.0 0.0 12457.063649431226 1.8189894035458565e-12 1.4152197720423433e+25 2147483648.0 0.09999999999999999 1.3877787807814457e-17 6.030542124834723e+32 5.1309925921282685e+32 3.380565642182492e+33 1.316539032362994e+34 1.2265621562961726e+33 9.67773864079419e+32 6.767163312104114e+33 2.575971292084682e+34 7.760234423512716e+35 5.507208163228158e+35 3.120806115036157e+34 1.0664355356868621e+35 8.192088488246995e+35 6.273675330256983e+35 5.3912904077554215e+20 2.695645203877711e+19 6.9678121947883405e+19 1.877004646642677e+19 3197978162.0497746 1708628483.0710962 0.01733288619545301 0.058902558728381016 0.01912550976594111 0.06345557765277582 0.01748131522306582 0.05928316568083837 1.4352488454292888e+36 6.062352339392986e+35 1.6245373720076263e+34 4.9835443724464e+34 3.143876342402215e+33 1.774697595149254e+33 1.3101497377674044e+34 4.89316830804086e+34 1.3888793499974562e+36 5.2124705883061515e+35 4.636949543183276e+34 1.587892873328625e+35 1453543152.5251331 905731623.6354024 1453534106.5760274 905734775.7658544 9045.949106081496 29449.09136639557 1744435009.518047 805192962.4886996 1744269242.3215163 805242506.4187628 165767.19653082747 563677.3205908604 4.4145897259623696e+51 1.4043216522946123e+52 7.044471551328524e+50 4.838769311284798e+50 3.710142570829518e+51 1.3719016145371455e+52 0.04794339209333121 0.0019856129091145387 0.008375106355913304 0.0006417236595443007 0.031029175063760583 0.0007992312694040495 0.019895928793779584 0.0008220387803533175 0.04172623887333794 0.0014688920110146578 0.05835900710719939 0.006987469122698057 0.04467159054177852 0.00739418571357555 0.18940721183720738 0.12985691543426883 0.27093938678755647 0.1902777448225148 5.892080458133542 0.7365100572666927 0.05 1.2491939785928596 0.25 0.25 0.25 0.25 0.25 0.25 2.5494274321608996 1.0110417179789262 1.0 0.0 0.0 217.5 35.0 2.0 0.0 0.0 300.0 -2.0 0.005690998305157191 0.1635071870089905 2.584307063318549 1291.0 20.0 1291.0 0.0 50.0 129.1549665014884 1030.2009172503535 1.0 0.05 10.0 12457.063649431228 1.4152197720423436e+25 0.1 1.6531326350969224e+32 3.963727171171442e+32 3.743458464338709e+32 8.975715386794185e+32 4.0557875795024284e+35 1.156713970906643e+34 4.189795010250494e+35 5.509691853740429e+20 5.098054629662822e+19 1750775427.6292913 0.006224216793337007 0.00895551920970949 0.00642368924578031 1.123235052700782e+36 3.370104864462389e+33 1.3161153850564838e+33 2.0539894794059047e+33 1.1074763814371366e+36 1.575867126364549e+34 694218705.2392335 694213685.9894239 5019.2498096636355 1056556722.4000013 1056497504.7474086 59217.6525925848 9.212478845825407e+50 2.711381440066936e+50 6.501097405758471e+50 0.008350853521867865 0.020504360549851032 0.03067039203930872 0.04019102800657864 0.04729413025152934 0.06323571021756548 0.05087646070163933 0.09962104574289546 0.13191570009051434
Content source: FRBs/FRB
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