Line parameters of Hb_whole_br in output fits table do not seem right

[smat-astr]

Hi,

I am using PyQSOFit version 2.1 and MCMC to compute errors. I have noticed that, while the parameters for Ha_whole_br in the output fits table align with the printed outputs of the code and the parameter limits I set, this is not true for the parameters of Hb_whole_br. The issue is illustrated in the following example of the output of the code:

fwhm, sigma, ew, peak, area, snr = q_hprior.line_prop_from_name('Hb_br', 'broad')
print("Broad Hb:")
print("FWHM (km/s)", np.round(fwhm, 1))
print("Sigma (km/s)", np.round(sigma, 1))
print("EW (A)", np.round(ew, 1))
print("Peak (A)", np.round(peak, 1))
print("Area (10^(-17) erg/s/cm^2)", np.round(area, 1))
print("")

fwhm, sigma, ew, peak, area, snr = q_hprior.line_prop_from_name('Ha_br', 'broad')
print("Broad Ha:")
print("FWHM (km/s)", np.round(fwhm, 1))
print("Sigma (km/s)", np.round(sigma, 1))
print("EW (A)", np.round(ew, 1))
print("Peak (A)", np.round(peak, 1))
print("Area (10^(-17) erg/s/cm^2)", np.round(area, 1))
print("")

print(q_hprior.line_result_name)
print('')
print(q_hprior.line_result)

My output:

Broad Hb:
FWHM (km/s) 9298.4
Sigma (km/s) 3896.4
EW (A) 23.2
Peak (A) 4841.6
Area (10^(-17) erg/s/cm^2) 11.9

Broad Ha:
FWHM (km/s) 19619.0
Sigma (km/s) 8217.5
EW (A) 356.4
Peak (A) 6536.8
Area (10^(-17) erg/s/cm^2) 88.3

['1_complex_name' '1_line_status' '1_line_min_chi2' '1_line_bic'
'1_line_red_chi2' '1_niter' '1_ndof' '2_complex_name' '2_line_status'
'2_line_min_chi2' '2_line_bic' '2_line_red_chi2' '2_niter' '2_ndof'
'3_complex_name' '3_line_status' '3_line_min_chi2' '3_line_bic'
'3_line_red_chi2' '3_niter' '3_ndof' 'OII3728_1_scale'
'OII3728_1_scale_err' 'OII3728_1_centerwave' 'OII3728_1_centerwave_err'
'OII3728_1_sigma' 'OII3728_1_sigma_err' 'Hb_br_1_scale'
'Hb_br_1_scale_err' 'Hb_br_1_centerwave' 'Hb_br_1_centerwave_err'
'Hb_br_1_sigma' 'Hb_br_1_sigma_err' 'Hb_br_2_scale' 'Hb_br_2_scale_err'
'Hb_br_2_centerwave' 'Hb_br_2_centerwave_err' 'Hb_br_2_sigma'
'Hb_br_2_sigma_err' 'Hb_na_1_scale' 'Hb_na_1_scale_err'
'Hb_na_1_centerwave' 'Hb_na_1_centerwave_err' 'Hb_na_1_sigma'
'Hb_na_1_sigma_err' 'OIII4959c_1_scale' 'OIII4959c_1_scale_err'
'OIII4959c_1_centerwave' 'OIII4959c_1_centerwave_err' 'OIII4959c_1_sigma'
'OIII4959c_1_sigma_err' 'OIII5007c_1_scale' 'OIII5007c_1_scale_err'
'OIII5007c_1_centerwave' 'OIII5007c_1_centerwave_err' 'OIII5007c_1_sigma'
'OIII5007c_1_sigma_err' 'OIII4959w_1_scale' 'OIII4959w_1_scale_err'
'OIII4959w_1_centerwave' 'OIII4959w_1_centerwave_err' 'OIII4959w_1_sigma'
'OIII4959w_1_sigma_err' 'OIII5007w_1_scale' 'OIII5007w_1_scale_err'
'OIII5007w_1_centerwave' 'OIII5007w_1_centerwave_err' 'OIII5007w_1_sigma'
'OIII5007w_1_sigma_err' 'Ha_br_1_scale' 'Ha_br_1_scale_err'
'Ha_br_1_centerwave' 'Ha_br_1_centerwave_err' 'Ha_br_1_sigma'
'Ha_br_1_sigma_err' 'Ha_br_2_scale' 'Ha_br_2_scale_err'
'Ha_br_2_centerwave' 'Ha_br_2_centerwave_err' 'Ha_br_2_sigma'
'Ha_br_2_sigma_err' 'Ha_na_1_scale' 'Ha_na_1_scale_err'
'Ha_na_1_centerwave' 'Ha_na_1_centerwave_err' 'Ha_na_1_sigma'
'Ha_na_1_sigma_err' 'NII6549_1_scale' 'NII6549_1_scale_err'
'NII6549_1_centerwave' 'NII6549_1_centerwave_err' 'NII6549_1_sigma'
'NII6549_1_sigma_err' 'NII6585_1_scale' 'NII6585_1_scale_err'
'NII6585_1_centerwave' 'NII6585_1_centerwave_err' 'NII6585_1_sigma'
'NII6585_1_sigma_err' 'SII6718_1_scale' 'SII6718_1_scale_err'
'SII6718_1_centerwave' 'SII6718_1_centerwave_err' 'SII6718_1_sigma'
'SII6718_1_sigma_err' 'SII6732_1_scale' 'SII6732_1_scale_err'
'SII6732_1_centerwave' 'SII6732_1_centerwave_err' 'SII6732_1_sigma'
'SII6732_1_sigma_err' 'OII_whole_br_fwhm' 'OII_whole_br_fwhm_err'
'OII_whole_br_sigma' 'OII_whole_br_sigma_err' 'OII_whole_br_ew'
'OII_whole_br_ew_err' 'OII_whole_br_peak' 'OII_whole_br_peak_err'
'OII_whole_br_area' 'OII_whole_br_area_err' 'OII_whole_br_snr'
'OII_whole_br_snr_err' 'Hb_whole_br_fwhm' 'Hb_whole_br_fwhm_err'
'Hb_whole_br_sigma' 'Hb_whole_br_sigma_err' 'Hb_whole_br_ew'
'Hb_whole_br_ew_err' 'Hb_whole_br_peak' 'Hb_whole_br_peak_err'
'Hb_whole_br_area' 'Hb_whole_br_area_err' 'Hb_whole_br_snr'
'Hb_whole_br_snr_err' 'Ha_whole_br_fwhm' 'Ha_whole_br_fwhm_err'
'Ha_whole_br_sigma' 'Ha_whole_br_sigma_err' 'Ha_whole_br_ew'
'Ha_whole_br_ew_err' 'Ha_whole_br_peak' 'Ha_whole_br_peak_err'
'Ha_whole_br_area' 'Ha_whole_br_area_err' 'Ha_whole_br_snr'
'Ha_whole_br_snr_err']

['OII' '1' '132.72738471801887' '59.748778656048195' '3.3181846179504717'
'53' '40' 'Hb' '1' '499.23971661142843' '249.27929066638114'
'4.230845056029055' '132' '118' 'Ha' '1' '485.36347667516674'
'221.31122573125992' '4.372643934010511' '183' '111' '1.1483558548519568'
'0.26326059996082646' '8.224241979557378' '0.00028730754668426783'
'0.0009591574906740917' '0.00020760284380930719' '0.03722855357324306'
'0.015272657022779423' '8.485116742267978' '0.000844865776095105'
'0.013226182405518368' '0.0066861916819435695' '0.03722855357324306'
'0.015004897753986184' '8.485116742300768' '0.0021344218621486277'
'0.01322618241090765' '0.009826920000161738' '0.12554235429007576'
'0.05220279011527776' '8.488974702863302' '0.00013432613368813406'
'0.0016607914230403404' '0.00028227495785784263' '0.137848066295021'
'0.05891032937538315' '8.508851182965842' '0.0' '0.0016607914230403404'
'0.0' '0.4986450141686305' '0.16997332641749996' '8.518469942121115'
'0.0' '0.0016607914230403404' '0.0' '0.06668166019352384'
'0.035657278836588' '8.508317195508802' '0.00032604672055924766'
'0.0038754327612850396' '0.0006301478500668671' '0.1983763153745599'
'0.09775727516909963' '8.517935954664075' '0.0' '0.0038754327612850396'
'0.0' '0.19303891818367447' '0.01795127151906674' '8.785208929087982'
'0.0005433168278932143' '0.027903731471066007' '0.0034875218955351533'
'0.007773781618425346' '0.005012689905572788' '8.793688716042691'
'0.001337664771662972' '0.0018878588816741712' '0.0015009525897937512'
'0.7115996680795433' '0.07975361431610523' '8.789624187190398'
'3.283416287480634e-05' '0.0011098838123932858' '4.335528301836267e-05'
'0.9282580260006057' '0.03445582920265977' '8.787372562129363' '0.0'
'0.0011098838123932858' '0.0' '2.784774078001817' '0.0'
'8.792767497737273' '0.0' '0.0011098838123932858' '0.0'
'0.45816517246777266' '0.042172673141741834' '8.81276414080368' '0.0'
'0.0011098838123932858' '0.0' '0.45816517246777266' '0.0'
'8.814902278621062' '0.0' '0.0011098838123932858' '0.0' '0.0' '0.0' '0.0'
'0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '3560.522782682529'
'825.0964716195697' '5458.389872903858' '1016.9791593849909'
'48.84811234523053' '12.85102486862834' '5002.913093402928'
'0.4901713571921391' '24.773157153462876' '6.479202501310278'
'1.3735257711683038' '0.8171204303286859' '19619.01285524007'
'2817.6215679216803' '8217.48423617239' '1041.9145525478257'
'356.3540085509411' '40.57390315653569' '6536.8385738849065'
'22.244716832328322' '88.29776939529299' '9.161242093903802'
'0.9615227075716244' '0.13533562174720581']

Since I would like to use the new method to decompose the host galaxy, is there a way I can obtain the Hb_whole_br parameters (along with the corresponding errors) using the latest version of PyQSOFit?

Thank you for your help,

[legolason]

you mean here are 0.0 for the whole components?