Fitting Quality Assessment


One of novelty of our database is that, we provide fitting quality assessment parameter. By simply taking Nσ of continuum, users easily judge the reliability of the measurements. In general, approximately 1.0 of Nσ or below 1.0 indicates very accurate fit and we suggest do not use above 3.0 of Nσ.


DOWNLOAD :  for continuum (27 MB)

DOWNLOAD :  for emission (47 MB)


  • Assessing the quality of our fits to the SDSS spectra is a key to the accuracy of our measurements for the stellar and gaseous kinematics, as well as for the strengths of both the emission and absorption lines.
  • An inadequate model for the observed spectrum, or artificial features in the data themselves is likely to introduce biases to the measured parameters that are not accounted for by our formal errors.
  • The quality of the data, as routinely estimated by dividing the average level of the flux density (hereafter S, for signal) by the level of the formal uncertainties for the latter (hereafter sN, for statistical noise), does not guarantee a good fit.
  • We compare the level of fluctuations in the fit residuals (hereafter rN, for residual noise) to the expected statistical fluctuations, sN.
  • A rN/sN ratio close to unity indicates a good fit, as this ration corresponds to a reduced Χ2, which is also close to 1.


Fitting Quality Assessing Parameter (continuum)
column      Name Data Type/Units Description
0      SDSS_ID string[1] 18-digit
1       S_sN double[1] Signal/statistical Noise
2       rN_sN double[1] residual Noise/statistical Noise
3       Nsigma double[1] quality assessing Nσ



 Figure : Quality assessment for continuum with examples inducing bad fits. This figure shows a quality assessment for a continuum region using randomly selected ~46,000 objects. Red crosses on the central panel denote the median (lower) and 1 σ(upper) distribution for each S/sN bin and orange solid lines fit (note that there is a demarcation line for 1 σ in the direction of larger rN/sN.). The vertical dashed lines indicate a specific bin and its Gaussian distribution has been inserted on the top side as an example. The color filled dots correspond to the colored left and right panels and clarify the trend for quality assessments. The black solid lines represent the observed spectra and the coloured lines are the fits. Furthermore, the top and bottom examples with gray fits are the major reasons for the bad fits. These telluric contaminated spectra (top) and Broad Line Regions (bottom) are marked with black crosses on the central panel. A minor reason which brought about bad fits is also denoted by red and green filled dots.




Fitting Quality Assessing Parameter (emission)
column      Name Data Type/Units Description
0      SDSS_ID string[1] 18-digit
1      Nsigma_OII_3726_3729 double[1] [OII]3726&3729
2      Nsigma_Hb_4861 double[1] Hb
3     Nsigma_OIII_5007 double[1] [OIII]5007
4     Nsigma_OI_6300 double[1] [OI]6300
5     Nsigma_OI_6363 double[1] [OI]6363
6     Nsigma_Ha_NII_6547_6583 double[1] Ha + [NII]6547&6583
7     Nsigma_SII_6716_6730 double[1] [SII]6716&6730


Figure : Quality assessment process for [OIII] λ5007 with three examples. Left : The quality assessment plane drawn by a central emission region using randomly selected ∼46000 objects. The three examples which have different emission line widths are shown as Ex.1,2 and 3. Middle : Quality assessment plane given by a typical passband. The lower and upper red filled dots are the median and 1σ at each S/sN bin. The black dashed and solid lines trace each point. Moreover, [OIII] λ5007 emissions for these three examples are shown on left sub-panels in corresponding colors. The black line represents the observed spectrum and the colored one the fit. Once we derived the median and 1σ from the typical passband, we measured the Nσ of every [OIII] λ5007 emission. The newly derived 1σ are also shown using the same colors. Right : 1,000 objects shown in different colors depend on Nσ. The objects below the median are indicated in gray.