GLYPH<Minus> 1.5 GLYPH<Minus> 1.0 GLYPH<Minus> 0.5 0.0 0.5 1.0 1.5 0.00 0.01 0.02 0.03 0.04 0.05 q P GLYPH<LParen1> q GLYPH<RParen1> GLYPH<Minus> 3 GLYPH<Minus> 2 GLYPH<Minus> 1 0 1 2 3 0.00 0.01 0.02 0.03 0.04 0.05 0.06 q P GLYPH<LParen1> q GLYPH<RParen1> 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 z weight function 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 z weight function w$_{L}$ GLYPH<Equal> $_{GLYPH<Minus>}$1 w$_{L}$ GLYPH<Equal> $_{GLYPH<Minus>}$1 w$_{L}$ unfixed w$_{L}$ unfixed q$_{1}$ q$_{2}$ q$_{3}$ q$_{1}$ q$_{2}$ q$_{3}$ q$_{4}$ FIG. 3: Likelihoods and weight functions of uncorrelated parameters q$_{i}$ and corresponding weight functions. The left panels are for the model with w$_{L}$ = − 1 and the right panels are for the case with a floating w$_{L}$ . TABLE III: The best-fitted parameters and 2 σ errors for Model II with w$_{L}$ fixed to − 1 or floating. ML is for “Maximum Likelihood”, and the value in {} means this parameter has been fixed. parameters h Ω (0) b Ω (0) m z$_{1}$ w (0) w ( z$_{1}$ ) w (0 . 9) w$_{L}$ best-fitted values 0.684 0.050 0.282 0.45 -0.63 -1.59 1.24 { -1 } ML and 2 σ errors 0 . 684 +0 . 025 − 0 026 0 . 050 +0 . 004 − 0 003 0 . 286 +0 . 03 − 0 { 0.45 } − 0 . 64 +0 . 44 − 0 38 − 1 . 64 +0 . 72 − 0 1 . 23 +1 . 97 − 2 09 { -1 } best-fitted values 0.687 0.049 0.280 { 0.45 } -0.59 -1.69 2.54 -1.72 ML and 2 σ errors 0 . 687 +0 . 027 − 0 026 0 . 049 +0 . 004 − 0 0 . 285 +0 . 033 − 0 030 { 0 . 45 } − 0 . 57 +0 . 41 − 0 42 − 1 . 70 +0 . 69 − 0 79 3 . 03 +2 . 58 − 3 66 − 1 . 77 +1 . 78 − 2 87 points of w$_{de}$ in this region. We found that with the additional 2 parameters ( z$_{2}$ and w ( z$_{2}$ )), there is almost no improvement of χ 2 $_{min}$, compared to the 2 bins case (Model II). This indicates that there is no more turning points and w$_{de}$ ( z ) can be well approximated by just two linear expansions in the region z ∈ (0 , 0 . 9). Of course, there is another possibility that the current data are not enough to find out more turning points. 12