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Table C.1

Best-fit scaled beta distribution description for polarization fraction curve for Fig. 6

id Target Date θ[P] α β
(1) (2) (3) (4) (5) (6) (7)
b CQ Tau 2021-01-01 63°.3 0.522 3.170 5.000
h HD 34282 2020-12-27 83°.7 0.257 4.478 5.000
i HD 97048 2021-01-28 56°.3 0.200 2.012 3.227
j HD 100453 2022-06-09 94°.0 0.375 3.069 2.892
k HD 100546 2020-12-22 125.9 0.274 4.035 2.305
n HD 169142 2021-09-06 73°.0 0.273 3.209 4.241
q LkCa 15 2020-12-08 90.0 0.316 5.000 5.000
r LkHa 330 2020-12-08 77°.4 0.334 4.016 5.000
s MWC 758 2020-12-19 86°.9 0.408 3.576 3.759
s′ MWC 758 2020-12-23 90.9 0.439 4.362 4.293
s MWC 758 2020-12-26 90.4 0.377 2.806 2.790
u PDS 201 2022-02-07 87°.4 0.396 3.768 3.932
v SAO 206462 2021-06-04 77°.7 0.415 3.210 3.912
x SZ Cha 2020-12-30 83°.6 0.374 3.769 4.190
z V1247 Ori 2020-12-24 83°.4 0.458 3.329 3.698

Notes. The modeling results are obtained directly from modeling Fig. 6 in Section 5, instead of performing negative injection for KLIP RDI in Appendix C.3. Column (1): Letter identifiers of the targets in this paper. Column (2): Target name. Column (3): UTC observation date. Columns (4): Scattering angle with peak polarization. Columns (5), (6), and (7): maximum polarization fraction, and parameters used to generate the polarization fraction curves in Fig. 7, see Eq. (C.2) for the mathematical profile using scaled beta distribution. In addition, we did not report the uncertainties from emcee modeling due to them being extremely small (see Wolff et al. 2017 for a way to obtain more realistic uncertainties). With the values from Columns (5), (6), and (7), to generate an array of polarization fraction in Fig. 7, readers can use the following pseudocode with scipy (Virtanen et al. 2020): *scipy.stats.beta.pdf(θscat/π,α,β)/scipy.stats.beta.pdf , where θscat is an array of scattering angles in units of radians which is divided by π so that 0 ≤ θscat/π ≤ 1.

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