2014-12-08T11:51:00 High-Resolution Very Large Array Imaging of Sloan Digital Sky Survey Stripe 82 at 1.4 GHz This is a high-resolution radio survey of the Sloan Digital Sky Survey (SDSS) Southern Equatorial Stripe, a.k.a. Stripe 82. This 1.4 GHz survey was conducted with the Very Large Array (VLA) primarily in the A-configuration, with supplemental B-configuration data to increase sensitivity to extended structure. The survey has an angular resolution of 1.''8 and achieves a median rms noise of 52 μJy per beam over 92 deg^2. The catalog contains 17,969 isolated radio components, for an overall source density of ∼195 sources/deg^2. See also J.A. Hodge et al, :bibcode:`2011AJ....142....3H` . Hodge, J. A.; Becker, R. H.; White, R. L.; Richards, G. T.; Zeimann, G. R. 2011AJ....142....3H surveys radio-continuum-emission Radio //scs#pgs-pos-index Position ICRS "raj2000" "dej2000" The positional errors are a function of source brightness, size, and noise in the map. They are best found using a simple rule-of-thumb approach, as the HAPPY-derived errors tend to be underestimated. An empirical equation for the accuracy at 90\% confidence is f_Size * (1/SNR + 1/20), where f_Size is the fitted semimajor or semiminor axis size, and SNR is the signal to noise ratio :bibcode:`1997ApJ...475..479W`. Systematic errors are smaller than 0.05". P(S) values are computed using a custom algorithm based on multiple voting oblique decision tree classifiers, which were trained on deep VLA fields. Note that the algorithm is optimized for the FIRST survey, whereas the RMS computation for this catalog has changed significantly. The values of P(S) are therefore not very reliable for this catalog. The uncertainty in f_int can be considerably greater than that of f_peak depending on source size and morphology. An expression to estimate the uncertainty can be found in :bibcode:`2004AJ....128.1974S` For point sources, the relative uncertainty reduces to: sigma_I/I = sqrt{ 2.5 sigma^2/I^2 + 0.01^2 }. 8.637e-25 9.932e-25 4/1114,1125-1126,1177 5/4442,4451-4454,4460,4499,4508,4707,4713-4716,4755-4758,4764 6/17661-17663,17719,17723,17725-17727,17762,17774,17799,17803,17820-17822,17844,17848,17856-17858,17860,17975,17981-17983,17991,17995,18036,18040,18068-18069,18071,18112-18114,18116,18176-18178,18180,18184,18679,18683,18685-18687,18743,18747,18749-18751,18792,18794-18795,18823,18827,18849-18851,18868,18872,18880-18882,18884,19015,19036-19038,19045,19060,19200-19202 raj2000:17-26 dej2000:27-36 p_s:38-41 f_peak:43-49 f_int: 50-57 rms_noise: 59-64 smaj_ax: 66-70 smin_ax: 72-76 pa: 78-82 smaj_ax_raw: 84-88 smin_ax_raw: 90-94 pa_raw: 96-100 field: 102-113 n_sdss: 115-116 d_sdss: 118-122 imag: 124-128 c_sdss: 130 hmsToDeg(@raj2000, sepChar="") dmsToDeg(@dej2000, sepChar="") float(@d_sdss) float(@imag) parseWithNull(@c_sdss, str, nullLiteral="-") float(@smaj_ax)*DEG_ARCSEC/2. float(@smin_ax)*DEG_ARCSEC/2. float(@smaj_ax_raw)*DEG_ARCSEC/2. float(@smin_ax_raw)*DEG_ARCSEC/2. \rowsMade vlastripe82 cone 331.308 -1.073 0.01 cone/scs.xml row = self.getFirstVOTableRow() self.assertAlmostEqual(row['smin_ax_raw'], 0.000259722000) self.assertEqual(row['f_int'], 4.5) self.assertAlmostEqual(row["dej2000"], -1.07355555) self.assertAlmostEqual(row["c_sdss"], 'g') self.assertAlmostEqual(row["field"], '22060-00520H')