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')