Bayestar17 3D Dust Mapping with Pan-STARRS 1
A 3D map of interstellar dust reddening, covering three
quarters of the sky (declinations greater than -30 degrees) out to a
distance of several kiloparsecs. The map is based on high-quality stellar
photometry of 800 million stars from Pan-STARRS 1 and 2MASS.
The map is available for five HEALPix levels (6 through 10), published
here as separate tables, map6 through map10. A union of the coverage is
provided as map_union. Use its coverage column to match against other
tables.
See http://argonaut.rc.fas.harvard.edu/ for more details.
These tables lets you do dereddening of photometry provided you have:
* Positions
* An estimate for the distance modulus
* Some idea of how to get from E(B-V) to your extinction.
You will usually join two of the arrays in map_union to your table.
Because of boring technicalities, that is a bit ideomatic; you need to
compute an order-10 healpix and see if that is contained in this tables
coverage. In a cartoon::
SELECT
mytable.*,
best_fit, grdiagnostic
FROM mytable
JOIN prdust.map_union
ON (1=ivo_interval_has(
CAST(ivo_healpix_index(10, l, b) AS INTEGER), coverage))
Yes, you have to use galactic longitude and latitude in the arguments
of ivo_healpix_index; and the odd cast is necessary because coverage
contains normal integer intervals.
If you have equatorial coordinates ``ra`` and ``dec``, on this service
there's the ``gavo_transform`` user defined function to your rescue. Just
write::
CAST(ivo_healpix_index(10,
gavo_transform('ICRS', 'GALACTIC', POINT(ra, dec))
) AS INTEGER), coverage)
in the second argument of ``ivo_interval_has`` above.
The next step is to estimate a distance modulus. If you have a good
parallax estimate and your TAP service supports the ``in_unit`` UDF,
you might get away with::
5*log10(1./in_unit(parallax, 'arcsec'))-5 AS dist_mod
You will certainly want to do better than this in almost all science
use cases (see :bibcode:`2018arXiv180409376L` for a through discussion
in the context of Gaia DR2).
From ``dist_mod``, you can compute the distance bin in the ``best_fit``
and ``grdiagostic`` columns with::
ROUND((dist_mod-4)*2)+1 AS dist_mod_bin
With this, you can add an E(B-V) to a local table somewhat like this::
SELECT
q.*,
best_fit[dist_mod_bin] as eb_v
FROM (
SELECT (
CAST(ivo_healpix_index(10, l, b) AS INTEGER) AS cat_hpx,
ROUND((dist_mod-4)*2)+1 AS dist_mod_bin,
mytable.*
FROM mytable) AS q
JOIN prdust.map_union
ON (1=ivo_interval_has(cat_hpx, coverage))
WHERE grdiagnostic[dist_mod_bin]<1.2
To turn E(B-V) into extinctions, there are several recipes. See, for
instance :bibcode:`1998ApJ...500..525S`.
You'll find a worked-out example on this at
https://blog.g-vo.org/deredden-using-tap/.
interstellar-extinction
interstellar-dust
galaxy-structure
2018-03-03T12:22:00
100
Green, G.M.; Schlafly, E.F.; Finkbeiner, D.;
Rix, H.-W.; Martin, N.; Burgett, W.; Draper, P.W.; Flewelling, H.;
Hodapp, K.; Kaiser, N.; Kudritzki, R.-P.; Magnier, E.A.; Metcalfe, N.;
Tonry, J.L.; Wainscoat, R; Waters, C.
Pan-STARRS 1
2018arXiv180103555G
Research
Survey
Optical
Infrared
healpix
The HEALPix map for order \order (nside=\nside); the pixel scale
is about \pixelscale.
In ADQL, all array indices are 1-based.
order, nside, pixelscale
6, 64, 55'
7, 128, 27'
8, 256, 14'
9, 512, 6.9'
10, 1024, 3.4'
healpix:HEALPIX_INDEX, converged:CONVERGED,
dm_reliable_min:DM_RELIABLE_MIN, dm_reliable_max:DM_RELIABLE_MAX,
n_stars:N_STARS, n_good:N_GOOD, n_dwarfs: N_DWARFS,
grdiagnostic:GRDIAGNOSTIC, best_fit:BEST_FIT, samples: SAMPLES
order = int(round(math.log(@NSIDE)/math.log(2)))
lowerBound = @HEALPIX_INDEX*2**(10-order)
upperBound = (@HEALPIX_INDEX+1)*2**(10-order)
# todo: make a MOC out of that when we have them
result["coverage"] = '[%s,%s)'%(lowerBound, upperBound)
from gavo.utils import pyfits
hdus = pyfits.open(self.sourceToken)
fitsTable = hdus[1].data
names = [n for n in fitsTable.dtype.names]
for row in fitsTable:
role = str(int(round(math.log(row["NSIDE"])/math.log(2))))
yield role, dict(zip(names, row))
Joined Bayestar17 3D Dust Map
This is a view of the dust maps in the
five orders, generated by splitting the larger pixels from the higher
orders into HEALPixes of order 10. This means, in particular,
that the spatial resolution for all pixels with original_order!=10.
Use this for convenient joins to other tables.
CREATE VIEW \curtable AS (SELECT \colNames FROM (
SELECT *, 10 AS hpx_order FROM \schema.map10
UNION ALL
SELECT *, 9 AS hpx_order FROM \schema.map9
UNION ALL
SELECT *, 8 AS hpx_order FROM \schema.map8
UNION ALL
SELECT *, 7 AS hpx_order FROM \schema.map7
UNION ALL
SELECT *, 6 AS hpx_order FROM \schema.map6
) AS q)
0/0-2,5-6,9 1/15,17,33,35,42 2/53-55,58-59,76-77,79,122,129,131,137,139,162,172,174-175 3/207,209-211,227,229-231,261,263,313,315,494-495,506-507,509-513,515,521,523,640,642,654-655,694-695 4/821-823,833-835,903,907,913-915,1040-1041,1043,1049,1077,1079,1085,1249,1251,1257,1259,1930-1931,1968,1970-1971,1974-1975,2018-2019,2022-2023,2034-2035,2056-2057,2059,2081,2083,2180-2181,2183,2189,2191,2213,2215,2221,2572,2574-2575,2608,2610-2611,2666-2667,2770 5/3191,3197,3199,3303,3307,3309-3311,3329-3331,3622-3623,3625-3627,3649-3651,4169,4204-4205,4207,4304-4305,4307,4313,4315,4337,4349,4351,4693,4993,4995,5001,5003,5025,5027,5033,5035,7712,7714-7715,7736,7738-7739,7742-7743,7876,7878-7879,7890-7891,7894-7895,8063,8066-8067,8070-8071,8082-8083,8086-8087,8130-8131,8134-8135,8233,8235,8321,8357,8359,8365,8728-8729,8731,8753,8755,8761,8763,8849,8851,8857,8859,8893,10266,10292-10294,10344,10346-10347,10436,10438,10458,10658-10659,10662-10663,10680,10682-10683,11074-11075,11084,11086-11087,11098-11099,11103,11264-11266,11268-11269,11280 6/12757,12759,12787,12793,12795,13133-13135,13203,13206-13207,13209,13211,13233-13235,13313,13315,14429,14431,14439,14445-14447,14479,14487,14497-14499,14593-14595,16469,16471,16673,16684-16685,16687,16773,16805,16824-16825,16827,17172-17173,17224-17225,17227,17249,17357,17393,17401,17403,18769,18771,18780-18781,18783,18805-18807,18813,18815,18901,19968-19969,19971,19977,19979,20001,20009,20011,20097,20099,20105,20107,20137,20139,30760,30762,30854,30874,30878-30879,30950-30951,30962,31146-31147,31150,31508,31510-31511,31554-31555,31558-31559,31571,32238-32239,32249-32251,32515,32518,32530-32531,32534-32535,32928-32929,32931,32939,33293,33295,33425,33427,33469,33471,34837,34839,34845,34869,34920-34921,34923,35009,35011,35017,35019,35041,35043,35049,35051,35393,35395,35401,35403,35525,35527,35569,35580-35581,35583,41034,41056,41058-41059,41068,41070,41083,41180,41182-41183,41382,41402,41758-41759,41802,41824,41826-41827,41830,41836,41838-41839,41850-41851,41854-41855,42624,42626-42627,42642-42643,42646-42647,42698,42726,42746-42747,44290-44291,44314-44315,44318,44340,44342-44343,44385-44388,44390-44391,44410-44411,45068-45070,45084-45085,45088-45090,45092,45124-45125,45128,45136