Table information for 'dfbsspec.spectra'

General

Table Description: This table contains basic metadata as well as the spectra from the Digital First Byurakan Survey (DFBS).

This table is available for ADQL queries and through the TAP endpoint.

Resource Description:

The First Byurakan Survey (FBS) is the largest and the first systematic objective prism survey of the extragalactic sky. It covers 17,000 sq.deg. in the Northern sky together with a high galactic latitudes region in the Southern sky. The FBS has been carried out by B.E. Markarian, V.A. Lipovetski and J.A. Stepanian in 1965-1980 with the Byurakan Observatory 102/132/213 cm (40"/52"/84") Schmidt telescope using 1.5 deg. prism. Each FBS plate contains low-dispersion spectra of some 15,000-20,000 objects; the whole survey consists of about 20,000,000 objects.

For a list of all services and tables belonging to this table's resource, see Information on resource 'Digitized First Byurakan Survey (DFBS) Extracted Spectra'

Further information

Usage Hints

Note that the spectra are not flux calibrated. Indeed, they were scanned off of different emulsions, and only spectra from compatible emulsions should be compared. The following emulsions occur in the database:

n emulsion
334409 103aF
18695 103aO
3622 IF
13555 IIAD
2409058 IIAF
16545 IIAF bkd
3254370 IIF
313287 IIF bkd
7871 IIIAJ bkd
6645 IIIF
18967 IIIaF
8122 IIaD
3565737 IIaF
6735 IIaO
16755 OAF
8794 ORWO CP-3
8097 ZP-3
23702 Zu-2

Upper- and lowercase versions of the emulsions are actually different (e.g., IIAD was produced in England, IIaD in the US). Their properties are different enough to make mixing spectra for the different emulsions unwise.

Also note the sp_class column. Unless you take great precaution, you probably should only use spectra with sp_class='OK'.

Spectra can be retrieved in VOTable form (via SSA or the accref field from the TAP table), but it will usually be faster to directly pull them from the spectral and flux arrays.

Actually, array indices in the flux arrays correspond to fixed wavelengths. In other words, the spectral column is constant in the database.

The flux arrays are actually of different length. The always start at index 1, corresponding to 690 nm. The blue end depends on how far some signal was suspected.

Use Cases

Locate Spectra by Features

While ADQL support for array operations is rather weak, you can subscript arrays. Because of the fixed bins, you can therefore select by flux ratios (never use absolute numbers here; they are meaningless). For instance, to select objects with a high (apparent) Halpha emission (656 nm, corresponding to array index 3), you might so something like:

select * from dfbsspec.spectra
where
  flux[3]/(flux[40]+flux[41]+flux[42])>30
  and sp_class='OK'

Since the table needs to be sequentially scanned for this, it will take a minute or so. Combine with an object selection (see below) or other criteria if possible.

Get Average Spectra

You cannot currently use the ADQL aggregate function AVG with arrays (which should be fixed at some time in the future). Meanwhile, you can work around this with a clumsy construction like this (this query will give you average spectra by magnitude bin; don't run it just for fun, it'll take a while):

select round(magb) as bin, avg(flux[1]) as col1, avg(flux[2]) as col2,
  avg(flux[3]) as col3, avg(flux[4]) as col4, avg(flux[5]) as col5,
  avg(flux[6]) as col6, avg(flux[7]) as col7, avg(flux[8]) as col8,
  avg(flux[9]) as col9, avg(flux[10]) as col10, avg(flux[11]) as col11,
  avg(flux[12]) as col12, avg(flux[13]) as col13, avg(flux[14]) as col14,
  avg(flux[15]) as col15, avg(flux[16]) as col16, avg(flux[17]) as col17,
  avg(flux[18]) as col18, avg(flux[19]) as col19, avg(flux[20]) as col20,
  avg(flux[21]) as col21, avg(flux[22]) as col22, avg(flux[23]) as col23,
  avg(flux[24]) as col24, avg(flux[25]) as col25, avg(flux[26]) as col26,
  avg(flux[27]) as col27, avg(flux[28]) as col28, avg(flux[29]) as col29,
  avg(flux[30]) as col30, avg(flux[31]) as col31, avg(flux[32]) as col32,
  avg(flux[33]) as col33, avg(flux[34]) as col34, avg(flux[35]) as col35,
  avg(flux[36]) as col36, avg(flux[37]) as col37, avg(flux[38]) as col38,
  avg(flux[39]) as col39, avg(flux[40]) as col40, avg(flux[41]) as col41,
  avg(flux[42]) as col42, avg(flux[43]) as col43, avg(flux[44]) as col44,
  avg(flux[45]) as col45, avg(flux[46]) as col46, avg(flux[47]) as col47,
  avg(flux[48]) as col48, avg(flux[49]) as col49, avg(flux[50]) as col50,
  avg(flux[51]) as col51, avg(flux[52]) as col52, avg(flux[53]) as col53,
  avg(flux[54]) as col54, avg(flux[55]) as col55, avg(flux[56]) as col56,
  avg(flux[57]) as col57, avg(flux[58]) as col58, avg(flux[59]) as col59
from dfbsspec.spectra
where sp_class='OK'
group by bin

To map col<n> to wavelenghts, see the contents of (any) spectral column.

Build Templates

To compute an average spectrum for a class of objects, we suggest to pull positions of such objects from SIMBAD and then fetch the associate spectra from this database. Since the response function of the photographic plates had a strong magnitude dependence, restrict the objects to a small magnitude range, for instance:

select
    otype, ra, dec, flux
from basic
join flux
on (oid=oidref)
where
    otype='HS*'
    and dec>-15
    and filter='G'
    and flux between 12.5 and 13.5

(to be executed on SIMBAD's TAP service, see also SIMBAD object types).

With the resulting table, go do this service and execute a query like:

SELECT
specid, spectral, flux
FROM dfbsspec.spectra AS db
JOIN TAP_UPLOAD.t1 AS tc
ON DISTANCE(tc.ra, tc.dec, db.ra, db.dec)<5./3600.
WHERE sp_class='OK'

(adjust t1 according to your client's rules; in TOPCAT, that's t plus the table number from the control window).

History of this Data Collection

The original aim of the First Byurakan Survey was to search for galaxies with UV excess (1986ApJS...62..751M, Markarian et al. 1989,1997- catalogue No. VII/172 at CDS). Successively, the amount of spectral information contained in the plates allowed the development of several other projects concerning the spectral classification of Seyfert Galaxies (Weedman and Kachikian 1971), the first definition of starburst galaxies (Weedman 1977 ), the discovery and investigation of blue stellar objects (Abrahamian and Mickaelian, 1996, Mickaelian et al 2001, 2002, CDS catalogue No II/223) and a survey for late-type stars (Gigoyan et al. 2002). All these results were obtained by eye inspection of the plates performed with the aid of a microscope at the Byurakan Observatory. The number and classes of new objects discovered FBS made clear the need of open access to FBS for the entire astronomical community.

Citing this table

This table has an associated publication. If you use data from it, it may be appropriate to reference 2007A&A...464.1177M (ADS BibTeX entry for the publication) either in addition to or instead of the service reference.

To cite the table as such, we suggest the following BibTeX entry:

@MISC{vo:dfbsspec_spectra,
  year=2017,
  title={Digitized First Byurakan Survey ({DFBS}) Extracted Spectra},
  author={Markarian, B.E. and N.N.},
  url={http://dc.zah.uni-heidelberg.de/tableinfo/dfbsspec.spectra},
  howpublished={{VO} resource provided by the {GAVO} Data Center}
}

Fields

Sorted by DB column index. [Sort alphabetically]

NameTable Head DescriptionUnitUCD
accref Product key Access key for the data N/A N/A
plate Src. Plate Number of the plate this spectrum was extracted from. Technically, this is a foreign key into dfbs.plates. N/A N/A
specid ID Identifier of the spectrum built from the plate identifier, a -, and the object position as in objectid. N/A meta.id;meta.main
ra RA ICRS RA of the source of this spectrum. deg pos.eq.ra;meta.main
dec Dec ICRS Dec of the source of this spectrum. deg pos.eq.dec;meta.main
pos Pos The object position as s pgsphere spoint. N/A pos.eq
sp_class Sp. Class Quality indicator: OK of undisturbed spectra of sufficiently bright objects, NL if disturbers are nearby, U for objects unclassifiable because of lack of signal. N/A meta.code.qual
px_length # Number of points in this spectrum N/A N/A
flux Flux[] Flux points of the extracted spectrum (arbitrary units) N/A phot.flux.density;em.wl
magb mag. B Source object magnitude in Johnson B mag phot.mag;em.opt.B
magr mag. R Source object magnitude in Johnson R mag phot.mag;em.opt.R
snr SNR Estimated signal-to-noise ratio for this spectrum. N/A stat.snr
lam_min λ_min Minimal wavelength in this spectrum (the longest wavelength is always 690 nm). m stat.min;em.wl
px_x X Location of the spectrum on the plate scan, x coordinate. pixel pos.cartesian.x;instr
px_y Y Location of the spectrum on the plate scan, y coordinate. pixel pos.cartesian.y;instr
pos_ang P.A. Position angle of the spectrum on the plate, north over east. deg pos.posAng
epoch Date Obs. Date of observation from WFPDB (this probably does not include the time). d time.epoch
exptime Exp. Time Exposure time from WFPDB. s time.duration;obs.exposure
emulsion Emulsion Emulsion used in this plate from WFPDB. N/A instr.plate.emulsion
spectral Spectral[] Spectral points of the extracted spectrum (wavelengths) as an array (that's actually the same for all spectra and only given here as a convenience). m N/A
cutout_link Image Cutout of the image this spectrum was extracted from N/A meta.ref.url

Columns that are parts of indices are marked like this.

Other

The following services may use the data contained in this table:

VOResource

VO nerds may sometimes need VOResource XML for this table.

More Information on the Base Resource

Usage Hints

Note that the spectra are not flux calibrated. Indeed, they were scanned off of different emulsions, and only spectra from compatible emulsions should be compared. The following emulsions occur in the database:

n emulsion
334409 103aF
18695 103aO
3622 IF
13555 IIAD
2409058 IIAF
16545 IIAF bkd
3254370 IIF
313287 IIF bkd
7871 IIIAJ bkd
6645 IIIF
18967 IIIaF
8122 IIaD
3565737 IIaF
6735 IIaO
16755 OAF
8794 ORWO CP-3
8097 ZP-3
23702 Zu-2

Upper- and lowercase versions of the emulsions are actually different (e.g., IIAD was produced in England, IIaD in the US). Their properties are different enough to make mixing spectra for the different emulsions unwise.

Also note the sp_class column. Unless you take great precaution, you probably should only use spectra with sp_class='OK'.

Spectra can be retrieved in VOTable form (via SSA or the accref field from the TAP table), but it will usually be faster to directly pull them from the spectral and flux arrays.

Actually, array indices in the flux arrays correspond to fixed wavelengths. In other words, the spectral column is constant in the database.

The flux arrays are actually of different length. The always start at index 1, corresponding to 690 nm. The blue end depends on how far some signal was suspected.

Use Cases

Locate Spectra by Features

While ADQL support for array operations is rather weak, you can subscript arrays. Because of the fixed bins, you can therefore select by flux ratios (never use absolute numbers here; they are meaningless). For instance, to select objects with a high (apparent) Halpha emission (656 nm, corresponding to array index 3), you might so something like:

select * from dfbsspec.spectra
where
  flux[3]/(flux[40]+flux[41]+flux[42])>30
  and sp_class='OK'

Since the table needs to be sequentially scanned for this, it will take a minute or so. Combine with an object selection (see below) or other criteria if possible.

Get Average Spectra

You cannot currently use the ADQL aggregate function AVG with arrays (which should be fixed at some time in the future). Meanwhile, you can work around this with a clumsy construction like this (this query will give you average spectra by magnitude bin; don't run it just for fun, it'll take a while):

select round(magb) as bin, avg(flux[1]) as col1, avg(flux[2]) as col2,
  avg(flux[3]) as col3, avg(flux[4]) as col4, avg(flux[5]) as col5,
  avg(flux[6]) as col6, avg(flux[7]) as col7, avg(flux[8]) as col8,
  avg(flux[9]) as col9, avg(flux[10]) as col10, avg(flux[11]) as col11,
  avg(flux[12]) as col12, avg(flux[13]) as col13, avg(flux[14]) as col14,
  avg(flux[15]) as col15, avg(flux[16]) as col16, avg(flux[17]) as col17,
  avg(flux[18]) as col18, avg(flux[19]) as col19, avg(flux[20]) as col20,
  avg(flux[21]) as col21, avg(flux[22]) as col22, avg(flux[23]) as col23,
  avg(flux[24]) as col24, avg(flux[25]) as col25, avg(flux[26]) as col26,
  avg(flux[27]) as col27, avg(flux[28]) as col28, avg(flux[29]) as col29,
  avg(flux[30]) as col30, avg(flux[31]) as col31, avg(flux[32]) as col32,
  avg(flux[33]) as col33, avg(flux[34]) as col34, avg(flux[35]) as col35,
  avg(flux[36]) as col36, avg(flux[37]) as col37, avg(flux[38]) as col38,
  avg(flux[39]) as col39, avg(flux[40]) as col40, avg(flux[41]) as col41,
  avg(flux[42]) as col42, avg(flux[43]) as col43, avg(flux[44]) as col44,
  avg(flux[45]) as col45, avg(flux[46]) as col46, avg(flux[47]) as col47,
  avg(flux[48]) as col48, avg(flux[49]) as col49, avg(flux[50]) as col50,
  avg(flux[51]) as col51, avg(flux[52]) as col52, avg(flux[53]) as col53,
  avg(flux[54]) as col54, avg(flux[55]) as col55, avg(flux[56]) as col56,
  avg(flux[57]) as col57, avg(flux[58]) as col58, avg(flux[59]) as col59
from dfbsspec.spectra
where sp_class='OK'
group by bin

To map col<n> to wavelenghts, see the contents of (any) spectral column.

Build Templates

To compute an average spectrum for a class of objects, we suggest to pull positions of such objects from SIMBAD and then fetch the associate spectra from this database. Since the response function of the photographic plates had a strong magnitude dependence, restrict the objects to a small magnitude range, for instance:

select
    otype, ra, dec, flux
from basic
join flux
on (oid=oidref)
where
    otype='HS*'
    and dec>-15
    and filter='G'
    and flux between 12.5 and 13.5

(to be executed on SIMBAD's TAP service, see also SIMBAD object types).

With the resulting table, go do this service and execute a query like:

SELECT
specid, spectral, flux
FROM dfbsspec.spectra AS db
JOIN TAP_UPLOAD.t1 AS tc
ON DISTANCE(tc.ra, tc.dec, db.ra, db.dec)<5./3600.
WHERE sp_class='OK'

(adjust t1 according to your client's rules; in TOPCAT, that's t plus the table number from the control window).

History of this Data Collection

The original aim of the First Byurakan Survey was to search for galaxies with UV excess (1986ApJS...62..751M, Markarian et al. 1989,1997- catalogue No. VII/172 at CDS). Successively, the amount of spectral information contained in the plates allowed the development of several other projects concerning the spectral classification of Seyfert Galaxies (Weedman and Kachikian 1971), the first definition of starburst galaxies (Weedman 1977 ), the discovery and investigation of blue stellar objects (Abrahamian and Mickaelian, 1996, Mickaelian et al 2001, 2002, CDS catalogue No II/223) and a survey for late-type stars (Gigoyan et al. 2002). All these results were obtained by eye inspection of the plates performed with the aid of a microscope at the Byurakan Observatory. The number and classes of new objects discovered FBS made clear the need of open access to FBS for the entire astronomical community.