apparentsens – Imaging sensitivity estimataion – imaging task

Description

Estimates the expected imaging sensitivity as a function of the

visibility weights and imaging parameters.

Parameters

Title

Parameter	Default	Description
vis	''	Name of input visibility file(s)
field	''	field(s) to select
spw	''	spw(s)/channels to select
intent	''	Scan Intent(s)
selectdata	True	Enable data selection parameters
timerange	''	Range of time to select from data
uvrange	''	Select data within uvrange
antenna	''	Select data based on antenna/baseline
scan	''	Scan number range
observation	''	Observation ID range
imsize	numpy.array( [  ] )	Number of pixels
cell	numpy.array( [  ] )	Cell size
stokes	'I'	Stokes Planes to make (I only, for now)
specmode	'mfs'	Spectral definition mode (mfs only, for now)
weighting	'natural'	Weighting scheme (natural,uniform,briggs)
robust	float(0.5)	Robustness parameter
npixels	int(0)	Number of pixels to determine uv-cell size (0 : -/+ 3 pixels)
uvtaper	numpy.array( [ '' ] )	uv-taper on outer baselines in uv-plane

Parameter Explanations

vis

''

Name(s) of input visibility file(s)

default: none; example: vis=’ngc5921.ms’

vis=[‘ngc5921a.ms’,’ngc5921b.ms’]; multiple MSes

field

''

Select fields to image or mosaic. Use field id(s) or name(s).

[‘go listobs’ to obtain the list id’s or names]

default: ‘’= all fields

If field string is a non-negative integer, it is assumed to be a field index otherwise, it is assumed to be a field name field=’0~2’; field ids 0,1,2 field=’0,4,5~7’; field ids 0,4,5,6,7 field=’3C286,3C295’; field named 3C286 and 3C295 field = ‘3,4C*’; field id 3, all names starting with 4C For multiple MS input, a list of field strings can be used: field = [‘0~2’,’0~4’]; field ids 0-2 for the first MS and 0-4

for the second

field = ‘0~2’; field ids 0-2 for all input MSes

spw

''

Select spectral window/channels

NOTE: channels de-selected here will contain all zeros if

selected by the parameter mode subparameters.

default: ‘’=all spectral windows and channels

spw=’0~2,4’; spectral windows 0,1,2,4 (all channels) spw=’0:5~61’; spw 0, channels 5 to 61 spw=’<2’; spectral windows less than 2 (i.e. 0,1) spw=’0,10,3:3~45’; spw 0,10 all channels, spw 3,

channels 3 to 45.

spw=’0~2:2~6’; spw 0,1,2 with channels 2 through 6 in each. For multiple MS input, a list of spw strings can be used: spw=[‘0’,’0~3’]; spw ids 0 for the first MS and 0-3 for the second spw=’0~3’ spw ids 0-3 for all input MS spw=’3:10~20;50~60’ for multiple channel ranges within spw id 3 spw=’3:10~20;50~60,4:0~30’ for different channel ranges for spw ids 3 and 4 spw=’0:0~10,1:20~30,2:1;2;3’; spw 0, channels 0-10,

spw 1, channels 20-30, and spw 2, channels, 1,2 and 3

spw=’1~4;6:15~48’ for channels 15 through 48 for spw ids 1,2,3,4 and 6

intent

''

Scan Intent(s)

default: ‘’ (all) example: intent=’TARGET_SOURCE’ example: intent=’TARGET_SOURCE1,TARGET_SOURCE2’ example: intent=’TARGET_POINTING*’

selectdata

True

Enable data selection parameters.

timerange

''

Range of time to select from data

default: ‘’ (all); examples, timerange = ‘YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss’ Note: if YYYY/MM/DD is missing date defaults to first

day in data set

timerange=’09:14:0~09:54:0’ picks 40 min on first day timerange=’25:00:00~27:30:00’ picks 1 hr to 3 hr

30min on NEXT day

timerange=’09:44:00’ pick data within one integration

of time

timerange=’> 10:24:00’ data after this time For multiple MS input, a list of timerange strings can be used: timerange=[‘09:14:0~09:54:0’,’> 10:24:00’] timerange=’09:14:0~09:54:0’’; apply the same timerange for

all input MSes

uvrange

''

Select data within uvrange (default unit is meters)

default: ‘’ (all); example: uvrange=’0~1000klambda’; uvrange from 0-1000 kilo-lambda uvrange=’> 4klambda’;uvranges greater than 4 kilo lambda For multiple MS input, a list of uvrange strings can be used: uvrange=[‘0~1000klambda’,’100~1000klamda’] uvrange=’0~1000klambda’; apply 0-1000 kilo-lambda for all

input MSes

antenna

''

Select data based on antenna/baseline

default: ‘’ (all) If antenna string is a non-negative integer, it is assumed to be an antenna index, otherwise, it is considered an antenna name. antenna=’5&6’; baseline between antenna index 5 and

index 6.

antenna=’VA05&VA06’; baseline between VLA antenna 5

and 6.

antenna=’5&6;7&8’; baselines 5-6 and 7-8 antenna=’5’; all baselines with antenna index 5 antenna=’05’; all baselines with antenna number 05

(VLA old name)

antenna=’5,6,9’; all baselines with antennas 5,6,9

index number

For multiple MS input, a list of antenna strings can be used: antenna=[‘5’,’5&6’]; antenna=’5’; antenna index 5 for all input MSes antenna=’!DV14’; use all antennas except DV14

scan

''

Scan number range

default: ‘’ (all) example: scan=’1~5’ For multiple MS input, a list of scan strings can be used: scan=[‘0~100’,’10~200’] scan=’0~100; scan ids 0-100 for all input MSes

observation

''

Observation ID range

default: ‘’ (all) example: observation=’1~5’

imsize

numpy.array( [  ] )

Number of pixels

exampleimsize = [350,250]

imsize = 500 is equivalent to [500,500]

To take proper advantage of internal optimized FFT routines, the number of pixels must be even and factorizable by 2,3,5,7 only.

cell

numpy.array( [  ] )

Cell size

example: cell=[‘0.5arcsec,’0.5arcsec’] or cell=[‘1arcmin’, ‘1arcmin’] cell = ‘1arcsec’ is equivalent to [‘1arcsec’,’1arcsec’]

stokes

'I'

Stokes Planes to make (I only, for now)

default=’I’; example: stokes=’IQUV’;

Options: ‘I’,’Q’,’U’,’V’,’IV’,’QU’,’IQ’,’UV’,’IQUV’,’RR’,’LL’,’XX’,’YY’,’RRLL’,’XXYY’,’pseudoI’

NoteDue to current internal code constraints, if any correlation pair

is flagged, by default, no data for that row in the MS will be used. So, in an MS with XX,YY, if only YY is flagged, neither a Stokes I image nor an XX image can be made from those data points. In such a situation, please split out only the unflagged correlation into a separate MS.

NoteThe ‘pseudoI’ option is a partial solution, allowing Stokes I imaging

when either of the parallel-hand correlations are unflagged.

The remaining constraints shall be removed (where logical) in a future release.

specmode

'mfs'

Spectral definition mode (mfs only, for now)

mode=’mfs’Continuum imaging with only one output image channel.

(mode=’cont’ can also be used here)

mode=’cube’Spectral line imaging with one or more channels

Parameters start, width,and nchan define the spectral coordinate system and can be specified either in terms of channel numbers, frequency or velocity in whatever spectral frame is specified in ‘outframe’. All internal and output images are made with outframe as the base spectral frame. However imaging code internally uses the fixed spectral frame, LSRK for automatic internal software Doppler tracking so that a spectral line observed over an extended time range will line up appropriately. Therefore the output images have additional spectral frame conversion layer in LSRK on the top the base frame.

(NoteEven if the input parameters are specified in a frame

other than LSRK, the viewer still displays spectral axis in LSRK by default because of the conversion frame layer mentioned above. The viewer can be used to relabel the spectral axis in any desired frame - via the spectral reference option under axis label properties in the data display options window.)

mode=’cubedata’Spectral line imaging with one or more channels

There is no internal software Doppler tracking so a spectral line observed over an extended time range may be smeared out in frequency. There is strictly no valid spectral frame with which to label the output images, but they will list the frame defined in the MS.

weighting

'natural'

Weighting scheme (natural,uniform,briggs,superuniform,radial)

During gridding of the dirty or residual image, each visibility value is multiplied by a weight before it is accumulated on the uv-grid. The PSF’s uv-grid is generated by gridding only the weights (weightgrid).

weighting=’natural’Gridding weights are identical to the data weights

from the MS. For visibilities with similar data weights, the weightgrid will follow the sample density pattern on the uv-plane. This weighting scheme provides the maximum imaging sensitivity at the expense of a possibly fat PSF with high sidelobes. It is most appropriate for detection experiments where sensitivity is most important.

weighting=’uniform’Gridding weights per visibility data point are the

original data weights divided by the total weight of all data points that map to the same uv grid cell : ‘ data_weight / total_wt_per_cell ‘.

The weightgrid is as close to flat as possible resulting in a PSF with a narrow main lobe and suppressed sidelobes. However, since heavily sampled areas of the uv-plane get down-weighted, the imaging sensitivity is not as high as with natural weighting. It is most appropriate for imaging experiments where a well behaved PSF can help the reconstruction.

weighting=’briggs’Gridding weights per visibility data point are given by

‘data_weight / ( A / total_wt_per_cell + B ) ‘ where A and B vary according to the ‘robust’ parameter.

robust = -2.0 maps to A=1,B=0 or uniform weighting. robust = +2.0 maps to natural weighting. (robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)

Robust/Briggs weighting generates a PSF that can vary smoothly between ‘natural’ and ‘uniform’ and allow customized trade-offs between PSF shape and imaging sensitivity.

weighting=’superuniform’This is similar to uniform weighting except that

the total_wt_per_cell is replaced by the total_wt_within_NxN_cells around the uv cell of interest. ( N = subparameter ‘npixels’ )

This method tends to give a PSF with inner sidelobes that are suppressed as in uniform weighting but with far-out sidelobes closer to natural weighting. The peak sensitivity is also closer to natural weighting.

weighting=’radial’ : Gridding weights are given by ‘ data_weight * uvdistance ‘

This method approximately minimizes rms sidelobes for an east-west synthesis array.

For more details on weighting please see Chapter3 of Dan Briggs’ thesis (http://www.aoc.nrao.edu/dissertations/dbriggs)

robust

float(0.5)

Robustness parameter for Briggs weighting.

robust = -2.0 maps to uniform weighting. robust = +2.0 maps to natural weighting. (robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)

npixels

int(0)

Number of pixels to determine uv-cell size for super-uniform weighting

(0 defaults to -/+ 3 pixels)

npixels – uv-box used for weight calculation

a box going from -npixel/2 to +npixel/2 on each side

around a point is used to calculate weight density.

npixels=2 goes from -1 to +1 and covers 3 pixels on a side.

npixels=0 implies a single pixel, which does not make sense for

superuniform weighting. Therefore, if npixels=0 it will be forced to 6 (or a box of -3pixels to +3pixels) to cover 7 pixels on a side.

uvtaper

numpy.array( [ '' ] )

uv-taper on outer baselines in uv-plane

Apply a Gaussian taper in addition to the weighting scheme specified via the ‘weighting’ parameter. Higher spatial frequencies are weighted down relative to lower spatial frequencies to suppress artifacts arising from poorly sampled areas of the uv-plane. It is equivalent to smoothing the PSF obtained by other weighting schemes and can be specified either as a Gaussian in uv-space (eg. units of lambda) or as a Gaussian in the image domain (eg. angular units like arcsec).

uvtaper = [bmaj, bmin, bpa]

NOTE: the on-sky FWHM in arcsec is roughly the uv taper/200 (klambda). default: uvtaper=[]; no Gaussian taper applied example: uvtaper=[‘5klambda’] circular taper

FWHM=5 kilo-lambda

uvtaper=[‘5klambda’,’3klambda’,’45.0deg’] uvtaper=[‘10arcsec’] on-sky FWHM 10 arcseconds uvtaper=[‘300.0’] default units are lambda

in aperture plane