FPtest: False Positive Reference Data
In ProFound: Photometry Tools
Description
Usage
Format
Details
Source
Examples
Description
This data consists of 1,000 runs of a random 1000 x 1000 noise matrix through profoundProFound. The catalogue is a concatenation of all the segstats outputs for all of these run.
Usage
1
data("FPtest")
Format
A data frame with 7012 observations on the following 56 variables. See profoundProFound for a detailed discussion on each of these parameters.
segIDa numeric vector
uniqueIDa numeric vector
xcena numeric vector
ycena numeric vector
xmaxa numeric vector
ymaxa numeric vector
RAcena logical vector
Deccena logical vector
RAmaxa logical vector
Decmaxa logical vector
sepa numeric vector
fluxa numeric vector
maga numeric vector
cenfraca numeric vector
N50a numeric vector
N90a numeric vector
N100a numeric vector
R50a numeric vector
R90a numeric vector
R100a numeric vector
SB_N50a numeric vector
SB_N90a numeric vector
SB_N100a numeric vector
xsda numeric vector
ysda numeric vector
covxya numeric vector
corxya numeric vector
cona numeric vector
asymma logical vector
flux_reflecta logical vector
mag_reflecta logical vector
semimaja numeric vector
semimina numeric vector
axrata numeric vector
anga numeric vector
signifa numeric vector
FPlima numeric vector
flux_erra numeric vector
mag_erra numeric vector
flux_err_skya numeric vector
flux_err_skyRMSa numeric vector
flux_err_shota numeric vector
sky_meana numeric vector
sky_suma numeric vector
skyRMS_meana numeric vector
Nedgea logical vector
Nskya logical vector
Nobjecta logical vector
Nbordera logical vector
Nmaska logical vector
edge_fraca logical vector
edge_excessa logical vector
flag_bordera logical vector
itera numeric vector
origfraca numeric vector
flag_keepa logical vector
Details
Specifically we ran with defaults the following command 1,000 times in a loop:
profoundProFound(matrix(rnorm(1e6),1e3))
The output is then a reference of the false positive rate, since we have not injected any sources into the images. The fact we find 7,012 false detections mean we expect 7 false positives per 1e6 pixels (the size in pixels of the input matrix). To compare against any target data we need to adjust the magnitudes by the sky RMS magnitude level, i.e. add on profoundFlux2Mag(skyRMS, 0) (if the zero point is 0 for our target data). See Examples for a comparison to our included VIKING data.
Source
FPtest=
for(i in 1:1000)FPtest=rbind(FPtest,profoundProFound(matrix(rnorm(1e6),1e3))$segstats)
Examples
1
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17
## Not run:
image=readFITS(system.file("extdata", 'VIKING/mystery_VIKING_Z.fits', package="ProFound"))
profound=profoundProFound(image, magzero=30, rotstats=TRUE)
skyRMS=median(profound$skyRMS)
magoff=profoundFlux2Mag(skyRMS, 30)
totpix=prod(profound$dim)
#We can easily compute the expected number of false positives on an image this size:
data("FPtest")
dim(FPtest)[1]*totpix/1e6/1e3
#And plot the detections and expected false positive distributions:
maghist(profound$segstats$mag, seq(-11,-1,by=0.2)+magoff)
maghist(FPtest$mag+magoff, seq(-6,-1,by=0.2)+magoff, scale=totpix/1e6/1e3, add=TRUE,
border='red')
## End(Not run)
ProFound documentation built on Jan. 8, 2021, 5:37 p.m.
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Description Usage Format Details Source Examples
Description
This data consists of 1,000 runs of a random 1000 x 1000 noise matrix through profoundProFound. The catalogue is a concatenation of all the segstats outputs for all of these run.
Usage
1 | data("FPtest")
|
Format
A data frame with 7012 observations on the following 56 variables. See profoundProFound for a detailed discussion on each of these parameters.
segIDa numeric vector
uniqueIDa numeric vector
xcena numeric vector
ycena numeric vector
xmaxa numeric vector
ymaxa numeric vector
RAcena logical vector
Deccena logical vector
RAmaxa logical vector
Decmaxa logical vector
sepa numeric vector
fluxa numeric vector
maga numeric vector
cenfraca numeric vector
N50a numeric vector
N90a numeric vector
N100a numeric vector
R50a numeric vector
R90a numeric vector
R100a numeric vector
SB_N50a numeric vector
SB_N90a numeric vector
SB_N100a numeric vector
xsda numeric vector
ysda numeric vector
covxya numeric vector
corxya numeric vector
cona numeric vector
asymma logical vector
flux_reflecta logical vector
mag_reflecta logical vector
semimaja numeric vector
semimina numeric vector
axrata numeric vector
anga numeric vector
signifa numeric vector
FPlima numeric vector
flux_erra numeric vector
mag_erra numeric vector
flux_err_skya numeric vector
flux_err_skyRMSa numeric vector
flux_err_shota numeric vector
sky_meana numeric vector
sky_suma numeric vector
skyRMS_meana numeric vector
Nedgea logical vector
Nskya logical vector
Nobjecta logical vector
Nbordera logical vector
Nmaska logical vector
edge_fraca logical vector
edge_excessa logical vector
flag_bordera logical vector
itera numeric vector
origfraca numeric vector
flag_keepa logical vector
Details
Specifically we ran with defaults the following command 1,000 times in a loop:
profoundProFound(matrix(rnorm(1e6),1e3))
The output is then a reference of the false positive rate, since we have not injected any sources into the images. The fact we find 7,012 false detections mean we expect 7 false positives per 1e6 pixels (the size in pixels of the input matrix). To compare against any target data we need to adjust the magnitudes by the sky RMS magnitude level, i.e. add on profoundFlux2Mag(skyRMS, 0) (if the zero point is 0 for our target data). See Examples for a comparison to our included VIKING data.
Source
FPtest=
for(i in 1:1000)FPtest=rbind(FPtest,profoundProFound(matrix(rnorm(1e6),1e3))$segstats)
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## Not run:
image=readFITS(system.file("extdata", 'VIKING/mystery_VIKING_Z.fits', package="ProFound"))
profound=profoundProFound(image, magzero=30, rotstats=TRUE)
skyRMS=median(profound$skyRMS)
magoff=profoundFlux2Mag(skyRMS, 30)
totpix=prod(profound$dim)
#We can easily compute the expected number of false positives on an image this size:
data("FPtest")
dim(FPtest)[1]*totpix/1e6/1e3
#And plot the detections and expected false positive distributions:
maghist(profound$segstats$mag, seq(-11,-1,by=0.2)+magoff)
maghist(FPtest$mag+magoff, seq(-6,-1,by=0.2)+magoff, scale=totpix/1e6/1e3, add=TRUE,
border='red')
## End(Not run)
|
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