Dear Community,
I'd appreciate help on the following issue.
Im doing apperture photometry with direct local background subtraction using the code
flux_lcbkg, fluxerr_lcbkg, flag_lcbkg = sep.sum_circle(datac_c, posx, posy, R, bkgann=(R_in, R_out), subpix=0)
, with R = 6, R_in = 32., R_out = 46. (pixels)
However, when using 1-channel images (from a MUSE datacube) I get many NaNs for the estimated flux (see bottom image below).
On the contrary, when using coadded 3-channel images (ie adding three wavelength bins along the spectral dimension of thre datacube), I don´t get any NaNs.
Any suggestion why I'm getting NaNs when using the former??? Plots of both images shown below.
Txs vm in advance
Jose
Dear Community,
I'd appreciate help on the following issue.
Im doing apperture photometry with direct local background subtraction using the code
flux_lcbkg, fluxerr_lcbkg, flag_lcbkg = sep.sum_circle(datac_c, posx, posy, R, bkgann=(R_in, R_out), subpix=0), with R = 6, R_in = 32., R_out = 46. (pixels)
However, when using 1-channel images (from a MUSE datacube) I get many NaNs for the estimated flux (see bottom image below).
On the contrary, when using coadded 3-channel images (ie adding three wavelength bins along the spectral dimension of thre datacube), I don´t get any NaNs.
Any suggestion why I'm getting NaNs when using the former??? Plots of both images shown below.
Txs vm in advance
Jose