pz-hyperbolic-temp 0.0

Implementation of hyperbolic magnitudes (Lupton et al. 1999).

hyperbolic

Implementation of hyperbolic magnitudes (Lupton et al. 1999).

Installation

This code is written for python3.6+. All dependencies are listed in the requirements.txt.

Usage

The code is split in two parts. The first script, hyp_smoothing.py, reads an input FITS table with photometric data (flux and flux errors and optional classical magnitudes) and calculates statistics that determine the smoothing paramter for the hyperbolical magnitudes, such as the photometric zeropoint and the median flux uncertainty (see implementation section below). The input data is specified in the configuration file. An empty configration file can be generated with

hyp_smoothing.py -d

An example usage might look like this:

hyp_smoothing.py -v \
   --field pointing_identifier_column_name \
   -c configration.json \
   input_catalogue.fits \
   field_statistics.csv

The second part of the code, hyp_magnitudes.py, computes the actual value for the smoothing parameter and computes hyperbolic magnitudes consistently. The code corrects automatically variations in the photometric zeropoint (if magnitudes are provided in the configuration file) based on the field_statistics.csv computed in the first step. An example usage might look like this:

hyp_magnitudes.py -v \
   --field pointing_identifier_column_name \
   -c configration.json \
   -s field_statistics.csv \
   --plot \
   input_catalogue.fits \
   output_catalogue_with_hyperb_mags.fits

It is also possible to apply the smoothing parameter from an external data cataloge. In this case, the field statistics of the external data should be computed with hyp_smoothing.py. The output .csv file can be provided to hyp_magnitudes.py by adding the --smoothing parameter to the call above.

Implementation

Hyperbolical magnitudes approximate the classical magnitudes at high signal-to- noise, but have linear behaviour around flux values of f=0 and are therefore well defined if f<0. Lupton et al. (1999) suggest to define hyperbolical magnitudes in terms of the normalised flux x=f/f0 as

mu = a * [arcsinh(0.5*x/b) + log(b)] ,

with a=2.5log10(e). In this parameterisation f0 is the flux of an object with magnitude zero and m0=a*log(f0) is the corresponding photometric zeropoint.

The free parameter b is a smoothing factor that determines the transition between linear and logarithmic behaviour of mu. Lupton et al. (1999) show that the optimal value for the smoothing parameter is

b = sqrt(a) Dx ,

i.e. is determined by the variance of the normalised flux.

When applied to observational data, the hyperbolic magnitudes can be written as

mu = a * [arcsinh(0.5*f/b') + log(b')] + m0 ,

where f is the measured flux and

b' = f0 * b = sqrt(a) * Df

is determined from the variance of the measured fluxes. In this formulation, b depends on the photometric zeropoint.

Estimating the smoothing parameter

For a given set of flux measurements the hyperbolic magnitudes can be computed once an appropriate smoothing parameter is chosen. This determines b globally from the complete input data and for each photometric filter specified in the configuration file:

1. It computes the photometric zeropoint m0 in each telescope pointing by comparing the individual flux measurements fi and magnitudes mi, since the latter may include a number of corrections, such as extinction or stellar locus regressions:

   m0 = median(mi + a*log(fi))
   

2. It computes the smoothing parameter for each pointing from the zeropoint according to

   b = b' / f0 = sqrt(a) * Df / f0 = sqrt(a) * e^(-m0/a) * Df ,
   

   where Df=median(fi) is the median of the measured flux errors.

3. It computes the global value for b in each filter by taking the median of all pointings.

Computation

The code computes the hyperbolical magnitudes from their normalised flux x=f/f0 with uncertainty Dx=Df/f0 based on these global values for b. In each pointing it calcualtes the flux f0=e^(m0/a) from the zeropoint m0 (see above) to compensate variations in the observing conditions.

Maintainers

Jan Luca van den Busch, Ruhr-University Bochum, Astronomical Institute.