Fast computation of broadband fluxes and magnitudes

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Project description

Broadband fluxes (`bbf`)

Table of contents

Installation
[Getting started](#getting started)
License

A module to evaluate the broadband fluxes and magnitudes of spectrophotometric standards.

Installation

Below are instruction to install the bbf package on Linux or MacOS systems. It should work on Windows too, but is has not been tested.

In brief

The quick way for end users. If you are a developer or have an issue with the above instructions, follow the [detailed instructions](#detailed instructions)

We recommand using conda (or its faster equivalent mamba), which comes with a compiled version of suitesparse. Conda packages for bbf are in preparation (but not ready yet), meanwhile, follow the instructions below.

Do not forget to activate your conda environment (or to create a new one):

conda create --name <my-env>
conda activate <my-env>

On Linux:

conda install libgomp gcc cmake ipython matplotlib numpy pandas scikit-sparse scipy h5py pyarrow
pip install git+https://github.com/nregnault/sncosmo
pip install bbf

On macos:

conda install openmp clangxx cmake ipython matplotlib numpy pandas scikit-sparse scipy h5py pyarrow
pip install git+https://github.com/nregnault/sncosmo
pip install bbf

Detailed instructions

Prerequisites

You need a C++ compiler (usually gcc on Linux or clang on macos).

You also need some Python dependencies installable with conda

conda install ipython matplotlib numpy pandas scikit-sparse scipy h5py pyarrow

Moreover, bbf relies for the moment on a modified version of sncosmo for passbands and magsys definition. You need to install it before installing bbf:
```
pip install git+https://github.com/nregnault/sncosmo
```

Installing bbf

pip install bbf

If you prefer installing from sources:

git clone clone git@gitlab.in2p3.fr:lemaitre/bbf.git
cd bbf
pip install .

If your you are a developper and want to work on the bbf package:

pip install nanobind ninja scikit-build-core[pyproject]
pip install --no-build-isolation -Ceditable.rebuild=true -ve .

Installing the Lemaitre bandpasses

If you plan to use the latest version of the megacam6, ztf and hsc passbands, install the lemaitre.bandpasses package:

pip install lemaitre-bandpasses

git clone https://gitlab.in2p3.fr/lemaitre/lemaitre/bandpasses
cd bandpasses
git lfs pull
pip install .

Getting started

The goal of bbf is to efficiently compute broadband fluxes and magnitudes, i.e. quantities of the form:

$$f_{xys} = \int S(\lambda) \lambda T_{xys}(\lambda) d\lambda$$

where $\lambda$ is the SED of an object, $T_{xyz}(\lambda)$ is the bandpass of the instrument used to observe it. $T$ may depend on the focal plane position of the object and, if the focal plane is a mosaic of sensors, on the specific sensor $s$ where the observation is made. In practice, $x,y$ are coordinates, in pixels, in the sensor frame, and $s$ is a unique sensor index (or amplifier index).

Computing magnitudes requires an additional ingredient: the flux of a reference spectrum $S_{ref}(\lambda)$, usually the AB spectrum, integrated in the same passband (same sensor, same position).

$$m = -2.5 \log_{10} \left(\frac{\int S(\lambda) \lambda T_{xyz}(\lambda) d\lambda}{\int S_{ref}(\lambda) \lambda T_{xyz}(\lambda) d\lambda}\right)$$

To compute these integrales, bbf uses the technique implemented in nacl, which consists in projecting the bandpasses and SED on spline bases:

$$S(\lambda) = \sum_i \theta_i {\cal B}_i(\lambda)$$

and

$$T(\lambda) = \sum_j t_j {\cal B}_j(\lambda)$$

If we precompute the products $G_{ij} = \int \lambda {\cal B}_i(\lambda) {\cal B}_j(\lambda) d\lambda$ the integrals above can be expressed as a simple contraction:

$$f = \theta_i G_{ij} t_j$$

where $G$ is very sparse, since the B-Splines ${\cal B}_i$ have a compact support. If the bandpass $T$ is spatially variable, the $t_j$ coefficients are themselves developped on a spatial spline basis.

$$t_j = \sum_{kj} \tau_{kj} {\cal K}(x,y)$$

The contraction above is then of the form: ...

FilterSets and StellarLibs

bbf implements two main kind of objects: FilterLib, which holds a set of band passes, projected on spline bases (${\cal K_j(x,y)}$ and ${\cal B}i(\lambda)$), and StellarLib which manages a set of spectra, also projected on a spline basis (not necessily the splines used for the filters).

Loading a filter lib

Building a complete version of a FilterLib requires some care. The standard FilterLib used in the Lemaître analysis is build and maintained within the package lemaitre.bandpasses. To access it:

from lemaitre import bandpasses

flib = bandpasses.get_filterlib()

The first time this function is called, the `FilterLib`` is built and cached. The subsequent calls access the cached version, and never take more than a few milliseconds.

Loading Stellar Libraries

As of today, bbf implements two kinds of StellarLibs: pickles and Calspec. An interface to gaiaXP is in development.

To load the pickles library:

import bbf.stellarlib.pickles
pickles = bbf.stellarlib.pickles.fetch()

To load the most recent version of Calspec:

import bbf.stellarlib.calspec
calspec = bbf.stellarlib.calspec.fetch()

Computing Broadband fluxes

With a FilterSet and a StellarLib in hand, one can compute broadband fluxes and broadband mags.