The Everest Pipeline¶
Below are the basic instructions for reproducing the everest catalog. For
large photometric surveys like K2, processing all light curves will take
a lot of time and a lot of computing power. It took on the order of 100,000 CPU hours
to generate the catalog for K2 campaigns 0-8.
Warning
Users do not need to follow these instructions, unless they wish to tweak model parameters and generate catalogs of their own. Most users will simply want to interface with the existing catalog. See the user interface section.
1. Download the data¶
Each everest mission implements its own functionality for interfacing
with the raw data. For K2, individual raw light curves can be downloaded
by running
everest.missions.k2.GetData(EPIC, download_only = True)
Batch downloading for entire campaigns is implemented for individual missions.
For K2, you can submit a PBS job to a cluster with
everest.missions.k2.pbs.Download().
2. De-trend the data¶
Individual light curves can be de-trended by instantiating one of the everest models:
everest.nPLD(EPIC, **kwargs)
Currently, the available models are everest.nPLD,
everest.sPLD, everest.pPLD, and
the experimental everest.iPLD. These are all subclasses
of the everest.detrender.Detrender class, which accepts a bunch
of keyword arguments.
De-trending generates a log log file, a npz model file,
and a pdf data validation summary (DVS) file. These are all stored
in the directory given by the TargetDirectory() function of the mission
(for K2, see everest.k2.TargetDirectory).
Batch de-trending is also implemented for individual missions.
For K2, see everest.missions.k2.pbs.Run().
Note
You can check the status of the de-trending of each K2 campaign with the everest.k2.Status function. This is also implemented as the everest-status command line utility in everest/bin.
3. Compute CBVs¶
Co-trending basis vectors (CBVs) can be computed for each of the K2
campaigns once they are done running. To do this, run
everest.k2.GetCBVs
on each campaign. This will compile all of the de-trended light curves and
compute the top signals that are shared among all light curves using SysRem.