etsfit

(Early Time Supernova FITting)

A wrapper package to do Bayesian modelling (emcee) including Gaussian Processes ( tinygp ,celerite) noise modeling for early time supernovae serendipitiously observed by TESS. Supernova data was generally retrieved via tessreduce.

INSTALLING ME:

• git clone this repo
• cd into directory
• pip install .

Tutorials + Paper Reproducer Code

Can be found in the tutorials directory above.

Quickest of quickstarts (from Tutorial 1):

Imports:

import numpy as np
from etsfit import etsMAIN
import etsfit.utils.utilities as ut
import pandas as pd
from astropy.time import Time

Info load:

TNSFile = "./tutorials/tutorial_data/2018hzh_TNS.csv"
TNSinfo = pd.read_csv(TNSFile)
dataFile = "./tutorials/tutorial_data/2018hzh0431-tessreduce"
save_dir = "."

(time, flux, error, targetlabel, 
                 sector, camera, ccd) = ut.tr_load_lc(dataFile)

discoverytime = ut.get_disctime(TNSFile, targetlabel)

Run it

ets = etsMAIN(save_dir, TNSFile)
                                      
ets.load_single_lc(time, flux, error, discoverytime, 
                   targetlabel, sector, camera, ccd)
   
#(optional) run a window RMS filter over the data 
filt = ets.window_rms_filt(plot=False)    
ets.pre_run_clean(1, flux_mask=filt, 
                    binning = False, fraction = None)
ets.run_MCMC(n1=10000, n2=50000)

Example fit:

Citing etsfit

If you make use of etsfit, please cite (our paper when it comes out) and our major dependencies emcee, tinygp, and celerite.