DES SN 5YR Tutorial Part 3: Load DES SN Classification

38ef90261b934e01991e1472ad44c990 Dark Energy Survey Supernova Program

Tutorial Part 3 Load SN classfication Contact author: Bruno Sánchez (bsanchez@cppm.in2p3.fr)

This tutorial is intended to show how to load the data from the classification files provided with this release.

We will load the package utils module and data module.

utils provides functionality to read the data formats

data provides with the location of the downloaded DR files.

[1]:

import warnings
warnings.filterwarnings("ignore")

[2]:

import os
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

[3]:

from dessndr import utils, data

This is the loacation of the cloned DR repository in our system:

[4]:

print(data.DES5YRDR_DATA)

/Users/sanchez/Data/DES/DESSNDR/DES-SN5YR

[5]:

probas = pd.read_csv(os.path.join(data.DES5YRDR_DATA, '3_CLASSIFICATION/DES_classification.csv'))

[6]:

probas

[6]:
CID CIDint PROB_SCONE PROB_SNIRFV19 PROB_SNNDESCC PROB_SNNJ17 PROB_SNNV19
0 1246275 1246275 0.9902 0.8486 1.0000 0.9999 1.0000
1 1246281 1246281 0.9432 1.0000 1.0000 0.9999 1.0000
2 1246314 1246314 0.9891 0.7823 0.9993 0.9970 0.9998
3 1246527 1246527 0.9757 1.0000 0.9997 0.9998 1.0000
4 1246529 1246529 0.9549 0.9407 0.9996 0.9993 1.0000
... ... ... ... ... ... ... ...
1630 1948484 1948484 0.8789 0.9146 0.9640 0.9935 0.9996
1631 1950024 1950024 0.9000 0.9380 0.9999 0.9937 0.9355
1632 1950043 1950043 0.9573 0.9454 0.9964 0.9874 0.2608
1633 1959025 1959025 0.9867 0.8921 0.9996 0.9996 0.9998
1634 1968046 1968046 1.0000 0.9225 1.0000 0.9998 1.0000

1635 rows × 7 columns

[7]:

plt.hist(probas.PROB_SNNV19)
plt.title('PROB SNN V19')
plt.xlabel('P(SNIa)')

[7]:

Text(0.5, 0, 'P(SNIa)')
../_images/tutorial_03-Classification_11_1.png

Now we are going to load the DES photometry files:

[8]:

phot_version = os.path.join(
    data.DES5YRDR_DATA_ROOT,
    'DES-SN5YR/0_DATA/DES-SN5YR_DESDR/DES-SN5YR_DESDR'
)
phot = utils.PhotFITS(phot_version)

Let’s select a few of the high probability!

[9]:

random_snias = probas[probas.PROB_SNIRFV19==1.].sample(3).CID.values

[10]:

from dessndr import plot

[11]:

lc = phot.get_lc(random_snias[0])

[12]:

plot.plot_sn_light_curve(lc, CID=random_snias[0], figsize=(10, 6))

[12]:

(<Figure size 1000x600 with 1 Axes>,
 <Axes: title={'center': 'Supernova Light CurveCID: 1387101'}, xlabel='MJD', ylabel='Flux'>)
../_images/tutorial_03-Classification_18_1.png 
[13]:

lc = phot.get_lc(random_snias[1])
plot.plot_sn_light_curve(lc, CID=random_snias[1], figsize=(10, 6))

[13]:

(<Figure size 1000x600 with 1 Axes>,
 <Axes: title={'center': 'Supernova Light CurveCID: 1341743'}, xlabel='MJD', ylabel='Flux'>)
../_images/tutorial_03-Classification_19_1.png 
[14]:

lc = phot.get_lc(random_snias[2])
plot.plot_sn_light_curve(lc, CID=random_snias[2], figsize=(10, 6))

[14]:

(<Figure size 1000x600 with 1 Axes>,
 <Axes: title={'center': 'Supernova Light CurveCID: 1655086'}, xlabel='MJD', ylabel='Flux'>)
../_images/tutorial_03-Classification_20_1.png

Now let’s check a few of the bad ones!

[15]:

random_snias = probas[probas.PROB_SNIRFV19<0.1].sample(3).CID.values

[16]:

lc = phot.get_lc(random_snias[0])
plot.plot_sn_light_curve(lc, CID=random_snias[0], figsize=(10, 6))

[16]:

(<Figure size 1000x600 with 1 Axes>,
 <Axes: title={'center': 'Supernova Light CurveCID: 1298369'}, xlabel='MJD', ylabel='Flux'>)
../_images/tutorial_03-Classification_23_1.png 
[17]:

lc = phot.get_lc(random_snias[1])
plot.plot_sn_light_curve(lc, CID=random_snias[1], figsize=(10, 6))

[17]:

(<Figure size 1000x600 with 1 Axes>,
 <Axes: title={'center': 'Supernova Light CurveCID: 1253324'}, xlabel='MJD', ylabel='Flux'>)
../_images/tutorial_03-Classification_24_1.png 
[18]:

lc = phot.get_lc(random_snias[2])
plot.plot_sn_light_curve(lc, CID=random_snias[2], figsize=(10, 6))

[18]:

(<Figure size 1000x600 with 1 Axes>,
 <Axes: title={'center': 'Supernova Light CurveCID: 1457045'}, xlabel='MJD', ylabel='Flux'>)
../_images/tutorial_03-Classification_25_1.png