Working with the Host Class

Similar to the transient_objects.ipynb notebook, this will go through useful methods of the Host class. The general thing to keep in mind here is that we want to minimize the amount of data that OTTER stores locally for Hosts. This is because much of the data is already available in an easy format online. So, instead, we provide code to query those services for data on the hosts and just store host metadata.

We will start by setting up the OTTER dataset connection and pulling the Host information from the ASASSN-14li Transient object. If you are unsure what this means, we recommend you return to the transient_objects.ipynb notebook.

# imports
import os
import otter
import matplotlib.pyplot as plt

# connect to the dataset
db = otter.Otter()

# grab the host information for ASASSN-14li
t = db.query(names='ASASSN-14li')[0]
host = t.get_host()[0] # we can just take the last one

host

Attempting to login to https://otter.idies.jhu.edu/api with the following credentials:
username: user-guest
password: test

SDSS J124815.23+174626.4 @ (RA, Dec)=(192.06249999999997 deg,17.77401111111111 deg)

Before we do anything else, you should take a look at the host attributes and methods with the documentation. The notable attributes are:

• name: The name of the host

• coord: The astropy SkyCoord for this host

• z or redshift: The redshift of this host

• transient_name: The default name from OTTER for the associated transient

• bibcodes: The references that say this is the host galaxy of transient_name

So, as you can see, this has many methods to pull data from a wide variety of sources for this host galaxy! I will go through three here; querying simbad, ZTF forced photometry, and sparcl (for spectra).

Simbad

Querying simbad will give you more meta information on the host, in case we are missing any in OTTER.

host.query_simbad()

Table length=1

main_id	ra	dec	coo_err_maj	coo_err_min	coo_err_angle	coo_wavelength	coo_bibcode
	deg	deg	mas	mas	deg
object	float64	float64	float32	float32	int16	str1	object
ZW VIII 211	192.06343876082	17.77402095173	0.1712	0.1079	90	O	2020yCat.1350....0G

ZTF Forced Photometry

The query_ztf method will pull all of the ZTF forced photometry for this Host. There are other services that will give you forced photometry like the query_asassn, query_ptf, and query_atlas methods. Most of these are easy and do not require authentication. But, please note, you need to set some special environment variables for querying ATLAS forced photometry.

ztf_phot = host.query_ztf()
ztf_phot

Table length=1380

oid	expid	hjd	mjd	mag	magerr	catflags	filtercode	ra	dec	chi	sharp	filefracday	field	ccdid	qid	limitmag	magzp	magzprms	clrcoeff	clrcounc	exptime	airmass	programid
int64	int32	float64	float64	float32	float32	int32	str2	float64	float64	float32	float32	int64	int32	uint8	uint8	float32	float32	float32	float32	float32	float32	float32	int32
577108400005378	44831063	2458202.8161852155	58202.3106366	16.37723	0.015439002	0	zg	192.063438	17.7740715	2.716	0.277	20180325310625	577	8	4	20.85	26.241505	0.0337535	-0.024271995	4.0729796e-05	30.0	1.072	1
577108400005378	44833559	2458202.841138935	58202.3355903	16.419819	0.0155790625	0	zg	192.0634047	17.7740339	2.877	0.218	20180325335579	577	8	4	20.9	26.23738	0.03151846	-0.021219717	3.6010155e-05	30.0	1.042	1
577108400005378	45130554	2458205.811087029	58205.305544	16.422834	0.015589281	0	zg	192.0634317	17.7740395	2.272	0.102	20180328305521	577	8	4	20.21	26.258638	0.040733323	-0.019816667	8.143837e-05	30.0	1.067	1
577108400005378	45131854	2458205.824084676	58205.3185417	16.42617	0.015600641	0	zg	192.0634323	17.7740673	2.819	0.159	20180328318530	577	8	4	20.3	26.264067	0.041553747	-0.01758011	8.442703e-05	30.0	1.05	1
577108400005378	45629962	2458210.805131635	58210.2996296	16.441822	0.015654594	0	zg	192.0634537	17.7740789	1.981	0.149	20180402299468	577	8	4	19.75	26.169762	0.04299412	-0.008474942	0.00015637281	30.0	1.056	1
577108400005378	45632116	2458210.826670778	58210.321169	16.468002	0.01574737	32768	zg	192.0634521	17.7740457	1.635	0.101	20180402321157	577	8	4	19.27	26.04908	0.049283765	-0.020842288	0.0003108623	30.0	1.037	1
577108400005378	46028217	2458214.7876171353	58214.2821759	16.390034	0.015480272	0	zg	192.0633939	17.7741309	2.509	0.032	20180406282164	577	8	4	20.37	25.73719	0.04760029	-0.011636164	0.00010886254	30.0	1.065	1
577108400005378	47329508	2458227.8001540815	58227.295081	16.373016	0.0154255815	0	zg	192.0633978	17.7740765	2.409	0.065	20180419295069	577	8	4	21.0	26.226395	0.032994743	-0.01994036	3.9443865e-05	30.0	1.034	1
577108400005378	47633460	2458230.839557638	58230.3346065	16.440844	0.015651187	0	zg	192.0634178	17.7740803	3.118	0.266	20180422334595	577	8	4	21.69	26.210157	0.025991382	-0.04899406	2.3768815e-05	30.0	1.082	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1572215400026709	155830789	2459312.8132893397	59312.3078935	15.926317	0.013343082	0	zr	192.0634878	17.7740314	3.806	0.404	20210408307882	1572	15	4	21.59	26.310152	0.027659263	0.105552204	2.350647e-05	30.0	1.051	3
1572215400026709	157023746	2459324.7424926427	59324.2374653	15.9509535	0.013394092	0	zr	192.0634652	17.7740607	3.048	0.129	20210420237465	1572	15	4	20.77	26.269865	0.029191252	0.10533256	2.7050333e-05	30.0	1.083	2
1572215400026709	157023792	2459324.742955525	59324.2379282	15.909086	0.013308326	0	zr	192.0634262	17.7740245	4.364	0.331	20210420237928	1572	15	4	21.09	26.256872	0.02895346	0.11206503	2.616994e-05	30.0	1.083	3
1572215400026709	158922392	2459343.7279607314	59343.2239236	15.832029	0.013161831	0	zr	192.0634817	17.7740642	3.345	0.211	20210509223924	1572	15	4	21.67	26.220495	0.02971997	0.10488721	2.7938207e-05	30.0	1.051	2
1572215400026709	158922439	2459343.7284351992	59343.2243981	15.840847	0.0131778745	0	zr	192.0634497	17.7740663	3.974	0.385	20210509224398	1572	15	4	21.71	26.220335	0.027456189	0.11674291	2.385869e-05	30.0	1.051	3
1572215400026709	195320190	2459707.7060267725	59707.2019097	15.911115	0.013312379	0	zr	192.0634865	17.7740796	3.267	0.081	20220508201910	1572	15	4	20.6	26.284805	0.031224055	0.11465285	3.0734343e-05	30.0	1.067	2
1572215400026709	226340540	2460017.9108466255	60017.4054051	15.925154	0.013340711	32768	zr	192.0634879	17.7740955	2.279	0.331	20230314405394	1572	15	4	19.5	25.12099	0.049222887	0.12120484	9.146372e-05	30.0	1.061	2
1572215400026709	228833269	2460042.838102637	60042.3326968	15.956323	0.013405417	0	zr	192.063459	17.7741067	3.161	0.322	20230408332697	1572	15	4	19.46	25.769245	0.04868074	0.111889996	9.221749e-05	30.0	1.057	2
1572215400026709	230130518	2460055.8101903414	60055.3051852	15.904069	0.013298345	0	zr	192.0634863	17.7741079	2.844	0.462	20230421305174	1572	15	4	21.11	26.269793	0.02943827	0.1203238	2.7261045e-05	30.0	1.064	2
1572215400026709	230523637	2460059.7412128258	60059.2363773	15.909904	0.013309958	0	zr	192.0634857	17.7740972	3.704	0.424	20230425236366	1572	15	4	21.06	26.213598	0.027670497	0.12003717	2.4489718e-05	30.0	1.069	2

Which gives us a lot of time-series photometry for this host! Let’s plot the light curve, color coded by the filter. And, we can easily add a line for the discovery date of ASASSN-14li.

fig, ax = plt.subplots()
for label, grp in ztf_phot.to_pandas().groupby('filtercode'):
    ax.errorbar(grp.mjd, grp.mag, yerr=grp.magerr, fmt='o', label=label)

ax.axvline(t.get_discovery_date().mjd, linestyle='--', label='ASASSN-14li Discovery')

ax.set_ylabel('Magnitude')
ax.set_xlabel('MJD')
fig.gca().invert_yaxis()
ax.legend();

Get Host Spectra

To get host spectra, we use the query_sparcl method which queries the SPARCL database. This will give an astropy table with columns for the wavelength and flux.

For a description of the columns see https://astrosparcl.datalab.noirlab.edu/sparc/sfc/

spec = host.query_sparcl()
spec

Table length=1

sparcl_id	dec	ra	specid	flux	wavelength	_dr
str36	float64	float64	int64	object	object	str9
8aa3ea9d-a072-11ee-870c-525400ad1336	17.774016	192.06346	2928594919838738432	[75.78482819 75.77787018 78.90979004 ... 78.68370819 81.32446289\n 81.32581329]	[3803.64520329 3804.5211258 3805.39725003 ... 9215.09888735 9217.22098659\n 9219.34357452]	SDSS-DR16

fig, ax = plt.subplots()

for row in spec:
    ax.plot(row['wavelength'], row['flux'], label=row['_dr'])

ax.set_ylabel(r'Flux [$10^{-17}~erg~cm^{-2}~s^{-1}~AA^{-1}$]')
ax.set_xlabel(r'Wavelength [$\AA$]')
ax.legend();