Euclid Quick Release 1: Cloud Access

Euclid Quick Release 1: Cloud Access#

Learning Goals#

By the end of this tutorial, you will:

  • Learn where Euclid Q1 data are stored in the cloud.

  • Retrieve an image cutout from the cloud.

  • Retrieve a spectrum from the cloud.

1. Introduction#

Euclid launched in July 2023 as a European Space Agency (ESA) mission with involvement by NASA. The primary science goals of Euclid are to better understand the composition and evolution of the dark Universe. The Euclid mission is providing space-based imaging and spectroscopy as well as supporting ground-based imaging to achieve these primary goals. These data will be archived by multiple global repositories, including IRSA, where they will support transformational work in many areas of astrophysics.

Euclid Quick Release 1 (Q1) consists of consists of ~30 TB of imaging, spectroscopy, and catalogs covering four non-contiguous fields: Euclid Deep Field North (22.9 sq deg), Euclid Deep Field Fornax (12.1 sq deg), Euclid Deep Field South (28.1 sq deg), and LDN1641.

Euclid Q1 data were released on-premises at IPAC and in the cloud via Amazon Web Services’ Open Data Repository. This notebook introduces users to accessing Euclid Q1 data from the cloud. Additional Euclid-specific notebooks can be found at “Accessing Euclid data” section and additional notebooks about how to access IRSA-curated data from the AWS ODR can be found at “Accessing IRSA’s cloud holdings” section.

2. Imports#

  • s3fs for browsing S3 buckets

  • astropy for handling coordinates, units, FITS I/O, tables, images, etc.

  • astroquery for querying Euclid data products from IRSA

  • matplotlib for visualization

  • json for decoding JSON strings

# Uncomment the next line to install dependencies if needed.
# !pip install s3fs astropy astroquery>=0.4.10 matploltib

import s3fs
from astropy.coordinates import SkyCoord
import astropy.units as u
from astropy.visualization import ImageNormalize, PercentileInterval, AsinhStretch
from astropy.io import fits
from astropy.nddata import Cutout2D
from astropy.wcs import WCS
from astropy.table import Table
from astroquery.ipac.irsa import Irsa
from matplotlib import pyplot as plt
import json

3. Browse Euclid Q1 cloud-hosted data#

BUCKET_NAME = 'nasa-irsa-euclid-q1'

s3fs provides a filesystem-like python interface for AWS S3 buckets. First we create a s3 client:

s3 = s3fs.S3FileSystem(anon=True)

Then we list the q1 directory that contains Euclid Q1 data products:

s3.ls(f'{BUCKET_NAME}/q1')

['nasa-irsa-euclid-q1/q1/MER',
 'nasa-irsa-euclid-q1/q1/MER_SEG',
 'nasa-irsa-euclid-q1/q1/NIR',
 'nasa-irsa-euclid-q1/q1/RAW',
 'nasa-irsa-euclid-q1/q1/SIR',
 'nasa-irsa-euclid-q1/q1/VIS',
 'nasa-irsa-euclid-q1/q1/VMPZ',
 'nasa-irsa-euclid-q1/q1/catalogs']

Let’s navigate to MER images (available as FITS files):

s3.ls(f'{BUCKET_NAME}/q1/MER')[:10] # ls only top 10 to limit the long output

['nasa-irsa-euclid-q1/q1/MER/102018211',
 'nasa-irsa-euclid-q1/q1/MER/102018212',
 'nasa-irsa-euclid-q1/q1/MER/102018213',
 'nasa-irsa-euclid-q1/q1/MER/102018664',
 'nasa-irsa-euclid-q1/q1/MER/102018665',
 'nasa-irsa-euclid-q1/q1/MER/102018666',
 'nasa-irsa-euclid-q1/q1/MER/102018667',
 'nasa-irsa-euclid-q1/q1/MER/102018668',
 'nasa-irsa-euclid-q1/q1/MER/102018669',
 'nasa-irsa-euclid-q1/q1/MER/102018670']

s3.ls(f'{BUCKET_NAME}/q1/MER/102018211') # pick any tile ID from above

['nasa-irsa-euclid-q1/q1/MER/102018211/DECAM',
 'nasa-irsa-euclid-q1/q1/MER/102018211/NISP',
 'nasa-irsa-euclid-q1/q1/MER/102018211/VIS']

s3.ls(f'{BUCKET_NAME}/q1/MER/102018211/VIS') # pick any instrument from above

['nasa-irsa-euclid-q1/q1/MER/102018211/VIS/EUC_MER_BGMOD-VIS_TILE102018211-4CD9D_20241018T142710.276652Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102018211/VIS/EUC_MER_BGSUB-MOSAIC-VIS_TILE102018211-ACBD03_20241018T142710.276838Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102018211/VIS/EUC_MER_CATALOG-PSF-VIS_TILE102018211-55C665_20241018T204235.689380Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102018211/VIS/EUC_MER_GRID-PSF-VIS_TILE102018211-9A5C08_20241018T142525.109781Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102018211/VIS/EUC_MER_MOSAIC-VIS-FLAG_TILE102018211-40A124_20241018T142525.109767Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102018211/VIS/EUC_MER_MOSAIC-VIS-RMS_TILE102018211-B3070B_20241018T142525.109753Z_00.00.fits']

As per “Browsable Directories” section in user guide, we need MER/{tile_id}/{instrument}/EUC_MER_BGSUB-MOSAIC*.fits for displaying background-subtracted mosiac images. But these images are stored under TILE IDs so first we need to find TILE ID for a coordinate search we are interested in. We will use astroquery (in next section) to retrieve FITS file paths for our coordinates by doing spatial search.

4. Do a spatial search for MER mosaics#

Pick a target and search radius:

target_name = 'TYC 4429-1677-1'
coord = SkyCoord.from_name(target_name)
search_radius = 10 * u.arcsec

List all Simple Image Access (SIA) collections for IRSA with names containing “euclid”:

collections = Irsa.list_collections(servicetype='SIA', filter='euclid')
collections
Table length=2
collection
object
euclid_DpdMerBksMosaic
euclid_ero

As per “Data Products Overview” in user guide and above table, we identify that MER Mosiacs are available as the following collection:

img_collection = 'euclid_DpdMerBksMosaic'

Now query this collection for our target’s coordinates and search radius:

img_tbl = Irsa.query_sia(pos=(coord, search_radius), collection=img_collection)
img_tbl
Table length=49
s_ras_decfacility_nameinstrument_namedataproduct_subtypecalib_leveldataproduct_typeenergy_bandpassnameenergy_embandobs_ids_resolutionem_minem_maxem_res_powerproposal_titleaccess_urlaccess_formataccess_estsizet_exptimes_regionobs_collectionobs_intentalgorithm_namefacility_keywordsinstrument_keywordsenvironment_photometricproposal_idproposal_piproposal_projecttarget_nametarget_typetarget_standardtarget_movingtarget_keywordsobs_release_dates_xel1s_xel2s_pixel_scaleposition_timedependentt_mint_maxt_resolutiont_xelobs_publisher_dids_fovem_xelpol_statespol_xelcloud_accesso_ucdupload_row_id
degdegarcsecmmkbytesdegarcsecddsdeg
float64float64objectobjectobjectint16objectobjectobjectobjectfloat64float64float64float64objectobjectobjectint64float64objectobjectobjectobjectobjectobjectboolobjectobjectobjectobjectobjectboolboolobjectobjectint64int64float64boolfloat64float64float64int64objectfloat64int64objectint64objectobjectint64
273.0716226240449568.00001388888474EuclidNISPnoise3imageYInfrared102160339_NISP0.08789.2e-071.146e-064.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_MOSAIC-NIR-Y-RMS_TILE102160339-975287_20241024T223523.501123Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/Y0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_MOSAIC-NIR-Y-RMS_TILE102160339-975287_20241024T223523.501123Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidNISPscience3imageYInfrared102160339_NISP0.08789.2e-071.146e-064.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-Y_TILE102160339-3A9BC7_20241024T231706.521789Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/Y0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-Y_TILE102160339-3A9BC7_20241024T231706.521789Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidNISPauxiliary3imageYInfrared102160339_NISP0.08789.2e-071.146e-064.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_CATALOG-PSF-NIR-Y_TILE102160339-E72766_20241025T072629.031850Z_00.00.fitsimage/fits549787--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/Y0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_CATALOG-PSF-NIR-Y_TILE102160339-E72766_20241025T072629.031850Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidNISPauxiliary3imageYInfrared102160339_NISP0.08789.2e-071.146e-064.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_GRID-PSF-NIR-Y_TILE102160339-3306DF_20241024T223523.501156Z_00.00.fitsimage/fits1475188--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/Y0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_GRID-PSF-NIR-Y_TILE102160339-3306DF_20241024T223523.501156Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidNISPauxiliary3imageYInfrared102160339_NISP0.08789.2e-071.146e-064.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_BGMOD-NIR-Y_TILE102160339-83950E_20241024T231706.521466Z_00.00.fitsimage/fits1474563--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/Y0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_BGMOD-NIR-Y_TILE102160339-83950E_20241024T231706.521466Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidNISPauxiliary3imageYInfrared102160339_NISP0.08789.2e-071.146e-064.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_MOSAIC-NIR-Y-FLAG_TILE102160339-257E27_20241024T223523.501139Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/Y0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_MOSAIC-NIR-Y-FLAG_TILE102160339-257E27_20241024T223523.501139Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474CFHTMegaCamscience3imageUOptical102160339_MegaCam--3.14751e-074.01839e-074.1Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/MEGACAM/EUC_MER_BGSUB-MOSAIC-CFIS-U_TILE102160339-B76075_20241024T224001.853984Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_MegaCam/U0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/MEGACAM/EUC_MER_BGSUB-MOSAIC-CFIS-U_TILE102160339-B76075_20241024T224001.853984Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474CFHTMegaCamauxiliary3imageUOptical102160339_MegaCam--3.14751e-074.01839e-074.1Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/MEGACAM/EUC_MER_BGMOD-CFIS-U_TILE102160339-B46410_20241024T224001.853720Z_00.00.fitsimage/fits1474563--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_MegaCam/U0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/MEGACAM/EUC_MER_BGMOD-CFIS-U_TILE102160339-B46410_20241024T224001.853720Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474CFHTMegaCamauxiliary3imageUOptical102160339_MegaCam--3.14751e-074.01839e-074.1Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/MEGACAM/EUC_MER_MOSAIC-CFIS-U-FLAG_TILE102160339-FB39D_20241024T222309.855577Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_MegaCam/U0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/MEGACAM/EUC_MER_MOSAIC-CFIS-U-FLAG_TILE102160339-FB39D_20241024T222309.855577Z_00.00.fits", "region": "us-east-1"}}1
.........................................................................................................................................................
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camscience3imageZOptical102160339_WISHES1.158.36385e-079.49057e-077.9Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_BGSUB-MOSAIC-WISHES-Z_TILE102160339-50455D_20241024T223629.659159Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/Z0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_BGSUB-MOSAIC-WISHES-Z_TILE102160339-50455D_20241024T223629.659159Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camnoise3imageZOptical102160339_WISHES1.158.36385e-079.49057e-077.9Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-Z-RMS_TILE102160339-D3B0D9_20241024T222306.537259Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/Z0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-Z-RMS_TILE102160339-D3B0D9_20241024T222306.537259Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camauxiliary3imageZOptical102160339_WISHES1.158.36385e-079.49057e-077.9Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_BGMOD-WISHES-Z_TILE102160339-F48933_20241024T223629.659029Z_00.00.fitsimage/fits1474563--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/Z0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_BGMOD-WISHES-Z_TILE102160339-F48933_20241024T223629.659029Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camauxiliary3imageZOptical102160339_WISHES1.158.36385e-079.49057e-077.9Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-Z-FLAG_TILE102160339-383905_20241024T222306.537289Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/Z0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-Z-FLAG_TILE102160339-383905_20241024T222306.537289Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camauxiliary3imageGOptical102160339_WISHES1.583.95422e-075.54729e-073.0Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_CATALOG-PSF-WISHES-G_TILE102160339-3FBE37_20241024T222302.191374Z_00.00.fitsimage/fits549150--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/G0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_CATALOG-PSF-WISHES-G_TILE102160339-3FBE37_20241024T222302.191374Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camauxiliary3imageGOptical102160339_WISHES1.583.95422e-075.54729e-073.0Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_BGMOD-WISHES-G_TILE102160339-809205_20241024T224427.004630Z_00.00.fitsimage/fits1474563--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/G0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_BGMOD-WISHES-G_TILE102160339-809205_20241024T224427.004630Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camauxiliary3imageGOptical102160339_WISHES1.583.95422e-075.54729e-073.0Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-G-FLAG_TILE102160339-7F63CF_20241024T222302.191361Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/G0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-G-FLAG_TILE102160339-7F63CF_20241024T222302.191361Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camscience3imageGOptical102160339_WISHES1.583.95422e-075.54729e-073.0Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_BGSUB-MOSAIC-WISHES-G_TILE102160339-3B55EE_20241024T224427.004846Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/G0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_BGSUB-MOSAIC-WISHES-G_TILE102160339-3B55EE_20241024T224427.004846Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474Subaru TelescopeHyper Suprime-Camnoise3imageGOptical102160339_WISHES1.583.95422e-075.54729e-073.0Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-G-RMS_TILE102160339-5EB01E_20241024T222302.191348Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_WISHES/G0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/HSC/EUC_MER_MOSAIC-WISHES-G-RMS_TILE102160339-5EB01E_20241024T222302.191348Z_00.00.fits", "region": "us-east-1"}}1

Let’s narrow it down to the images with science dataproduct subtype and Euclid facility:

euclid_sci_img_tbl = img_tbl[[row['facility_name']=='Euclid'
                              and row['dataproduct_subtype']=='science'
                              for row in img_tbl]]
euclid_sci_img_tbl
Table length=4
s_ras_decfacility_nameinstrument_namedataproduct_subtypecalib_leveldataproduct_typeenergy_bandpassnameenergy_embandobs_ids_resolutionem_minem_maxem_res_powerproposal_titleaccess_urlaccess_formataccess_estsizet_exptimes_regionobs_collectionobs_intentalgorithm_namefacility_keywordsinstrument_keywordsenvironment_photometricproposal_idproposal_piproposal_projecttarget_nametarget_typetarget_standardtarget_movingtarget_keywordsobs_release_dates_xel1s_xel2s_pixel_scaleposition_timedependentt_mint_maxt_resolutiont_xelobs_publisher_dids_fovem_xelpol_statespol_xelcloud_accesso_ucdupload_row_id
degdegarcsecmmkbytesdegarcsecddsdeg
float64float64objectobjectobjectint16objectobjectobjectobjectfloat64float64float64float64objectobjectobjectint64float64objectobjectobjectobjectobjectobjectboolobjectobjectobjectobjectobjectboolboolobjectobjectint64int64float64boolfloat64float64float64int64objectfloat64int64objectint64objectobjectint64
273.0716226240449568.00001388888474EuclidNISPscience3imageYInfrared102160339_NISP0.08789.2e-071.146e-064.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-Y_TILE102160339-3A9BC7_20241024T231706.521789Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/Y0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-Y_TILE102160339-3A9BC7_20241024T231706.521789Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidVISscience3imageVISOptical102160339_VIS0.165.5e-079e-072.1Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/VIS/EUC_MER_BGSUB-MOSAIC-VIS_TILE102160339-ADF1FF_20241025T022657.824470Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_VIS/VIS0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/VIS/EUC_MER_BGSUB-MOSAIC-VIS_TILE102160339-ADF1FF_20241025T022657.824470Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidNISPscience3imageJInfrared102160339_NISP0.0941.146e-061.372e-065.6Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-J_TILE102160339-B6EC5E_20241024T230609.320185Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/J0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-J_TILE102160339-B6EC5E_20241024T230609.320185Z_00.00.fits", "region": "us-east-1"}}1
273.0716226240449568.00001388888474EuclidNISPscience3imageHInfrared102160339_NISP0.10261.372e-062e-062.7Euclid on-the-flyhttps://irsa.ipac.caltech.edu/ibe/data/euclid/q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-H_TILE102160339-539320_20241024T230405.104628Z_00.00.fitsimage/fits1474566--POLYGON ICRS 273.7917384920759 68.26512213776137 272.3515059037093 68.2651218134747 272.3679070260387 67.73183349848344 273.7753390775804 67.73183381419538 273.7917384920759 68.26512213776137euclid_DpdMerBksMosaicSCIENCEmosaic--field--False2025-05-01 00:00:0019200192000.100000000000008False--------ivo://irsa.ipac/euclid_DpdMerBksMosaic?102160339_NISP/H0.533333333333376----{"aws": {"bucket_name": "nasa-irsa-euclid-q1", "key":"q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-H_TILE102160339-539320_20241024T230405.104628Z_00.00.fits", "region": "us-east-1"}}1

We can see there’s a cloud_access column that gives us the location info of the image files we are interested in. So let’s extract the S3 bucket file path from it:

def get_s3_fpath(cloud_access):
    cloud_info = json.loads(cloud_access) # converts str to dict
    bucket_name = cloud_info['aws']['bucket_name']
    key = cloud_info['aws']['key']

    return f'{bucket_name}/{key}'

[get_s3_fpath(row['cloud_access']) for row in euclid_sci_img_tbl]

['nasa-irsa-euclid-q1/q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-Y_TILE102160339-3A9BC7_20241024T231706.521789Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102160339/VIS/EUC_MER_BGSUB-MOSAIC-VIS_TILE102160339-ADF1FF_20241025T022657.824470Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-J_TILE102160339-B6EC5E_20241024T230609.320185Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/MER/102160339/NISP/EUC_MER_BGSUB-MOSAIC-NIR-H_TILE102160339-539320_20241024T230405.104628Z_00.00.fits']

Let’s also extract filter names to use when displaying the images:

def get_filter_name(instrument, bandpass):
    return f'{instrument}_{bandpass}' if instrument!=bandpass else instrument

[get_filter_name(row['instrument_name'], row['energy_bandpassname']) for row in euclid_sci_img_tbl]

['NISP_Y', 'VIS', 'NISP_J', 'NISP_H']

5. Efficiently retrieve mosaic cutouts#

These image files are very big (~1.4GB), so we use astropy’s lazy-loading capability of FITS for better performance. (See Obtaining subsets from cloud-hosted FITS files.)

cutout_size = 1 * u.arcmin

cutouts = []
filters = []

for row in euclid_sci_img_tbl:
    s3_fpath = get_s3_fpath(row['cloud_access'])
    filter_name = get_filter_name(row['instrument_name'], row['energy_bandpassname'])

    with fits.open(f's3://{s3_fpath}', fsspec_kwargs={"anon": True}) as hdul:
        print(f'Retrieving cutout for {filter_name} ...')
        cutout = Cutout2D(hdul[0].section,
                          position=coord,
                          size=cutout_size,
                          wcs=WCS(hdul[0].header))
        cutouts.append(cutout)
        filters.append(filter_name)

Retrieving cutout for NISP_Y ...
Retrieving cutout for VIS ...
Retrieving cutout for NISP_J ...
Retrieving cutout for NISP_H ...

fig, axes = plt.subplots(2, 2, figsize=(4 * 2, 4 * 2), subplot_kw={'projection': cutouts[0].wcs})

for idx, ax in enumerate(axes.flat):
    norm = ImageNormalize(cutouts[idx].data, interval=PercentileInterval(99), stretch=AsinhStretch())
    ax.imshow(cutouts[idx].data, cmap='gray', origin='lower', norm=norm)
    ax.set_xlabel('RA')
    ax.set_ylabel('Dec')
    ax.text(0.95, 0.05, filters[idx], color='white', fontsize=14, transform=ax.transAxes, va='bottom', ha='right')

plt.tight_layout()
../../_images/193d7ce38bc130d233deb788daed2f5d05503ad113bbceeac320ed331f140486.png

6. Find the MER catalog for a given tile#

Let’s navigate to MER catalog in the Euclid Q1 bucket:

s3.ls(f'{BUCKET_NAME}/q1/catalogs')

['nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG',
 'nasa-irsa-euclid-q1/q1/catalogs/NIR_CAL_CATALOG',
 'nasa-irsa-euclid-q1/q1/catalogs/PHZ_PF_OUTPUT_CATALOG',
 'nasa-irsa-euclid-q1/q1/catalogs/PHZ_PF_OUTPUT_FOR_L3',
 'nasa-irsa-euclid-q1/q1/catalogs/SPE_PF_OUTPUT_CATALOG',
 'nasa-irsa-euclid-q1/q1/catalogs/VIS_CAL_CATALOG']

s3.ls(f'{BUCKET_NAME}/q1/catalogs/MER_FINAL_CATALOG')[:10] # ls only top 10 to limit the long output

['nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018211',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018212',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018213',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018664',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018665',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018666',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018667',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018668',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018669',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102018670']

mer_tile_id = 102160339 # from the image paths for the target we picked
s3.ls(f'{BUCKET_NAME}/q1/catalogs/MER_FINAL_CATALOG/{mer_tile_id}')

['nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102160339/EUC_MER_FINAL-CAT_TILE102160339-52AA9D_20241026T145149.163369Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102160339/EUC_MER_FINAL-CUTOUTS-CAT_TILE102160339-7449E7_20241026T034412.641243Z_00.00.fits',
 'nasa-irsa-euclid-q1/q1/catalogs/MER_FINAL_CATALOG/102160339/EUC_MER_FINAL-MORPH-CAT_TILE102160339-52C86_20241026T145146.071857Z_00.00.fits']

As per “Browsable Directiories” section in user guide, we can use catalogs/MER_FINAL_CATALOG/{tile_id}/EUC_MER_FINAL-CAT*.fits for listing the objects catalogued. We can read the identified FITS file as table and do filtering on ra, dec columns to find object ID(s) only for the target we picked. But it will be an expensive operation so we will instead use astroquery (in next section) to do a spatial search in the MER catalog provided by IRSA.

Note

Once the catalogs are available as Parquet files in the cloud, we can efficiently do spatial filtering directly on the cloud-hosted file to identify object ID(s) for our target. But for the time being, we can use catalog VO services through astroquery to do the same.

7. Find the MER Object ID for our target#

First, list the Euclid catalogs provided by IRSA:

catalogs = Irsa.list_catalogs(full=True, filter='euclid')
catalogs
Table length=17
table_indexschema_nametable_namedescriptiontable_typeutypeirsa_dbmsirsa_posirsa_sptlevelirsa_missionidirsa_groupidirsa_access_flagirsa_nrowsirsa_odbc_datasourceirsa_spatial_idx_name
int32objectobjectobjectobjectobjectint32objectint32int32int32int32int64objectobject
937euclideuclid_q1_mer_catalogueEuclid Q1 MER Catalogtable21y20-1-13029953430euclidNIX_EUCLID_Q1_MER_CATALOGUE_HTM20
938euclideuclid_q1_mer_morphologyEuclid Q1 MER Morphologytable21n20-1-13029953430euclid
939euclideuclid_q1_phz_photo_zEuclid Q1 PHZ Photo-z Catalogtable21n20-1-13029643313euclid
940euclideuclid_q1_spectro_zcatalog_spe_qualityEuclid Q1 SPE Redshift Catalog - Qualitytable21n20-1-13029205770euclid
941euclideuclid_q1_spectro_zcatalog_spe_classificationEuclid Q1 SPE Redshift Catalog - Classificationtable21n20-1-1303766170euclid
942euclideuclid_q1_spectro_zcatalog_spe_galaxy_candidatesEuclid Q1 SPE Redshift Catalog - Galaxy Candidatestable21n20-1-13018409905euclid
943euclideuclid_q1_spectro_zcatalog_spe_star_candidatesEuclid Q1 SPE Redshift Catalog - Star Candidatestable21n20-1-1306545058euclid
944euclideuclid_q1_spectro_zcatalog_spe_qso_candidatesEuclid Q1 SPE Redshift Catalog - QSO Candidatestable21n20-1-13016781027euclid
945euclideuclid_q1_spe_lines_line_featuresEuclid Q1 SPE Lines Catalog - Spectral Linestable21n20-1-130202905141euclid
946euclideuclid_q1_spe_lines_continuum_featuresEuclid Q1 SPE Lines Catalog - Continuum Featurestable21n20-1-1306133772euclid
947euclideuclid_q1_spe_lines_atomic_indicesEuclid Q1 SPE Lines Catalog - Atomic Indicestable21n20-1-130110407896euclid
948euclideuclid_q1_spe_lines_molecular_indicesEuclid Q1 SPE Lines Catalog - Molecular Indicestable21n20-1-13018401316euclid
949euclideuclid.tileid_association_q1Euclid Q1 TILEID to Observation ID Association Tabletable22n20-1-1307572euclid_postgres
950euclideuclid.objectid_spectrafile_association_q1Euclid Q1 Object ID to Spectral File Association Tabletable22n20-1-13029952509euclid_postgres
951euclideuclid.observation_euclid_q1Euclid Q1 CAOM Observation Tableview22n20-1-1301420euclid_postgres
952euclideuclid.plane_euclid_q1Euclid Q1 CAOM Plane Tableview22y20-1-1304525euclid_postgres
953euclideuclid.artifact_euclid_q1Euclid Q1 CAOM Artifact Tableview22n20-1-13037519euclid_postgres

From this table, we can extract the MER catalog name. We also see several other interesting catalogs, let’s also extract spectral file association catalog for retrieving spectra later.

euclid_mer_catalog = 'euclid_q1_mer_catalogue'
euclid_spec_association_catalog = 'euclid.objectid_spectrafile_association_q1'

Now, we do a region search within a cone of 5 arcsec around our target to pinpoint its object ID in Euclid catalog:

search_radius = 5 * u.arcsec

mer_catalog_tbl = Irsa.query_region(coordinates=coord, spatial='Cone',
                                    catalog=euclid_mer_catalog, radius=search_radius)
mer_catalog_tbl
Table length=1
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NAdegdegdegdegNANAuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyNANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANANANANANANANANANANANANANAmagNAmag / arcsec2mag / arcsec2pixpixpixdegdegNANApixpixarcsecmagmagNANANA
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2731173428682078045273.117342895468.2078045025----1021603390923261------15.8093824387----------------------------------------------------------------------------------------------------------------4875.361328125012928.961914062516977.681640625018710.50976562501233.40625000004855.167480468810355.670898437515934.2138671875--------------11.5900001526------------------------------1495.2513427734----------------------------------------------------------------------------------------------------------------0.06763066350.13767333330.20814146100.28125664590.03858586400.07920518520.12011121960.1619603336--------------1.5817222595------------------------12848.434570312514002.187500000013081.4218750000--------------------40729.179687500026445.1718750000--30498.3554687500--5874.598144531211243.15332031257.81155872348.38819694526.8755660057--------------------0.45246958730.2596883774--8.5716934204--0.46092996002.5831463337--------------------------------------------------------------------------------833.54455566411.33340907109262.95898437502.15729594239372.35253906252.38729739199373.87695312509372.28222656259372.17578125009371.8046875000--------------------3327.687988281216259.2363281250--46777.2421875000--8571.042968750016432.236328125078.167175293069.323905944867.966903686556.0157890320--------------------28.174657821729.7284183502--230.1270751953--22.215751647976.6047515869------------------------------------------------------------------------------------------------------------------------------------------------------------------------22222--------------------22--2--22----------------------------3682.77001953126425.6977539062--7535.6406250000--4730.21484375008908.5263671875168232722----------------00.023591479313.970378875739014.81733608250.846957206711033446.1892967224---54.6229858398--0.1192200407--323.3251037598--1.29566407200.06533759830.000833673922582377678000821760.00388439771021603390.0201884979526048-0.37069201670729000.92853640386357492013200021370972428802429269339
object_id = int(mer_catalog_tbl['object_id'][0])
object_id

2731173428682078045

8. Find the spectrum of an object in the MER catalog#

Using the object ID(s) we extracted above, we can narrow down the spectral file association catalog to identify spectra file path(s). So we do the following TAP search:

adql_query = f"SELECT * FROM {euclid_spec_association_catalog} \
    WHERE objectid = {object_id}"

spec_association_tbl = Irsa.query_tap(adql_query).to_table()
spec_association_tbl
Table length=1
objectidtileidurihducntr
int64int64objectint64int64
2731173428682078045102160339ibe/data/euclid/q1/SIR/102160339/EUC_SIR_W-COMBSPEC_102160339_2024-11-05T16:26:34.614296Z.fits16443602837

Warning

If you picked a target other than what this notebook uses, it’s possible that there is no spectrum associated for your target’s object ID. In that case, spec_association_tbl will contain 0 rows.

In above table, we can see that the uri column gives us location of spectra file on IBE. We can map it to S3 bucket key to retrieve spectra file from the cloud. This is a very big FITS spectra file with multiple extensions where each extension contains spectrum of one object. The hdu column gives us the extension number for our object. So let’s extract both of these.

spec_fpath_key = spec_association_tbl['uri'][0].replace('ibe/data/euclid/', '')
spec_fpath_key

'q1/SIR/102160339/EUC_SIR_W-COMBSPEC_102160339_2024-11-05T16:26:34.614296Z.fits'

object_hdu_idx = int(spec_association_tbl['hdu'][0])
object_hdu_idx

1644

Again, we use astropy’s lazy-loading capability of FITS to only retrieve the spectrum table of our object from the S3 bucket.

with fits.open(f's3://{BUCKET_NAME}/{spec_fpath_key}', fsspec_kwargs={'anon': True}) as hdul:
    spec_hdu = hdul[object_hdu_idx]
    spec_tbl = Table.read(spec_hdu)

WARNING: UnitsWarning: 'erg/s/cm2/Angstrom' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]
WARNING: UnitsWarning: 'Number' did not parse as fits unit: At col 0, Unit 'Number' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]
WARNING: UnitsWarning: 'Number' did not parse as fits unit: At col 0, Unit 'Number' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]
WARNING: UnitsWarning: 'erg2/s2/cm4/Angstrom2' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]
WARNING: UnitsWarning: 'Number' did not parse as fits unit: At col 0, Unit 'Number' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]

spec_tbl
Table length=531
WAVELENGTHSIGNALMASKQUALITYVARNDITH
Angstromerg / (Angstrom s cm2)NumberNumbererg2 / (Angstrom2 s2 cm4)Number
float32float32int32float32float32int16
11900.00.010.00.00
11913.40.010.00.00
11926.80.010.00.00
11940.20.010.00.00
11953.6533.2775670.59190076.1487281
11967.00.010.00.00
11980.4450.44067650.990100262.48395821
11993.8253.39111650.79506320.7241251
12007.20.010.00.00
..................
18894.8106.68797640.8753910.0636822362
18908.2117.300804640.981550460.081569262
18921.6132.819660.877314570.0341483764
18935.0139.23103640.989570260.0579522774
18948.4126.06506640.743000860.0911312853
18961.8128.77884640.723687770.105317683
18975.2131.90378640.73138870.143622553
18988.60.010.00.00
19002.0126.94632660.492347240.81005652
plt.plot(spec_tbl['WAVELENGTH'], spec_tbl['SIGNAL'])
plt.xlabel(spec_tbl['WAVELENGTH'].unit.to_string('latex_inline'))
plt.ylabel(spec_tbl['SIGNAL'].unit.to_string('latex_inline'))

plt.title(f'Spectrum of Target: {target_name}\n(Euclid Object ID: {object_id})');
../../_images/079d93770b2357943b3e31c4e6e06e7586ea1a7039d54b5d89ead5d1fa2bf6a3.png

About this Notebook#

Author: Jaladh Singhal (IRSA Developer) in conjunction with Vandana Desai, Brigitta Sipőcz, Tiffany Meshkat and the IPAC Science Platform team

Updated: 2025-03-17

Contact: the IRSA Helpdesk with questions or reporting problems.

Contents