Package: lenscat
$\texttt{lenscat}$A public and community-contributed catalog of known strong gravitational lenses.QuickstartThe catalog is available as aplain csv fileunderlenscat/data/catalog.csv. Alternatively, one can interact with the catalog using aweb app(mobile-friendly).We also provide apython packagelenscat, available in pypi. Simply dopipinstalllenscatto install the latest version. Here we adopt thecontinuous deploymentparadigm (similar toastroquery). Whenever there is a change in the catalog content, or a major change in the code, a new release will be available instantaneously on bothGitHubandPyPI.The code converts the catalog in the csv file into a customCatalogobject that is inherited from theTableobject inastropy. To access the catalog, simply runIn[1]:importlenscat;lenscat.catalogOut[1]:<Cataloglength=4587>nameRADECzlenstypegradingdegdegstr20float64float64str15str7str9-------------------------------------------------------J0011-08452.83435-8.7643-galaxyconfirmedJ0013+51193.34807751.3183-galaxyconfirmedPSJ0028+06317.093696.5317-galaxyconfirmedPSJ0030-15257.5636-15.4177measuredgalaxyconfirmedJ0047+251411.946594325.2411-galaxyconfirmedHE0047-175612.6158-17.66930.407galaxyconfirmed..................235614+023115359.059232.521070.372galaxyprobable235730+010133359.3776431.025960.638galaxyprobable235811+003309359.54750.55270.639clusterprobable235853+012406359.7217081.4018160.481galaxyprobable235933+020823359.88972.13980.43clusterconfirmed235948-005913359.95245-0.987020.758galaxyprobable235952+004154359.96980.69850.267clusterprobableNote that the code will try to assign the unit for each of the columns inferred from its name, and that it will hide the 'ref' columnby default. One can show or hide the 'ref' column by calling.show_ref()and.hide_ref()on theCatalogobject respectively.EveryCatalogobject supports two features: basic searching with.search()and crossmatching with a skymap with.crossmatch(). Note that these function will return aCatalogobject, and hencethey can be composed together(e.g.,.crossmatch().search()).Basic searchingThis feature is implemented as.search(). One can search/filter by any combination ofranges of right ascension (specified asRA_range=(RA_min, RA_max))ranges of declination (specified asDEC_range=(DEC_min, DEC_max))ranges of lens redshift if available (specified aszlens_range=(zlens_min, zlens_max))type of the lenses (specified aslens_type)grading of the lenses (specified asgrading)For example, to get a list of the cluster-scale lenses which are confirmed and with a redshift $z_{\mathrm{lens}} \geq 1$ together with the reference, runIn[1]:importlenscat,numpy;lenscat.catalog.search(grading="confirmed",lens_type="cluster",zlens_range=(1,numpy.inf)).show_ref()Out[1]:<Cataloglength=3>nameRADECzlenstypegradingrefdegdegstr20float64float64str15str7str9str171----------------------------------------------------------------------------------------------------------------------021118-04272932.827087-4.4580691.02clusterconfirmedhttps://arxiv.org/abs/2004.00634Moreetal.2012Moreetal.2016023100-06213937.7516-6.36081.17clusterconfirmedhttps://arxiv.org/abs/2002.01611220859+020655332.24952.11531.04clusterconfirmedhttps://arxiv.org/abs/2002.01611Crossmatching with a skymapThis feature is implemented as.crossmatch(). This function is simply a wrapper to thecrossmatch()function inligo.skymapwhich performs the cross-matching of a gravitational-wave (GW) skymap with a given list of coordinates. For example, to cross-match the GW skymap of GW190814 (download fromhere) with only the confirmed lenses in thelenscatcatalog, simply runIn[1]:importlenscat;lenscat.catalog.search(grading="confirmed").crossmatch("GW190814_PublicationSamples.multiorder.fits")Out[1]:<Cataloglength=761>nameRADECzlenstypegradingsearchedprobabilitysearchedareadegdegdeg2str20float64float64str15str7str9float64float64-------------------------------------------------------------------------------------------------------MACSJ0035.4-20158.85-20.270833330.352clusterconfirmed0.988140028466847946.213606339437370047-28112.42458333-27.873805560.484galaxyconfirmed0.989938402406781148.32495441567177A281310.8625-20.616944440.2924clusterconfirmed0.992450190352911951.82638259178721DESJ0145-354126.44493-35.690930.49galaxyconfirmed0.999876645953825182.04095381939021DESJ0138-284424.59567-28.735550.44galaxyconfirmed0.9999999999999968151.91214978321142DESJ0130-374422.51201-37.749380.68galaxyconfirmed0.9999999999999968173.7862404115262AGEL014253-18311610.72041667-18.521055560.63627galaxyconfirmed0.9999999999999968187.58212970468517Format['name'] = Names of galaxies/galaxy clusters['RA [deg]'] = RA in dergees['DEC [deg]'] = DEC in degrees['zlens'] = Lens redshift (if known)['type'] = Type of lens (i.e. galaxy or galaxy cluster)['grading'] = Grading whether it is a confirmed lens or a probable lens (see individual references for internal grading systems)['ref'] = Reference to the corresponding catalog or studyReferencesThis catalog contains the known strong lenses from the following studies:GLQ Database:https://research.ast.cam.ac.uk/lensedquasars/index.htmlCLASH (Postman+2012):https://archive.stsci.edu/prepds/clash/MUSES Cluster Followups (Richards+2020):https://cral-perso.univ-lyon1.fr/labo/perso/johan.richard/MUSE_data_release/RELICShttps://relics.stsci.edu/clusters.html37 Clusters from SDSS Giant Arcs Surveyhttps://iopscience.iop.org/article/10.3847/1538-4365/ab5f13An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networkshttps://iopscience.iop.org/article/10.3847/1538-4365/ab26b6#apjsab26b6t5The AGEL Survey: Spectroscopic Confirmation of Strong Gravitational Lenses in the DES
and DECaLS Fields Selected Using Convolutional Neural Networkshttps://arxiv.org/ftp/arxiv/papers/2205/2205.05307.pdfLSD Surveyhttps://web.physics.ucsb.edu/~tt/LSD/(COSMOS) LensFlow: A Convolutional Neural Network in Search of Strong Gravitational Lenseshttps://ui.adsabs.harvard.edu/abs/2018ApJ...856...68P/abstractSLACS. XIII. Galaxy-scale strong lens candidateshttps://ui.adsabs.harvard.edu/abs/2019yCat..18510048S/abstractRINGFINDER: Automated Detection of Galaxy-scale Gravitational Lenses in Ground-based Multi-filter Imaging Datahttps://iopscience.iop.org/article/10.1088/0004-637X/785/2/144Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI) Candidate Listhttps://www-utap.phys.s.u-tokyo.ac.jp/~oguri/sugohi/See alsoMaster Lens DatabaseAcknowledgementsThis project was supported by the research grant no. VIL37766 and no. VIL53101 from Villum Fonden, and the DNRF Chair program grant no. DNRF162 by the Danish National Research Foundation.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101131233.We would also like to thank Jonah Kanner for introducing us the amazingstreamlitservice that hosts theweb appforlenscat.