lenscat 1.1.3
A public and community-maintained catalog of known strong gravitational lenses
Requires Python: >=3.9

# $\texttt{lenscat}$
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A public and community-contributed catalog of known strong gravitational lenses. 

![Known Lenses](https://raw.githubusercontent.com/lenscat/lenscat/main/catalog.png)

## Quickstart

The catalog is available as a _plain csv file_ under [lenscat/data/catalog.csv](https://github.com/lenscat/lenscat/blob/main/lenscat/data/catalog.csv). Alternatively, one can interact with the catalog using a [web app](https://lenscat.streamlit.app/) (mobile-friendly).

We also provide a _python package_ `lenscat`, available in pypi. Simply do
```bash
pip install lenscat
```
to install the latest version. Here we adopt the _continuous deployment_ paradigm (similar to [`astroquery`](https://github.com/astropy/astroquery)). Whenever there is a change in the catalog content, or a major change in the code, a new release will be available instantaneously on both [GitHub](https://github.com/lenscat/lenscat/releases) and [PyPI](https://pypi.org/project/lenscat/).

The code converts the catalog in the csv file into a custom `Catalog` object that is inherited from the `Table` object in `astropy`. To access the catalog, simply run
```python
import lenscat; lenscat.catalog
```
and this will show a formatted table to the output. For example,
```output_catalog
<Catalog length=32838>
                                       name                                            RA      DEC                zlens               type   grading 
                                                                                      deg      deg                                                   
                                      str479                                        float64  float64              str234              str7     str9  
---------------------------------------------------------------------------------- --------- -------- ------------------------------ ------ ---------
                                                                        J0011-0845   2.83435  -8.7643                              - galaxy confident
                                                                        J0013+5119  3.348077  51.3183                              - galaxy confident
                                                                               ...       ...      ...                            ...    ...       ...
                          SDSS J153713.2+655621 | SDSS J1537+6556 | SDSSJ1537+6556   234.305 65.93913        0.251 | 0.2595 | 0.2595  group  probable
HERMES J105750.9+573026 | 5438733 | DESI-164.4623+57.5071 | HERMESJ105751.1+573027 164.46256 57.50734 0.6 | 0.64259666 | 0.643 | nan  group  probable
 
```
Note 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' column *by default*. One can show or hide the 'ref' column by calling `.show_ref()` and `.hide_ref()` on the `Catalog` object respectively.

Every `Catalog` object supports three features: basic searching with `.search()`, crossmatching with a skymap with `.crossmatch()`, and visualizing with `.plot()`. Note that these function will return a `Catalog` object, and hence *they can be composed together* (e.g., `.crossmatch().search()`).

### Basic searching
This feature is implemented as `.search()`. One can search/filter by any combination of
- ranges of right ascension (specified as `RA_range=(RA_min, RA_max)`)
- ranges of declination (specified as `DEC_range=(DEC_min, DEC_max)`)
- ranges of lens redshift if available (specified as `zlens_range=(zlens_min, zlens_max)`)
- type of the lenses (specified as `lens_type`)
- grading of the lenses (specified as `grading`)

For example, to get a list of the cluster-scale lenses which are confidently identified and with a redshift $z_{\mathrm{lens}} \geq 1$ together with the reference, run
```python
import lenscat, numpy
lenscat.catalog.search(grading="confident", lens_type="cluster", zlens_range=(1,numpy.inf)).show_ref()
```
The output would be something like
```output_search_confident_cluster
<Catalog length=5>
      name            RA         DEC      zlens    type   grading                                                 ref                                               
                     deg         deg                                                                                                                                
     str479        float64     float64    str234   str7     str9                                                str1587                                             
---------------- ----------- ------------ ------ ------- --------- -------------------------------------------------------------------------------------------------
   021118-042729   32.827087    -4.458069   1.02 cluster confident https://arxiv.org/abs/2004.00634 https://arxiv.org/abs/1109.1821 https://arxiv.org/abs/1504.05587
   023100-062139     37.7516      -6.3608   1.17 cluster confident                                                                  https://arxiv.org/abs/2002.01611
   220859+020655    332.2495       2.1153   1.04 cluster confident                                                                  https://arxiv.org/abs/2002.01611
SPT-CLJ0356-5337 59.08672611 -53.63073944 1.0359 cluster confident                                              Moustakas et al. (2012) - adsabs:2012hst..prop12833M
IDCSJ1426.5+3508   216.63729     35.13989   1.75 cluster confident                                              Moustakas et al. (2012) - adsabs:2012hst..prop12833M
 
```

### Crossmatching with a skymap
This feature is implemented as `.crossmatch()`. This function is simply a wrapper to the `crossmatch()` function in `ligo.skymap` which 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 GW170814 (download from [here](https://dcc.ligo.org/public/0157/P1800381/007/GW170814_skymap.fits.gz)) with only galaxy-scale lenses in the `lenscat` catalog, simply run
```python
import lenscat
lenscat.catalog.search(lens_type="galaxy").crossmatch("GW170814_skymap.fits.gz")
```
Running this will give
```output_search_GW170814
<CrossmatchResult length=31755>
         name             RA       DEC       zlens      type  grading  searched probability   searched area   
                         deg       deg                                                             deg2       
        str479         float64   float64     str234     str7    str9         float64             float64      
--------------------- --------- --------- ------------ ------ -------- -------------------- ------------------
DESI-047.5128-44.2451  47.51275 -44.24506        0.278 galaxy probable 0.019409452342101355  0.383583423787917
DESI-047.1501-45.3903  47.15012 -45.39029        0.683 galaxy probable  0.03333914444622844 0.6720906143292572
                  ...       ...       ...          ...    ...      ...                  ...                ...
  NSCS J145222+081201 223.09342   8.20025 not measured galaxy probable   0.9999999999999997  41252.96124941707
DESI-225.5679+15.1661 225.56792   15.1661     0.551534 galaxy probable   0.9999999999999997  41252.96124941707
 
```
The cross-matching can be done to the sky localization from any type of transients as long as it is in the FITS format. For example, to cross-match the localization of GRB 240229A (download from [here](https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/triggers/2024/bn240229588/quicklook/glg_healpix_all_bn240229588.fit)), simply run
```python
import lenscat
lenscat.catalog.crossmatch("glg_healpix_all_bn240229588.fit")
```
In this case, the output would be
```output_search_GRB
<CrossmatchResult length=32838>
         name              RA         DEC        zlens      type   grading  searched probability   searched area   
                          deg         deg                                                               deg2       
        str479          float64     float64      str234     str7     str9         float64             float64      
--------------------- ----------- ----------- ------------ ------ --------- -------------------- ------------------
DESI-199.7131+16.3005   199.71308    16.30045        0.783 galaxy  probable   0.0780098544673723 3.1473511695418765
DESI-199.0121+15.7349   199.01208    15.73486        0.336 galaxy  probable  0.08790310861826113 3.5669979921474595
                  ...         ...         ...          ...    ...       ...                  ...                ...
           J0113+4549    18.26925    45.81806 not measured galaxy  probable                  1.0  41038.10207619734
            Q0957+561 0.336666667 55.89705556        0.356 galaxy confident                  1.0  41038.10207619734
 
```

To generate a visualization of a crossmatching result, simply invoke `.plot()` to a crossmatching result. For example,
```python
import lenscat
lenscat.catalog.crossmatch("GW170814_skymap.fits.gz").plot(searched_prob_threshold=0.7)
```
will generate a figure like this
![image](https://github.com/lenscat/lenscat/assets/55488840/12d398e4-6f58-40e5-8cea-edc6bf19d701)


## Format

<table width="300">
  <tr>
    <th width="20%">Column name</th>
    <th width="80%">Description</th>
  </tr>
  <tr>
    <td><code>name</code></td>
    <td>Names of galaxies/galaxy clusters</td>
  </tr>
  <tr>
    <td><code>RA [deg]</code></td>
    <td>Right ascension in dergees</td>
  </tr>
  <tr>
     <td><code>DEC [deg]</code></td>
     <td>Declination in degress</td>
  </tr>
  <tr>
     <td><code>zlens</code></td>
     <td>Lens redshift (if known)</td>
  </tr>
  <tr>
     <td><code>type</code></td>
     <td>Type of lens (i.e. galaxy or galaxy cluster). Either<code>galaxy</code>, <code>group</code> or <code>cluster</code>.</td>
  </tr>
  <tr>
     <td><code>grading</code></td>
     <td>Grading whether it is a confidently identified lens or a probable lens (see individual references for internal grading systems). Either <code>confident</code> or <code>probable</code>.</td>
  </tr>
  <tr>
     <td><code>ref</code></td>
     <td>Reference to the corresponding catalog or study</td>
  </tr>
</table>

## References

`lenscat` makes use of the LaStBeRu catalog, which should be cited as “R. Alves de Oliveira, J. P. C. França, M. Makler, The Last Stand Before Rubin: a consolidated sample of strong lensing systems in wide-field surveys, in prep.".

This catalog also contains the known strong lenses from the following studies:

  - [GLQ Database](https://research.ast.cam.ac.uk/lensedquasars/index.html)

  - [CLASH (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/)

  - [RELICS](https://relics.stsci.edu/clusters.html)

  - [37 Clusters from SDSS Giant Arcs Survey](https://iopscience.iop.org/article/10.3847/1538-4365/ab5f13)

  - [An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks](https://iopscience.iop.org/article/10.3847/1538-4365/ab26b6#apjsab26b6t5)

  - [The AGEL Survey: Spectroscopic Confirmation of Strong Gravitational Lenses in the DES
    and DECaLS Fields Selected Using Convolutional Neural Networks](https://arxiv.org/ftp/arxiv/papers/2205/2205.05307.pdf)

  - [LSD Survey](https://web.physics.ucsb.edu/~tt/LSD/)

  - [(COSMOS) LensFlow: A Convolutional Neural Network in Search of Strong Gravitational Lenses](https://ui.adsabs.harvard.edu/abs/2018ApJ...856...68P/abstract)

  - [SLACS. XIII. Galaxy-scale strong lens candidates](https://ui.adsabs.harvard.edu/abs/2019yCat..18510048S/abstract)

  - [RINGFINDER: Automated Detection of Galaxy-scale Gravitational Lenses in Ground-based Multi-filter Imaging Data](https://iopscience.iop.org/article/10.1088/0004-637X/785/2/1440)

  - [Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI) Candidate List](https://www-utap.phys.s.u-tokyo.ac.jp/~oguri/sugohi/)

## See also

[Master Lens Database](https://test.masterlens.org/index.php)

## How to cite
If you have used this code in your research that leads to a publication, please cite the following article:
```
@article{Vujeva:2024scq,
    author = "Vujeva, L. and Lo, R. K. L. and Ezquiaga, J. M. and Chan, J. C. L.",
    title = "{lenscat: a Public and Community-Contributed Catalog of Known Strong Gravitational Lenses}",
    journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
    volume = {383},
    number = {2294},
    pages = {20240168},
    year = {2025},
    doi = {10.1098/rsta.2024.0168},
    eprint = "2406.04398",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.GA",
}
```

## Acknowledgements
This 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 amazing [streamlit](https://streamlit.io) service that hosts the [web app](https://lenscat.streamlit.app/) for `lenscat`.