LAISS: Lightcurve Anomaly Identification and Similarity Search

This is the official Zenodo version of the code LAISS (Lightcurve Anomaly Identification and Similarity Search), associated with the paper, "Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams" by Aleo et al (in review). This repository contains all datasets and code needed to run a local instance of LAISS, though slight modifications will be needed (e.g., renaming hard-coded file paths). See Aleo et al. for details on the LAISS pipeline, now on arXiv and currently submitted to The Astrophysical Journal. The live version of the code can be found on Github.

Moreover, the results of all objects processed by LAISS via the ANTARES broker is available on the main page and selecting “LAISS_RFC_AD_filter” under ‘Tags’. Those we consider anomalies are objects that have a Locus Property feature “LAISS_RFC_anomaly_score” > 0.5. Note that the version on ANTARES has no similarity search functionality.

A demo can be found on Google Colab, written by current code maintainer Alex Gagliano.

Below we list the files with a brief description:

"LAISS_ANNOY_pseudo_Filter.ipynb" -- The notebook version of LAISS. Preferred method because it doesn't need to reload the large .ann files for each instance.

"LAISS.py" -- The .py version of LAISS. Same functionality but a little slower because of the many arguments and longer runtimes due to needing to reload the .ann files for each run. Can be run, e.g., with

LAISS(l_or_ztfid_ref="ZTF18abydmfv",
           lc_and_host_features=lc_and_host_features,
           n=8,
           use_lc_for_ann_only_bool=True, 
           use_ysepz_phot_snana_file=False,
           show_lightcurves_grid=False,
           show_hosts_grid=False,
           run_AD_model=False,
           savetables=False,
           savefigs=False)

"*.ann" & "*.npy"-- The ANNOY index files, used for similarity search functionality.

"*.csv.gz" -- Datafiles with objects (rows) and light curve + host features (columns), used for anomaly detection and similarity search functionality.

NOTE: With either choice of running LAISS, you'll need to add the following hardcoded directories (or manually change the filepaths). See Github for directory structure:

tables/custom/timeseries/
notebooks/ysepz_snana_phot_files/
notebooks/LAISS_run/
loci_dbs/alerce_cut/
ps1_psc/
ps1_cutouts/
dataframes/
RFC/SMOTE_train_test_70-30_min14_kneighbors8/cls=binary_n_estimators=100_max_depth=35_rs=11_max_feats=35_cw=balanced/figures
RFC/SMOTE_train_test_70-30_min14_kneighbors8/cls=binary_n_estimators=100_max_depth=35_rs=11_max_feats=35_cw=balanced/model