astromcad 1.2.1

Classifier-Based Anomaly Detection for Astronomical Transients

astromcad Package Documentation

Overview

For sample usage, see: https://colab.research.google.com/drive/1SrYVt9PAjai0NeLr4cI40Lriz_ruLhLt?usp=sharing

The astromcad package implements the transient anomaly detection methodology presented in https://arxiv.org/abs/2403.14742 and allows anyone to train custom light curve anomaly detectors. In short, the methodology is to repurpose the penultimate layer of a neural network classifier for anomaly detection. Then, to extract anomalies from this latent space, a separate isolation forest is trained on the observations from each class, and the minimum score from any detector is used as the final anomaly score. This isolation forest approach is called MCIF (Multi-Class Isolation Forest).

Installation

Type pip install astromcad in your command line or terminal

Usage

astromcad has 3 major classes, which are Detect, Custom, and mcif

Detect

This class allows you to use the trained model from the research work for anomaly detection. The model from the work was trained from ZTF simulations which are described in more detail in the manuscript.

Sample Usage

from astromcad.astromcad import Detect

Detect.init() # Load the pretrained model

Detect.classify(light_curves, host_gals) # Uses the pretrained classifier to get a classification output
Detect.anomaly_score(light_curves, host_gals) # Generates the anomaly score for the given simple

The input host_gals must be an array of shape (N_SAMPLES, 2). Each 2-element entry should be [host redshift, milky way extinction] of that object.

The input light_curves must be an array of shape (N_SAMPLES, 656, 4). For each sample, the 2-dimensional array should store the [median passband wavelength, scaled time since trigger, flux / 500, and flux error / 500] for each observation. If there are fewer than 656 observations, you can call

x_data = Detect.pad(x_data)

You can also generate a real-time anomaly score plot for any transient.

Detect.plot_real_time(light_curve, host_gal)

Note that this function takes a single light curve, not a list of samples.

Custom

This class allows you to create a custom light curve classifer to then use for anomaly detection. Start by creating your classifer

from astromcad.astromcad import Custom

det = Custom(656, 4, 2, 9, 12) # n_timesteps, n_features per time step, n_host, latent_size, n_classes
det.create_model()
det.train(X_train, y_train, X_val, y_val, host_gal_train, host_gal_val) # EarlyStopping is initalized in the class
det.create_encoder()
det.init_mcif(X_train, y_train) # Values and labels to init MCIF with
det.score(light_curves, host_gals)

NOTE: If n_host is set to 0 during initalization, don't pass host galaxy information to any other function (or pass None). The class will not use the host galaxy information.

In the case that you want to create your own classifer, be sure to name the input layers and latent layer something memorable. To initalize a Custom object with this classifier, use

det.custom_model(model, lc_name, latent_name, context_name) # Names of the respective layers (input/latent)
det.create_encoder()
...

To use the trained classifier, you can use det.classify(light_curves, host_gals). To plot a real-time score evoluation, use

det.plot_real_time(lc, unique_passbands, time, flux, flux_error, host_gal, names=['g', 'r'], colors=['g', 'r'])

unique_passbands is a list of all the unique values in the first column of lc. time, flux, and flux_error are the unscaled time, flux, and flux error values (likely to also be columns in lc). names and colors signify what plot label/color to be associated with each passband in unique_passbands.

MCIF

This is an implementation of the Multi-Class Isolation Forest Algorithm, which trains a separate isolation forest for each class of data and uses the minimum score from any detector as the final anomaly score. MCIF is very simple to use.

from astromcad.astromcad import mcif

multi = mcif(n_estimators=100)
mutli.train(x_data, labels)
multi.score(x_data)
multi.score_discrete(x_data)

.score(x_data) returns the minimum anomaly score, while .score_discrete reports the anomaly score per detector in a list. To see which unique entry in labels each element of the output refers to, use multi.classes

Example:

multi.train(x_data, labels) # labels = [Class 1, Class 3, Class 2, Class 1, ...]
multi.score_discrete(x_data) # [0.45, -0.3, 0.2]
multi.labels # 				   [Class 1, Class 2, Class 3]