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SQUEzE

Spectroscopic QUasar Extractor and redshift (z) Estimator

requirements:

  • python 2.7 or 3.6
  • argparse
  • numpy
  • pandas
  • sklearn - only for training
  • astropy

Description

SQUEzE is a software package to identify quasars and estimate their redshift in a sample of spectra. The quasars id and redshif is estimated in a two-step process: 1. Generate a high completeness and low purity sample of candidates 2. Filter the candidates to improve purity while keeping a target completeness See Perez-Rafols et al. 2019 (https://arxiv.org/abs/1903.00023) for more details. Consider referencing this paper if you are using SQUEzE in your analysis.

SQUEzE can run in different modes: 1. training - Use a known sample to decide on model options 2. test - Use a known sample to assess model performance 3. operation - Apply cuts to unknown sample to build a catalogue 4. merge - Merge different candidates objects into a single candidate objects

Installation

download

git clone https://github.com/iprafols/SQUEzE.git

Add <path_to_SQUEzE>/SQUEzE/py/ to your PYTHONPATH.

It is recommended to pretrain SQUEzE using the provided results from BOSS. To do so run

cd <path_to_SQUEzE>/SQUEzE/data
tar -xzvf BOSS_train_64plates.tar.gz
cd ../bin
python pretrain_squeze.py

from the bin/ folder. This will generate a trained model named BOSS_train_64plates_model.json stored in the data/ folder.

Usage

SQUEzE presents four modes of operation: training, test, operation, and merge. The training and test modes are used on a controlled sample where a truth table is available, and allows the code to learn or test performance of the model parameters. The operation mode is used on an uncontrolled sample to generate a quasar catalogue. The merge mode is used to join different (and possibly parallel) runs on a different mode.

Formatting data (training, test, and operation modes)

Before running SQUEzE data must be formatted so that the code can use it properly. This section explains both the optional and required pre-steps to format all data correctly.

  1. Formatting spectra (required):

The spectra variable sets the spectra where SQUEzE will be run. Format of the spectra must be a Spectra object (defined in py/squeze_spectra.py) containing a set of Spectrum instances. The package provides the file `format_superset_dr12q.py´ with a working example of how to format BOSS spectra.

2.1 Formatting lines (optional):

The lines variable sets the characteristics of the lines used by the code. For SQUEzE to use a value different from the default, it needs to be formatted as a pandas data frame and saved into a csv file. The package provides the file `format_lines.py´ with the instructions to properly create this object. It is a modifyiable working example.

Usage in training mode

run

python squeze_training.py

for an explanation on the optional arguments add -h to the previous line

Usage in test mode

run

python squeze_test.py

for an explanation on the optional arguments add -h to the previous line

Usage in operation mode

run

python squeze_operation.py

for an explanation on the optional arguments add -h to the previous line

Usage in merge mode

run

python squeze_merge_candidates.py

for an explanation on the optional arguments add -h to the previous line