21cmLSTM (Dorigo Jones et al. accepted, referred to as DJ+24) is a long short-term memory (LSTM) recurrent neural network (RNN) emulator of the global 21 cm signal that has unprecedented emulation accuracy compared to previous, fully connected neural network emulators. 21cmLSTM takes advantage of the unique pattern learning capabilities of LSTM RNNs to leverage the intrinsic correlation between neighboring frequency channels (i.e., autocorrelation) in the 21 cm signal. Given input astrophysical parameters, 21cmLSTM outputs, or predicts, a realization of the global 21 cm signal across redshifts z=5-50 (spanning the EoR to the Dark Ages) and can emulate two different popular models of the signal (see below).
The average relative rms error of 21cmLSTM is 0.22% when trained and tested on the same simulated set of signals as used for previous emulators globalemu (Bevins et al. 2021, GitHub) and 21cmVAE (Bye et al. 2022, GitHub); this data set was originally created for the 21cmGEM emulator (Cohen et al. 2020) and the exact version that we used is publicly available on Zenodo. We also trained and tested 21cmLSTM on a different but nearly equivalent set of signals created by the ARES model (Mirocha 2014, Mirocha et al. 2017, GitHub), which is publicly available on Zenodo. When trained/tested on the ARES set, 21cmLSTM performs slightly worse compared to when trained/tested on the 21cmGEM set, which can be attributed to differences in parameterizations and statistical variation between the two sets (see Section 3.3 of DJ+24).
The average evaluation speed of 21cmLSTM is 46 ms when emulating one signal at a time and using GPU acceleration (see Section 3.2 of DJ+24). This timing test was done using the eval_21cmGEM.py script and the computational resources stated in Section 2.3 of DJ+24. We used 21cmLSTM to fit mock data of the global 21 cm signal and showed that it can sufficiently exploit even outstandingly optimisic measurements of the signal (i.e., with 5 mK noise) and obtain unbiased Bayesian posterior constraints (see Section 3.2 of DJ+24). 21cmLSTM is thus useful for performing efficient and accurate multi-parameter inference of upcoming 21 cm experiments, which may require jointly fitting various complementary data sets or summary statistics (e.g., Dorigo Jones et al. 2023; Breitman et al. 2024).
21cmLSTM is free to use on the MIT open source license. This GitHub contains the full code for training and running the emulator, including data preprocessing, network training, and model evaluation, to facilitate the community in retraining 21cmLSTM on other data sets and models. The sample notebook 'evaluate.ipynb' evaluates the provided representative trained instances of 21cmLSTM on the 21cmGEM and ARES model data sets and prints accuracy metrics, and the notebook 'train.ipynb' trains a new instance of 21cmLSTM on the 21cmGEM set with the same training settings described in Section 2.3 of DJ+24. The instance of 21cmLSTM trained on 21cmGEM included in this repository is the same one used for Bayesian analyses in DJ+24 and has test set mean rms error of 0.20%, and the instance of 21cmLSTM trained on ARES is a representative trial with mean emulation error of 0.38%.
Please reach out to me at johnny.dorigojones@colorado.edu about any comments, questions, or contributions (can also open an issue or make a pull request). Please reference Dorigo Jones et al. 2024 and provide a link to this GitHub repository if you utilize this work or emulator in any way, and Dorigo Jones et al. 2023 regarding posterior emulation bias or if you perform joint fits using 21cmLSTM.
We recommend to pip install 21cmLSTM within a virtual environment containing the following dependencies: python>=3.6, tensorflow>=2.5, h5py, jupyter, matplotlib, numpy
git clone https://github.com/jdorigojones/21cmLSTM
cd 21cmLSTM
python -m pip install .
Authors: Johnny Dorigo Jones and Shah Bahauddin
Additional contributions from: Jordan Mirocha, David Rapetti, Christian H. Bye