hyperseti

Hyperseti is a GPU-accelerated code for searching radio astronomy spectral datasets for narrowband technosignatures that indicate the presence of intelligent (i.e. technologically capable) life beyond Earth. It was developed as part of the Breakthrough Listen initiative, which seeks to quantify the prevalence of intelligent life within the Universe.

Hyperseti is centered around a brute-force dedoppler CUDA code, and has a Python-based frontend. Hyperseti is intended as the spiritual successor to the turboSETI package.

Example Usage

The following code searches a filterbank file which contains telemetry data from the Voyager space mission (data available here).

from hyperseti.pipeline import find_et

voyager_h5 = '../test/test_data/Voyager1.single_coarse.fine_res.h5'

config = {
    'preprocess': {
        'sk_flag': True,                        # Apply spectral kurtosis flagging
        'normalize': True,                      # Normalize data
        'blank_edges': {'n_chan': 32},          # Blank edges channels
        'blank_extrema': {'threshold': 10000}   # Blank ridiculously bright signals before search
    },
    'dedoppler': {
        'kernel': 'ddsk',                       # Doppler + kurtosis doppler (ddsk)
        'max_dd': 10.0,                         # Maximum dedoppler delay, 10 Hz/s
        'apply_smearing_corr': True,            # Correct  for smearing within dedoppler kernel 
        'plan': 'stepped'                       # Dedoppler trial spacing plan (stepped = less memory)
    },
    'hitsearch': {
        'threshold': 20,                        # SNR threshold above which to consider a hit
    },
    'pipeline': {
        'merge_boxcar_trials': True             # Merge hits at same frequency that are found in multiple boxcars
    }
}

hit_browser = find_et(voyager_h5, config, gulp_size=2**20)
display(hit_browser.hit_table)

hit_browser.view_hit(0, padding=128, plot='dual')

Hyperseti can natively load data generated with setigen:

import numpy as np
import cupy as cp
import pylab as plt
from astropy import units as u

import setigen as stg
from hyperseti.io import from_setigen
from hyperseti.dedoppler import dedoppler
from hyperseti.plotting import imshow_waterfall, imshow_dedopp

# Create data using setigen
frame = stg.Frame(...)

# Convert data into hyperseti DataArray
d = from_setigen(frame)
d.data = cp.asarray(d.data) # Copy to GPU

# Run dedoppler
dedopp_array = dedoppler(d, boxcar_size=1, max_dd=8.0, plan='optimal')

# Plot waterfall / dedoppler
plt.figure(figsize=(8, 3))
plt.subplot(1,2,1)
imshow_waterfall(d)
plt.subplot(1,2,2)
imshow_dedopp(dedopp_array)
plt.tight_layout()

Installation

Hyperseti uses the GPU heavily, so a working CUDA environment is needed, and requires Python 3.7 or above. hyperseti relies upon cupy, which is easiest to install using conda (or mamba).

To install from conda/mamba package:

conda install -c technosignatures hyperseti

If starting from scratch, this should get you most of the way there:

conda create -n hyper -c nvidia -c conda-forge python=3.10 cupy jupyterlab ipywidgets

Jupyterlab and ipywidgets are optional, but useful for a base environment.

From there:

conda activate hyper
pip install git+https://github.com/ucberkeleyseti/hyperseti