Open-source software tools and libraries for interferometry / correlator / radio astronomy
There are several open-source software tools and libraries for interferometry / correlator / radio astronomy work. Depending on how much of the pipeline you want (just correlation, calibration + imaging, or full control) and what your hardware is like (CPU, GPU, real-time vs offline), different tools may fit. Here are some options + what you’d need to consider. If you tell me your computer specs (CPU cores, GPU?, RAM, etc.), I can suggest ones best suited to you.
What “correlation” entails
Since you said your setup is two cables joined in the middle, held aloft etc — sounds like a very small baseline interferometer / maybe even a single baseline. In effect, you’ll need at least:
- precise time-tagging / synchronization of the data streams
- digitization (sample the RF or IF signals, likely via SDRs or similar)
- a correlate step: multiply the signals (with delay compensation if needed), integrate to get visibilities
- optional calibration and imaging
Some open source tools handle parts; few are turnkey for small ad hoc systems.
Open Source Tools / Libraries
Here are some useful software tools & libraries:
| Name | What it does / strengths | Pros / cons for a home setup |
| DiFX (Distributed FX) | A software correlator used in VLBI / many-station arrays. Open source. (NRAO Science Site) | Powerful; perhaps heavy for a small system but usable if you have a decent machine. Needs good understanding of formats (VEX etc) and delay models. |
| Bifrost | A Python/C++ framework for high-throughput stream processing (including interferometry, pulsar dedispersion, beamforming) (arXiv) | Good if you want to do real-time or near real-time processing; can use GPU/CPU. More flexible but more work to set up. |
| pyuvsim | Simulator of interferometer output; generates data for testing / development. (GitHub) | Useful for testing, but does not itself do full correlation of real signals (unless you adapt it). Good for prototyping. |
| pyuvdata | Python library for dealing with interferometric datasets: reading/writing formats, converting, handling metadata. (GitHub) | Helps with data management and follow-up (after correlation) but not correlation itself. |
| GALARIO | For analysing interferometer observations; accelerates computing synthetic visibilities etc. Good when you have model images and want to compare/fit them to observed visibilities. (arXiv) | More about analysis (imaging / modelling) rather than doing the raw correlation of time streams. |
| PORT (Potsdam Open-source Radio interferometry Tool) | For analysis of VLBI observations: estimating Earth orientation, coordinates, etc. More specialized in geodetic/astrometric work. (helmholtz.software) | Probably more than you need unless you’re doing VLBI/geodesy; also may expect higher data complexity. |
What you’ll need / challenges for home setup
To run a correlator at home (with your antenna/cable setup), you’ll need to consider:
- Hardware: Digitisers or SDRs for each antenna/cable. Must sample at enough bandwidth and with stable clocks (synchronization).
- Clock / timing: To correlate, you need to know the time offset between the two channels very precisely. That probably means GPS disciplined clock or similar, or synchronizing via a shared clock reference.
- Data rates and storage: If sampling high bandwidth, data sizes grow fast. Correlation requires reading the raw data (maybe in buffers or streaming) and multiplying/integrating.
- Software engineering: You’ll need to adapt tools (or write some glue code) to take in your digitiser outputs, apply delays, do multiplication, etc. Many tools assume standard VLBI or array formats.
- Calibration / imaging: After correlation, you’ll want to calibrate out delays, phase offsets, maybe do Fourier inversion (imaging). Tools like CASA, AIPS etc can help with imaging.
Recommendation for a home‐build small two‐channel setup
Here’s what I’d suggest if I were you and building something modest:
- Use an SDR (or two SDRs) as digitizers for the two cable ends.
- Use Bifrost or write small correlation code in Python + C/CUDA if you have a GPU. Bifrost particularly for streaming / GPU might help.
- For offline, use DiFX (if you can format your data appropriately) to do the correlation + produce “visibilities”.
- Use pyuvdata to convert/manipulate the data, and something like CASA for imaging and calibration.
- If your needs are simple, maybe even smaller simpler custom scripts: e.g. buffer, multiply, integrate, FFT etc.