VESPER

Vetting Exoplanet Signals with Pixel-informed Evidence, Rapidly.

VESPER vets transiting-planet candidates around TESS M dwarfs. Give it a candidate ephemeris; it returns a calibrated probability that the signal is a planet, seven named impostor scores, and a keep/reject verdict — in about a tenth of a second per star once the data are in.

Everything else is fetched automatically from public archives: the photometry, the pixel data for the centroid measurement, the PRF models, the stellar parameters. One CSV in, one table out.

The frozen model, its certified performance and its limitations are documented in the model card: https://gitlab.com/yohanntschudi/exoplanets-project/-/blob/main/documentation/pipeline_ml/VETTER_MODEL_CARD.md

Install

Python ≥ 3.10, in a fresh virtual environment. CPU-only torch is enough.

macOS / Linux:

python3.11 -m venv vesper-env
source vesper-env/bin/activate
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install "tess-vesper[acquire,spatial]"

Windows (PowerShell):

python -m venv vesper-env
vesper-env\Scripts\Activate.ps1
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install "tess-vesper[acquire,spatial]"

If PowerShell blocks Activate.ps1, skip activation and call vesper-env\Scripts\vesper.exe directly — it works identically.

The wheel bundles the frozen model (2.5 MB); its SHA-256 fingerprints are verified at every load. Nothing else to configure.

Try it — two commands

vesper example
vesper vet-batch --candidates candidates_example.csv --curves-dir example_curves --out example_results.csv

The bundled example is twelve signals on six M-dwarf hosts: the confirmed planets of L 98-59 and TOI-700 (including the habitable-zone TOI-700 d), one unconfirmed candidate signal, and four TFOPWG-certified false positives. VESPER downloads the photometry and the pixel data itself — the first run takes a while (TOI-700 alone has 30+ sectors). Everything is cached, and an interrupted run resumes with the same command.

For each host you get a table like:

TIC 307210830 (4 signals)
 sig      P [d]   T0 [BTJD]   dur[h]  P(pl)   verdict  BEB    alias  EB     rot    comp   empty  syst
 .02     2.2531   1354.9054     0.24  1.000   KEEP     0.000  0.000  0.013  0.000  0.008  0.000  0.000
 .01     1.0492   1354.7153     0.32  0.017   REJECT   0.071  0.113  0.811  0.024  0.033  0.008  0.085

The .01 line is the unconfirmed candidate: rejected, and the EB head says why.

Your own candidates

One CSV row per candidate:

vesper vet-batch --candidates my_candidates.csv --curves-dir curves --out results.csv

Two classic traps:

• ExoFOP lists full BJD epochs — subtract 2,457,000, or the row comes back blocked:t0_not_btjd.
• Header exactly as above, no spaces after the commas.

Single candidate:

vesper vet --tic 307210830 --period 7.450726 --t0 1355.29 --duration-h 1.68 --curves-dir curves

What VESPER fetches for you

Everything is cached, nothing is downloaded twice. Centroid measurements are saved one by one to a <out>_spatial.csv sidecar: an interrupted run resumes where it stopped, and the sidecar is reusable forever via --spatial.

Bringing your own photometry: one pickle per host, named TIC{tic}_stitched.pkl, a dict with time (BTJD), flux (PDCSAP before any detrending, median ≈ 1.0) and optionally flux_err/sector — exactly what vesper fetch writes. Never feed detrended flux: VESPER does its own detrending, and that is part of the certified contract.

How it works

1. You provide the ephemeris. VESPER judges the signal at that period; it does not search for periods.
2. It prepares its own photometry — stellar variability is removed with the same Gaussian-process model the network was trained with, on a 200 s grid.
3. It folds and judges: shape views (global, transit zoom, secondary, odd/even, per-observing-block), the star's parameters, and the difference-image centroid — the pixel evidence that separates a background eclipsing binary from an on-target signal.
4. It answers in calibrated units: p_cal, the shipped decision threshold, and the named impostor heads.

Reading the results

The results CSV (--out) echoes your inputs, then:

p_cal is calibrated against real M-dwarf light curves carrying injected planets and constructed impostors. It is not a TRICERATOPS-style FPP, and a high value is not by itself statistical validation: use it to rank and triage; validation needs independent evidence.

Domain of validity

• TESS M dwarfs (Teff ≲ 4,000 K); periods ~0.5–100 d; native cadences 20–300 s (served on a 200 s grid; coarser products are refused with an explicit status).
• VESPER checks the data, not the star: a G-dwarf curve will serve and return a number — an uncertified one. Check Teff yourself.
• Known weak spot: ultra-short periods (P < 1 d). Full list in the model card.

License and citation

MIT. The reference article is in preparation; until it is out, cite this package and its model card (linked above), which carries the full provenance of the frozen model.