---
id: "2021-11-22-exominer-301-exoplanets"
url: "https://postcutoff.com/e/2021-11-22-exominer-301-exoplanets/"
as_of: "2026-11-13T13:49:00+01:00"
date: "2021-11-22"
date_precision: day
category: science
importance: 2
confidence: high
status: [Result confirmed]
verification: Unverified claim
sources: 2
editor: human editor
human_review: null
version: "2026-10-04"
---

As of: 2026-11-13 13:49 CET.
Researched and written by AI agents (Claude Opus 5.5 in Claude Code), with a human editor. How we work: https://postcutoff.com/about/
Canonical page: https://postcutoff.com/e/2021-11-22-exominer-301-exoplanets/

# NASA's ExoMiner deep-learning model validates 301 new exoplanets from Kepler data

NASA's ExoMiner neural network statistically validated 301 Kepler planet candidates as real planets in one batch, bringing the validated count to 4,569 (Astrophysical Journal, 2021).

## Key facts
- 301 new validated planets
- Explainable classifier mimicking the vetting steps of human experts

## What happened

ExoMiner vetted thousands of Kepler signals and confidently validated hundreds as planets.

## Why it matters

AI vetting has become standard for the flood of survey data from Kepler, TESS and, soon, other surveys.

## Your AI and this story

- GPT-6 Astra (training cutoff April 2026): in its training data
- Claude Opus 5.5 (training cutoff June 2026): in its training data
- Gemini 4 Hydrogen (training cutoff March 2026): in its training data
- Grok 4.7 (training cutoff May 2026): in its training data

## Sources

1. [ExoMiner paper (arXiv 2111.10009)](https://arxiv.org/abs/2111.10009) (arxiv.org, paper)
2. [NASA JPL: new deep learning method adds 301 planets to Kepler's total count](https://www.jpl.nasa.gov/news/new-deep-learning-method-adds-301-planets-to-keplers-total-count/) (jpl.nasa.gov, official)

## Changes

- 2026-09-29 (filed): Created

## Related

- 2026-03-25: [RAVEN machine-learning pipeline validates 118 new planets in TESS data](https://postcutoff.com/e/2026-03-25-raven-tess-118-new-planets/index.md)