---
title: "Exploring Long-period Architectures: Four New Planet Candidates from Kepler with Periods >342 days"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.23425v1"
permalink: "https://feed.astralor.com/items/itm_1d06a648530e82e8e838fd62"
published: "2026-08-24T16:04:30.000Z"
score: 73
channel: "ai"
tags: ["Transit Timing Variations","Transit Method","Convolutional Neural Network","Kepler Space Telescope","Long-period Exoplanet"]
---

# Exploring Long-period Architectures: Four New Planet Candidates from Kepler with Periods >342 days

## AI commentary

> This paper is worth reading because it targets Kepler's long-period blind spot with a CNN-based single-transit search rather than reprocessing known signals. It is especially useful for exoplanet researchers working on TTV dynamics and follow-up scheduling, and it demonstrates that machine-learning reanalysis can yield actionable new discoveries.

## At a glance

Long-period planet samples are scarce. The team built a single-transit detection pipeline using a classification convolutional neural network and Kepler spacecraft diagnostics, scanned all known multi-planet systems in the Kepler field, and after manual vetting identified four long-period planet candidates. Two of them show two transit events, with periods around 777.78 and 505.495 days; the other two are single-transit candidates. These candidates cannot explain the transit timing variations observed in the inner systems, so follow-up observations are needed.

## Key points

- Built a single-transit detection pipeline using a classification CNN and Kepler spacecraft diagnostics.
- Identified four long-period planet candidates: Kepler 1752.02, Kepler 199.03, Kepler 1897.02, and Kepler 1811.02.
- The first two have two transits with periods around 777.78 and 505.495 days; the latter two are single-transit candidates.
- The candidates alone cannot reproduce the observed TTVs, calling for follow-up observations.

## Full text

The Kepler detection pipeline, as well as the transit method, has a bias towards shorter periods, leaving a dearth of detections at longer orbital periods. This relative lack of detections has left an incomplete picture of the architectures of exoplanet systems within the long-period regime. We have built a single transit detection pipeline, utilizing a classification convolutional neural network and the onboard spacecraft diagnostics of the Kepler spacecraft, to detect long-period planets. We apply our pipeline to all currently known planetary systems in the Kepler field hosting at least one planet with an orbital period longer than 6 days. We manually vet all new signals from our pipeline, and identify four new planetary candidates, all of which are in systems where the inner planets exhibit transit timing variations (TTVs). Two of these candidates, Kepler 1752.02 and Kepler 199.03, cause two transit events that are consistent with periods of $777.78^{+0.01}_{-0.02}$ and $505.495^{+0.004}_{-0.004}$ days, and radii of $3.55^{+0.15}_{-0.15}$ and $2.74^{+0.05}_{-0.05}$ $R_{\oplus}$, respectively. Our remaining two candidates, Kepler 1897.02 and Kepler 1811.02, are single transit candidates with radii $4.81^{+0.20}_{-0.19}$ and $3.25^{+0.28}_{-0.30}$ $R_{\oplus}$, respectively. The shortest orbital periods for these candidates, consistent with the Kepler dataset (gaps and coverage), are 342 days for Kepler 1897.02 and 544 days for Kepler 1811.02. The new planetary candidates, on their own, are incapable of reproducing the observed TTV signals in the inner system. Although difficult to schedule, follow-up observations are needed to further constrain the new candidates and potentially discover the planets causing the perturbations.

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