Uncertainty-Aware Tidal Disruption Event Classification : A Host-Agnostic Probabilistic Random Forest Approach

Clicks: 16
ID: 332526
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #845 of 1,339 articles by views in monthly notices of the royal astronomical society

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,339 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract The classification of Tidal Disruption Events in large-scale photometric surveys is challenging because deterministic machine learning models produce overconfident misclassifications under varying data quality and low signal-to-noise conditions. Existing lightcurve-based approaches do not propagate measurement uncertainties properly into the classification itself, consequently generating brittle outputs. We present a host-agnostic, uncertainty-aware classification framework utilizing a Probabilistic Random Forest (PRF). Our pipeline extracts 11 characteristic features-including rise and decay timescales, blackbody temperature evolution, and pre-transient variability metrics–from nuclear transients in the ZTF alert stream. Relying exclusively on photometric lightcurve data without host galaxy information ensures effectiveness for the faint transient population expected from the Rubin Observatory. The PRF classifier, which treats feature measurements as distributions, was evaluated against XGBoost through a Leave-One-Out Cross-Validation strategy. The analysis demonstrates the PRF yields higher stability and robustness for ambiguous candidates compared to the predictions of XGBoost. The two classifiers occupy complementary regimes: XGBoost achieves higher recall in balanced and high-precision scenarios, where its rigid decision boundaries efficiently isolate TDE-like sources, while PRF rejects more false positives by penalizing sources with large feature uncertainties. Applying this validated framework to archival data identified 11 new candidate TDEs from the unclassified population and isolated 3 potential photometric TDEs within existing training labels previously misclassified as supernovae or active galactic nuclei. This work demonstrates that TDEs can be reliably identified using photometric lightcurve features, providing a host-agnostic framework. Uncertainty-aware, probabilistic classifiers are essential for the Rubin era to prevent the overconfident misclassifications inherent in deterministic models operating at low signal-to-noise.
Reference Key
openalex_W7171978399 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vysakh Anilkumar, Sjoert van Velzen, Marek Kowalski, Simeon Reusch
Are you one of these authors? Claim your profile
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1973
URL
https://doi.org/10.1093/mnras/stag1973
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.