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DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

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Elizabeth J. Melton, Eric D. Feigelson, Marco Montalto, Gabriel A. Caceres, Andrew W. Rosenswie, Cullen S. Abelson

Earth and Planetary Astrophysics · Submitted 13 Feb 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates.

how it is stated · asserted 1.00 read the record →

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arXiv ID2302.06700 (version 2)
Pith ID6WYISFVB, Pith's permanent record of this paper and its review
CitationAndrew W. Rosenswie, Cullen S. Abelson, Elizabeth J. Melton, Eric D. Feigelson, Gabriel A. Caceres, Marco Montalto. "DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves." arXiv:2302.06700 (2023). https://pith.science/paper/6WYISFVB
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,445 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.96 · human participants 0.02 · controlled comparison 0.20 · randomized assignment 0.02 · states quantitative result 0.99 · compares with prior results 0.37 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends and lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. Classifier training sets include injections of both planetary transit signals and contaminating eclipsing binaries. The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. The completeness map of the injected planetary signals shows high recall rates for planets with 8 - 30 R(Earth) radii and periods 0.6-13 days and poor completeness for planets with radii < 2 R(Earth) or periods < 1 day. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. method 0.72 0.92 0.04 asserted 0.99
s1 ARIMA models remove trends and lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. method 1.00 0.87 0.04 asserted 1.00
s2 Classifier training sets include injections of both planetary transit signals and contaminating eclipsing binaries. method 1.00 0.89 0.02 asserted 0.99
s3 The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. result 0.98 0.95 0.08 asserted 1.00
s4 The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. result 1.00 0.96 0.69 asserted 1.00
s5 The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. result 0.99 0.92 0.08 asserted 1.00
s6 The completeness map of the injected planetary signals shows high recall rates for planets with 8 - 30 R(Earth) radii and periods 0.6-13 days and poor completeness for planets with radii < 2 R(Earth) or periods < 1 day. result 0.90 0.94 0.07 asserted 1.00
s7 The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II). limitation 0.56 0.78 0.22 asserted 0.99

What kind of paper the classifier reports this is: method or tool 0.57 · evidence from analysis of existing data 1.00.

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not examined
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  • S sections
  • T body sentences
  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends and lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. Classifier training sets include injections of both planetary transit signals and contaminating eclipsing binaries. The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. The completeness map of the injected planetary signals shows high recall rates for planets with 8 - 30 R(Earth) radii and periods 0.6-13 days and poor completeness for planets with radii < 2 R(Earth) or periods < 1 day. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).

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