[論文レビュー] X-ray transients in the Chandra archive: Introducing the cumulative distribution discriminator (CuDiDi)

I. Saathoff, J. Larsson|arXiv (Cornell University)|Mar 23, 2026

Gamma-ray bursts and supernovae被引用数 0

ひとこと要約

論文は CuDiDi を提出し、20,212 件の Chandra ACIS 観測で sub-observation X 線トランジェントを特定する多段パイプラインに適用、765 の高信頼トランジェントという金の標本を生成する。

ABSTRACT

X-ray transients on sub-observation timescales represent a diverse and underexplored class of astrophysical phenomena, from stellar flares and magnetar bursts to extragalactic fast transients and supernova shock breakouts. We present a systematic search for such events across 20,212 Chandra ACIS observations using a new detection pipeline that combines source identification, light-curve analysis, catalogue cross-matching, and a novel statistical classifier, the cumulative distribution discriminator (CuDiDi). From 1420 initial candidates, we identified a high-confidence golden sample of 765 transients spanning a broad range of timescales, fluxes, and spectral shapes. The candidates are distributed across the whole sky and show a wide range of durations with a median of 10 ks. A subset of fast events lasting < 30 s displays very soft spectra and is likely due to flaring dwarf stars, although extragalactic phenomena cannot be ruled out for all of them. The comparison with previously published samples showed that CuDiDi identifies most known transients while imposing somewhat stricter variability criteria, and it also extends the total sample of Chandra transients to include shorter events. We deliver a comprehensive catalogue of sub-observation Chandra X-ray transients and establish a general method for exploiting archival datasets to uncover rare short-lived high-energy phenomena.

研究の動機と目的

• Motivate and search for sub-observation X-ray transients across a large Chandra ACIS archive.
• Develop a multi-stage pipeline combining source detection, light-curve analysis, cross-matching, and a new CuDiDi classifier.
• Produce a high-purity golden sample of transients and a broader candidate catalog for archival population studies.

提案手法

• Detect X-ray sources in 0.3–2 keV images with wavdetect across multiple scales.
• Extract 0.5–7 keV light curves with 1 s binning and compute CuDiDi using the lower and upper halves of the cumulative distribution of photon arrival times.
• Classify sources as transient, persistent, or repeating based on the CuDiDi diagram with a band around mu_upper = mu_lower + 0.5 (width ±0.2).
• Cross-match detected sources with SIMBAD, NED, Gaia, STONKS, and eROSITA catalogs to remove known or stellar sources.
• Apply a quick-look visual inspection to flag artefacts such as readout streaks and edge effects.
• Generate diagnostic flags to annotate artefacts and ambiguities and define a golden sample (no flags true).

実験結果

リサーチクエスチョン

• RQ1 Can CuDiDi robustly separate true X-ray transients from persistent or repeating sources in the Chandra ACIS archive?
• RQ2 What is the efficiency (completeness/purity) of CuDiDi relative to previous transient searches in archival Chandra data?
• RQ3 What are the temporal and spectral characteristics of the identified transient sample across a broad range of timescales?
• RQ4 How does cross-matching with multi-wavelength and X-ray catalogs affect sample contamination and recovery of known transients?
• RQ5 What is the distribution of sub-observation transients across sky, duration, and flux in the Chandra archive?

主な発見

• A golden sample of 765 high-confidence Chandra transients was identified from 1,420 candidates after multi-stage screening.
• CuDiDi classifies transients by a distinct asymmetry in the light-curve cumulative distribution, improving purity over previous methods.
• The search spanned 20,212 ACIS observations with a median transient duration around 10 ks and full-sky distribution.
• Cross-matching and artefact flags remove large fractions of candidates, with 441,306 sources excluded by cross-matching and 1,307 by quick-look artefact screening.
• The pipeline demonstrates that archival Chandra data can reveal a broad diversity of short-lived high-energy phenomena, including very short events and softer spectra.