Discovery of the Tadpole Molecular Cloud near the Galactic Nucleus

Miyuki, Kaneko,; Oka, Tomoharu; Yokozuka, Hiroki; Enokiya, Rei; Takekawa, Shunya; Iwata, Yuhei; Tsujimoto, Shiho

doi:10.3847/1538-4357/aca66a

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Intermediate-mass black holes (IMBHs) bridge the gap between stellar-mass remnants and supermassive black holes, potentially playing an important role as seeds for the latter (see, e.g., Woods et al 2019;Volonteri et al 2021 for two recent reviews). Searches for IMBHs in the present-day Universe are being actively carried out, with focus on high-density environments such as the Galactic center (Oka et al 2016;Ballone et al 2018;Takekawa et al 2019aTakekawa et al , 2019bTakekawa et al , 2020Kaneko et al 2023;The GRAVITY Collaboration et al 2023), dwarf galaxies (Mezcua et al 2016(Mezcua et al , 2018, and globular clusters (GCs; see, e.g., Maccarone & Servillat 2008;Farrell et al 2009;Cseh et al 2010;Strader et al 2012;Bachetti et al 2014;Kains et al 2016;Kızıltan et al 2017;Lin et al 2018;Su et al 2022). Regarding Galactic GCs, the most comprehensive study providing direct IMBH mass upper limits based on radio observations is by Tremou et al (2018, hereafter T18).…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for Finding Intermediate-mass Black Holes

Pasquato,

Trevisan,

Askar

et al. 2024

ApJ

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Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine-learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to simulate GCs realistically, the simulation physics or initial conditions may fail to reflect reality fully. Therefore our training data may be biased, leading to a failure in generalization to observational data. Both the first issue—explainability/interpretability—and the second—out of distribution generalization and fairness—are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an Extreme Gradient Boosting (XGBoost) classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost, and a global rule from CORELS.

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Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for Finding Intermediate-mass Black Holes

Pasquato,

Trevisan,

Askar

et al. 2024

ApJ

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Parabolic-like Trend in SiO Ratios throughout the Central Molecular Zone: Possible Signature of a Past Nuclear Activity in the Galactic Center

Takekawa,

Oka,

Tsujimoto

et al. 2024

ApJL

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We report the discovery of a characteristic trend in the intensity ratios of SiO emissions across the Central Molecular Zone (CMZ) of our Galaxy. Using the Nobeyama Radio Observatory 45 m telescope, we conducted large-scale, high-sensitivity imaging observations in molecular lines including SiO J = 2–1 and CS J = 2–1. By identifying SiO-emitting clouds and examining their intensity ratios relative to the other molecular lines, we unveiled a parabolic-like trend showing lower ratios near the Galactic nucleus, Sgr A*, with gradual increases toward the edges of the CMZ. This pattern suggests a possible outburst of the nucleus within the last ∼105 yr, which may have propagated through the entire CMZ with strong shocks. Alternatively, the observed trend may also be attributed to the destruction of small dust grains by high-energy photons. Our results can potentially lead to a new perspective on the history of nuclear activity and its impact on the surrounding molecular environment.

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Discovery of the Tadpole Molecular Cloud near the Galactic Nucleus

Cited by 2 publications

References 48 publications

Interpretable Machine Learning for Finding Intermediate-mass Black Holes

Interpretable Machine Learning for Finding Intermediate-mass Black Holes

Parabolic-like Trend in SiO Ratios throughout the Central Molecular Zone: Possible Signature of a Past Nuclear Activity in the Galactic Center

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