The KLEVER survey: Nitrogen abundances at $zsim$2 and probing the existence of a fundamental nitrogen relation

Kavli Affiliate: Yingjie Peng | First 5 Authors: Connor Hayden-Pawson, Mirko Curti, Roberto Maiolino, Michele Cirasuolo, Francesco Belfiore | Summary: We present a comparison of the nitrogen-to-oxygen ratio (N/O) in 37 high-redshift galaxies at $zsim$2 taken from the KMOS Lensed Emission Lines and VElocity Review (KLEVER) Survey with a comparison sample of local galaxies, taken […]


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The HASHTAG project: The First Submillimeter Images of the Andromeda Galaxy from the Ground

Kavli Affiliate: Yingjie Peng | First 5 Authors: Matthew W. L. Smith, Stephen A. Eales, Thomas G. Williams, Bumhyun Lee, Zongnan Li | Summary: Observing nearby galaxies with submillimeter telescopes on the ground has two major challenges. First, the brightness is significantly reduced at long submillimeter wavelengths compared to the brightness at the peak of […]


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Cosmological constraints without fingers of God

Kavli Affiliate: Masahiro Takada | First 5 Authors: Mikhail M. Ivanov, Oliver H. E. Philcox, Marko Simonović, Matias Zaldarriaga, Takahiro Nishimichi | Summary: Non-linear redshift-space distortions ("fingers of God") are challenging to model analytically, a fact that limits the applicability of perturbation theory in redshift space as compared to real space. We show how this […]


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Investigating the existence of gravitomagnetic monopole in M87*

Kavli Affiliate: Qingjuan Yu | First 5 Authors: M. Ghasemi-Nodehi, Chandrachur Chakraborty, Qingjuan Yu, Youjun Lu, | Summary: We examine the possibility for the existence of gravitomagnetic monopole ($n_*$) in M87* by using the results obtained from its first Event Horizon Telescope image. By numerically deducing the shadow sizes in Kerr-Taub-NUT (KTN) spacetime, we show […]


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The Eccentric and Accelerating Stellar Binary Black Hole Mergers in Galactic Nuclei: Observing in Ground and Space Gravitational Wave Observatories

Kavli Affiliate: Kohei Inayoshi | First 5 Authors: Fupeng Zhang, Xian Chen, Lijing Shao, Kohei Inayoshi, | Summary: We study the stellar binary black holes (BBHs) inspiralling/merging in galactic nuclei based on our numerical method GNC. We find that $3-40%$ of all new born BBHs will finally merge due to various dynamical effects. In a […]


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Bounded multiplicity theorems for induction and restriction

Kavli Affiliate: Toshiyuki Kobayashi | First 5 Authors: Toshiyuki Kobayashi, , , , | Summary: We prove a geometric criterion for the bounded multiplicity property of "small" infinite-dimensional representations of real reductive Lie groupsin both induction and restrictions. Applying the criterion to symmetric pairs, we give a full description of the triples $H subset G […]


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Visualizing the Energy-gap Modulations of the Cuprate Pair Density Wave State

Kavli Affiliate: J. C. Davis | First 5 Authors: Zengyi Du, Hui Li, Sanghyun Joo, Elizabeth P. Donoway, Jinho Lee | Summary: When Cooper pairs are formed with finite center-of-mass momentum, the defining characteristic is a spatially modulating superconducting energy gap $Delta(r)$. Recently, this concept has been generalized to the pair density wave (PDW) state […]


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Probing Early Super-massive Black Hole Growth and Quasar Evolution with Near-infrared Spectroscopy of 37 Reionization-era Quasars at 6.3 < z <= 7.64

Kavli Affiliate: Linhua Jiang | First 5 Authors: Jinyi Yang, Feige Wang, Xiaohui Fan, Aaron J. Barth, Joseph F. Hennawi | Summary: We report the results of near-infrared spectroscopic observations of 37 quasars in the redshift range $6.3< zle7.64$, including 32 quasars at $z>6.5$, forming the largest quasar near-infrared spectral sample at this redshift. The […]


Continue.. Probing Early Super-massive Black Hole Growth and Quasar Evolution with Near-infrared Spectroscopy of 37 Reionization-era Quasars at 6.3 < z <= 7.64

Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning

Kavli Affiliate: Max Tegmark | First 5 Authors: Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark, | Summary: Integrating physical inductive biases into machine learning can improve model generalizability. We generalize the successful paradigm of physics-informed learning (PIL) into a more general framework that also includes what we term physics-augmented learning (PAL). PIL and PAL […]


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