GA-NIFS: Early-stage feedback in a heavily obscured AGN at $z=4.76$

Kavli Affiliate: Roberto Maiolino | First 5 Authors: Eleonora Parlanti, Stefano Carniani, Hannah Übler, Giacomo Venturi, Francesco D’Eugenio | Summary: Dust-obscured galaxies are thought to represent an early evolutionary phase of massive galaxies in which the active galactic nucleus (AGN) is still deeply buried in significant amounts of dusty material and its emission is strongly […]


Continue.. GA-NIFS: Early-stage feedback in a heavily obscured AGN at $z=4.76$

The Cosmic Ultraviolet Baryon Survey: Empirical Characterization of Turbulence in the Cool Circumgalactic Medium

Kavli Affiliate: Robert A. Simcoe | First 5 Authors: Hsiao-Wen Chen, Zhijie Qu, Michael Rauch, Mandy C. Chen, Fakhri S. Zahedy | Summary: This paper reports the first measurement of the relationship between turbulent velocity and cloud size in the diffuse circumgalactic medium (CGM) in typical galaxy halos at redshift z~0.4-1. Through spectrally-resolved absorption profiles […]


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Nested solitons in two-field fuzzy dark matter

Kavli Affiliate: Mark Vogelsberger | First 5 Authors: Hoang Nhan Luu, Philip Mocz, Mark Vogelsberger, Simon May, Josh Borrow | Summary: Dark matter as scalar particles consisting of multiple species is well motivated in string theory where axion fields are ubiquitous. A two-field fuzzy dark matter (FDM) model features two species of ultralight axion particles […]


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Nested solitons in two-field fuzzy dark matter

Kavli Affiliate: Mark Vogelsberger | First 5 Authors: Hoang Nhan Luu, Philip Mocz, Mark Vogelsberger, Simon May, Josh Borrow | Summary: Dark matter as scalar particles consisting of multiple species is well motivated in string theory where axion fields are ubiquitous. A two-field fuzzy dark matter (FDM) model features two species of ultralight axion particles […]


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Amalgame: Cosmological Constraints from the First Combined Photometric Supernova Sample

Kavli Affiliate: Richard Kessler | First 5 Authors: Brodie Popovic, Daniel Scolnic, Maria Vincenzi, Mark Sullivan, Dillon Brout | Summary: Future constraints of cosmological parameters from Type Ia supernovae (SNe Ia) will depend on the use of photometric samples, those samples without spectroscopic measurements of the SNe Ia. There is a growing number of analyses […]


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Progress in Direct Measurements of the Hubble Constant

Kavli Affiliate: Wendy L. Freedman | First 5 Authors: Wendy L. Freedman, Barry F. Madore, , , | Summary: One of the most exciting and pressing issues in cosmology today is the discrepancy between some measurements of the local Hubble constant and other values of the expansion rate inferred from the cosmic microwave background (CMB) […]


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On the Comparison of AGN with GRMHD Simulations: II. M87

Kavli Affiliate: Roger Blandford | First 5 Authors: Richard Anantua, Angelo Ricarte, George Wong, Razieh Emami, Roger Blandford | Summary: Horizon-scale observations of the jetted active galactic nucleus M87 are compared with simulations spanning a broad range of dissipation mechanisms and plasma content in three-dimensional general relativistic flows around spinning black holes. Observations of synchrotron […]


Continue.. On the Comparison of AGN with GRMHD Simulations: II. M87

On the Comparison of AGN with GRMHD Simulations: II. M87

Kavli Affiliate: Roger Blandford | First 5 Authors: Richard Anantua, Angelo Ricarte, George Wong, Razieh Emami, Roger Blandford | Summary: Horizon-scale observations of the jetted active galactic nucleus M87 are compared with simulations spanning a broad range of dissipation mechanisms and plasma content in three-dimensional general relativistic flows around spinning black holes. Observations of synchrotron […]


Continue.. On the Comparison of AGN with GRMHD Simulations: II. M87

Introspective Deep Metric Learning

Kavli Affiliate: Zheng Zhu | First 5 Authors: Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu | Summary: This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods focus on learning a discriminative embedding to describe the semantic features of images, which ignore […]


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