Extracting cosmological information from the abundance of galaxy clusters with simulation-based inference

Kavli Affiliate: Anthony Challinor | First 5 Authors: Íñigo Zubeldia, Boris Bolliet, Anthony Challinor, William Handley, | Summary: The abundance of galaxy clusters as a function of mass and redshift is a well-established and powerful cosmological probe. Cosmological analyses based on galaxy cluster number counts have traditionally relied on explicitly computed likelihoods, which are often […]


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Rapid and Late Cosmic Reionization Driven by Massive Galaxies: a Joint Analysis of Constraints from 21-cm, Lyman Line & CMB Data Sets

Kavli Affiliate: George Efstathiou | First 5 Authors: Peter H. Sims, Harry T. J. Bevins, Anastasia Fialkov, Dominic Anstey, Will J. Handley | Summary: Observations of the Epoch of Reionization (EoR) have the potential to answer long-standing questions of astrophysical interest regarding the nature of the first luminous sources and their effects on the intergalactic […]


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GenEDA: Towards Generative Netlist Functional Reasoning via Cross-Modal Circuit Encoder-Decoder Alignment

Kavli Affiliate: Jing Wang | First 5 Authors: Wenji Fang, Wenji Fang, , , | Summary: The success of foundation AI has motivated the research of circuit foundation models, which are customized to assist the integrated circuit (IC) design process. However, existing pre-trained circuit foundation models are typically limited to standalone encoders for predictive tasks […]


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The X-ray statistical properties of dust-obscured galaxies detected by eROSITA

Kavli Affiliate: Kohei Inayoshi | First 5 Authors: Akatoki Noboriguchi, Kohei Ichikawa, Yoshiki Toba, Tom Dwelly, Kohei Inayoshi | Summary: Dust-obscured galaxies (DOGs) are considered to be in a co-evolution phase, with the associated active galactic nuclei (AGN) obscured by dust and gas. Although the DOGs are thought to harbor rapidly growing SMBHs, their X-ray […]


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Sample Efficient Algorithms for Linear System Identification under Noisy Observations

Kavli Affiliate: Jia Liu | First 5 Authors: Yuyang Zhang, Xinhe Zhang, Jia Liu, Na Li, | Summary: In this paper, we focus on learning linear dynamical systems under noisy observations. In this setting, existing algorithms either yield biased parameter estimates, or suffer from large sample complexities. To address these issues, we adapt the instrumental […]


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Error-In-Variables Methods for Efficient System Identification with Finite-Sample Guarantees

Kavli Affiliate: Jia Liu | First 5 Authors: Yuyang Zhang, Yuyang Zhang, , , | Summary: This paper addresses the problem of learning linear dynamical systems from noisy observations. In this setting, existing algorithms either yield biased parameter estimates or have large sample complexities. We resolve these issues by adapting the instrumental variable method and […]


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The Effect of Nonlinear Gravity on the Cosmological Background During Preheating

Kavli Affiliate: Joshua Frieman | First 5 Authors: Ryn Grutkoski, Hayley J. Macpherson, John T. Giblin Jr, Joshua Frieman, | Summary: We use numerical relativity to study the violent preheating era at the end of inflation. This epoch can result in highly nonlinear fluctuations in density and gravitational potential which feed back onto the averaged […]


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A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English

Kavli Affiliate: Xian Chen | First 5 Authors: Julian Bäumler, Louis Blöcher, Lars-Joel Frey, Xian Chen, Markus Bayer | Summary: The dissemination of online hate speech can have serious negative consequences for individuals, online communities, and entire societies. This and the large volume of hateful online content prompted both practitioners’, i.e., in content moderation or […]


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Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits

Kavli Affiliate: Elise Jennings | First 5 Authors: Ronny Müller, Maximilian Zanner, Mika Schielein, Martin Rüfenacht, Elise Jennings | Summary: We develop a parametric error model to construct a digital twin of a superconducting transmon qubit device. The model parameters are extracted from hardware calibration data and supplementary benchmarking circuits, providing a dynamic, system-specific representation […]


Continue.. Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits

Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits

Kavli Affiliate: Elise Jennings | First 5 Authors: Ronny Müller, Ronny Müller, , , | Summary: We develop a parametric error model to construct a digital twin of a superconducting transmon qubit device. The model parameters are extracted from hardware calibration data and supplementary benchmarking circuits, providing a dynamic, system-specific representation of noise and gate […]


Continue.. Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits