Directly Imaging the Cooling Flow in the Phoenix Cluster

Kavli Affiliate: Michael McDonald | First 5 Authors: Michael Reefe, Michael McDonald, Marios Chatzikos, Jerome Seebeck, Richard Mushotzky | Summary: In the centers of many galaxy clusters, the hot ($sim$10$^7$ K) intracluster medium (ICM) can become dense enough that it should cool on short timescales. However, the low measured star formation rates in massive central […]


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Long-Term X-ray Variability on the Benchmark YSO HL Tau

Kavli Affiliate: David A. Principe | First 5 Authors: Steven M. Silverberg, Scott J. Wolk, David A. Principe, P. Christian Schneider, Hans Moritz Guenther | Summary: HL Tau is one of the most well-studied Class I young stellar objects, including frequent observations at near- and mid-infrared, (sub-) millimeter, and X-ray wavelengths. We present the results […]


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Cryoscope: A Cryogenic Infrared Survey Telescope

Kavli Affiliate: Robert A. Simcoe | First 5 Authors: Mansi M. Kasliwal, Nicholas Earley, Roger Smith, Tristan Guillot, Tony Travouillon | Summary: We present Cryoscope — a new 50 sq. deg field-of-view, 1.2 m aperture, K-dark survey telescope to be located at Dome C, Antarctica. Cryoscope has an innovative optical-thermal design wherein the entire telescope […]


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Cryoscope: A Cryogenic Infrared Survey Telescope in Antarctica

Kavli Affiliate: Robert A. Simcoe | First 5 Authors: Mansi M. Kasliwal, Nicholas Earley, Roger Smith, Tristan Guillot, Tony Travouillon | Summary: We present Cryoscope–a new 50 deg$^2$ field-of-view, 1.2 m aperture, $K_{dark}$ survey telescope to be located at Dome C, Antarctica. Cryoscope has an innovative optical-thermal design wherein the entire telescope is cryogenically cooled. […]


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Multidisciplinary Science in the Multimessenger Era

Kavli Affiliate: Dheeraj R. Pasham | First 5 Authors: Eric Burns, Christopher L. Fryer, Ivan Agullo, Jennifer Andrews, Elias Aydi | Summary: Astrophysical observations of the cosmos allow us to probe extreme physics and answer foundational questions on our universe. Modern astronomy is increasingly operating under a holistic approach, probing the same question with multiple […]


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Multidisciplinary Science in the Multimessenger Era

Kavli Affiliate: Dheeraj R. Pasham | First 5 Authors: Eric Burns, Christopher L. Fryer, Ivan Agullo, Jennifer Andrews, Elias Aydi | Summary: Astrophysical observations of the cosmos allow us to probe extreme physics and answer foundational questions on our universe. Modern astronomy is increasingly operating under a holistic approach, probing the same question with multiple […]


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Harmonic Loss Trains Interpretable AI Models

Kavli Affiliate: Max Tegmark | First 5 Authors: David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark, | Summary: In this paper, we introduce **harmonic loss** as an alternative to the standard cross-entropy loss for training neural networks and large language models (LLMs). Harmonic loss enables improved interpretability and faster convergence, owing to its scale […]


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Language Models Use Trigonometry to Do Addition

Kavli Affiliate: Max Tegmark | First 5 Authors: Subhash Kantamneni, Max Tegmark, , , | Summary: Mathematical reasoning is an increasingly important indicator of large language model (LLM) capabilities, yet we lack understanding of how LLMs process even simple mathematical tasks. To address this, we reverse engineer how three mid-sized LLMs compute addition. We first […]


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Characterization of the Teledyne COSMOS Camera: A Large Format CMOS Image Sensor for Astronomy

Kavli Affiliate: Gabor Furesz | First 5 Authors: Christopher Layden, Jill Juneau, Gustav Pettersson, Nathan Lourie, Benjamin Schneider | Summary: The Teledyne COSMOS-66 is a next-generation CMOS camera designed for astronomical imaging, featuring a large-format sensor ($8120 times 8120$ pixels, each $10 mu m$), high quantum efficiency, high frame rates, and a correlated multi-sampling mode […]


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Low-Rank Adapting Models for Sparse Autoencoders

Kavli Affiliate: Max Tegmark | First 5 Authors: Matthew Chen, Joshua Engels, Max Tegmark, , | Summary: Sparse autoencoders (SAEs) decompose language model representations into a sparse set of linear latent vectors. Recent works have improved SAEs using language model gradients, but these techniques require many expensive backward passes during training and still cause a […]


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