The Compton Spectrometer and Imager Project for MeV Astronomy

Kavli Affiliate: Tom Melia | First 5 Authors: John A. Tomsick, Steven E. Boggs, Andreas Zoglauer, Eric Wulf, Lee Mitchell | Summary: The Compton Spectrometer and Imager (COSI) is a 0.2-5 MeV Compton telescope capable of imaging, spectroscopy, and polarimetry of astrophysical sources. Such capabilities are made possible by COSI’s germanium cross-strip detectors, which provide […]


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AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators

Kavli Affiliate: Xiang Zhang | First 5 Authors: Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, Xiang Zhang | Summary: Contrastive learning has been widely applied to graph representation learning, where the view generators play a vital role in generating effective contrastive samples. Most of the existing contrastive learning methods employ pre-defined view generation methods, […]


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A Horizon Study for Cosmic Explorer: Science, Observatories, and Community

Kavli Affiliate: Rainer Weiss | Summary:This Horizon Study describes a next-generation ground-based gravitational-wave observatory: Cosmic Explorer. With ten times the sensitivity of Advanced LIGO, Cosmic Explorer will push gravitational-wave astronomy towards the edge of the observable universe ($z sim 100$). The goals of this Horizon Study are to describe and evaluate design concepts for Cosmic […]


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A Horizon Study for Cosmic Explorer: Science, Observatories, and Community

Kavli Affiliate: David H. Shoemaker | First 5 Authors: Matthew Evans, Rana X Adhikari, Chaitanya Afle, Stefan W. Ballmer, Sylvia Biscoveanu | Summary: This Horizon Study describes a next-generation ground-based gravitational-wave observatory: Cosmic Explorer. With ten times the sensitivity of Advanced LIGO, Cosmic Explorer will push gravitational-wave astronomy towards the edge of the observable universe […]


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Hyper Suprime-Cam Subaru Strategic Program: A Mass-Dependent Slope of the Galaxy Size-Mass Relation at $z

Kavli Affiliate: Naoki Yasuda | Summary:We present the galaxy size-mass ($R_e-M_ast$) distributions using a stellar-mass complete sample of $sim1.5$ million galaxies, covering $sim100$ deg$^2$, with $log(M_ast/M_odot)>10.2~(9.2)$ over the redshift range $0.2<z<1.0$ $(z<0.6)$ from the second public data release of the Hyper Suprime-Cam Subaru Strategic Program. We confirm that, at fixed redshift and stellar mass over […]


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Hyper Suprime-Cam Subaru Strategic Program: A Mass-Dependent Slope of the Galaxy Size-Mass Relation at $z<1$

Kavli Affiliate: John D. Silverman | First 5 Authors: Lalitwadee Kawinwanichakij, John D. Silverman, Xuheng Ding, Angelo George, Ivana Damjanov | Summary: We present the galaxy size-mass ($R_{e}-M_{ast}$) distributions using a stellar-mass complete sample of $sim1.5$ million galaxies, covering $sim100$ deg$^2$, with $log(M_{ast}/M_{odot})>10.2~(9.2)$ over the redshift range $0.2<z<1.0$ $(z<0.6)$ from the second public data release […]


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Superconductivity in a quintuple-layer square-planar nickelate

Kavli Affiliate: Lena F. Kourkoutis | First 5 Authors: Grace A. Pan, Dan Ferenc Segedin, Harrison LaBollita, Qi Song, Emilian M. Nica | Summary: Since the discovery of high-temperature superconductivity in the copper oxide materials, there have been sustained efforts to both understand the origins of this phase and discover new cuprate-like superconducting materials. One […]


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Quantum criticality of fermion velocities and critical temperature nearby a putative quantum phase transition in the $d$-wave superconductors

Kavli Affiliate: Jing Wang | First 5 Authors: Xiao-Yue Ren, Ya-Hui Zhai, Jing Wang, , | Summary: Quantum critical behaviors induced by a putative quantum phase transition are vigilantly investigated, which separates a $d$-wave superconducting state and $d$-wave superconducting+$X$ state below the superconducting dome of the $d-$wave superconductors with tuning the non-thermal doping variable. Within […]


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Assisted Learning for Organizations with Limited Imbalanced Data

Kavli Affiliate: Yi Zhou | First 5 Authors: Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou, | Summary: In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available […]


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