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arXiv 2023年

作者： DeRose, Joseph Kokron, Nickolas Banerjee, Arka Chen, Shi-Fan White, Martin Wechsler, Risa Storey-Fisher, Kate Tinker, Jeremy Zhai, Zhongxu Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Rd BerkeleyCA United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States Kavli Institute for Particle Astrophysics and Cosmology SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Physics Indian Institute of Science Education and Research Homi Bhabha Road Pashan Pune411008 India Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Berkeley Center for Cosmological Physics Department of Physics UC Berkeley Campbell Hall 341 CA94720 United States Department of Physics University of California 366 Physics North MC 7300 BerkeleyCA94720 United States Center for Cosmology and Particle Physics Department of Physics New York University 726 Broadway New YorkNY10003 United States Department of Astronomy School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Waterloo Center for Astrophysics University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada

We present the Aemulus ν simulations: a suite of 150 (1.05h−1Gpc)3 N-body simulations with a mass resolution of 3.51 × 1010 Ω***3 h−1M☉ in a wνCDM cosmological parameter space. The simulations have been explicitly designed to span a broad range in σ8 to facilitate investigations of tension between large scale structure and cosmic microwave background cosmological probes. Neutrinos are treated as a second particle species to ensure accuracy to 0.5 eV, the maximum neutrino mass that we have simulated. By employing Zel’dovich control variates, we increase the effective volume of our simulations by factors of 10 − 105 depending on the statistic in question. As a first application of these simulations, we build new hybrid effective field theory and matter power spectrum surrogate models, demonstrating that they achieve ≤ 1% accuracy for k ≤ 1 hMpc−1 and 0 ≤ z ≤ 3, and ≤ 2% accuracy for k ≤ 4 hMpc−1 for the matter power spectrum. We publicly release the trained surrogate models, and estimates of the surrogate model errors in the hope that they will be broadly applicable to a range of cosmological analyses for many years to come. © 2023, CC BY.

关键词： Power spectrum

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Heavy Primordial Black Holes from Strongly Clustered Light Black Holes

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Physical Review Letters 2023年第17期130卷 171401-171401页

作者： Valerio De Luca Gabriele Franciolini Antonio Riotto Center for Particle Cosmology Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street Philadelphia Pennsylvania 19104 USA Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 00185 Roma Italy INFN Sezione di Roma Piazzale Aldo Moro 2 00185 Roma Italy Département de Physique Théorique and Gravitational Wave Science Center (GWSC) Université de Genève CH-1211 Geneva Switzerland CH-1211 Geneva Switzerland

We show that heavy primordial black holes may originate from much lighter ones if the latter are strongly clustered at the time of their formation. While this population is subject to the usual constraints from late-time universe observations, its relation to the initial conditions is different from the standard scenario and provides a new mechanism to generate massive primordial black holes even in the absence of efficient accretion, opening new scenarios, e.g., for the generation of supermassive black holes.

关键词： cosmology Dark matter Astronomical black holes

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Prospects for kSZ2-21cm2 Cross-Correlations during Reionization

arXiv

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arXiv 2025年

作者： Zhou, Meng Plante, Paul La Lidz, Adam Mao, Yi Ma, Yin-Zhe National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Bejing100101 China Department of Computer Science University of Nevada Las VegasNV89154 United States Nevada Center for Astrophysics University of Nevada Las VegasNV89154 United States Center for Particle Cosmology Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Department of Astronomy Tsinghua University Beijing100084 China Department of Physics Stellenbosch University Matieland7602 South Africa Matieland Stellenbosch7602 South Africa

The 21 cm line and the patchy kinetic Sunyaev-Zel’dovich (kSZ) effect are promising and complementary probes of the Epoch of Reionization (EoR). A challenge for cross-correlating these two signals is that foreground avoidance or removal algorithms applied to the 21 cm data inevitably sacrifice Fourier modes with long wavelengths along the line-of-sight (i.e., low- modes), yet only these same modes contribute to the kSZ signal. Here we show that a suitable kSZ2 × 21 cm2 cross-correlation statistic nevertheless remains non-vanishing, even after filtering out the corrupted low- Fourier modes from the 21 cm data. We simulate the kSZ2 × 21 cm2 cross-correlation signal across reionization-era redshifts and find distinctive redshift evolution. This signal peaks early in the reionization history, when the volume-averaged fraction is around 0.1 HII 0.2, after which it changes sign and reaches a minimum near reionization’s midpoint (HII ∼ 0.5), while the signal gradually vanishes as reionization completes. These trends appear generic across three simulated models which differ in their reionization histories. We forecast the detectability of the kSZ2 × 21 cm2 cross-power spectrum for the HERA and SKA1-Low 21 cm experiments in combination with current and next-generation CMB surveys including the Simons Observatory, CMB-S4, and CMB-HD. We find that a high-significance detection (S/N 5) is possible with SKA1-Low and CMB-S4. Copyright © 2025, The Authors. All rights reserved.

关键词： cosmology

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ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST

arXiv

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arXiv 2025年

作者： Shah, Ved G. Gagliano, Alex Malanchev, Konstantin Narayan, Gautham Center for Interdisciplinary Exploration and Research in Astrophysics Northwestern University EvanstonIL United States Department of Astronomy University of Illinois Urbana-Champaign UrbanaIL United States Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States McWilliams Center for Cosmology and Astrophysics Department of Physics Carnegie Mellon University PittsburghPA15213 United States Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States Illinois Center for Advanced Studies of the Universe University of Illinois Urbana-Champaign UrbanaIL61801 United States

We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with Gated Recurrent Units (GRUs), and has been trained using a custom hierarchical cross-entropy loss function to provide high-confidence classifications along an observationally-driven taxonomy with as little as a single photometric observation. Contextual information for each object, including host galaxy photometric redshift, offset, ellipticity and brightness, is concatenated to the light curve embedding and used to make a final prediction. Training on ∼0.5M events from the Extended LSST Astronomical Time-Series Classification Challenge, we achieve a top-level (Transient vs Variable) macro-averaged precision of 0.96 using only 1 day of photometric observations after the first detection in addition to contextual information, for each event;this increases to >0.99 once 64 days of the light curve has been obtained, and 0.83 at 1024 days after first detection for 19-way classification (including supernova sub-types, active galactic nuclei, variable stars, microlensing events, and kilonovae). We also compare ORACLE with other state-of-the-art classifiers and report comparable performance for the 19-way classification task, in addition to delivering accurate top-level classifications much earlier. The code and model weights used in this work are publicly available at our associated GitHub repository. Copyright © 2025, The Authors. All rights reserved.

关键词： Supernovae

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Interpretability of deep-learning methods applied to large-scale structure surveys

arXiv

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arXiv 2025年

作者： Aymerich, Gaspard Kacprzak, Tomasz Refregier, Alexandre Université Paris-Saclay CNRS Institut d’Astrophysique Spatiale Orsay91405 France Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Institute for Particle Physics and Astrophysics ETH Zurich Zurich8093 Switzerland Swiss Data Science Center Paul Scherrer Institute Villigen5232 Switzerland

Deep learning and convolutional neural networks in particular are powerful and promising tools for cosmological analysis of large-scale structure surveys. They are already providing similar performance to classical analysis methods using fixed summary statistics, are showing potential to break key degeneracies by better probe combination and will likely improve rapidly in the coming years as progress is made in the physical modelling through both software and hardware improvement. One key issue remains: unlike classical analysis, a convolutional neural network’s decision process is hidden from the user as the network optimises millions of parameters with no direct physical meaning. This prevents a clear understanding of the potential limitations and biases of the analysis, making it hard to rely on as a main analysis method. In this work, we explore the behaviour of such a convolutional neural network through a novel method. Instead of trying to analyse a network "a posteriori", i.e. after training has been completed, we study the impact on the constraining power of training the network and predicting parameters with degraded data where we removed part of the information. This allows us to gain an understanding of which parts and features of a large-scale structure survey are most important in the network’s prediction process. We find that the network’s prediction process relies on a mix of both Gaussian and non-Gaussian information, and seems to put an emphasis on structures whose scales are at the limit between linear and non-linear regimes. © 2025, CC BY.

关键词： Convolutional neural networks

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SKA sensitivity for possible radio emission from dark matter in Omega Centauri

arXiv

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arXiv 2023年

作者： Wang, Guan-Sen Chen, Zhan-Fang Zu, Lei Gong, Hao Feng, Lei Fan, Yi-Zhong Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Hefei Anhui230026 China School of Mathematics and Physics Bengbu University Bengbu Anhui233030 China Joint Center for Particle Nuclear Physics and Cosmology Nanjing University-Purple Mountain Observatory Nanjing210093 China

Omega Centauri, the largest known globular cluster in the Milky Way, is believed to be the remains of a dwarf galaxy's core. Giving its potential abundance of dark matter (DM), it is an attractive target for investigating the nature of this elusive substance in our local environment. Our study demonstrates that by observing Omega Centauri with the SKA for 100 hours, we can detect synchrotron radio or Inverse Compton (IC) emissions from the DM annihilation products. It enables us to constrain the cross-section of DM annihilation down to ∼ 10-30cm3s-1for DM mass from several GeV to 100 GeV, which is much stronger compared with other observations. Additionally, we explore the axion, another well-motivated DM candidate, and provide stimulated decay calculations. It turns out that the sensitivity can reach gaγγ ∼ 10-10GeV-1for 2×10-7eV -4eV. Copyright © 2023, The Authors. All rights reserved.

关键词： Dark Matter

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Fitting very flexible models: Linear regression with large numbers of parameters

arXiv

引用

arXiv 2021年

作者： Hogg, David W. Villar, Soledad Flatiron Institute Center for Cosmology and Particle Physics Department of Physics New York University Center for Data Science New York University Max-Planck-Institut für Astronomie Heidelberg United States Department of Applied Mathematics & Statistics Johns Hopkins University Mathematical Institute for Data Science Johns Hopkins University United States

There are many uses for linear fitting;the context here is interpolation and denoising of data, as when you have calibration data and you want to fit a smooth, flexible function to those data. Or you want to fit a flexible function to de-trend a time series or normalize a spectrum. In these contexts, investigators often choose a polynomial basis, or a Fourier basis, or wavelets, or something equally general. They also choose an order, or number of basis functions to fit, and (often) some kind of regularization. We discuss how this basis-function fitting is done, with ordinary least squares and extensions thereof. We emphasize that it is often valuable to choose far more parameters than data points, despite folk rules to the contrary: Suitably regularized models with enormous numbers of parameters generalize well and make good predictions for held-out data;over-fitting is not (mainly) a problem of having too many parameters. It is even possible to take the limit of infinite parameters, at which, if the basis and regularization are chosen correctly, the least-squares fit becomes the mean of a Gaussian process. We recommend cross-validation as a good empirical method for model selection (for example, setting the number of parameters and the form of the regularization), and jackknife resampling as a good empirical method for estimating the uncertainties of the predictions made by the model. We also give advice for building stable computational implementations. Copyright © 2021, The Authors. All rights reserved.

关键词： Functions

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Cosmological perturbations in the energy-momentum squared gravity theory:constraints from gravitational wave standard sirens and redshift space distortions

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Chinese physics C 2025年第4期 202-211页

作者：付启铭张鑫 Institute of Physics Shaanxi University of Technology Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Provinceand Key Laboratory of Quantum Theory andApplications of MoELanzhou University Key Laboratory of Cosmology and Astrophysics (Liaoning) & College of Sciences Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University

We investigate the linear cosmological perturbations in the context of the so-called energy-momentum squared gravity(EMSG) *** research shows that the EMSG theory can reproduce a viable background cosmological evolution comparable to ΛCDM,whereas the matter-dominated era exhibits slight *** this paper,we focus on power-law EMSG models and derive the equations for the linear cosmological *** explore the propagation of the gravitational wave(GW) and the growth of matter density perturbation at the first order,and we estimate the model parameters from the simulated GW and observed redshift space distortion *** analysis reveals that the model parameters should be small and positive in the 1 σ confidence interval,which indicates that the theory agrees closely with the observational data and can be considered an alternative to the standard cosmological model.

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Pre-Merger Detection and Characterization of Inspiraling Binary Neutron Stars Derived from Neural Posterior Estimation

arXiv

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arXiv 2024年

作者： van Straalen, Wouter Kolmus, Alex Janquart, Justin Van Den Broeck, Chris Department of Physics Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Radboud University Nijmegen Toernooiveld 212 Nijmegen6525 EC Netherlands Nikhef Science Park 105 Amsterdam1098 XG Netherlands Center for Cosmology Particle Physics and Phenomenology - CP3 Université Catholique de Louvain Louvain-la-NeuveB-1348 Belgium Royal Observatory of Belgium Avenue Circulaire 3 Uccle1180 Belgium

As the sensitivity of the international gravitational wave detector network increases, observing binary neutron star signals will become more common. Moreover, since these signals will be louder, the chances of detecting them before their mergers increase. However, this requires an efficient framework. In this work, we present a machine-learning-based framework capable of detecting and analyzing binary neutron star mergers during their inspiral. Using a residual network to summarize the strain data, we use its output as input to a classifier giving the probability of having a signal in the data, and to a normalizing-flow network to perform neural posterior estimation. We train a network for several maximum frequencies reached by the signal to improve the estimate over time. Our framework shows good results both for detection and characterization, with improved parameter estimation as we get closer to the merger time. Thus, we can effectively evolve the precision of the sky location as the merger draws closer. Such a setup would be important for future multi-messenger searches where one would like to have the most precise information possible, as early as possible. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravity waves

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Holographic screen sequestration

arXiv

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arXiv 2023年

作者： Chatwin-Davies, Aidan Leung, Pompey Remmen, Grant N. Okinawa Institute of Science and Technology 1919-1 Tancha Onna-son Kunigami-gun Okinawa904-0495 Japan Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Canada Center for Cosmology and Particle Physics Department of Physics New York University New YorkNY10003 United States

Holographic screens are codimension-one hypersurfaces that extend the notion of apparent horizons to general (non-black hole) spacetimes and that display interesting thermodynamic properties. We show that if a spacetime contains a codimension-two, boundary-homologous, minimal extremal spacelike surface X (known as an HRT surface in AdS/CFT), then any holographic screens are sequestered to the causal wedges of X. That is, any single connected component of a holographic screen can be located in at most one of the causal future, causal past, inner wedge, or outer wedge of X. We comment on how this result informs possible coarse grained entropic interpretations of generic holographic screens, as well as on connections to semiclassical objects such as quantum extremal surfaces. Copyright © 2023, The Authors. All rights reserved.

关键词： Holography

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