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Journal of experimental & clinical cancer research : CR 2024年第1期43卷 37页

作者： Ying Lin Yun Wu Qiangzu Zhang Xunwei Tu Sufang Chen Junfan Pan Nengluan Xu Ming Lin Peiwei She Gang Niu Yusheng Chen Hongru Li Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. Department of General Practice Medicine Fujian Provincial Hospital Fuzhou 350001 China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. The Centre for Experimental Research in Clinical Medicine Fujian Provincial Hospital Fuzhou 350001 Fujian China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. g.niu@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. cysktz@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. muzi131122@***. Fujian Provincial Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Shengli Clinical College of Fujian Medical University Fuzhou 350001 Fujian China. muzi131122@***.

eROSITA clusters dynamical state and their impact on the BCG luminosity

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arXiv 2025年

作者： Zenteno, A. Kluge, M. Kharkrang, R. Hernandez-Lang, D. Damke, G. Saro, A. Monteiro-Oliveira, R. Carrasco, E.R. Salvato, M. Comparat, J. Fabricius, M. Snigula, J. Arevalo, P. Cuevas, H. Castellon, J.L. Nilo Ramirez, A. Astudillo, S. Véliz Landriau, M. Myers, A.D. Schlafly, E. Valdes, F. Weaver, B. Mohr, J.J. Grandis, S. Klein, M. Liu, A. Bulbul, E. Zhang, X. Sanders, J.S. Bahar, Y.E. Ghirardini, V. Ramos, M. Balzer, F. Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse 1 Garching85748 Germany Universitè Paris-Saclay CNRS Institut dÁstrophysique Spatiale Orsay91405 France Faculty of Physics Ludwig-Maximilians-Universität München Scheinerstr. 1 Munich81679 Germany INAF - Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 Trieste34143 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 Trieste34131 Italy INFN - National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan International Gemini Observatory NSF NOIRLab Casilla 603 La Serena Chile Instituto de Física y Astronomía Universidad de Valparaíso Gran Bretaña 1111 Valparaíso Chile Chile Departamento de Astronomía Universidad de La Serena Av. Raúl Bitrán 1305 La Serena Chile Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics & Astronomy University of Wyoming 1000 E. University Dept. 3905 LaramieWY82071 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States 950 N. Cherry Ave TucsonAZ85732 United States Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria

Context. The SRG (Spectrum Roentgen Gamma)/eROSITA first public release contains 12,247 clusters and groups from the first 6 months of operation. We use the offset between the Brightest Cluster Galaxy (BCG) and the X–ray peak (DBCG−X) to classify the cluster dynamical state of 3,946 galaxy clusters and groups. Aims. We aim to investigate the evolution of the merger and relaxed cluster distributions with redshift and mass, and their impact on the BCG. Methods. We use the X–ray peak from the eROSITA survey and the BCG position from the DECaLS DR10 optical data, which includes the DECam eROSITA Survey optical data, to measure the DBCG−X offset. We model the distribution of DBCG−X, in units of r500, as the sum of two Rayleigh distributions representing the cluster’s relaxed and disturbed populations, and explore their evolution with redshift and mass. To explore the impact of the cluster’s dynamical state on the BCG luminosity, we separate the main sample according to the dynamical state. We define clusters as relaxed if DBCG−X500, disturbed if DBCG−X>0.5r500, and as ‘diverse’ if 0.25BCG−X Copyright © 2025, The Authors. All rights reserved.

关键词： Photons

Multi-thread concurrent compression algorithm for genomic big data 20

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Multi-thread concurrent compression algorithm for genomic bi...

20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Ji, Yimu Chen, Shuai Yao, Haichang Fang, Houzhi Li, Kui Liu, Shangdong Xie, Zhengyuan Wang, Kairui School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecom. School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9781728126166

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC. © 2019 IEEE.

关键词： big data

The impact of assembly history on the X-ray detectability of halos From galaxy groups to galaxy clusters

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arXiv 2025年

作者： Marini, I. Popesso, P. Dolag, K. Biffi, V. Vladutescu-Zopp, S. Castro, T. Toptun, V. de Isídio, N. Dev, A. Mazengo, D. Comparat, J. Gouin, C. Malavasi, N. Merloni, A. Mroczkowski, T. Ponti, G. Shreeram, S. Zhang, Y. European Southern Observatory Karl Schwarzschildstrasse 2 Garching bei München85748 Germany Excellence Cluster ORIGINS Boltzmannstr. 2 Garching bei MünchenD-85748 Germany Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr.1 München81679 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschildstr. 1 Garching bei München85741 Germany INAF Osservatorio Astronomico di Trieste Via Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 TriesteI-34014 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Sorbonne Université UMR7095 Institut d’Astrophysique de Paris 98 bis Boulevard Arago ParisF-75014 France Université Paris-Saclay CNRS Institut d’Astrophysique Spatiale Orsay91405 France International Centre for Radio Astronomy Research University of Western Australia M468 35 Stirling Highway PerthWA6009 Australia Department of Physics College of Natural and Mathematical Sciences University of Dodoma P.O. Box 338 Dodoma Tanzania United Republic of Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse 1 Garching85748 Germany INAF Osservatorio Astronomico di Brera Via E. Bianchi 46 LC Merate23807 Italy DiSAT Università degli Studi dell’Insubria via Valleggio 11 ComoI-22100 Italy

Galaxy groups represent a significant fraction of the halo population, playing a crucial role in galaxy formation and evolution. However, their detection in X-rays remains challenging, raising questions about the physical mechanisms driving their detectability in current surveys. Using the Magneticum simulations, we construct a mock X-ray lightcone of the local Universe (z 500 = 1013 M☉ can have hot gas fractions in the range fgas = 0.02 − 0.06 and a central SMBH with a median mass of MBH = 109 M☉ and a scatter of 0.5 dex) compatible with the most recent measurements of the baryonic fraction. These findings will aid the interpretation of future X-ray surveys, demonstrating the power of simulation-based inference. Copyright © 2025, The Authors. All rights reserved.

关键词： Black holes

KiDS-1000 and DES-Y1 combined: Cosmology from peak count statistics

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arXiv 2024年

作者： Harnois-Déraps, Joachim Heydenreich, Sven Giblin, Benjamin Martinet, Nicolas Tröster, Tilman Asgari, Marika Burger, Pierre Castro, Tiago Dolag, Klaus Heymans, Catherine Hildebrandt, Hendrik Joachimi, Benjamin Wright, Angus H. School of Mathematics Statistics and Physics Newcastle University Herschel Building Newcastle-upon-TyneNE1 7RU United Kingdom Department of Astronomy and Astrophysics University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Institute for Astronomy University of Edinburgh Blackford Hill EdinburghEH9 3HJ United Kingdom Université d’Aix-Marseille CNRS CNES LAM Marseille France Institute for Particle Physics and Astrophysics ETH Zürich Zürich8092 Switzerland E.A Milne Centre University of Hull Cottingham Road HullHU6 7RX United Kingdom University of Hull Cottingham Road Kingston-upon-HullHU6 7RX United Kingdom Waterloo Centre for Astrophysics University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Argelander-Institut für Astronomie Auf dem Hügel 71 Bonn53121 Germany INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy INFN - Sezione di Trieste Via Valerio 2 TS Trieste34100 Italy IFPU - Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Max-Planck-Institut fur Astrophysik Karl-Schwarzschild Strasse 1 Garching85748 Germany German Centre for Cosmological Lensing Bochum44780 Germany Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

We analyse the fourth data release of the Kilo Degree Survey (KiDS-1000) and extract cosmological parameter constraints based on the cosmic shear peak count statistics. Peaks are identified in aperture mass maps in which the filter is maximally sensitive to angular scales in the range 2-4 arcmin, probing deep into the non-linear regime of structure formation. We interpret our results with a simulation-based inference pipeline, sampling over a broad wCDM prior volume and marginalising over uncertainties on shape calibration, photometric redshift distribution, intrinsic alignment and baryonic feedback. Our measurements constrain the structure growth parameter and the amplitude of the non-linear intrinsic alignment model to Σ8 ≡ σ8 [Ωm/0.3]0.60 = 0.765+−00030030 and AIA = 0.71+−004242, respectively, in agreement with previous KiDS-1000 results based on two-point shear statistics. These results are robust against modelling of the non-linear physics, different scale cuts and selections of tomographic bins. The posterior is also consistent with that from the Dark Energy Survey Year-1 peak count analysis presented in Harnois-Déraps et al (2021), and hence we jointly analyse both surveys with a common pipeline. We obtain Σjoint8 ≡ σ8 [Ωm/0.3]0.57 = 0.759+−00020017, in agreement with the Planck wCDM results. The shear-CMB tension on this parameter increases to 3.1σ when forcing w = −1.0, and to 4.1σ if comparing instead with S8joint,ΛCDM = 0.736+−00016018, one of the tightest constraints to date on this quantity. Residual biases in the photometric redshifts of the DES-Y1 data and in the modelling of small scales physics could lower this tension, however it is robust against other systematics. Limits in the accuracy of our emulator prevent us from constraining Ωm © 2024, CC BY.

关键词： Dark energy

Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects

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arXiv 2023年

作者： de Santi, Natalí S.M. Villaescusa-Navarro, Francisco Abramo, L. Raul Shao, Helen Perez, Lucia A. Castro, Tiago Ni, Yueying Lovell, Christopher C. Hernández-Martínez, Elena Marinacci, Federico Spergel, David N. Dolag, Klaus Hernquist, Lars Vogelsberger, Mark Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Instituto de Física Universidade de São Paulo R. do Matão 1371 São Paulo05508-900 Brazil Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34143 Italy INFN Sezione di Trieste TriesteI-34100 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr. 1 München81679 Germany Department of Physics and Astronomy Augusto Righi University of Bologna via Gobetti 93/2 Bologna40129 Italy Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA United States Kavli Institute for Astrophysics and Space Research Department of Physics MIT CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA02139 United States

It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale. In particular, de Santi et al. [58] developed models that could accurately infer the value of Ωm from catalogs that only contain the positions and radial velocities of galaxies that are robust to different astrophysics and subgrid models. However, observations are affected by many effects, including 1) masking, 2) uncertainties in peculiar velocities and radial distances, and 3) different galaxy population selections. Moreover, observations only allow us to measure redshift, which entangles the galaxy radial positions and velocities. In this paper we train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project, that incorporate these observational effects. We find that while such effects degrade the precision and accuracy of the models, the fraction of galaxy catalogs for which the models retain high performance and robustness is over 90%, demonstrating the potential for applying them to real data. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

Cosmology with Galaxy Cluster Properties using Machine Learning

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arXiv 2023年

作者： Qiu, Lanlan Napolitano, Nicola R. Borgani, Stefano Zhong, Fucheng Li, Xiaodong Radovich, Mario Lin, Weipeng Dolag, Klaus Tortora, Crescenzo Wang, Yang Remus, Rhea-Silvia Wu, Sirui Longo, Giuseppe School of Physics and Astronomy Sun Yat-sen University Zhuhai Campus 2 Daxue Road Tangjia Zhuhai519082 China CSST Science Center for Guangdong-Hong Kong-Macau Great Bay Area Zhuhai519082 China Department of Physics E. Pancini University Federico II Via Cinthia 6 Naples80126-I Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy INAF-Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 TriesteI-34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing INAF - Osservatorio Astronomico di Padova via dell’Osservatorio 5 Padova35122 Italy Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr.1 München81679 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany INAF – Osservatorio Astronomico di Capodimonte Salita Moiariello 16 Napoli80131 Italy Peng Cheng Laboratory No.2 Xingke 1st Street Shenzhen518000 China

Context. Galaxy clusters are the largest gravitating structures in the universe and their mass assembly is sensitive to the underlying cosmology. Their mass function, baryon fraction, and mass distribution have been used to infer cosmological parameters, despite the presence of systematics. However, the complexity of the scaling relations among galaxy cluster properties has never been fully exploited, limiting their potential as a cosmological probe. Aims. We propose the first Machine Learning (ML) method using galaxy cluster properties from hydrodynamical simulations in different cosmologies to predict cosmological parameters combining a series of canonical cluster observables, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, and velocity dispersion at different redshifts. Methods. The machine learning model is trained on mock "measurements" of these observable quantities from Magneticum multi-cosmology simulations to derive unbiased constraints on a set of cosmological parameters. These include the mass density parameter, Ωm, the power spectrum normalization, σ8, the baryonic density parameter, Ωb, and the reduced Hubble constant, h0. Results. We test the ML model on catalogs of a few hundred clusters taken, in turn, from each simulation and find that the ML model can correctly predict the cosmology they have been picked from. The cumulative accuracy depends on the cosmology, ranging from 21% to 75%. We demonstrate that this is sufficient to derive unbiased constraints on the main cosmological parameters with errors of the order of ∼ 14% for Ωm, ∼ 8% for σ8, ∼ 6% for Ωb, and ∼ 3% for h0. Conclusions. This proof-of-concept analysis, yet based on a limited variety of multi-cosmology simulations, shows that machine learning can efficiently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a

关键词： Cosmology

Blockchain and IoT data Analytics for Fine-Grained Transportation Insurance 24

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Blockchain and IoT Data Analytics for Fine-Grained Transport...

24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018

作者： Li, Zengxiang Xiao, Zhe Xu, Quanqing Sotthiwat, Ekanut Mong Goh, Rick Siow Liang, Xueping Institute of High Performance Computing ASTAR Singapore Big Data Experience Center Computer Engineering Department King Mongkut's University of Technology Thonburi Thailand Old Dominion University NorfolkVA United States

ISBN: (纸本)9781538673089

Innovations such as the Cloud, Internet of Things (IoT), and data analytics have already dramatically altered the customer experience in many, if not all, industries. Blockchain, as another emerging technology, is expected to be the next generation infrastructure to established trusted multiparty collaborations. In this paper, we investigated the convergence of aforementioned technologies, by presenting a prototype of fine-grained transportation insurance. Insurance premium were assessed based on vehicle's usage and driver's behavior, which were deduced from streaming IoT data collected from mobile sensors. This incentive mechanism promotes fairness among drivers and encourages safer driving style. The prototype takes advantage of both private blockchain (e.g., high transaction rate in Hyperledger) and public blockchain (e.g., inbuilt cryptocurrency). Besides system architecture and implementation details, preliminary performance evaluations are presented and discussed. © 2018 IEEE.

关键词： Insurance

Euclid's Sensitivity to Neutrino Parameters 12

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Euclid's Sensitivity to Neutrino Parameters

12th Neutrino Oscillation Workshop, NOW 2024

作者： Archidiacono, M. Lesgourgues, J. Casas, S. Pamuk, S. Schöneberg, N. Sakr, Z. Parimbelli, G. Schneider, A. Hervas Peters, F. Pace, F. Sabarish, V.M. Costanzi, M. Camera, S. Carbone, C. Clesse, S. Frusciante, N. Fumagalli, A. Monaco, P. Scott, D. Viel, M. Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN-Sezione di Milano Via Celoria 16 Milano20133 Italy RWTH Aachen University Aachen52056 Germany CSIC Univ. de Cantabria Avda. de los Castros s/n SantanderE-39005 Spain Martí i Franquès 1 Barcelona08028 Spain Institut für Theoretische Physik University of Heidelberg Philosophenweg 16 Heidelberg69120 Germany Université de Toulouse CNRS UPS CNES 14 Av. Edouard Belin Toulouse31400 France Université St Joseph Faculty of Sciences Beirut Lebanon Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy Department of Astrophysics University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy INAF-Osservatorio Astrofisico di Torino Via Osservatorio 20 TO Pino Torinese10025 Italy Hamburger Sternwarte University of Hamburg Gojenbergsweg 112 Hamburg21029 Germany Dipartimento di Fisica Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Service de Physique Théorique CP225 Boulevard du Triophe Bruxelles1050 Belgium Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany INFN Sezione di Trieste Via Valerio 2 TS Tri

The European Space Agency launched its newest mission in July 2023: Euclid, which is designed to create the largest galaxy clustering and weak gravitational lensing survey to date. The complementarity of wide imaging and spectroscopy will provide excellent sensitivity to the history of structure formation, and hence to physics that affects that history. Here we present the latest forecasts of how Euclid's main cosmological probes will be able to constrain parameters from neutrino physics. Specifically we focus on the summed mass of neutrino species Í mv, as well as the effective number of additional relativistic species ΔNeff. We show how the forthcoming Euclid data should lead to unprecedented sensitivity for these parameters, and together with data from future cosmic microwave background experiments, could enable a detection of the neutrino mass scale. © Copyright owned by the author(s)

关键词： Cosmology

Constraining Mixed Dark Matter models with high redshift Lyman-alpha forest data

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arXiv 2025年

作者： Garcia-Gallego, Olga Iršič, Vid Haehnelt, Martin G. Viel, Matteo Bolton, James S. Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Center for Astrophysics Research Department of Physics Astronomy and Mathematics University of Hertfordshire College Lane HatfieldAL10 9AB United Kingdom SISSA- International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy INFN – National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom

This study sets new constraints on Cold+Warm Dark Matter (CWDM) models by leveraging the small-scale suppression of structure formation imprinted in the Lyman-α forest. Using the Sherwood-Relics suite, we extract high-fidelity flux power spectra from simulated Lyman-α forest data, spanning a broad range of cosmologies and thermal histories. This enables precise constraints on the warm dark matter (WDM) fraction, fWDM, and the mass of the WDM particle, mWDM. A key advancement of our analysis is the integration of a neural network emulator directly at the likelihood level, significantly accelerating Bayesian parameter inference. With new observations of high-redshift (z = 4.2−5.0) quasar spectra from UVES and HIRES, we establish stringent upper limits: for mWDM = 1 keV, we find fWDM WDM = 2, 3, and 4 keV, respectively. Our results for pure WDM reaffirm the lower bounds of previous work. Crucially, we account for the fixed resolution of simulations and the impact of patchy reionization, demonstrating their minimal influence on mixed dark matter constraints. This robustness paves the way for tighter bounds with improved statistical samples in the future. Our findings suggest that CWDM models can naturally accommodate mild suppression of matter clustering in the high redshift Lyman-α forest 1D flux power, potentially offering a resolution to some of the ongoing cosmological tensions at low redshifts, namely the S8 tension. © 2025, CC BY.

关键词： Dark Matter