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Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs

IEEE Network 2024年

作者： Chen, Xianfu Wu, Celimuge Shen, Yi Ji, Yusheng Yoshinaga, Tsutomu Ni, Qiang Zarakovitis, Charilaos C. Zhang, Honggang Shenzhen CyberAray Network Technology Co. Ltd China Chinese Academy of Sciences Shanghai Advanced Research Institute China Meta-Networking Research Center China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan Lancaster University School of Computing and Communications Ukraine AXON Logic Greece City University of Macau Faculty of Data Science Macau China China

This article investigates a control system within the context of sixth-generation wireless networks. The remote control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control sub-systems, asking for a co-design. Considering the system dynamics, we formulate the sequential co-design decision-makings of communication and control over a discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed. Our proposed framework integrates large language models into the elements of reinforcement learning. We present a case study on the age of semantics-aware communication and control co-design to showcase the potential of our proposed learning framework. Furthermore, we discuss the open issues remaining to make our offline learning framework feasible for real-world implementations and highlight the research directions for future explorations. © 1986-2012 IEEE.

关键词： Markov processes

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State-of-the-art TCAD: 25 years ago and today

State-of-the-art TCAD: 25 years ago and today

引用

International Electron Devices Meeting (IEDM)

作者： M. Stettler S. Cea S. Hasan L. Jiang A. Kaushik P. Keys R. Kotlyar C. Landon D. Pantuso A. Slepko S. Smith V. Tiwari C. Weber J. R. Weber Logic Technology Division Intel Corporation Hillsboro OR USA

In the past 25 years, process and device TCAD used in direct support of industrial process development has undergone radical change. In the era of Dennard scaling [1], TCAD efforts in manufacturers such as Intel could arguably be described as advanced applications work. However, with the advent of nanometer device dimensions, the need to calculate fundamental material properties on the fly, resolve quantum effects, and understand the role of atomic-scale defects has shifted TCAD from engineering towards research. Rigorous solutions to Schrodinger's equations based on NEGF and DFT and semi-classical solutions of the BTE are now in routine use. Concurrently, aging continuum models such as drift-diffusion continue to be infused with more rigorous approaches to maintain accuracy while still affording the fast turn-around-time required by today's development, which now involves a significant number of novel options under simultaneous consideration. This talk will contrast Intel's TCAD environment of 25 years ago with today's and give examples of studies which illustrate the evolution.

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Reduction of annotation efforts for multiclass object detection by using a domain awareness data combination strategy

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IOP Conference Series: Materials Science and Engineering 2021年第1期1140卷

作者： C. Briese B. Hummel V. Hoang M. Schlüter J. Krüger Fraunhofer IPK-Division of Automation Technology Pascalstraße 8-9 10587 Berlin Germany https://www.ipk.fraunhofer.de/logic Technische Universität Berlin-Division of Industrial Automation Technology Straße des 17. Juni 135 10623 Berlin Germany https://www.iat.tu-berlin.de/

To train convolutional neural networks (CNN) it is common practise to collect a huge amount of data. This is cost intensive and often not applicable. Up to date several studies have investigated the concept of few shoot learning, e.g. 1-3 samples per class. Suboptimal is still the over fitting resulting from the gap between training data and representative test data in the application. Since this is still a field of intensive research, an alternative and common approach is transfer learning with data- and image augmented pictures. However, collecting and labelling data for fine-tuning can still take an enormous amount of time, when it comes to multiclass pictures in industrial applications like assembly kit verification. The kits often contain stock lists with a small interclass and a high intraclass-distance. A specific characteristic of stock lists is that parts are easily adaptable and exchangeable. To bring object detection closer to the industry, we successfully show a dataset driven approach that combines a single class collection of pictures, which we call single class (SC) dataset and adapt with a few samples the specific multiclass use case. In result, we use a model trained on a huge SC dataset that can easily and fast be adapted to specific industrial use cases.

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Methane emissions from abandoned oil and gas wells: A case study in Oil creek State Park, Pennsylvania 112

Methane emissions from abandoned oil and gas wells: A case s...

引用

A and WMA''s 112th Annual Conference and Exhibition, ACE 2019

作者： Pekney, Natalie Reeder, Matt Sams, Jim DeWitt, Gabe Tinker, Andrew LRST PittsburghPA United States Key Logic PittsburghPA United States SSBC PittsburghPA United States National Energy Technology Laboratory Geological and Environmental Systems Division Geo-Analysis and Monitoring Team 626 Cochrans Mill Rd. MS 84-100A PittsburghPA15236 United States

This paper discusses a case study conducted in Oil Creek State Park, PA, the country's oldest oilfield, in order to locate abandoned wells and measure their methane emission rates. To locate wells with intact casing, an aerial magnetic survey of the park was completed in 2014. Measurements of methane emission rates were collected using high flow sampling, with other tools and techniques, e.g., optical gas imaging, laser-based methane concentration measurements, gas composition measurements, and carbon isotopic signatures, providing qualitative information for further characterize the emissions. Methane emissions measurements were collected at 137 wells, 70 of which were unplugged. Of the plugged wells, none registered measurable methane leaks. For the unplugged wells, 10 wells were leaking. Leaks ranged from 0.15-1.02 kg/day of methane. The average emission rate for unplugged wells in the park was 0.05 kg/well/day of methane.

关键词： Parks

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Risk-based decision making: estimands for sequential prediction under interventions

arXiv

引用

arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

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28NM high density SRAM BIT cell design and manufacture study

28NM high density SRAM BIT cell design and manufacture study

引用

China Semiconductor technology International Conference (CSTIC)

作者： Meili Hu Lijun Song Lei Wu Division of Design Technology Co-optimization SMIC ATD Shanghai China Division of Logic and Device Eng SMIC TD Shanghai China

SRAM is a basic component in logic circuit. To qualify 28nm technology, 0.127cm 2 high-density SRAM is chosen as a factor. To get good nominal and VMIN yield, some key processes need to focus on. For examples, Active Area formation, Gate dielectric process and MG (metal gate) boundary. This paper will give study result for these three key steps to improve SRAM yield.

关键词： Random access memory logic gates Dielectrics Silicon germanium Aerospace electronics Metals Sensitivity

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First Constraints on the Epoch of Reionization Using the Non-Gaussianity of the Kinematic Sunyaev-Zel’dovich Effect from the South Pole Telescope and Herschel-SPIRE Observations

引用

Physical Review Letters 2024年第12期133卷 121004页

作者： S. Raghunathan P. A. R. Ade A. J. Anderson B. Ansarinejad M. Archipley J. E. Austermann L. Balkenhol J. A. Beall K. Benabed A. N. Bender B. A. Benson F. Bianchini L. E. Bleem J. Bock F. R. Bouchet L. Bryant E. Camphuis J. E. Carlstrom T. W. Cecil C. L. Chang P. Chaubal H. C. Chiang P. M. Chichura T.-L. Chou R. Citron A. Coerver T. M. Crawford A. T. Crites A. Cukierman C. Daley K. R. Dibert M. A. Dobbs A. Doussot D. Dutcher W. Everett C. Feng K. R. Ferguson K. Fichman A. Foster S. Galli J. Gallicchio A. E. Gambrel R. W. Gardner F. Ge E. M. George N. Goeckner-Wald R. Gualtieri F. Guidi S. Guns N. Gupta T. de Haan N. W. Halverson E. Hivon G. P. Holder W. L. Holzapfel J. C. Hood J. D. Hrubes A. Hryciuk N. Huang J. Hubmayr K. D. Irwin F. Kéruzoré A. R. Khalife L. Knox M. Korman K. Kornoelje C.-L. Kuo A. T. Lee K. Levy D. Li A. E. Lowitz C. Lu A. Maniyar E. S. Martsen J. J. McMahon F. Menanteau M. Millea J. Montgomery C. Corbett Moran Y. Nakato T. Natoli J. P. Nibarger G. I. Noble V. Novosad Y. Omori S. Padin Z. Pan P. Paschos S. Patil K. A. Phadke K. Prabhu C. Pryke W. Quan M. Rahimi A. Rahlin C. L. Reichardt M. Rouble J. E. Ruhl B. R. Saliwanchik K. K. Schaffer E. Schiappucci C. Sievers G. Smecher J. A. Sobrin A. A. Stark J. Stephen A. Suzuki C. Tandoi K. L. Thompson B. Thorne C. Trendafilova C. Tucker C. Umilta T. Veach J. D. Vieira M. P. Viero Y. Wan G. Wang N. Whitehorn W. L. K. Wu V. Yefremenko M. R. Young J. A. Zebrowski M. Zemcov Center for AstroPhysical Surveys School of Physics and Astronomy Cardiff University Cardiff CF24 3YB United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA School of Physics University of Melbourne Parkville Victoria 3010 Australia Department of Astronomy University of Illinois Urbana-Champaign 1002 West Green Street Urbana Illinois 61801 USA NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut d’Astrophysique de Paris UMR 7095 CNRS and Sorbonne Université 98 bis boulevard Arago 75014 Paris France High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA California Institute of Technology Pasadena California 91125 USA Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Department of Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Department of Physics and McGill Space Institute McGill University 3600 Rue University Montreal Quebec H3A 2T8 Canada School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Durban South Africa University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Department of Physics

We report results from an analysis aimed at detecting the trispectrum of the kinematic Sunyaev-Zel’dovich (kSZ) effect by combining data from the South Pole Telescope (SPT) and Herschel-SPIRE experiments over a 100 deg2 field. The SPT observations combine data from the previous and current surveys, namely SPTpol and SPT-3G, to achieve depths of 4.5, 3, and 16 μK−arcmin in bands centered at 95, 150, and 220 GHz. For SPIRE, we include data from the 600 and 857 GHz bands. We reconstruct the velocity-induced large-scale correlation of the small-scale kSZ signal with a quadratic estimator that uses two cosmic microwave background (CMB) temperature maps, constructed by optimally combining data from all the frequency bands. We reject the null hypothesis of a zero trispectrum at 10.3σ level. However, the measured trispectrum contains contributions from both the kSZ and other undesired components, such as CMB lensing and astrophysical foregrounds, with kSZ being sub-dominant. We use the agora simulations to estimate the expected signal from CMB lensing and astrophysical foregrounds. After accounting for the contributions from CMB lensing and foreground signals, we do not detect an excess kSZ-only trispectrum and use this nondetection to set constraints on reionization. By applying a prior based on observations of the Gunn-Peterson trough, we obtain an upper limit on the duration of reionization of Δzre,50<4.5 (95% confidence level). We find these constraints are fairly robust to foregrounds assumptions. This trispectrum measurement is independent of, but consistent with, Planck’s optical depth measurement. This result is the first constraint on the epoch of reionization using the non-Gaussian nature of the kSZ signal.

关键词： Cosmic microwave background Evolution of the Universe Formation & evolution of stars & galaxies

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Monitoring Architecture for Fog and Mobile Cloud

Monitoring Architecture for Fog and Mobile Cloud

引用

International Symposium on Parallel and Distributed Computing

作者： Fabrice Mourlin Charif Mahmoudi Advanced Network Technology Division National Institute of Standards and Technology Gaithersburg MD USA Algorithmic Complexity and Logic Laboratory UPEC University Creteil France

Mobile devices in daily life have seen an increasingly steady use and they serve as essential tools for convenient communication. With the development of IoT and Fog, especially regarding the Smart City model, sensors are also finding themselves as ubiquitous as mobile devices. Mobile Cloud Computing becomes thus a key area that changes the game in terms of mobility of workspaces and the interaction with the connected devices and sensors. This paper aims to describe an architectural model for a monitoring system on a Cloudlet based system. Our work focuses on the data collection to supply a dynamic, distributed and real-time resource allocator dedicated to mobile complex systems, which is adapted to the new urban model. We present our proposed architecture description as well as its implementation. A case study on a security-critical system has been employed to show the efficiency of our Cloudlet approach.

关键词： Monitoring Cloud computing Sensors Computer architecture Tools Mobile handsets Wireless sensor networks

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Atomic-scale ferroic HfO2-ZrO2 superlattice gate stack for advanced transistors

Research Square

引用

Research Square 2021年

作者： Cheema, Suraj S. Shanker, Nirmaan Wang, Li-Chen Hsu, Cheng-Hsiang Hsu, Shang-Lin Liao, Yu-Hung Jose, Matthew San Gomez, Jorge Li, Wenshen Bae, Jong-Ho Volkman, Steve Kwon, Daewoong Rho, Yoonsoo Pinelli, Gianni Rastogi, Ravi Pipitone, Dominick Stull, Corey Cook, Matthew Tyrrell, Brian Stoica, Vladimir A. Zhang, Zhan Freeland, John W. Tassone, Christopher J. Mehta, Apurva Soheli, Ghazal Thompson, David Suh, Dong Ik Koo, Won-Tae Nam, Kab-Jin Jung, Dong Jin Song, Woo-Bin Lin, Chung-Hsun Nam, Seunggeol Heo, Jinseong Grigoropoulos, Costas P. Shafer, Padraic Fay, Patrick Ramesh, Ramamoorthy Ciston, Jim Datta, Suman Mohamed, Mohamed Hu, Chenming Salahuddin, Sayeef Department of Materials Science and Engineering University of California BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Electrical Engineering University of Notre Dame Notre DameIN United States Applied Science & Technology University of California BerkeleyCA United States Laser Thermal Laboratory Department of Mechanical Engineering University of California BerkeleyCA United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA United States Department of Materials Science and Engineering Pennsylvania State University University ParkPA United States Advanced Photon Source Argonne National Laboratory LemontIL United States Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo ParkCA United States Applied Materials Santa ClaraCA United States SK Hynix Inc. Gyeonggi-do Icheon17336 Korea Republic of Semiconductor R&D Center Samsung Electronics Gyeonggi-do 445-330 Korea Republic of Logic Technology Development Intel Corporation HillsboroOR97124 United States Samsung Advanced Institute of Technology Samsung Electronics Gyeonggi-do 445-330 Korea Republic of Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Physics University of California BerkeleyCA United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States

With the scaling of lateral dimensions in advanced transistors, an increased gate capacitance is desirable both to retain the control of the gate electrode over the channel and to reduce the operating voltage1. This led to the adoption of high-κ dielectric HfO2 in the gate stack in 20082, which remains as the material of choice to date. Here, we report HfO2-ZrO2 superlattice heterostructures as a gate stack, stabilized with mixed ferroelectric-antiferroelectric order, directly integrated onto Si transistors and scaled down to ∼ 20 Å, the same gate oxide thickness required for high performance transistors. The overall EOT (equivalent oxide thickness) in metal-oxide-semiconductor capacitors is equivalent to ∼ 6.5 Å effective SiO2 thickness, which is, counterintuitively, even smaller than the interfacial SiO2 thickness (8.0-8.5 Å) itself. Such a low effective oxide thickness and the resulting large capacitance cannot be achieved in conventional HfO2-based high-κ dielectric gate stacks without scavenging the interfacial SiO2, which has adverse effects on the electron transport and gate leakage current3. Accordingly, our gate stacks, which do not require such scavenging, provide substantially lower leakage current and no mobility degradation. Therefore, our work demonstrates that HfO2-ZrO2 multilayers with competing ferroelectric-antiferroelectric order, stabilized in the 2 nm thickness regime, provides a new path towards advanced gate oxide stacks in electronic devices beyond the conventional HfO2-based high-κ dielectrics. © 2021, CC BY.

关键词： Silica

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First Constraints on the Epoch of Reionization Using the non-Gaussianity of the Kinematic Sunyaev-Zel’dovich Effect from the South Pole Telescope and Herschel-SPIRE Observations

arXiv

引用

arXiv 2024年

作者： Raghunathan, S. Ade, P.A.R. Anderson, A.J. Ansarinejad, B. Archipley, M. Austermann, J.E. Balkenhol, L. Beall, J.A. Benabed, K. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Bock, J. Bouchet, F.R. Bryant, L. Camphuis, E. Carlstrom, J.E. Cecil, T.W. Chang, C.L. Chaubal, P. Chiang, H.C. Chichura, P.M. Chou, T.-L. Citron, R. Coerver, A. Crawford, T.M. Crites, A.T. Cukierman, A. Daley, C. Dibert, K.R. Dobbs, M.A. Doussot, A. Dutcher, D. Everett, W. Feng, C. Ferguson, K.R. Fichman, K. Foster, A. Galli, S. Gallicchio, J. Gambrel, A.E. Gardner, R.W. Ge, F. George, E.M. Goeckner-Wald, N. Gualtieri, R. Guidi, F. Guns, S. Gupta, N. de Haan, T. Halverson, N.W. Hivon, E. Holder, G.P. Holzapfel, W.L. Hood, J.C. Hrubes, J.D. Hryciuk, A. Huang, N. Hubmayr, J. Irwin, K.D. Kéruzoré, F. Khalife, A.R. Knox, L. Korman, M. Kornoelje, K. Kuo, C.-L. Lee, A.T. Levy, K. Li, D. Lowitz, A.E. Lu, C. Maniyar, A. Martsen, E.S. McMahon, J.J. Menanteau, F. Millea, M. Montgomery, J. Moran, C. Corbett Nakato, Y. Natoli, T. Nibarger, J.P. Noble, G.I. Novosad, V. Omori, Y. Padin, S. Pan, Z. Paschos, P. Patil, S. Phadke, K.A. Prabhu, K. Pryke, C. Quan, W. Rahimi, M. Rahlin, A. Reichardt, C.L. Rouble, M. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Schiappucci, E. Sievers, C. Smecher, G. Sobrin, J.A. Stark, A.A. Stephen, J. Suzuki, A. Tandoi, C. Thompson, K.L. Thorne, B. Trendafilova, C. Tucker, C. Umilta, C. Veach, T. Vieira, J.D. Viero, M.P. Wan, Y. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Young, M.R. Zebrowski, J.A. Zemcov, M. Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States School of Physics and Astronomy Cardiff University CardiffCF24 3YB United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Astronomy University of Illinois Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Institut d’Astrophysique de Paris UMR 7095 CNRS & Sorbonne Université 98 bis boulevard Arago Paris75014 France High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue. LemontIL60439 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics McGill Space Institute McGill University 3600 Rue University MontrealQCH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa Unive

We report results from an analysis aimed at detecting the trispectrum of the kinematic Sunyaev-Zel’dovich (kSZ) effect by combining data from the South Pole Telescope (SPT) and Herschel-SPIRE experiments over a 100 deg2 field. The SPT observations combine data from the previous and current surveys, namely SPTpol and SPT-3G, to achieve depths of 4.5, 3, and 16 µK-arcmin in bands centered at 95, 150, and 220 GHz. For SPIRE, we include data from the 600 and 857 GHz bands. We reconstruct the velocity-induced large-scale correlation of the small-scale kSZ signal with a quadratic estimator that uses two cosmic microwave background (CMB) temperature maps, constructed by optimally combining data from all the frequency bands. We reject the null hypothesis of a zero trispectrum at 10.3σ level. However, the measured trispectrum contains contributions from both the kSZ and other undesired components, such as CMB lensing and astrophysical foregrounds, with kSZ being sub-dominant. We use the Agora simulations to estimate the expected signal from CMB lensing and astrophysical foregrounds. After accounting for the contributions from CMB lensing and foreground signals, we do not detect an excess kSZ-only trispectrum and use this non-detection to set constraints on reionization. By applying a prior based on observations of the Gunn-Peterson trough, we obtain an upper limit on the duration of reionization of (formula presented). We find these constraints are fairly robust to foregrounds assumptions. This trispectrum measurement is independent of, but consistent with, Planck’s optical depth measurement. This result is the first constraint on the epoch of reionization using the non-Gaussian nature of the kSZ signal. © 2024, CC BY.

关键词： Kinematics

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