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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Global Analysis of the Mammalian MHC Class I Immunopeptidome at the Organism-Wide Scale

SSRN

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SSRN 2020年

作者： Kubiniok, Peter Marcu, Ana Bichmann, Leon Kuchenbecker, Leon Schuster, Heiko Hamelin, David Despault, Jérome Kovalchik, Kevin Wessling, Laura Kohlbacher, Oliver Stevanovic, Stefan Rammensee, Hans-Georg Neidert, Marian C. Sirois, Isabelle Caron, Etienne CHU Sainte-Justine Research Center MontrealQCH3T 1C5 Canada Department of Immunology Interfaculty Institute for Cell Biology University of Tübingen TübingenBaden-Württemberg72076 Germany Image-Guided and Functionally Instructed Tumor Therapies University of Tübingen TübingenBaden-Württemberg72076 Germany Applied Bioinformatics Dept. of Computer Science University of Tübingen TübingenBaden-Württemberg72074 Germany Immatics Biotechnologies GmbH Tübingen Baden-Württemberg72076 Germany TübingenBaden-Württemberg72076 Germany Institute for Bioinformatics and Medical Informatics University of Tübingen TübingenBaden-Württemberg72076 Germany Biomolecular Interactions Max Planck Institute for Developmental Biology TübingenBaden-Württemberg72076 Germany University of Tübingen TübingenBaden-WürttembergPLZ Germany Translational Bioinformatics University Hospital Tübingen Tübingen TübingenBaden-Württemberg72076 Germany Clinical Neuroscience Center Department of Neurosurgery University Hospital and University of Zürich Zürich Zürich8057&8091 Switzerland Department of Pathology and Cellular Biology Faculty of Medicine Université de Montréal QCH3T 1J4 Canada

Understanding the molecular principles that govern the composition of the mammalian MHC-I immunopeptidome (MHC-Ii) across different primary tissues is fundamentally important to predict how T cell respond in different contexts in vivo. Here, we performed a global analysis of the mammalian MHC-Ii from 29 and 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, -B and -C allotypes do not contribute evenly to the global composition of the MHC-Ii across multiple human tissues. Second, w e found that peptides that are presented in a tissue-dependent and -independent manner share very distinct properties. Third, we discovered that proteins that were evolutionarily hyperconserved represent the primary source of the MHC-Ii at the organism-wide scale. Finally, we uncovered a remarkable antigen processing and presentation network that may drive the high level of heterogeneity of the MHC-Ii across different tissues in mammals. This study opens up new avenues toward a system-wide understanding of antigen presentation in vivo and may serve as ground work to understand tissue-dependent T cell responses in autoimmunity, infectious diseases and cancer. © 2020, The Authors. All rights reserved.

关键词： Mammals

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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Measurements of the Temperature and E-mode Polarization of the Cosmic Microwave Background from the Full 500-square-degree SPTpol Dataset

arXiv

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arXiv 2025年

作者： Chou, T.-L. Ade, P.A.R. Anderson, A.J. Austermann, J.E. Balkenhol, L. Beall, J.A. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Carlstrom, J.E. Chang, C.L. Chaubal, P. Chiang, H.C. Citron, R. Moran, C. Corbett Crawford, T.M. Crites, A.T. de Haan, T. Dobbs, M.A. Dutcher, D. Everett, W. Gallicchio, J. George, E.M. Gupta, N. Halverson, N.W. Holder, G.P. Holzapfel, W.L. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Knox, L. Lee, A.T. Li, D. Lowitz, A. McMahon, J.J. Montgomery, J. Natoli, T. Nibarger, J.P. Noble, G.I. Novosad, V. Omori, Y. Padin, S. Patil, S. Pryke, C. Quan, W. Reichardt, C.L. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Sievers, C. Smecher, G. Stark, A.A. Tucker, C. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Zebrowski, J.A. Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Cardiff University CardiffCF10 3XQ United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Sorbonne Universit’e CNRS UMR 7095 Institut d’Astrophysique de Paris 98 bis bd Arago Paris75014 France High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Jet Propulsion Laboratory PasadenaCA91109 United States Department of Physics Cornell University IthacaNY United States Department of Astronomy Cornell University IthacaNY United States Department of Physics University of California BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Ibaraki Tsukuba305-0801 Japan Canadian Institute for Advanced Research CIFAR Program in Gravity and the Extreme Universe TorontoONM5G 1Z8 Canada Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544

Using the full four-year SPTpol 500 deg2 dataset in both the 95 GHz and 150 GHz frequency bands, we present measurements of the temperature and E-mode polarization of the cosmic microwave background (CMB), as well as the E-mode polarization auto-power spectrum (EE) and temperature-E-mode cross-power spectrum (TE) in the angular multipole range 50 0 = 70.48 ± 2.16 km s−1 Mpc−1 and Ωm = 0.271 ± 0.026, when using only the SPTpol data and a Planck-based prior on the optical dept. to reionization. The ΛCDM parameter constraints are consistent across the 95 GHz-only, 150 GHz-only, TE-only, and EE-only data splits. Between the 1000 data splits, the ΛCDM parameter constraints are borderline consistent at the ∼ 2σ level. This consistency improves when including a parameter AL, the degree of lensing of the CMB inferred from the smearing of acoustic peaks. When marginalized over AL, the ΛCDM parameter constraints from SPTpol are consistent with those from Planck. The power spectra presented here are the most sensitive measurements of the lensed CMB damping tail to date for roughly > 1700 in TE and > 2000 in EE. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmology

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A low-cost, open-source, compliant hand for enabling sensorimotor control for people with transradial amputations

A low-cost, open-source, compliant hand for enabling sensori...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Aadeel Akhtar Kyung Yun Choi Michael Fatina Jesse Cornman Edward Wu Joseph Sombeck Chris Yim Patrick Slade Jason Lee Jack Moore Daniel Gonzales Alvin Wu Garrett Anderson David Rotter Cliff Shin Timothy Bretl Neuroscience Program and Medical Scholars Program Dept. of Aerospace Engineering Dept. of Electrical & Computer Engineering Dept. of Mechanical Science & Engineering Rehabilitation Counseling Program Center for Wounded Veterans Scheck & Siress Prosthetics Orthotics Chicago IL USA Industrial Design Program University of Illinois at Urbana-Champaign Urbana IL USA

In this paper, we describe the design and implementation of a low-cost, open-source prosthetic hand that enables both motor control and sensory feedback for people with transradial amputations. We integrate electromyographic pattern recognition for motor control along with contact reflexes and sensory substitution to provide feedback to the user. Compliant joints allow for robustness to impacts. The entire hand can be built for around $550. This low cost makes research and development of sensorimotor prosthetic hands more accessible to researchers worldwide, while also being affordable for people with amputations in developing nations. We evaluate the sensorimotor capabilities of our hand with a subject with a transradial amputation. We show that using contact reflexes and sensory substitution, when compared to standard myoelectric prostheses that lack these features, improves grasping of delicate objects like an eggshell and a cup of water both with and without visual feedback. Our hand is easily integrated into standard sockets, facilitating long-term testing of sensorimotor capabilities.

关键词： Visualization Grasping Electromyography Sockets Motor drives Pattern recognition Prosthetics

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Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Towards Automated Visual Monitoring of Individual Gorillas i...

引用

International Conference on computer Vision Workshops (ICCV Workshops)

作者： Clemens-Alexander Brust Tilo Burghardt Milou Groenenberg Christoph Kading Hjalmar S. Kühl Marie L. Manguette Joachim Denzler Computer Vision Group Friedrich Schiller University Jena Germany Dept. of Computer Science University of Bristol United Kingdom Wildlife Conservation Society Global Conservation Program USA Mbeli Bai Study Wildlife Conservation Society-Congo Program Republic of Congo Michael Stifel Center Jena Germany Max-Planck-Institut fur evolutionare Anthropologie Leipzig Sachsen DE Mbeli Bai Study Wildlife Conservation Soc.-Congo Program Congo German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Germany

In this paper we report on the context and evaluation of a system for an automatic interpretation of sightings of individual western lowland gorillas (Gorilla gorilla gorilla) as captured in facial field photography in the wild. This effort aligns with a growing need for effective and integrated monitoring approaches for assessing the status of biodiversity at high spatio-temporal scales. Manual field photography and the utilisation of autonomous camera traps have already transformed the way ecological surveys are conducted. In principle, many environments can now be monitored continuously, and with a higher spatio-temporal resolution than ever before. Yet, the manual effort required to process photographic data to derive relevant information delimits any large scale application of this methodology. The described system applies existing computer vision techniques including deep convolutional neural networks to cover the tasks of detection and localisation, as well as individual identification of gorillas in a practically relevant setup. We evaluate the approach on a relatively large and challenging data corpus of 12,765 field images of 147 individual gorillas with image-level labels (i.e. missing bounding boxes) photographed at Mbeli Bai at the Nouabal-Ndoki National Park, Republic of Congo. Results indicate a facial detection rate of 90.8% AP and an individual identification accuracy for ranking within the Top 5 set of 80.3%. We conclude that, whilst keeping the human in the loop is critical, this result is practically relevant as it exemplifies model transferability and has the potential to assist manual identification efforts. We argue further that there is significant need towards integrating computer vision deeper into ecological sampling methodologies and field practice to move the discipline forward and open up new research horizons.

关键词： Monitoring Biological system modeling Cameras Sociology Statistics Biodiversity

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Are Environmental Regulations Working? A Visual Analytic Approach To Answering Their Impact on Toxic Emissions 4

Are Environmental Regulations Working? A Visual Analytic App...

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4th Workshop on Visualisation in Environmental sciences, EnvirVis 2016 at EuroVis 2016

作者： Burlinson, David Koehrn, Kara Subramanian, Kalpathi Lu, Aidong Dept. of Computer Science The University of North Carolina CharlotteNC United States Environmental Protection Agency Office of Chemical Safety and Pollution Prevention/Toxics Release Inventory Program WashingtonDC United States

ISBN: (纸本)9783038680185

Acquisition and analysis of environmental data are essential to the quality, safety and health of human life, and typically consist of heterogeneous, multivariate, and spatio-temporal information collected from different resources. In the United States, the Environmental Protection Agency (EPA) is responsible for protecting human health and the environment;it does so by developing and enforcing regulations to monitor the environment and reduce pollution. In this work, our goal is to obtain a deeper understanding of the effectiveness and impact of EPA regulations;we present preliminary work on an interactive visualization system that attempts to answer specific questions as a function of the dimensions of interest. We demonstrate our system on toxic chemical release reductions resulting from the Clean Air Interstate Rule (CAIR), focusing on the electric utilities sector. We have received positive feedback from our domain expert (toxicologist at the US EPA), indicating that our initial results can more effectively communicate the impacts of environmental regulations, through providing strong visual correlation tools that are currently not available. © 2016 The Author(s) Eurographics Proceedings © 2016 The Eurographics Association.

关键词： Environmental Protection Agency

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Multiscale semi-markov dynamics for intracortical brain-computer interfaces 17

Multiscale semi-markov dynamics for intracortical brain-comp...

引用

Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Daniel J. Milstein Jason L. Pacheco Leigh R. Hochberg John D. Simeral Beata Jarosiewicz Erik B. Sudderth Department of Computer Science Brown University Providence RI Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA School of Engineering Brown University Providence RI and Department of Neurology Massachusetts General Hospital Boston MA and Rehabilitation R&D Service Department of Veterans Affairs Medical Center Providence RI and Brown Institute for Brain Science Brown University Providence RI Rehabilitation R&D Service Department of Veterans Affairs Medical Center Providence RI and Department of Neurology Harvard Medical School Boston MA and Dept. of Neurosurgery Stanford University Stanford CA Department of Computer Science Brown University Providence RI and Department of Neuroscience Brown University Providence RI

ISBN: (纸本)9781510860964

Intracortical brain-computer interfaces (iBCIs) have allowed people with tetraplegia to control a computer cursor by imagining the movement of their paralyzed arm or hand. State-of-the-art decoders deployed in human iBCIs are derived from a Kalman filter that assumes Markov dynamics on the angle of intended movement, and a unimodal dependence on intended angle for each channel of neural activity. Due to errors made in the decoding of noisy neural data, as a user attempts to move the cursor to a goal, the angle between cursor and goal positions may change rapidly. We propose a dynamic Bayesian network that includes the on-screen goal position as part of its latent state, and thus allows the person's intended angle of movement to be aggregated over a much longer history of neural activity. This multiscale model explicitly captures the relationship between instantaneous angles of motion and long-term goals, and incorporates semi-Markov dynamics for motion trajectories. We also introduce a multimodal likelihood model for recordings of neural populations which can be rapidly calibrated for clinical applications. In offline experiments with recorded neural data, we demonstrate significantly improved prediction of motion directions compared to the Kalman filter. We derive an efficient online inference algorithm, enabling a clinical trial participant with tetraplegia to control a computer cursor with neural activity in real time. The observed kinematics of cursor movement are objectively straighter and smoother than prior iBCI decoding models without loss of responsiveness.

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Performance analysis of accelerated biophysically-meaningful neuron simulations

Performance analysis of accelerated biophysically-meaningful...

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IEEE International Symposium on Performance Analysis of Systems and Software

作者： Georgios Smaragdos Georgios Chatzikostantis Sofia Nomikou Dimitrios Rodopoulos Ioannis Sourdis Dimitrios Soudris Chris I. De Zeeuw Christos Strydis Neuroscience dept. Erasmus Medical Center The Netherlands MicroLab National Technical University of Athens (NTUA) Greece Computer Science and Engineering dept. Chalmers University of Technology Sweden

In-vivo and in-vitro experiments are routinely used in neuroscience to unravel brain functionality. Although they are a powerful experimentation tool, they are also time-consuming and, often, restrictive. Computational neuroscience attempts to solve this by using biologically-plausible and biophysically-meaningful neuron models, most prominent among which are the conductance-based models. Their computational complexity calls for accelerator-based computing to mount large-scale or real-time neuroscientific experiments. In this paper, we analyze and draw conclusions on the class of conductance models by using a representative modeling application of the inferior olive (InfOli), an important part of the olivocerebellar brain circuit. We conduct an extensive profiling session to identify the computational and data-transfer requirements of the application under various realistic use cases. The application is, then, ported onto two acceleration nodes, an Intel Xeon Phi and a Maxeler Vectis Data Flow Engine (DFE). We evaluate the performance scalability and resource requirements of the InfOli application on the two target platforms. The analysis of InfOli, which is a real-life neuroscientific application, can serve as a useful guide for porting a wide range of similar workloads on platforms like the Xeon Phi or the Maxeler DFEs. As accelerators are increasingly populating High-Performance Computing (HPC) infrastructure, the current paper provides useful insight on how to optimally use such nodes to run complex and relevant neuron modeling workloads.

关键词： Biological system modeling Neurons Computational modeling Brain models Integrated circuit modeling Acceleration

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Simultaneous Millimeter-wave, Gamma-ray, and Optical Monitoring of the Blazar PKS 2326-502 During a Flaring State

arXiv

引用

arXiv 2023年

作者： Hood, J.C. Simpson, A. McDaniel, A. Foster, A. Ade, P.A.R. Ajello, M. Anderson, A.J. Austermann, J.E. Beall, J.A. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Carlstrom, J.E. Chang, C.L. Chaubal, P. Chiang, H.C. Chou, T.-L. Citron, R. Corbett Moran, C. Crawford, T.M. Crites, A.T. De Haan, T. Dobbs, M.A. Everett, W. Gallicchio, J. George, E.M. Gupta, N. Halverson, N.W. Hilton, G.C. Holder, G.P. Holzapfel, W.L. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Knox, L. Lee, A.T. Li, D. Lowitz, A. Madejski, G. Malkan, M. McMahon, J.J. Meyer, S.S. Montgomery, J. Natoli, T. Nibarger, J.P. Noble, G. Novosad, V. Omori, Y. Padin, S. Patil, S. Pryke, C. Reichardt, C.L. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Sievers, C. Smecher, G. Stark, A.A. Tucker, C. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Zebrowski, J.A. Zhang, L. 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 Department of Physics and Astronomy Vanderbilt University NashvilleTN37235 United States Department of Physics Rensselaer Polytechnic Institute TroyNY12180 United States Department of Physics and Astronomy Clemson University ClemsonSC29631 United States Department of Physics Case Western Reserve University ClevelandOH44106 United States Cardiff University CardiffCF10 3XQ United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Nist Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States School of Physics University of Melbourne VIC Parkville3010 Australia Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics McGill University 3600 Rue University QC MontrealH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Tapir Walter Burke Institute for Theoretical Physics California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St George St ON TorontoM5S 3H4 Canada Department of Astronomy & Astrophysics University of Toronto 50 St George St ON TorontoM5S 3H4 Canada Ibaraki Tsukuba305-0801 Japan Ibaraki Tsukuba305-0801 Japan Canadian Institute f

Including millimeter-wave (mm-wave) data in multi-wavelength studies of the variability of active galactic nuclei (AGN) can provide insights into AGN physics that are not easily accessible at other wavelengths. We demonstrate in this work the potential of cosmic microwave background (CMB) telescopes to provide long-term, high-cadence mm-wave AGN monitoring over large fractions of sky. We report on a pilot study using data from the SPTpol instrument on the South Pole Telescope (SPT), which was designed to observe the CMB at arcminute and larger angular scales. Between 2013 and 2016, SPTpol was used primarily to observe a single 500 deg2field, covering the entire field several times per day with detectors sensitive to radiation in bands centered at 95 and 150 GHz. We use SPT 150 GHz observations to create AGN light curves, and we compare these mm-wave light curves to those at other wavelengths, in particular γ-ray and optical. In this Letter, we focus on a single source, PKS 2326-502, which has extensive, day-timescale monitoring data in gamma-ray, optical, and now mm-wave between 2013 and 2016. We find PKS 2326-502 to be in a flaring state in the first two years of this monitoring, and we present a search for evidence of correlated variability between mm-wave, optical R band, and γ-ray observations. This pilot study is paving the way for AGN monitoring with current and upcoming CMB experiments such as SPT-3G, Simons Observatory, and CMB-S4, including multi-wavelength studies with facilities such as VRO-LSST. © 2023, CC BY.

关键词： Active Galactic Nuclei

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