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Book ; Online: Self-Adaptive Reconfigurable Arrays (SARA)

Samajdar, Ananda / Pellauer, Michael / Krishna, Tushar

Using ML to Assist Scaling GEMM Acceleration

2021

Abstract: With increasing diversity in Deep Neural Network(DNN) models in terms of layer shapes and sizes, the research community has been investigating flexible/reconfigurable accelerator substrates. This line of research has opened up two challenges. The first ... ...

Abstract	With increasing diversity in Deep Neural Network(DNN) models in terms of layer shapes and sizes, the research community has been investigating flexible/reconfigurable accelerator substrates. This line of research has opened up two challenges. The first is to determine the appropriate amount of flexibility within an accelerator array that that can trade-off the performance benefits versus the area overheads of the reconfigurability. The second is being able to determine the right configuration of the array for the current DNN model and/or layer and reconfigure the accelerator at runtime. This work introduces a new class of accelerators that we call Self Adaptive Reconfigurable Array (SARA). SARA architectures comprise of both a reconfigurable array and a hardware unit capable of determining an optimized configuration for the array at runtime. We demonstrate an instance of SARA with an accelerator we call SAGAR, which introduces a novel reconfigurable systolic array that can be configured to work as a distributed collection of smaller arrays of various sizes or as a single array with flexible aspect ratios. We also develop a novel recommendation neural network called ADAPTNET which recommends an array configuration and dataflow for the current layer parameters. ADAPTNET runs on an integrated custom hardware ADAPTNETX that runs ADAPTNET at runtime and reconfigures the array, making the entire accelerator self-sufficient. SAGAR is capable of providing the same mapping flexibility as a collection of 1024 4x4 arrays working as a distributed system while achieving 3.5x more power efficiency and 3.2x higher compute density Furthermore, the runtime achieved on the recommended parameters from ADAPTNET is 99.93% of the best achievable runtime.
Keywords	Computer Science - Hardware Architecture ; Computer Science - Machine Learning
Subject code	000
Publishing date	2021-01-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: CLAN

Mannan, Parth / Samajdar, Ananda / Krishna, Tushar

Continuous Learning using Asynchronous Neuroevolution on Commodity Edge Devices

2020

Abstract: Recent advancements in machine learning algorithms, especially the development of Deep Neural Networks (DNNs) have transformed the landscape of Artificial Intelligence (AI). With every passing day, deep learning based methods are applied to solve new ... ...

Abstract	Recent advancements in machine learning algorithms, especially the development of Deep Neural Networks (DNNs) have transformed the landscape of Artificial Intelligence (AI). With every passing day, deep learning based methods are applied to solve new problems with exceptional results. The portal to the real world is the edge. The true impact of AI can only be fully realized if we can have AI agents continuously interacting with the real world and solving everyday problems. Unfortunately, high compute and memory requirements of DNNs acts a huge barrier towards this vision. Today we circumvent this problem by deploying special purpose inference hardware on the edge while procuring trained models from the cloud. This approach, however, relies on constant interaction with the cloud for transmitting all the data, training on massive GPU clusters, and downloading updated models. This is challenging for bandwidth, privacy, and constant connectivity concerns that autonomous agents may exhibit. In this paper we evaluate techniques for enabling adaptive intelligence on edge devices with zero interaction with any high-end cloud/server. We build a prototype distributed system of Raspberry Pis communicating via WiFi running NeuroEvolutionary (NE) learning and inference. We evaluate the performance of such a collaborative system and detail the compute/communication characteristics of different arrangements of the system that trade-off parallelism versus communication. Using insights from our analysis, we also propose algorithmic modifications to reduce communication by up to 3.6x during the learning phase to enhance scalability even further and match performance of higher end computing devices at scale. We believe that these insights will enable algorithm-hardware co-design efforts for enabling continuous learning on the edge. Comment: Accepted and appears in ISPASS 2020
Keywords	Computer Science - Neural and Evolutionary Computing ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-08-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: AIRCHITECT

Samajdar, Ananda / Joseph, Jan Moritz / Denton, Matthew / Krishna, Tushar

Learning Custom Architecture Design and Mapping Space

2021

Abstract: Design space exploration is an important but costly step involved in the design/deployment of custom architectures to squeeze out maximum possible performance and energy efficiency. Conventionally, optimizations require iterative sampling of the design ... ...

Abstract	Design space exploration is an important but costly step involved in the design/deployment of custom architectures to squeeze out maximum possible performance and energy efficiency. Conventionally, optimizations require iterative sampling of the design space using simulation or heuristic tools. In this paper we investigate the possibility of learning the optimization task using machine learning and hence using the learnt model to predict optimal parameters for the design and mapping space of custom architectures, bypassing any exploration step. We use three case studies involving the optimal array design, SRAM buffer sizing, mapping, and schedule determination for systolic-array-based custom architecture design and mapping space. Within the purview of these case studies, we show that it is possible to capture the design space and train a model to "generalize" prediction the optimal design and mapping parameters when queried with workload and design constraints. We perform systematic design-aware and statistical analysis of the optimization space for our case studies and highlight the patterns in the design space. We formulate the architecture design and mapping as a machine learning problem that allows us to leverage existing ML models for training and inference. We design and train a custom network architecture called AIRCHITECT, which is capable of learning the architecture design space with as high as 94.3% test accuracy and predicting optimal configurations which achieve on average (GeoMean) of 99.9% the best possible performance on a test dataset with $10^5$ GEMM workloads.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Hardware Architecture
Subject code	720
Publishing date	2021-08-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: SCALE-Sim

Samajdar, Ananda / Zhu, Yuhao / Whatmough, Paul / Mattina, Matthew / Krishna, Tushar

Systolic CNN Accelerator Simulator

2018

Abstract: Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications. However, the research community lacks tools to insights on both ... ...

Abstract	Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications. However, the research community lacks tools to insights on both the design trade-offs and efficient mapping strategies for systolic-array based accelerators. We introduce Systolic CNN Accelerator Simulator (SCALE-Sim), which is a configurable systolic array based cycle accurate DNN accelerator simulator. SCALE-Sim exposes various micro-architectural features as well as system integration parameters to the designer to enable comprehensive design space exploration. This is the first systolic-array simulator tuned for running DNNs to the best of our knowledge. Using SCALE-Sim, we conduct a suite of case studies and demonstrate the effect of bandwidth, data flow and aspect ratio on the overall runtime and energy of Deep Learning kernels across vision, speech, text, and games. We believe that these insights will be highly beneficial to architects and ML practitioners.
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Hardware Architecture
Subject code	004
Publishing date	2018-10-16
Publishing country	us
Document type	Book ; Online
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Book ; Online: RASA

Jeong, Geonhwa / Qin, Eric / Samajdar, Ananda / Hughes, Christopher J. / Subramoney, Sreenivas / Kim, Hyesoon / Krishna, Tushar

Efficient Register-Aware Systolic Array Matrix Engine for CPU

2021

Abstract: As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency. Systolic arrays have been the premier architectural choice as matrix engines in offload accelerators. However, we ... ...

Abstract	As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency. Systolic arrays have been the premier architectural choice as matrix engines in offload accelerators. However, we demonstrate that incorporating them inside CPUs can introduce under-utilization and stalls due to limited register storage to amortize the fill and drain times of the array. To address this, we propose RASA, Register-Aware Systolic Array. We develop techniques to divide an execution stage into several sub-stages and overlap instructions to hide overheads and run them concurrently. RASA-based designs improve performance significantly with negligible area and power overhead. Comment: This paper is accepted to DAC 2021
Keywords	Computer Science - Hardware Architecture ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Publishing date	2021-10-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Second Data Release from the European Pulsar Timing Array: Challenging the Ultralight Dark Matter Paradigm.

Smarra, Clemente / Goncharov, Boris / Barausse, Enrico / Antoniadis, J / Babak, S / Nielsen, A-S Bak / Bassa, C G / Berthereau, A / Bonetti, M / Bortolas, E / Brook, P R / Burgay, M / Caballero, R N / Chalumeau, A / Champion, D J / Chanlaridis, S / Chen, S / Cognard, I / Desvignes, G /

Falxa, M / Ferdman, R D / Franchini, A / Gair, J R / Graikou, E / Grießmeier, J-M / Guillemot, L / Guo, Y J / Hu, H / Iraci, F / Izquierdo-Villalba, D / Jang, J / Jawor, J / Janssen, G H / Jessner, A / Karuppusamy, R / Keane, E F / Keith, M J / Kramer, M / Krishnakumar, M A / Lackeos, K / Lee, K J / Liu, K / Liu, Y / Lyne, A G / McKee, J W / Main, R A / Mickaliger, M B / Niţu, I C / Parthasarathy, A / Perera, B B P / Perrodin, D / Petiteau, A / Porayko, N K / Possenti, A / Leclere, H Quelquejay / Samajdar, A / Sanidas, S A / Sesana, A / Shaifullah, G / Speri, L / Spiewak, R / Stappers, B W / Susarla, S C / Theureau, G / Tiburzi, C / van der Wateren, E / Vecchio, A / Krishnan, V Venkatraman / Wang, J / Wang, L / Wu, Z

Physical review letters

2023 Volume 131, Issue 17, Page(s) 171001

Abstract: Pulsar Timing Array experiments probe the presence of possible scalar or pseudoscalar ultralight dark matter particles through decade-long timing of an ensemble of galactic millisecond radio pulsars. With the second data release of the European Pulsar ... ...

Abstract	Pulsar Timing Array experiments probe the presence of possible scalar or pseudoscalar ultralight dark matter particles through decade-long timing of an ensemble of galactic millisecond radio pulsars. With the second data release of the European Pulsar Timing Array, we focus on the most robust scenario, in which dark matter interacts only gravitationally with ordinary baryonic matter. Our results show that ultralight particles with masses 10^{-24.0} eV≲m≲10^{-23.3} eV cannot constitute 100% of the measured local dark matter density, but can have at most local density ρ≲0.3 GeV/cm^{3}.
Language	English
Publishing date	2023-11-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.131.171001
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Book ; Online: The second data release from the European Pulsar Timing Array II. Customised pulsar noise models for spatially correlated gravitational waves

Antoniadis, J. / Arumugam, P. / Arumugam, S. / Babak, S. / Bagchi, M. / Nielsen, A. S. Bak / Bassa, C. G. / Bathula, A. / Berthereau, A. / Bonetti, M. / Bortolas, E. / Brook, P. R. / Burgay, M. / Caballero, R. N. / Chalumeau, A. / Champion, D. J. / Chanlaridis, S. / Chen, S. / Cognard, I. /

Dandapat, S. / Deb, D. / Desai, S. / Desvignes, G. / Dhanda-Batra, N. / Dwivedi, C. / Falxa, M. / Ferdman, R. D. / Franchini, A. / Gair, J. R. / Goncharov, B. / Gopakumar, A. / Graikou, E. / Grießmeier, J. -M. / Guillemot, L. / Guo, Y. J. / Gupta, Y. / Hisano, S. / Hu, H. / Iraci, F. / Izquierdo-Villalba, D. / Jang, J. / Jawor, J. / Janssen, G. H. / Jessner, A. / Joshi, B. C. / Kareem, F. / Karuppusamy, R. / Keane, E. F. / Keith, M. J. / Kharbanda, D. / Kikunaga, T. / Kolhe, N. / Kramer, M. / Krishnakumar, M. A. / Lackeos, K. / Lee, K. J. / Liu, K. / Liu, Y. / Lyne, A. G. / McKee, J. W. / Maan, Y. / Main, R. A. / Mickaliger, M. B. / Niţu, I. C. / Nobleson, K. / Paladi, A. K. / Parthasarathy, A. / Perera, B. B. P. / Perrodin, D. / Petiteau, A. / Porayko, N. K. / Possenti, A. / Prabu, T. / Leclere, H. Quelquejay / Rana, P. / Samajdar, A. / Sanidas, S. A. / Sesana, A. / Shaifullah, G. / Singha, J. / Speri, L. / Spiewak, R. / Srivastava, A. / Stappers, B. W. / Surnis, M. / Susarla, S. C. / Susobhanan, A. / Takahashi, K. / Tarafdar, P. / Theureau, G. / Tiburzi, C. / van der Wateren, E. / Vecchio, A. / Krishnan, V. Venkatraman / Verbiest, J. P. W. / Wang, J. / Wang, L. / Wu, Z.

2023

Abstract: The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. ... ...

Abstract	The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar timing arrays, ensembles of extremely stable pulsars, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise. These must be carefully characterized to increase the sensitivity to the GWB. In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise and perform model selection. We also incorporate a new model to fit for scattering variations in the pulsar timing package temponest and created realistic simulations of the European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms. We present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian PTA collaboration. We use enterprise and temponest packages to compare noise models with those reported with the EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a more complicated way. We also find evidence of long-term scattering variations in PSR J1600$-$3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The results presented here directly help improve sensitivity to the GWB and are already being used as part of global PTA efforts. Comment: 20 pages, 6 figures, 9 tables
Keywords	Astrophysics - High Energy Astrophysical Phenomena ; Astrophysics - Instrumentation and Methods for Astrophysics
Subject code	612
Publishing date	2023-06-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Comparing recent PTA results on the nanohertz stochastic gravitational wave background

The International Pulsar Timing Array Collaboration / Agazie, G. / Antoniadis, J. / Anumarlapudi, A. / Archibald, A. M. / Arumugam, P. / Arumugam, S. / Arzoumanian, Z. / Askew, J. / Babak, S. / Bagchi, M. / Bailes, M. / Nielsen, A. -S. Bak / Baker, P. T. / Bassa, C. G. / Bathula, A. / Bécsy, B. / Berthereau, A. / Bhat, N. D. R. /

Blecha, L. / Bonetti, M. / Bortolas, E. / Brazier, A. / Brook, P. R. / Burgay, M. / Burke-Spolaor, S. / Burnette, R. / Caballero, R. N. / Cameron, A. / Case, R. / Chalumeau, A. / Champion, D. J. / Chanlaridis, S. / Charisi, M. / Chatterjee, S. / Chatziioannou, K. / Cheeseboro, B. D. / Chen, S. / Chen, Z. -C. / Cognard, I. / Cohen, T. / Coles, W. A. / Cordes, J. M. / Cornish, N. J. / Crawford, F. / Cromartie, H. T. / Crowter, K. / Curyło, M. / Cutler, C. J. / Dai, S. / Dandapat, S. / Deb, D. / DeCesar, M. E. / DeGan, D. / Demorest, P. B. / Deng, H. / Desai, S. / Desvignes, G. / Dey, L. / Dhanda-Batra, N. / Di Marco, V. / Dolch, T. / Drachler, B. / Dwivedi, C. / Ellis, J. A. / Falxa, M. / Feng, Y. / Ferdman, R. D. / Ferrara, E. C. / Fiore, W. / Fonseca, E. / Franchini, A. / Freedman, G. E. / Gair, J. R. / Garver-Daniels, N. / Gentile, P. A. / Gersbach, K. A. / Glaser, J. / Good, D. C. / Goncharov, B. / Gopakumar, A. / Graikou, E. / Grießmeier, J. -M. / Guillemot, L. / Gültekin, K. / Guo, Y. J. / Gupta, Y. / Grunthal, K. / Hazboun, J. S. / Hisano, S. / Hobbs, G. B. / Hourihane, S. / Hu, H. / Iraci, F. / Islo, K. / Izquierdo-Villalba, D. / Jang, J. / Jawor, J. / Janssen, G. H. / Jennings, R. J. / Jessner, A. / Johnson, A. D. / Jones, M. L. / Joshi, B. C. / Kaiser, A. R. / Kaplan, D. L. / Kapur, A. / Kareem, F. / Karuppusamy, R. / Keane, E. F. / Keith, M. J. / Kelley, L. Z. / Kerr, M. / Key, J. S. / Kharbanda, D. / Kikunaga, T. / Klein, T. C. / Kolhe, N. / Kramer, M. / Krishnakumar, M. A. / Kulkarni, A. / Laal, N. / Lackeos, K. / Lam, M. T. / Lamb, W. G. / Larsen, B. B. / Lazio, T. J. W. / Lee, K. J. / Levin, Y. / Lewandowska, N. / Littenberg, T. B. / Liu, K. / Liu, T. / Liu, Y. / Lommen, A. / Lorimer, D. R. / Lower, M. E. / Luo, J. / Luo, R. / Lynch, R. S. / Lyne, A. G. / Ma, C. -P. / Maan, Y. / Madison, D. R. / Main, R. A. / Manchester, R. N. / Mandow, R. / Mattson, M. A. / McEwen, A. / McKee, J. W. / McLaughlin, M. A. / McMann, N. / Meyers, B. W. / Meyers, P. M. / Mickaliger, M. B. / Miles, M. / Mingarelli, C. M. F. / Mitridate, A. / Natarajan, P. / Nathan, R. S. / Ng, C. / Nice, D. J. / Niţu, I. C. / Nobleson, K. / Ocker, S. K. / Olum, K. D. / Osłowski, S. / Paladi, A. K. / Parthasarathy, A. / Pennucci, T. T. / Perera, B. B. P. / Perrodin, D. / Petiteau, A. / Petrov, P. / Pol, N. S. / Porayko, N. K. / Possenti, A. / Prabu, T. / Leclere, H. Quelquejay / Radovan, H. A. / Rana, P. / Ransom, S. M. / Ray, P. S. / Reardon, D. J. / Rogers, A. F. / Romano, J. D. / Russell, C. J. / Samajdar, A. / Sanidas, S. A. / Sardesai, S. C. / Schmiedekamp, A. / Schmiedekamp, C. / Schmitz, K. / Schult, L. / Sesana, A. / Shaifullah, G. / Shannon, R. M. / Shapiro-Albert, B. J. / Siemens, X. / Simon, J. / Singha, J. / Siwek, M. S. / Speri, L. / Spiewak, R. / Srivastava, A. / Stairs, I. H. / Stappers, B. W. / Stinebring, D. R. / Stovall, K. / Sun, J. P. / Surnis, M. / Susarla, S. C. / Susobhanan, A. / Swiggum, J. K. / Takahashi, K. / Tarafdar, P. / Taylor, J. / Taylor, S. R. / Theureau, G. / Thrane, E. / Thyagarajan, N. / Tiburzi, C. / Toomey, L. / Turner, J. E. / Unal, C. / Vallisneri, M. / van der Wateren, E. / van Haasteren, R. / Vecchio, A. / Krishnan, V. Venkatraman / Verbiest, J. P. W. / Vigeland, S. J. / Wahl, H. M. / Wang, S. / Wang, Q. / Witt, C. A. / Wang, J. / Wang, L. / Wayt, K. E. / Wu, Z. / Young, O. / Zhang, L. / Zhang, S. / Zhu, X. -J. / Zic, A.

2023

Abstract: The Australian, Chinese, European, Indian, and North American pulsar timing array (PTA) collaborations recently reported, at varying levels, evidence for the presence of a nanohertz gravitational wave background (GWB). Given that each PTA made different ... ...

Abstract	The Australian, Chinese, European, Indian, and North American pulsar timing array (PTA) collaborations recently reported, at varying levels, evidence for the presence of a nanohertz gravitational wave background (GWB). Given that each PTA made different choices in modeling their data, we perform a comparison of the GWB and individual pulsar noise parameters across the results reported from the PTAs that constitute the International Pulsar Timing Array (IPTA). We show that despite making different modeling choices, there is no significant difference in the GWB parameters that are measured by the different PTAs, agreeing within $1\sigma$. The pulsar noise parameters are also consistent between different PTAs for the majority of the pulsars included in these analyses. We bridge the differences in modeling choices by adopting a standardized noise model for all pulsars and PTAs, finding that under this model there is a reduction in the tension in the pulsar noise parameters. As part of this reanalysis, we "extended" each PTA's data set by adding extra pulsars that were not timed by that PTA. Under these extensions, we find better constraints on the GWB amplitude and a higher signal-to-noise ratio for the Hellings and Downs correlations. These extensions serve as a prelude to the benefits offered by a full combination of data across all pulsars in the IPTA, i.e., the IPTA's Data Release 3, which will involve not just adding in additional pulsars, but also including data from all three PTAs where any given pulsar is timed by more than as single PTA. Comment: 21 pages, 9 figures, submitted to ApJ
Keywords	Astrophysics - High Energy Astrophysical Phenomena ; General Relativity and Quantum Cosmology
Subject code	551
Publishing date	2023-09-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Frequency-Dependent Squeezed Vacuum Source for the Advanced Virgo Gravitational-Wave Detector.

Acernese, F / Agathos, M / Ain, A / Albanesi, S / Alléné, C / Allocca, A / Amato, A / Amra, C / Andia, M / Andrade, T / Andres, N / Andrés-Carcasona, M / Andrić, T / Ansoldi, S / Antier, S / Apostolatos, T / Appavuravther, E Z / Arène, M / Arnaud, N /

Assiduo, M / Melo, S Assis de Souza / Astone, P / Aubin, F / Babak, S / Badaracco, F / Bagnasco, S / Baird, J / Baka, T / Ballardin, G / Baltus, G / Banerjee, B / Barneo, P / Barone, F / Barsuglia, M / Barta, D / Basti, A / Bawaj, M / Bazzan, M / Beirnaert, F / Bejger, M / Benedetto, V / Berbel, M / Bernuzzi, S / Bersanetti, D / Bertolini, A / Bhardwaj, U / Bianchi, A / Bilicki, M / Bini, S / Bischi, M / Bitossi, M / Bizouard, M-A / Bobba, F / Boër, M / Bogaert, G / Boileau, G / Boldrini, M / Bonavena, L D / Bondarescu, R / Bondu, F / Bonnand, R / Boschi, V / Boudart, V / Bouffanais, Y / Bozzi, A / Bradaschia, C / Braglia, M / Branchesi, M / Breschi, M / Briant, T / Brillet, A / Brooks, J / Bruno, G / Bucci, F / Bulashenko, O / Bulik, T / Bulten, H J / Buscicchio, R / Buskulic, D / Buy, C / Cabras, G / Cabrita, R / Cagnoli, G / Calloni, E / Canepa, M / Santoro, G Caneva / Cannavacciuolo, M / Capocasa, E / Carapella, G / Carbognani, F / Carpinelli, M / Carullo, G / Diaz, J Casanueva / Casentini, C / Caudill, S / Cavalieri, R / Cella, G / Cerdá-Durán, P / Cesarini, E / Chaibi, W / Chanial, P / Chassande-Mottin, E / Chaty, S / Chessa, P / Chiadini, F / Chiarini, G / Chierici, R / Chincarini, A / Chiofalo, M L / Chiummo, A / Christensen, N / Chua, S / Ciani, G / Ciecielag, P / Cieślar, M / Cifaldi, M / Ciolfi, R / Clesse, S / Cleva, F / Coccia, E / Codazzo, E / Cohadon, P-F / Colombo, A / Colpi, M / Conti, L / Cordero-Carrión, I / Corezzi, S / Cortese, S / Coulon, J-P / Coupechoux, J-F / Croquette, M / Cudell, J R / Cuoco, E / Curyło, M / Dabadie, P / Canton, T Dal / Dall'Osso, S / Dálya, G / D'Angelo, B / Dangoisse, G / Danilishin, S / D'Antonio, S / Dattilo, V / Davier, M / Degallaix, J / De Laurentis, M / Deléglise, S / De Lillo, F / Dell'Aquila, D / Del Pozzo, W / De Matteis, F / Depasse, A / De Pietri, R / De Rosa, R / De Rossi, C / De Simone, R / Di Fiore, L / Di Giorgio, C / Di Giovanni, F / Di Giovanni, M / Di Girolamo, T / Diksha, D / Di Lieto, A / Di Michele, A / Ding, J / Di Pace, S / Di Palma, I / Di Renzo, F / D'Onofrio, L / Dooney, T / Dorosh, O / Drago, M / Ducoin, J-G / Dupletsa, U / Durante, O / D'Urso, D / Duverne, P-A / Eisenmann, M / Errico, L / Estevez, D / Fabrizi, F / Faedi, F / Fafone, V / Favaro, G / Fays, M / Fenyvesi, E / Ferrante, I / Fidecaro, F / Figura, P / Fiori, A / Fiori, I / Fittipaldi, R / Fiumara, V / Flaminio, R / Font, J A / Frasca, S / Frasconi, F / Freise, A / Freitas, O / Fronzé, G G / Gadre, B / Gamba, R / Garaventa, B / Garcia-Bellido, J / Gargiulo, J / Garufi, F / Gasbarra, C / Gemme, G / Gennai, A / Ghosh, Archisman / Giacoppo, L / Giri, P / Gissi, F / Gkaitatzis, S / Glotin, F / Goncharov, B / Gosselin, M / Gouaty, R / Grado, A / Granata, M / Granata, V / Greco, G / Grignani, G / Grimaldi, A / Guerra, D / Guetta, D / Guidi, G M / Gulminelli, F / Guo, Y / Gupta, P / Gutierrez, N / Haegel, L / Halim, O / Hannuksela, O / Harder, T / Haris, K / Harmark, T / Harms, J / Haskell, B / Heidmann, A / Heitmann, H / Hello, P / Hemming, G / Hennes, E / Hennig, J-S / Hennig, M / Hild, S / Hofman, D / Holland, N A / Hui, V / Iandolo, G A / Idzkowski, B / Iess, A / Iorio, G / Iosif, P / Jacqmin, T / Jacquet, P-E / Janquart, J / Janssens, K / Jaraba, S / Jaranowski, P / Jasal, P / Juste, V / Kalaghatgi, C / Karathanasis, C / Katsanevas, S / Kéfélian, F / Koekoek, G / Koley, S / Kolstein, M / Kranzhoff, S L / Królak, A / Kuijer, P / Kuroyanagi, S / Lagabbe, P / Laghi, D / Lalleman, M / Lamberts, A / La Rana, A / La Rosa, I / Lartaux-Vollard, A / Lazzaro, C / Leaci, P / Lemaître, A / Lenti, M / Leonova, E / Lequime, M / Leroy, N / Letendre, N / Lethuillier, M / Leyde, K / Linde, F / London, L / Longo, A / Portilla, M Lopez / Lorenzini, M / Loriette, V / Losurdo, G / Lumaca, D / Macquet, A / Magazzù, C / Maggiore, R / Magnozzi, M / Majorana, E / Man, N / Mangano, V / Mantovani, M / Mapelli, M / Marchesoni, F / Pina, D Marín / Marion, F / Marquina, A / Marsat, S / Martelli, F / Martinez, M / Martinez, V / Masserot, A / Mastrodicasa, M / Mastrogiovanni, S / Meijer, Q / Menendez-Vazquez, A / Mereni, L / Merzougui, M / Miani, A / Michel, C / Miller, A / Miller, B / Milotti, E / Minenkov, Y / Mir, Ll M / Miravet-Tenés, M / Mitchell, A L / Mondal, C / Montani, M / Morawski, F / Morras, G / Moscatello, A / Mours, B / Mow-Lowry, C M / Msihid, E / Muciaccia, F / Mukherjee, Suvodip / Nagar, A / Napolano, V / Nardecchia, I / Narola, H / Naticchioni, L / Neilson, J / Nesseris, S / Nguyen, C / Nieradka, G / Nissanke, S / Nitoglia, E / Nocera, F / Novak, J / No Siles, J F Nu / Oertel, M / Oganesyan, G / Oliveri, R / Orselli, M / Palomba, C / Pang, P T H / Pannarale, F / Paoletti, F / Paoli, A / Paolone, A / Pappas, G / Parisi, A / Pascucci, D / Pasqualetti, A / Passaquieti, R / Passuello, D / Patricelli, B / Pedurand, R / Pegna, R / Pegoraro, M / Perego, A / Pereira, A / Périgois, C / Perreca, A / Perriès, S / Perry, J W / Pesios, D / Petrillo, C / Phukon, K S / Piccinni, O J / Pichot, M / Piendibene, M / Piergiovanni, F / Pierini, L / Pierra, G / Pierro, V / Pillant, G / Pillas, M / Pilo, F / Pinard, L / Pinto, I M / Pinto, M / Piotrzkowski, K / Placidi, A / Placidi, E / Plastino, W / Poggiani, R / Polini, E / Porcelli, E / Portell, J / Porter, E K / Poulton, R / Pracchia, M / Pradier, T / Principe, M / Prodi, G A / Prosposito, P / Puecher, A / Punturo, M / Puosi, F / Puppo, P / Raaijmakers, G / Radulesco, N / Rapagnani, P / Razzano, M / Regimbau, T / Rei, L / Rettegno, P / Revenu, B / Reza, A / Rezaei, A S / Ricci, F / Rinaldi, S / Robinet, F / Rocchi, A / Rolland, L / Romanelli, M / Romano, R / Romero, A / Ronchini, S / Rosa, L / Rosińska, D / Roy, S / Rozza, D / Ruggi, P / Morales, E Ruiz / Saffarieh, P / Salafia, O S / Salconi, L / Salemi, F / Sallé, M / Samajdar, A / Sanchis-Gual, N / Sanuy, A / Sasli, A / Sassi, P / Sassolas, B / Sayah, S / Schmidt, S / Seglar-Arroyo, M / Sentenac, D / Sequino, V / Servignat, G / Setyawati, Y / Shcheblanov, N S / Sieniawska, M / Silenzi, L / Singh, N / Singha, A / Sipala, V / Soldateschi, J / Sordini, V / Sorrentino, F / Sorrentino, N / Soulard, R / Spagnuolo, V / Spera, M / Spinicelli, P / Stachie, C / Steer, D A / Steinlechner, J / Steinlechner, S / Stergioulas, N / Stratta, G / Suchenek, M / Sur, A / Suresh, J / Swinkels, B L / Syx, A / Szewczyk, P / Tacca, M / Tamanini, N / Tanasijczuk, A J / Martín, E N Tapia San / Taranto, C / Tonelli, M / Torres-Forné, A / E Melo, I Tosta / Tournefier, E / Trapananti, A / Travasso, F / Trenado, J / Tringali, M C / Troiano, L / Trovato, A / Trozzo, L / Tsang, K W / Turbang, K / Turconi, M / Turski, C / Ubach, H / Utina, A / Valentini, M / Vallero, S / van Bakel, N / van Beuzekom, M / van Dael, M / van den Brand, J F J / Van Den Broeck, C / van der Sluys, M / Van de Walle, A / van Dongen, J / van Haevermaet, H / van Heijningen, J V / van Ranst, Z / van Remortel, N / Vardaro, M / Vasúth, M / Vedovato, G / Verdier, P / Verkindt, D / Verma, P / Vetrano, F / Viceré, A / Vinet, J-Y / Viret, S / Virtuoso, A / Vocca, H / Walet, R C / Was, M / Yadav, N / Zadrożny, A / Zelenova, T / Zendri, J-P / Zhao, Y / Zerrad, M / Vahlbruch, H / Mehmet, M / Lück, H / Danzmann, K

Physical review letters

2023 Volume 131, Issue 4, Page(s) 41403

Abstract: In this Letter, we present the design and performance of the frequency-dependent squeezed vacuum source that will be used for the broadband quantum noise reduction of the Advanced Virgo Plus gravitational-wave detector in the upcoming observation run. ... ...

Abstract	In this Letter, we present the design and performance of the frequency-dependent squeezed vacuum source that will be used for the broadband quantum noise reduction of the Advanced Virgo Plus gravitational-wave detector in the upcoming observation run. The frequency-dependent squeezed field is generated by a phase rotation of a frequency-independent squeezed state through a 285 m long, high-finesse, near-detuned optical resonator. With about 8.5 dB of generated squeezing, up to 5.6 dB of quantum noise suppression has been measured at high frequency while close to the filter cavity resonance frequency, the intracavity losses limit this value to about 2 dB. Frequency-dependent squeezing is produced with a rotation frequency stability of about 6 Hz rms, which is maintained over the long term. The achieved results fulfill the frequency dependent squeezed vacuum source requirements for Advanced Virgo Plus. With the current squeezing source, considering also the estimated squeezing degradation induced by the interferometer, we expect a reduction of the quantum shot noise and radiation pressure noise of up to 4.5 dB and 2 dB, respectively.
Language	English
Publishing date	2023-07-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.131.041403
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Quantum Backaction on kg-Scale Mirrors: Observation of Radiation Pressure Noise in the Advanced Virgo Detector.

Acernese, F / Agathos, M / Aiello, L / Ain, A / Allocca, A / Amato, A / Ansoldi, S / Antier, S / Arène, M / Arnaud, N / Ascenzi, S / Astone, P / Aubin, F / Babak, S / Badaracco, F / Bader, M K M / Bagnasco, S / Baird, J / Ballardin, G /

Baltus, G / Barbieri, C / Barneo, P / Barone, F / Barsuglia, M / Barta, D / Basti, A / Bawaj, M / Bazzan, M / Bejger, M / Belahcene, I / Bernuzzi, S / Bersanetti, D / Bertolini, A / Bischi, M / Bitossi, M / Bizouard, M-A / Blanch, O / Bobba, F / Boer, M / Bogaert, G / Boldrini, M / Bondu, F / Bonnand, R / Boom, B A / Boschi, V / Boudart, V / Bouffanais, Y / Bozzi, A / Bradaschia, C / Branchesi, M / Breschi, M / Briant, T / Brighenti, F / Brillet, A / Brooks, J / Bruno, G / Bulik, T / Bulten, H J / Buskulic, D / Cagnoli, G / Calloni, E / Canepa, M / Carapella, G / Carbognani, F / Carpinelli, M / Carullo, G / Diaz, J Casanueva / Casentini, C / Caudill, S / Cavalier, F / Cavalieri, R / Cella, G / Cerdá-Durán, P / Cesarini, E / Chaibi, W / Chanial, P / Chassande-Mottin, E / Chiadini, F / Chierici, R / Chincarini, A / Chiummo, A / Christensen, N / Chua, S / Ciani, G / Ciecielag, P / Cieślar, M / Cifaldi, M / Ciolfi, R / Cipriano, F / Cirone, A / Clesse, S / Cleva, F / Coccia, E / Cohadon, P-F / Cohen, D E / Colpi, M / Conti, L / Cordero-Carrión, I / Corezzi, S / Corre, D / Cortese, S / Coulon, J-P / Croquette, M / Cudell, J R / Cuoco, E / Curylo, M / D'Angelo, B / D'Antonio, S / D'Onofrio, L / D'Urso, D / Canton, T Dal / Dattilo, V / Davier, M / De Laurentis, M / De Matteis, F / De Pietri, R / De Rosa, R / De Rossi, C / Degallaix, J / Del Pozzo, W / Deléglise, S / Depasse, A / Di Fiore, L / Di Giorgio, C / Di Giovanni, F / Di Giovanni, M / Di Girolamo, T / Di Lieto, A / Di Pace, S / Di Palma, I / Di Renzo, F / Dietrich, T / Drago, M / Ducoin, J-G / Durante, O / Duverne, P-A / Eisenmann, M / Errico, L / Estevez, D / Fafone, V / Farinon, S / Fays, M / Feng, F / Fenyvesi, E / Ferrante, I / Fidecaro, F / Figura, P / Fiori, I / Fiorucci, D / Fittipaldi, R / Fiumara, V / Flaminio, R / Font, J A / Fournier, J-D / Frasca, S / Frasconi, F / Frey, V / Fronzé, G G / Gamba, R / Garaventa, B / Garufi, F / Gemme, G / Gennai, A / Ghosh, Archisman / Giacomazzo, B / Giacoppo, L / Giri, P / Gosselin, M / Gouaty, R / Grado, A / Granata, M / Granata, V / Greco, G / Grignani, G / Grimaldi, A / Grimm, S J / Gruning, P / Guidi, G M / Guixé, G / Guo, Y / Gupta, P / Haegel, L / Halim, O / Hannuksela, O / Harder, T / Haris, K / Harms, J / Heidmann, A / Heitmann, H / Hello, P / Hemming, G / Hennes, E / Hofman, D / Hui, V / Idzkowski, B / Iess, A / Intini, G / Jacqmin, T / Janssens, K / Jaranowski, P / Jonker, R J G / Karathanasis, C / Katsanevas, S / Kéfélian, F / Khan, I / Khetan, N / Koekoek, G / Koley, S / Kolstein, M / Królak, A / La Rosa, I / Laghi, D / Lamberts, A / Lartaux-Vollard, A / Lazzaro, C / Leaci, P / Leroy, N / Letendre, N / Linde, F / Llorens-Monteagudo, M / Longo, A / Lorenzini, M / Loriette, V / Losurdo, G / Lumaca, D / Macquet, A / Magazzù, C / Majorana, E / Maksimovic, I / Man, N / Mangano, V / Mantovani, M / Mapelli, M / Marchesoni, F / Marion, F / Marquina, A / Marsat, S / Martelli, F / Martinez, M / Martinez, V / Masserot, A / Mastrogiovanni, S / Menendez-Vazquez, A / Mereni, L / Merzougui, M / Metzdorff, R / Miani, A / Michel, C / Milano, L / Miller, A / Milotti, E / Minazzoli, O / Minenkov, Y / Mir, Ll M / Montani, M / Morawski, F / Mours, B / Muciaccia, F / Nagar, A / Nardecchia, I / Naticchioni, L / Neilson, J / Nelemans, G / Nguyen, C / Nissanke, S / Nocera, F / Oganesyan, G / Olivetto, C / Pagano, G / Pagliaroli, G / Palomba, C / Pang, T H / Pannarale, F / Paoletti, F / Paoli, A / Paolone, A / Pascucci, D / Pasqualetti, A / Passaquieti, R / Passuello, D / Patricelli, B / Pegoraro, M / Perego, A / Périgois, C / Perreca, A / Perriès, S / Phukon, K S / Piccinni, O J / Pichot, M / Piendibene, M / Piergiovanni, F / Pierini, L / Pierro, V / Pillant, G / Pilo, F / Pinard, L / Pinto, I M / Piotrzkowski, K / Placidi, E / Plastino, W / Poggiani, R / Polini, E / Popolizio, P / Porter, E K / Pracchia, M / Principe, M / Prodi, G A / Prosposito, P / Puecher, A / Punturo, M / Puosi, F / Puppo, P / Raaijmakers, G / Radulesco, N / Rapagnani, P / Razzano, M / Regimbau, T / Rei, L / Rettegno, P / Ricci, F / Riemenschneider, G / Robinet, F / Rocchi, A / Rolland, L / Romanelli, M / Romano, R / Romero, A / Ronchini, S / Rosińska, D / Ruggi, P / Salafia, O S / Salconi, L / Samajdar, A / Sanchis-Gual, N / Santos, E / Sassolas, B / Sauter, O / Sayah, S / Seglar-Arroyo, M / Sentenac, D / Sequino, V / Sharma, A / Sieniawska, M / Singh, N / Singhal, A / Sipala, V / Sordini, V / Sorrentino, F / Sorrentino, N / Soulard, R / Spera, M / Stachie, C / Steer, D A / Stratta, G / Sur, A / Swinkels, B L / Tacca, M / Tanasijczuk, A J / Martin, E N Tapia San / Tonelli, M / Torres-Forné, A / Tosta E Melo, I / Trapananti, A / Travasso, F / Tringali, M C / Trovato, A / Tsang, K W / Turconi, M / Valentini, M / van Bakel, N / van Beuzekom, M / van den Brand, J F J / Van Den Broeck, C / van der Schaaf, L / Vardaro, M / Vasúth, M / Vedovato, G / Verkindt, D / Vetrano, F / Viceré, A / Vinet, J-Y / Vocca, H / Walet, R C / Was, M / Zadrożny, A / Zelenova, T / Zendri, J-P / Mehmet, M / Vahlbruch, H / Lück, H / Danzmann, K

Physical review letters

2020 Volume 125, Issue 13, Page(s) 131101

Abstract: The quantum radiation pressure and the quantum shot noise in laser-interferometric gravitational wave detectors constitute a macroscopic manifestation of the Heisenberg inequality. If quantum shot noise can be easily observed, the observation of quantum ... ...

Abstract	The quantum radiation pressure and the quantum shot noise in laser-interferometric gravitational wave detectors constitute a macroscopic manifestation of the Heisenberg inequality. If quantum shot noise can be easily observed, the observation of quantum radiation pressure noise has been elusive, so far, due to the technical noise competing with quantum effects. Here, we discuss the evidence of quantum radiation pressure noise in the Advanced Virgo gravitational wave detector. In our experiment, we inject squeezed vacuum states of light into the interferometer in order to manipulate the quantum backaction on the 42 kg mirrors and observe the corresponding quantum noise driven displacement at frequencies between 30 and 70 Hz. The experimental data, obtained in various interferometer configurations, is tested against the Advanced Virgo detector quantum noise model which confirmed the measured magnitude of quantum radiation pressure noise.
Language	English
Publishing date	2020-10-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.125.131101
Database	MEDical Literature Analysis and Retrieval System OnLINE

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