TY - JOUR

T1 - Characterizing and Mitigating Intraday Variability: Reconstructing Source Structure in Accreting Black Holes with mm-VLBI

AU - Broderick, Avery E.

AU - Gold, Roman

AU - Georgiev, Boris

AU - Pesce, Dominic W.

AU - Tiede, Paul

AU - Ni, Chunchong

AU - Moriyama, Kotaro

AU - Akiyama, Kazunori

AU - Alberdi, Antxon

AU - Alef, Walter

AU - Algaba, Juan Carlos

AU - Anantua, Richard

AU - Asada, Keiichi

AU - Azulay, Rebecca

AU - Bach, Uwe

AU - Baczko, Anne-Kathrin

AU - Ball, David

AU - Baloković, Mislav

AU - Barrett, John

AU - Bauböck, Michi

AU - Benson, Bradford A.

AU - Bintley, Dan

AU - Blackburn, Lindy

AU - Blundell, Raymond

AU - Bouman, Katherine L.

AU - Bower, Geoffrey C.

AU - Boyce, Hope

AU - Bremer, Michael

AU - Brinkerink, Christiaan D.

AU - Brissenden, Roger

AU - Britzen, Silke

AU - Broguiere, Dominique

AU - Bronzwaer, Thomas

AU - Bustamante, Sandra

AU - Byun, Do-Young

AU - Carlstrom, John E.

AU - Ceccobello, Chiara

AU - Chael, Andrew

AU - Chan, Chi-kwan

AU - Chatterjee, Koushik

AU - Chatterjee, Shami

AU - Chen, Ming-Tang

AU - Chen, Yongjun

AU - Cheng, Xiaopeng

AU - Cho, Ilje

AU - Christian, Pierre

AU - Conroy, Nicholas S.

AU - Conway, John E.

AU - Cordes, James M.

AU - Crawford, Thomas M.

AU - Crew, Geoffrey B.

AU - Cruz-Osorio, Alejandro

AU - Cui, Yuzhu

AU - Davelaar, Jordy

AU - De Laurentis, Mariafelicia

AU - Deane, Roger

AU - Dempsey, Jessica

AU - Desvignes, Gregory

AU - Dexter, Jason

AU - Dhruv, Vedant

AU - Doeleman, Sheperd S.

AU - Dougal, Sean

AU - Dzib, Sergio A.

AU - Eatough, Ralph P.

AU - Emami, Razieh

AU - Falcke, Heino

AU - Farah, Joseph

AU - Fish, Vincent L.

AU - Fomalont, Ed

AU - Ford, H. Alyson

AU - Fraga-Encinas, Raquel

AU - Freeman, William T.

AU - Friberg, Per

AU - Fromm, Christian M.

AU - Fuentes, Antonio

AU - Galison, Peter

AU - Gammie, Charles F.

AU - García, Roberto

AU - Gentaz, Olivier

AU - Goddi, Ciriaco

AU - Gómez-Ruiz, Arturo I.

AU - Gómez, José L.

AU - Gu, Minfeng

AU - Gurwell, Mark

AU - Hada, Kazuhiro

AU - Haggard, Daryl

AU - Haworth, Kari

AU - Hecht, Michael H.

AU - Hesper, Ronald

AU - Heumann, Dirk

AU - Ho, Luis C.

AU - Ho, Paul

AU - Honma, Mareki

AU - Huang, Chih-Wei L.

AU - Huang, Lei

AU - Hughes, David H.

AU - Ikeda, Shiro

AU - Impellizzeri, C. M. Violette

AU - Inoue, Makoto

AU - Issaoun, Sara

AU - James, David J.

AU - Jannuzi, Buell T.

AU - Janssen, Michael

AU - Jeter, Britton

AU - Jiang, Wu

AU - Jiménez-Rosales, Alejandra

AU - Johnson, Michael D.

AU - Jorstad, Svetlana

AU - Joshi, Abhishek V.

AU - Jung, Taehyun

AU - Karami, Mansour

AU - Karuppusamy, Ramesh

AU - Kawashima, Tomohisa

AU - Keating, Garrett K.

AU - Kettenis, Mark

AU - Kim, Dong-Jin

AU - Kim, Jae-Young

AU - Kim, Jongsoo

AU - Kim, Junhan

AU - Kino, Motoki

AU - Koay, Jun Yi

AU - Kocherlakota, Prashant

AU - Kofuji, Yutaro

AU - Koch, Patrick M.

AU - Koyama, Shoko

AU - Kramer, Carsten

AU - Kramer, Michael

AU - Krichbaum, Thomas P.

AU - Kuo, Cheng-Yu

AU - Bella, Noemi La

AU - Lauer, Tod R.

AU - Lee, Daeyoung

AU - Lee, Sang-Sung

AU - Leung, Po Kin

AU - Levis, Aviad

AU - Li, Zhiyuan

AU - Lico, Rocco

AU - Lindahl, Greg

AU - Lindqvist, Michael

AU - Lisakov, Mikhail

AU - Liu, Jun

AU - Liu, Kuo

AU - Liuzzo, Elisabetta

AU - Lo, Wen-Ping

AU - Lobanov, Andrei P.

AU - Loinard, Laurent

AU - Lonsdale, Colin J.

AU - Lu, Ru-Sen

AU - Mao, Jirong

AU - Marchili, Nicola

AU - Markoff, Sera

AU - Marrone, Daniel P.

AU - Marscher, Alan P.

AU - Martí-Vidal, Iván

AU - Matsushita, Satoki

AU - Matthews, Lynn D.

AU - Menten, Karl M.

AU - Michalik, Daniel

AU - Mizuno, Izumi

AU - Mizuno, Yosuke

AU - Moran, James M.

AU - Moscibrodzka, Monika

AU - Müller, Cornelia

AU - Mus, Alejandro

AU - Musoke, Gibwa

AU - Myserlis, Ioannis

AU - Nadolski, Andrew

AU - Nagai, Hiroshi

AU - Nagar, Neil M.

AU - Nakamura, Masanori

AU - Narayan, Ramesh

AU - Narayanan, Gopal

AU - Natarajan, Iniyan

AU - Nathanail, Antonios

AU - Fuentes, Santiago Navarro

AU - Neilsen, Joey

AU - Neri, Roberto

AU - Noutsos, Aristeidis

AU - Nowak, Michael A.

AU - Oh, Junghwan

AU - Okino, Hiroki

AU - Olivares, Héctor

AU - Ortiz-León, Gisela N.

AU - Oyama, Tomoaki

AU - Palumbo, Daniel C. M.

AU - Paraschos, Georgios Filippos

AU - Park, Jongho

AU - Parsons, Harriet

AU - Patel, Nimesh

AU - Pen, Ue-Li

AU - Piétu, Vincent

AU - Plambeck, Richard

AU - PopStefanija, Aleksandar

AU - Porth, Oliver

AU - Pötzl, Felix M.

AU - Prather, Ben

AU - Preciado-López, Jorge A.

AU - Pu, Hung-Yi

AU - Ramakrishnan, Venkatessh

AU - Rao, Ramprasad

AU - Rawlings, Mark G.

AU - Raymond, Alexander W.

AU - Rezzolla, Luciano

AU - Ricarte, Angelo

AU - Ripperda, Bart

AU - Roelofs, Freek

AU - Rogers, Alan

AU - Ros, Eduardo

AU - Romero-Cañizales, Cristina

AU - Roshanineshat, Arash

AU - Rottmann, Helge

AU - Roy, Alan L.

AU - Ruiz, Ignacio

AU - Ruszczyk, Chet

AU - Rygl, Kazi L. J.

AU - Sánchez, Salvador

AU - Sánchez-Argüelles, David

AU - Sánchez-Portal, Miguel

AU - Sasada, Mahito

AU - Satapathy, Kaushik

AU - Savolainen, Tuomas

AU - Schloerb, F. Peter

AU - Schonfeld, Jonathan

AU - Schuster, Karl-Friedrich

AU - Shao, Lijing

AU - Shen, Zhiqiang

AU - Small, Des

AU - Sohn, Bong Won

AU - SooHoo, Jason

AU - Souccar, Kamal

AU - Sun, He

AU - Tazaki, Fumie

AU - Tetarenko, Alexandra J.

AU - Tilanus, Remo P. J.

AU - Titus, Michael

AU - Torne, Pablo

AU - Traianou, Efthalia

AU - Trent, Tyler

AU - Trippe, Sascha

AU - Turk, Matthew

AU - van Bemmel, Ilse

AU - van Langevelde, Huib Jan

AU - van Rossum, Daniel R.

AU - Vos, Jesse

AU - Wagner, Jan

AU - Ward-Thompson, Derek

AU - Wardle, John

AU - Weintroub, Jonathan

AU - Wex, Norbert

AU - Wharton, Robert

AU - Wielgus, Maciek

AU - Wiik, Kaj

AU - Witzel, Gunther

AU - Wondrak, Michael F.

AU - Wong, George N.

AU - Wu, Qingwen

AU - Yamaguchi, Paul

AU - Yoon, Doosoo

AU - Young, André

AU - Young, Ken

AU - Younsi, Ziri

AU - Yuan, Feng

AU - Yuan, Ye-Fei

AU - Zensus, J. Anton

AU - Zhao, Guang-Yao

AU - Zhang, Shuo

AU - Zhao, Shan-Shan

PY - 2022

Y1 - 2022

N2 - The extraordinary physical resolution afforded by the Event Horizon Telescope has opened a window onto the astrophysical phenomena unfolding on horizon scales in two known black holes, M87* and Sgr A*. However, with this leap in resolution has come a new set of practical complications. Sgr A* exhibits intraday variability that violates the assumptions underlying Earth aperture synthesis, limiting traditional image reconstruction methods to short timescales and data sets with very sparse (u, v) coverage. We present a new set of tools to detect and mitigate this variability. We develop a data-driven, model-agnostic procedure to detect and characterize the spatial structure of intraday variability. This method is calibrated against a large set of mock data sets, producing an empirical estimator of the spatial power spectrum of the brightness fluctuations. We present a novel Bayesian noise modeling algorithm that simultaneously reconstructs an average image and statistical measure of the fluctuations about it using a parameterized form for the excess variance in the complex visibilities not otherwise explained by the statistical errors. These methods are validated using a variety of simulated data, including general relativistic magnetohydrodynamic simulations appropriate for Sgr A* and M87*. We find that the reconstructed source structure and variability are robust to changes in the underlying image model. We apply these methods to the 2017 EHT observations of M87*, finding evidence for variability across the EHT observing campaign. The variability mitigation strategies presented are widely applicable to very long baseline interferometry observations of variable sources generally, for which they provide a data-informed averaging procedure and natural characterization of inter-epoch image consistency.

AB - The extraordinary physical resolution afforded by the Event Horizon Telescope has opened a window onto the astrophysical phenomena unfolding on horizon scales in two known black holes, M87* and Sgr A*. However, with this leap in resolution has come a new set of practical complications. Sgr A* exhibits intraday variability that violates the assumptions underlying Earth aperture synthesis, limiting traditional image reconstruction methods to short timescales and data sets with very sparse (u, v) coverage. We present a new set of tools to detect and mitigate this variability. We develop a data-driven, model-agnostic procedure to detect and characterize the spatial structure of intraday variability. This method is calibrated against a large set of mock data sets, producing an empirical estimator of the spatial power spectrum of the brightness fluctuations. We present a novel Bayesian noise modeling algorithm that simultaneously reconstructs an average image and statistical measure of the fluctuations about it using a parameterized form for the excess variance in the complex visibilities not otherwise explained by the statistical errors. These methods are validated using a variety of simulated data, including general relativistic magnetohydrodynamic simulations appropriate for Sgr A* and M87*. We find that the reconstructed source structure and variability are robust to changes in the underlying image model. We apply these methods to the 2017 EHT observations of M87*, finding evidence for variability across the EHT observing campaign. The variability mitigation strategies presented are widely applicable to very long baseline interferometry observations of variable sources generally, for which they provide a data-informed averaging procedure and natural characterization of inter-epoch image consistency.

U2 - 10.3847/2041-8213/ac6584

DO - 10.3847/2041-8213/ac6584

M3 - статья

VL - 930

SP - L21

JO - Astrophysical Journal Letters

JF - Astrophysical Journal Letters

SN - 2041-8205

IS - 2

ER -