Dated:

# The Virgo O3 run and the impact of the environment

F Acernese1,2, M Agathos3, A Ain4, S Albanesi5,6, A Allocca7,2, A Amato8, T
Andrade9, N Andres10, M Andrés-Carcasona11, T Andrić12, S Ansoldi13,14, S
Antier15,16, T Apostolatos17, E Z Appavuravther18,19, M Arène20, N
Arnaud21,22, M Assiduo23,24, S Assis de Souza Melo22, P Astone25, F Aubin24, T
Avgitas26, S Babak20, F Badaracco27, M K M Bader28, S Bagnasco6, J Baird20, T
Baka29, G Ballardin22, G Baltus30, B Banerjee12, C Barbieri31,32,33, P
Barneo9, F Barone34,2, M Barsuglia20, D Barta35, A Basti36,4, M Bawaj18,37, M
Bazzan38,39, F Beirnaert40, M Bejger41, I Belahcene21, V Benedetto42, M
Berbel43, S Bernuzzi3, D Bersanetti44, A Bertolini28, U Bhardwaj16,28, A
Bianchi28,45, S Bini46,47, M Bischi23,24, M Bitossi22,4, M-A Bizouard15, F
Bobba48,49, M Boër15, G Bogaert15, M Boldrini50,25, L D Bonavena38, F Bondu51,
R Bonnand10, B A Boom28, V Boschi4, V Boudart30, Y Bouffanais38,39, A Bozzi22,
C Bradaschia4, M Branchesi12,52, M Breschi3, T Briant53, A Brillet15, J
Brooks22, G Bruno27, F Bucci24, T Bulik54, H J Bulten28, D Buskulic10, C
Buy55, G Cabras13,14, R Cabrita27, G Cagnoli8, E Calloni7,2, M Canepa56,44, S
Canevarolo29, M Cannavacciuolo48, E Capocasa20, G Carapella48,49, F
Carbognani22, E Caredda57, M Carpinelli58,59,22, G Carullo36,4, J Casanueva
Diaz22, C Casentini60,61, S Caudill28,29, F Cavalier21, R Cavalieri22, G
Cella4, P Cerdá-Durán62, E Cesarini61, W Chaibi15, P Chanial22, E Chassande-
Mottin20, S Chaty20, F Chiadini63,49, G Chiarini39, R Chierici26, A
Chincarini44, M L Chiofalo36,4, A Chiummo22, N Christensen15, G Ciani38,39, P
Ciecielag41, M Cieślar41, M Cifaldi60,61, R Ciolfi64,39, F Cipriano15, S
Clesse65, F Cleva15, E Coccia12,52, E Codazzo12, P-F Cohadon53, D E Cohen21, A
Colombo31,32, M Colpi31,32, L Conti39, I Cordero-Carrión66, S Corezzi37,18, D
Corre21, S Cortese22, M Coughlin67, J-P Coulon15, M Croquette53, J R Cudell30,
E Cuoco22,68,4, M Curyło54, P Dabadie8, T Dal Canton21, S Dall’Osso12, G
Dálya40, B D’Angelo56,44, S Danilishin69,28, S D’Antonio61, V Dattilo22, M
Davier21, J Degallaix70, M De Laurentis7,2, S Deléglise53, F De Lillo27, D
Dell’Aquila58, W Del Pozzo36,4, F De Matteis60,61, A Depasse27, R De
Pietri71,72, R De Rosa7,2, C De Rossi22, R De Simone63, L Di Fiore2, C Di
Giorgio48,49, F Di Giovanni62, M Di Giovanni12, T Di Girolamo7,2, A Di
Lieto36,4, A Di Michele37, S Di Pace50,25, I Di Palma50,25, F Di Renzo36,4, L
D’Onofrio7,2, M Drago50,25, J-G Ducoin21, U Dupletsa12, O Durante48,49, D
D’Urso58,59, P-A Duverne21, M Eisenmann10, L Errico7,2, D Estevez73, F
Fabrizi23,24, F Faedi24, V Fafone60,61,12, S Farinon44, G Favaro38, M Fays30,
E Fenyvesi35,74, I Ferrante36,4, F Fidecaro36,4, P Figura54, A Fiori4,36, I
Fiori22, R Fittipaldi75,49, V Fiumara76,49, R Flaminio10,77, J A Font62,78, S
Frasca50,25, F Frasconi4, A Freise28,45, O Freitas79, G G Fronzé6, R Gamba3, B
Garaventa44,56, F Garufi7,2, G Gemme44, A Gennai4, Archisman Ghosh40, B
Giacomazzo31,32,33, L Giacoppo50,25, P Giri4,36, F Gissi42, C Giunchi80, S
Gkaitatzis4,36, B Goncharov12, M Gosselin22, R Gouaty10, A Grado81,2, M
Granata70, V Granata48, G Greco18, G Grignani37,18, A Grimaldi46,47, S J
Grimm12,52, P Gruning21, D Guerra62, G M Guidi23,24, G Guixé9, Y Guo28, P
Gupta28,29, L Haegel20, O Halim14, O Hannuksela29,28, T Harder15, K
Haris28,29, J Harms12,52, B Haskell41, A Heidmann53, H Heitmann15, P Hello21,
G Hemming22, E Hennes28, S Hild69,28, D Hofman70, V Hui10, B Idzkowski54, A
Iess60,61, P Iosif82, T Jacqmin53, P-E Jacquet53, J Janquart29,28, K
Janssens83,15, P Jaranowski84, V Juste73, C Kalaghatgi29,28,85, C
Karathanasis11, S Katsanevas22, F Kéfélian15, N Khetan12,52, G Koekoek28,69, S
Koley12, M Kolstein11, A Królak86,87, P Kuijer28, P Lagabbe10, D Laghi55, M
Lalleman83, A Lamberts15,88, I La Rosa10, A Lartaux-Vollard21, C Lazzaro38,39,
P Leaci50,25, A Lemaître89, M Lenti24,90, E Leonova16, N Leroy21, N
Letendre10, K Leyde20, F Linde85,28, L London16, A Longo91, M Lopez
Portilla29, M Lorenzini60,61, V Loriette92, G Losurdo4, D Lumaca60,61, A
Macquet15, C Magazzù4, M Magnozzi44,56, E Majorana50,25, I Maksimovic92, N
Man15, V Mangano50,25, M Mantovani22, M Mapelli38,39, F Marchesoni19,18,93, D
Marín Pina9, F Marion10, A Marquina66, S Marsat20, J Marteau26, F
Martelli23,24, M Martinez11, V Martinez8, A Masserot10, S Mastrogiovanni20, Q
Meijer29, A Menendez-Vazquez11, L Mereni70, M Merzougui15, A Miani46,47, C
Michel70, L Milano∗7, A Miller27, B Miller16,28, E Milotti94,14, Y Minenkov61,
Ll M Mir11, M Miravet-Tenés62, M Montani23,24, F Morawski41, B Mours73, C M
Mow-Lowry28,45, F Muciaccia50,25, Suvodip Mukherjee16, N Mukund95,96, R
Musenich44,56, A Nagar6,97, V Napolano22, I Nardecchia60,61, H Narola29, L
Naticchioni25, J Neilson42,49, C Nguyen20, S Nissanke16,28, E Nitoglia26, F
Nocera22, G Oganesyan12,52, C Olivetto22, M Olivieri98, G Pagano36,4, G
Pagliaroli12,52, C Palomba25, P T H Pang28,29, F Pannarale50,25, F Paoletti4,
A Paoli22, A Paolone25,99, G Pappas82, D Pascucci28,40, A Pasqualetti22, R
Passaquieti36,4, D Passuello4, B Patricelli22,4, R Pedurand49, M Pegoraro39, A
Perego46,47, A Pereira8, C Périgois10, A Perreca46,47, S Perriès26, D
Pesios82, K S Phukon28,85, O J Piccinni25, M Pichot15, M Piendibene36,4, F
Piergiovanni23,24, L Pierini50,25, V Pierro42,49, G Pillant22, M Pillas21, F
Pilo4, L Pinard70, I M Pinto42,49,100, M Pinto22, K Piotrzkowski27, A
Placidi18,37, E Placidi50,25, W Plastino101,91, R Poggiani36,4, E Polini10, E
K Porter20, R Poulton22, M Pracchia10, T Pradier73, M Principe42,100,49, G A
Prodi102,47, P Prosposito60,61, A Puecher28,29, M Punturo18, F Puosi4,36, P
Puppo25, G Raaijmakers16,28, N Radulesco15, P Rapagnani50,25, M Razzano36,4, T
Regimbau10, L Rei44, P Rettegno5,6, B Revenu20, A Reza28, F Ricci50,25, G
Riemenschneider5,6, S Rinaldi36,4, F Robinet21, A Rocchi61, L Rolland10, M
Romanelli51, R Romano1,2, A Romero11, S Ronchini12,52, L Rosa2,7, D
Rosińska54, S Roy29, D Rozza58,59, P Ruggi22, O S Salafia33,32,31, L
Salconi22, F Salemi46,47, A Samajdar32, N Sanchis-Gual103, A Sanuy9, B
Sassolas70, S Sayah70, S Schmidt29, M Seglar-Arroyo10, D Sentenac22, V
Sequino7,2, Y Setyawati29, A Sharma12,52, N S Shcheblanov89, M Sieniawska27, L
Silenzi18,19, N Singh54, A Singha69,28, V Sipala58,59, J Soldateschi90,104,24,
V Sordini26, F Sorrentino44, N Sorrentino36,4, R Soulard15, V Spagnuolo69,28,
M Spera38,39, P Spinicelli22, C Stachie15, D A Steer20, J Steinlechner69,28, S
Steinlechner69,28, N Stergioulas82, G Stratta105,25, M Suchenek41, A Sur41, B
L Swinkels28, P Szewczyk54, M Tacca28, A J Tanasijczuk27, E N Tapia San
Martín28, C Taranto60, K Thorne106, M Tonelli36,4, A Torres-Forné62, I Tosta e
Melo59, A Trapananti19,18, F Travasso18,19, M C Tringali22, L Troiano107,49, A
Trovato20, L Trozzo2, K W Tsang28,108,29, K Turbang109,83, M Turconi15, A
Utina69,28, M Valentini46,47, N van Bakel28, M van Beuzekom28, M van
Dael28,110, J F J van den Brand69,45,28, C Van Den Broeck29,28, H van
Haevermaet83, J V van Heijningen27, N van Remortel83, M Vardaro85,28, M
Vasúth35, G Vedovato39, D Verkindt10, P Verma87, F Vetrano23, A Viceré23,24,
J-Y Vinet15, A Virtuoso94,14, H Vocca37,18, R C Walet28, M Was10, A
Zadrożny87, T Zelenova22, J-P Zendri39 ${}{\ast}$Deceased, April 2021.
1Dipartimento di Farmacia, Università di Salerno, I-84084 Fisciano, Salerno,
Italy  2INFN, Sezione di Napoli, Complesso Universitario di Monte S. Angelo,
I-80126 Napoli, Italy  3Theoretisch-Physikalisches Institut, Friedrich-
Schiller-Universität Jena, D-07743 Jena, Germany  4INFN, Sezione di Pisa,
I-56127 Pisa, Italy  5Dipartimento di Fisica, Università degli Studi di
Torino, I-10125 Torino, Italy  6INFN Sezione di Torino, I-10125 Torino, Italy
7Università di Napoli “Federico II”, Complesso Universitario di Monte S.
Angelo, I-80126 Napoli, Italy  8Université de Lyon, Université Claude Bernard
Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France
9Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, C/ Martí i
Franquès 1, Barcelona, 08028, Spain  10Univ. Savoie Mont Blanc, CNRS,
Laboratoire d’Annecy de Physique des Particules - IN2P3, F-74000 Annecy,
France  11Institut de Física d’Altes Energies (IFAE), Barcelona Institute of
Science and Technology, and ICREA, E-08193 Barcelona, Spain  12Gran Sasso
Science Institute (GSSI), I-67100 L’Aquila, Italy  13Dipartimento di Scienze
Matematiche, Informatiche e Fisiche, Università di Udine, I-33100 Udine, Italy
14INFN, Sezione di Trieste, I-34127 Trieste, Italy  15Artemis, Université Côte
d’Azur, Observatoire de la Côte d’Azur, CNRS, F-06304 Nice, France  16GRAPPA,
Anton Pannekoek Institute for Astronomy and Institute for High-Energy Physics,
University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
17National and Kapodistrian University of Athens, School of Science Building,
2nd floor, Panepistimiopolis, 15771 Ilissia, Greece  18INFN, Sezione di
Perugia, I-06123 Perugia, Italy  19Università di Camerino, Dipartimento di
Fisica, I-62032 Camerino, Italy  20Université de Paris, CNRS, Astroparticule
et Cosmologie, F-75006 Paris, France  21Université Paris-Saclay, CNRS/IN2P3,
IJCLab, 91405 Orsay, France  22European Gravitational Observatory (EGO),
I-56021 Cascina, Pisa, Italy  23Università degli Studi di Urbino “Carlo Bo”,
I-61029 Urbino, Italy  24INFN, Sezione di Firenze, I-50019 Sesto Fiorentino,
Firenze, Italy  25INFN, Sezione di Roma, I-00185 Roma, Italy  26Université
Lyon, Université Claude Bernard Lyon 1, CNRS, IP2I Lyon / IN2P3, UMR 5822,
F-69622 Villeurbanne, France  27Université catholique de Louvain, B-1348
Louvain-la-Neuve, Belgium  28Nikhef, Science Park 105, 1098 XG Amsterdam,
Netherlands  29Institute for Gravitational and Subatomic Physics (GRASP),
Utrecht University, Princetonplein 1, 3584 CC Utrecht, Netherlands
30Université de Liège, B-4000 Liège, Belgium  31Università degli Studi di
Milano-Bicocca, I-20126 Milano, Italy  32INFN, Sezione di Milano-Bicocca,
I-20126 Milano, Italy  33INAF, Osservatorio Astronomico di Brera sede di
Merate, I-23807 Merate, Lecco, Italy  34Dipartimento di Medicina, Chirurgia e
Odontoiatria “Scuola Medica Salernitana”, Università di Salerno, I-84081
Baronissi, Salerno, Italy  35Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege
Miklós út 29-33, Hungary  36Università di Pisa, I-56127 Pisa, Italy
37Università di Perugia, I-06123 Perugia, Italy  38Università di Padova,
Dipartimento di Fisica e Astronomia, I-35131 Padova, Italy  39INFN, Sezione di
Padova, I-35131 Padova, Italy  40Universiteit Gent, B-9000 Gent, Belgium
41Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, 00-716,
Warsaw, Poland  42Dipartimento di Ingegneria, Università del Sannio, I-82100
Benevento, Italy  43Departamento de Matemáticas, Universitat Autònoma de
Barcelona, Edificio C Facultad de Ciencias 08193 Bellaterra (Barcelona), Spain
44INFN, Sezione di Genova, I-16146 Genova, Italy  45Vrije Universiteit
Amsterdam, 1081 HV Amsterdam, Netherlands  46Università di Trento,
Dipartimento di Fisica, I-38123 Povo, Trento, Italy  47INFN, Trento Institute
for Fundamental Physics and Applications, I-38123 Povo, Trento, Italy
48Dipartimento di Fisica “E.R. Caianiello”, Università di Salerno, I-84084
Fisciano, Salerno, Italy  49INFN, Sezione di Napoli, Gruppo Collegato di
Salerno, Complesso Universitario di Monte S. Angelo, I-80126 Napoli, Italy
50Università di Roma “La Sapienza”, I-00185 Roma, Italy  51Univ Rennes, CNRS,
Institut FOTON - UMR6082, F-3500 Rennes, France  52INFN, Laboratori Nazionali
del Gran Sasso, I-67100 Assergi, Italy  53Laboratoire Kastler Brossel,
Sorbonne Université, CNRS, ENS-Université PSL, Collège de France, F-75005
Paris, France  54Astronomical Observatory Warsaw University, 00-478 Warsaw,
Poland  55L2IT, Laboratoire des 2 Infinis - Toulouse, Université de Toulouse,
CNRS/IN2P3, UPS, F-31062 Toulouse Cedex 9, France  56Dipartimento di Fisica,
Università degli Studi di Genova, I-16146 Genova, Italy  57Università di
Bologna, Dipartimento di Fisica e Astronomia, I-40127 Bologna, Italy
58Università degli Studi di Sassari, I-07100 Sassari, Italy  59INFN,
Laboratori Nazionali del Sud, I-95125 Catania, Italy  60Università di Roma Tor
Vergata, I-00133 Roma, Italy  61INFN, Sezione di Roma Tor Vergata, I-00133
Roma, Italy  62Departamento de Astronomía y Astrofísica, Universitat de
València, E-46100 Burjassot, València, Spain  63Dipartimento di Ingegneria
Industriale (DIIN), Università di Salerno, I-84084 Fisciano, Salerno, Italy
64INAF, Osservatorio Astronomico di Padova, I-35122 Padova, Italy
65Université libre de Bruxelles, Avenue Franklin Roosevelt 50 - 1050
Bruxelles, Belgium  66Departamento de Matemáticas, Universitat de València,
E-46100 Burjassot, València, Spain  67University of Minnesota, Minneapolis, MN
55455, USA  68Scuola Normale Superiore, Piazza dei Cavalieri, 7 - 56126 Pisa,
Italy  69Maastricht University, P.O. Box 616, 6200 MD Maastricht, Netherlands
70Université Lyon, Université Claude Bernard Lyon 1, CNRS, Laboratoire des
Matériaux Avancés (LMA), IP2I Lyon / IN2P3, UMR 5822, F-69622 Villeurbanne,
France  71Dipartimento di Scienze Matematiche, Fisiche e Informatiche,
Università di Parma, I-43124 Parma, Italy  72INFN, Sezione di Milano Bicocca,
Gruppo Collegato di Parma, I-43124 Parma, Italy  73Université de Strasbourg,
CNRS, IPHC UMR 7178, F-67000 Strasbourg, France  74Institute for Nuclear
Research, Bem t’er 18/c, H-4026 Debrecen, Hungary  75CNR-SPIN, c/o Università
di Salerno, I-84084 Fisciano, Salerno, Italy  76Scuola di Ingegneria,
Università della Basilicata, I-85100 Potenza, Italy  77Gravitational Wave
Science Project, National Astronomical Observatory of Japan (NAOJ), Mitaka
City, Tokyo 181-8588, Japan  78Observatori Astronòmic, Universitat de
València, E-46980 Paterna, València, Spain  79Centro de Física das
Universidades do Minho e do Porto, Universidade do Minho, Campus de Gualtar,
PT-4710 - 057 Braga, Portugal  80Istituto Nazionale di Geofisica e
Vulcanologia, I-56125 Pisa, Italy  81INAF, Osservatorio Astronomico di
Capodimonte, I-80131 Napoli, Italy  82Aristotle University of Thessaloniki,
University Campus, 54124 Thessaloniki, Greece  83Universiteit Antwerpen,
Prinsstraat 13, 2000 Antwerpen, Belgium  84University of Białystok, 15-424
Białystok, Poland  85Institute for High-Energy Physics, University of
Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands  86Institute of
Mathematics, Polish Academy of Sciences, 00656 Warsaw, Poland  87National
Center for Nuclear Research, 05-400 Świerk-Otwock, Poland  88Laboratoire
Lagrange, Université Côte d’Azur, Observatoire Côte d’Azur, CNRS, F-06304
Nice, France  89NAVIER, École des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-
Vallée, France  90Università di Firenze, Sesto Fiorentino I-50019, Italy
91INFN, Sezione di Roma Tre, I-00146 Roma, Italy  92ESPCI, CNRS, F-75005
Paris, France  93School of Physics Science and Engineering, Tongji University,
Shanghai 200092, China  94Dipartimento di Fisica, Università di Trieste,
I-34127 Trieste, Italy  95Max Planck Institute for Gravitational Physics
(Albert Einstein Institute), D-30167 Hannover, Germany  96Leibniz Universität
Hannover, D-30167 Hannover, Germany  97Institut des Hautes Etudes
Scientifiques, F-91440 Bures-sur-Yvette, France  98Istituto Nazionale di
Geofisica e Vulcanologia, I-40100 Bologna, Italy  99Consiglio Nazionale delle
Ricerche - Istituto dei Sistemi Complessi, Piazzale Aldo Moro 5, I-00185 Roma,
Italy  100Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”,
I-00184 Roma, Italy  101Dipartimento di Matematica e Fisica, Università degli
Studi Roma Tre, I-00146 Roma, Italy  102Università di Trento, Dipartimento di
Matematica, I-38123 Povo, Trento, Italy  103Departamento de Matemática da
Universidade de Aveiro and Centre for Research and Development in Mathematics
and Applications, Campus de Santiago, 3810-183 Aveiro, Portugal  104INAF,
Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, I-50125 Firenze, Italy
105Istituto di Astrofisica e Planetologia Spaziali di Roma, Via del Fosso del
Cavaliere, 100, 00133 Roma RM, Italy  106LIGO Livingston Observatory,
Livingston, LA 70754, USA  107Dipartimento di Scienze Aziendali - Management
and Innovation Systems (DISA-MIS), Università di Salerno, I-84084 Fisciano,
Salerno, Italy  108Van Swinderen Institute for Particle Physics and Gravity,
University of Groningen, Nijenborgh 4, 9747 AG Groningen, Netherlands
109Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium
110Eindhoven University of Technology, Postbus 513, 5600 MB Eindhoven,
Netherlands

###### Abstract

Sources of geophysical noise (such as wind, sea waves and earthquakes) or of
anthropogenic noise impact ground-based gravitational-wave interferometric
detectors, causing transient sensitivity worsening and gaps in data taking.
During the one year-long third Observing Run (O3: from April 01, 2019 to March
27, 2020), the Virgo Collaboration collected a statistically significant
dataset, used in this article to study the response of the detector to a
variety of environmental conditions. We correlated environmental parameters to
global detector performance, such as observation range, duty cycle and control
losses. Where possible, we identified weaknesses in the detector that will be
used to elaborate strategies in order to improve Virgo robustness against
external disturbances for the next data taking period, O4, currently planned
to start at the end of 2022. The lessons learned could also provide useful
insights for the design of the next generation of ground-based
interferometers.

###### Contents

  1. 1 Introduction
  2. 2 The Virgo environmental monitoring during O3
  3. 3 Seismic noise
    1. 3.1 The seismic frequency bands and their evolution during the O3 run
    2. 3.2 Impact on the Virgo detector
      1. 3.2.1 Sensitivity
      2. 3.2.2 Duty cycle
  4. 4 Earthquakes
    1. 4.1 O3 Seismon setup at EGO
    2. 4.2 Earthquakes impact during O3
    3. 4.3 Plans for O4
  5. 5 Bad weather
    1. 5.1 Impact of sea activity
      1. 5.1.1 Microseism impact on strain noise
      2. 5.1.2 Microseism impact on glitch rates
      3. 5.1.3 Microseism and scattered light
      4. 5.1.4 Identification of scattered light culprits
    2. 5.2 Impact of wind
    3. 5.3 Disentangling sea activity and wind
  6. 6 Other environment impacts
    1. 6.1 Magnetic noise
    2. 6.2 Lightnings
    3. 6.3 Cosmic muons
  7. 7 Outlook and prospects for O4
  8. A Study of the control losses during O3

##  1 Introduction

The past decade has seen the ramp-up of the second-generation (’Advanced’)
earth-based gravitational-wave (GW) detectors. Design improvements and
technological upgrades have paved the way to the first direct detections of
GWs by the global network made up of the two aLIGO instruments [1] (located in
the USA: Hanford, WA and Livingston, LA) and of the Advanced Virgo detector
[2] (located in Cascina, Italy). The main results achieved by the LIGO
Scientific Collaboration and the Virgo Collaboration – recently joined by the
KAGRA collaboration whose detector [3] (located in Kamioka, Japan, under the
Ikenoue mountain) is nearing completion – include the first detection of a
binary black hole merger (GW150914 [4]); the first detection of a binary
neutron star (BNS) merger (GW170817 [5]) that lead to the birth of multi-
messenger astronomy with GW [6]; and now dozens of detections of compact
binary mergers that add up in a GW Transient Catalogue regularly updated [7,
8, 9]. These detections contribute to opening a new window onto the Universe
by providing insights to the populations of compact objects and the binary
merger rates [10]; they also allow scientists to perform stringent tests of
general relativity [11] in a new regime of gravitation never probed before.

The operation of ground-based GW detectors is organized into successive steps
forming a recurring sequence over the years: upgrades; commissioning and
sensitivity improvement (the so-called noise hunting phase); data-taking
periods called observing runs (or simply runs and labelled O$n$). So far there
have been three runs for the global network of advanced detectors.

  * •

O1 (09/2015 – 01/2016) with only the two LIGO detectors taking data;

  * •

O2 (11/2016 – 08/2017) with Virgo joining LIGO on August 01, 2017;

  * •

finally O3 (04/2019 – 03/2020), that saw the three detectors take data jointly
during 11 months in total: 6 months first (a run called O3a), followed by a
1-month break (October 2019) and then another period of 5 months of data
taking (O3b), interrupted about a month earlier than expected due to the
worldwide COVID-19 pandemic.

The above listing shows that the O3 run was the first long data-taking period
for the Advanced Virgo detector. Therefore, we have used the wealth of
unprecedented data collected during this year to make an in-depth analysis of
the instrument performance. In this article, we study the impact of the
environment on Advanced Virgo, along the lines of previous publications from
Virgo [12], LIGO [13] or KAGRA [14]. We focus on various types of seismic
noises, on earthquakes and on bad weather periods. We also briefly investigate
the effect of other possible disturbances: magnetic noise, lightning and
cosmic muons. Our goal is threefold: to quantify how the Virgo sensitivity and
duty cycle depend on these external parameters; to use this knowledge to
prepare the next run, O4, scheduled to start in the second semester 2022;
finally, to build experience for future GW detectors, in particular for the
Einstein Telescope project [15].

The Virgo detector is located in Italy at EGO, the European Gravitational
Observatory, in the municipality of Cascina. The EGO site is in the
countryside, about 12 km south-east of Pisa and about 17 km east of the
Tyrrhenian coast. Virgo is not far from some industrial and commercial sites
that can generate noise. Within 7 km from EGO there are: elevated highways,
railway tracks, wind turbines, earth quarries, high-voltage power lines
(electricity pylons and overhead line segments) and the Pisa airport. To avoid
pressure waves potentially shaking the ground, a no-fly zone has been enforced
in a cylindrical volume (600 m radius and height) above each of the Virgo
experimental buildings.

Advanced Virgo is a power-recycled Michelson interferometer with Fabry-Perot
cavities in its 3 km-long arms. All core optics are suspended to long
suspensions, called the superattenuators [16], that have a twofold use: first,
to isolate as much as possible the mirrors from seismic motions (both vertical
and longitudinal), and then to control very accurately their positions in all
six degrees of freedom. Many feedback systems are used to bring the detector
to its working point and maintain it there [17, 18]. This state – the same for
O2 and O3: the Michelson interferometer on a dark fringe, the Fabry-Perot and
power recycling cavities in resonance – is the only one in which the detector
is sensitive to the passing of GWs.

During a run, priority is obviously given to taking data of quality good
enough to be included in physics analysis. In that case, Virgo is said to be
in Science mode. During O3, the average duty cycle in Science mode has been
around 76% [19], with the remaining time almost equally divided into three
categories.

  * •

Control acquisition and adjustment phases, to restore the working point and
restart taking data in Science mode;

  * •

Recurring controlled actions on the detector: maintenance (usually a few hours
on Tuesday mornings local time), calibration (usually every Wednesday evening)
or commissioning (measurements, working point tuning or tailored improvements:
sessions organized when the need arises);

  * •

Problems preventing a smooth running of the detector.

The article is organized as follows. Section 2 describes the environmental
monitoring of the Virgo detector during the O3 run. Section 3 is dedicated to
the different seismic noise contributions (either natural or human-related):
how to disentangle them, how to monitor them and what their impacts on the
detector are in terms of sensitivity and duty cycle. Section 4 provides an
analysis of the impact of earthquakes on the detector. Section 5 studies the
impact of bad weather on data quality and duty cycle, disentangling
contributions from sea activity and wind. Section 6 goes through other
environment impacts: magnetic noise, lightning and a study of the cosmic muon
rate on the Virgo central building. Then, Section 7 concludes this article by
opening outlooks to the future O4 run. Finally, A provides a detailed and
quite complete classification of the control losses during the O3 run.
Although that study has a scope broader than the present article, it is
included here for reference and also because its results were used, in
particular to find out which control losses were due to earthquakes.

##  2 The Virgo environmental monitoring during O3

The Virgo detector is equipped with a large set of probes used to monitor the
conditions of the surrounding environment. Since these conditions can
influence the detector response, or even mimic a GW event, it is very
important to track their evolution, to assess the right working condition of
the detector or to use them as veto against possible fake signals.

Figure 1: Location of the probes used for the Virgo environmental monitoring
system. Maps of most relevant building are shown: left MCB, middle NEB, right
CEB. The WEB is very similar to NEB and is not shown.

The set of probes and their conditioning electronics constitute the
Environmental Monitoring System (EMS). The EMS was initially composed by a few
tens of environmental probes (EPs) [20] and then improved during the detector
upgrades that occurred in the past years. During O3, the total number of
channels belonging to EMS was about 420.

The EMS is also helpful to understand the origin of some noise sources
affecting the detector sensitivity. Indeed it was largely used during the
commissioning phase following each detector upgrade, to recover and improve
the Virgo performance in terms of sensitivity and duty cycle [12].

Data acquired for EMS can be grouped in two classes depending on the sample
rate used for the different EPs. High-rate, or fast class, includes those EPs
acquired at rate up to 20 kHz like seismometers, piezoelectric (PZT)
accelerometers, force balance (FB) accelerometer, magnetometers, microphones,
voltage and current sensors, radio-frequency (RF) antennas, while low-rate or
slow class includes temperature, pressure, humidity, weather and lightning
probes, acquired at 1 Hz rate.

Type | Model | Frequency Band
---|---|---
Seismometer | Guralp CMG-40T | 0.01 – 50 Hz
FB Accelerometer | Kinemetrics FBA ES-T | 0.1 – 200 Hz
PZT Accelerometer | Wilcoxon 731-207 or PCB 393B12 | 1 Hz – 1 kHz
Magnetometer | Metronics MFS-06 or MFS-06e | 0.1 mHz – 10 kHz
Microphone | Brüel & Kjær 4190 or 4193 | 0.1 – 10 kHz
RF antenna | AAS STA 5 A/D/0.01-100 | 10 kHz – 100 MHz
Voltage probe | Talema 0015P1-2-009 | DC – 10 kHz
Current probe | IME 0015P1-2-009 | DC – 10 kHz
Temperature probe | Analog Device AD590 | DC – 0.5 Hz
Humidity probe | Honeywell HIH-5031-001 | DC – 0.5 Hz
Pressure probe | NXP MPXA4115A6U | DC – 0.5 Hz
Weather station | Davis Advantage Pro 2 | DC – 0.3 Hz
Lightning detector | Boltek LD 250 | DC – 0.5 Hz
Table 1: Characteristics of the Virgo environmental probes used during O3.

The main characteristics (type, model and frequency band) of the EPs in use
during O3 are listed in Table 1. Figure 1 shows the arrangement of the EPs
inside the main Virgo buildings. Most probes are located in the experimental
halls of the relevant buildings of the detector: Central Building (CEB), North
and West End Buildings (NEB and WEB) and Mode Cleaner Building (MCB). Usually,
the probes are in contact with critical elements of the detector, like the
walls of the vacuum chambers containing the test mass suspensions, or the
optical benches hosting the laser injection and GW detection systems. Figure 2
shows a bird eye’s view of the Virgo detector at EGO, with an emphasis on the
location of the buildings that are identified in this article.

Few probes are placed outside the buildings, namely the weather station, the
lightning detector and two additional magnetometers – see Fig. 2. These two
low-noise induction coil magnetometers are deployed at 0.5 m depth in the
soil, at about 100 m from the CEB, oriented along the geographic North and
West directions. Their data are shared in real time with the EM antenna
network "Radio waves below 22 kHz" [21].

Figure 2: Map [22] of the EGO site showing the Virgo detector and in
particular the location of the main buildings identified in the text. The
central insert shows a zoom around the interferometer vertex, with the CEB and
MCB highlighted. The Mode-Cleaner cavity is 144 m-long, while the Virgo arms
are 3-km long.

##  3 Seismic noise

In this section we introduce the main sources of seismic noise at EGO. They
are disentangled and monitored by examining seismic probes in specific
frequency bands. We provide a statistical description of the noise and
evidence its main recurring features. Then, we describe how they impacted on
the detector during the O3 run.

###  3.1 The seismic frequency bands and their evolution during the O3 run

Figure 3:  Variability of horizontal velocity of the Virgo NEB ground floor
during O3. The quantity shown is the 2D histogram of the E-W velocity
amplitude spectral density computed for the whole dataset recorded during O3
divided into 128 second long chunks. All intereferometer maintenance periods
are excluded from the computation. The color scale indicates the percent
occupancy of histogram bins. The superposed continuous curves show different
percentile levels (labelled PL on the plot): 10% (gray), 50% (black) and 90%
(gray as well). The two red dashed curves correspond to the Peterson low-noise
(’NLNM’) and high-noise (’NHNM’) models [23].

The seismic wavefield at EGO, the site of the Virgo detector, is the sum of
several sources [24]. Seismic spectrum variability during the O3 run is
illustrated in Fig. 3. The largest contribution to seismic ground motion in
the frequency range between 0.1 Hz and 1 Hz, referred to as microseism, is due
to the interaction between shallow water sea waves and the bottom of the sea
[25, 26]. At EGO, the prevailing microseimic peak is around 0.35 Hz.

Figure 4: Evolution during O3 of seismic RMS in the 0.1 to 1 Hz frequency
band. Data colored in yellow and red exceed the 75th and 90th percentile,
respectively.

Figure 4 shows the time evolution of microseism during the O3 run, while Fig.
5 shows the corresponding cumulative distribution, split by season. Microseism
intensity follows seasonal variations, being larger in fall and winter, due to
the stronger wind and sea activity.

Figure 5: Cumulative distribution of microseism in the frequency band 0.1-1 Hz
(dominated by sea activity), measured at EGO during each season in 2019-2020.

Above 1 Hz, anthropogenic sources dominate the spectrum. Heavy vehicles
(trucks and alike) on ${\sim}1$ km distant elevated roads are the prevailing
source of seismic noise in the 1-10 Hz band [24].

As illustrated in Fig. 6, the RMS of seismic noise in the 1-5 Hz band follows
a working day/night cycle with higher levels during working hours (from 8:00
to 17:00 local time – LT), with small reduction during lunch break
(12:00-14:00 LT) and minima during week-ends and holidays. The blue curve,
used as reference, covers the whole O3 run. The green curve is based on a
4-week period, from Monday 16 December, 2019 to Sunday 12 January, 2020: the
noise reduction during the two consecutive Wednesdays, Christmas 2019 and the
New Year’s Day 2020, is quite impressive. A significant reduction of the
anthropogenic noise is also visible during the Spring 2020 lockdown in Italy,
due to the COVID-19 pandemic (red curve, covering a 8-week period from 09
March to 03 May). That decrease is smaller than for the Christmas and New Year
holidays but it is more global as it is visible for all days of the week.

Figure 6: Average evolution on a weekly basis of the seismic anthropogenic
noise (frequency band: 1-5 Hz) measured at EGO during different times in
2019-2020.

Finally, above 10 Hz, the dominant seismic contribution is generated locally:
vehicles in nearby and on-site roads, agricultural work on neighbouring land,
etc. Figure 7 shows the average day-night variations, computed in the 10-40 Hz
band on a weekly basis: in blue during the O3 run; in magenta during the
1-month commissioning break (October 2019) separating the two halves of O3;
finally in orange for the second semester of 2020, during which hardware
upgrades and construction or infrastructure works for the Advanced Virgo+
project [27] took place.

The common feature between the three curves is the dominant peak on Tuesday
mornings, the usual slot used for the weekly maintenance of the Virgo
detector. This activity includes in particular the refilling of
Nitrogen111Liquid Nitrogen is used to cool down the Advanced Virgo cryotraps
[2]. tanks by heavy trucks coming on-site, and the possibility to have people
moving around and working inside experimental areas whose access is forbidden
during data taking periods. The on-site seismic noise level was slightly
higher during the commissioning break compared to the O3 run, but not by much:
that 1-month shutdown was not long enough to allow for invasive works that
could have jeopardized the restart of data taking on November 01, 2019,
alongside the two LIGO detectors. On the other hand, on-site activites are
more evenly distributed over working days during the post-O3 upgrade. Though,
activities were the lowest on weekends during that period because of site
access restrictions, enforced because of the pandemic.

Figure 7: Average evolution on a weekly basis of the seismic on-site noise
(frequency band: 10-40 Hz) measured at EGO during different times in
2019-2020.

###  3.2 Impact on the Virgo detector

The previous sections have demonstrated that the Virgo collaboration is
accurately monitoring the seismic environment at EGO and that the recorded
data show significant variations over time, in agreement with expectations
from known noise sources. It is then interesting to see how these noises
impact the performance of the Virgo detector, namely its sensitivity and duty
cycle.

####  3.2.1 Sensitivity

A convenient way to monitor the sensitivity of a GW detector like Virgo is to
study the evolution of the BNS range, that is the average distance up to which
the merger of a standard BNS system can be detected with a signal-to-noise
ratio (SNR) set to 8, roughly corresponding to one false alarm per year with
purely Gaussian noise. The average is taken over the position of the BNS in
the sky and over the orientation of its orbital plane. Broadly speaking, the
lower (higher) the noise in the frequency band of interest –from a few tens of
Hz to a few hundreds of Hz depending on the actual sensitivity curve –, the
larger (smaller) the BNS range.

In addition to its potential dependence on the surrounding environment, the
BNS range can fluctuate significantly due to changes in the control accuracy
of the detector. Therefore, averaging raw BNS range values, especially over
long timescales, is not expected to provide meaningful information as one
would mix together too many effects that cause the BNS range to vary.
Therefore, the method used in the following consists in computing a moving
daily average of the BNS range and to focus on the local fluctuations around
this level. Figures 8 and 9 show these variations, averaged over the whole O3
run, and projected over a weekly or daily time range, respectively. On both
plots, the red dots show daily variations while the blue curve is a moving
median profile of the scatter plot. The variations seen are clearly of
anthropogenic origin, with a day-night pattern and a reduced spread during the
weekend. Although they are significant, they are also limited in size:
${\sim}1$ Mpc compared to an average BNS range of about 50 Mpc during the O3
run, hence a ${\sim}2\%$ fluctuation. This shows the robustness of the Virgo
detector.

Figure 8: Average variation of the BNS range around its local average,
computed on a weekly basis. The blue trace is a moving median profile of the
red scatter plot, each dot showing the fluctuation at a particular weekday and
time. The lack of available data on Tuesday morning corresponds to the weekly
maintenance period of the Virgo detector, while the sharper variations on
Wednesday and Thursday afternoons are due to the fact that these times have
often been used for calibration or detector activities. Therefore, the BNS
range is less stable than usually when nominal data taking gets restored.
Figure 9: Average variation of the BNS range around its local average,
computed on a daily basis. The blue trace is a moving median profile of the
red scatter plot, each dot showing the fluctuation at a particular time of the
day.

####  3.2.2 Duty cycle

Figure 10 shows the average duty cycle of the Virgo detector during the O3
run. The top plot displays its average variation over a week, while the bottom
one focuses on a day. The red curve normalizes the Science mode data taking by
the elapsed real time, while the green one is computed by excluding the
calibration, commissioning and maintenance periods. Thus, the latter curve
shows the fraction of the time available for data taking that is actually used
for that. Activities on the detector are concentrated during working hours as
expected, with maintenance on Tuesday morning, calibrations on Wednesday
evenings and commissioning slots from Monday to Friday depending on the needs.
There is a non-negligible recovery time from maintenance, while the transition
from calibration back to data taking is smoother and quicker on average.
During the quietest hours of the night, when no work takes place on the
interferometer except in case of an emergency, the average duty cycle reaches
a plateau around 85%.

Figure 10: Average weekly (top) and daily (bottom) duty cycle of the Virgo
detector during the O3 run. The red curve uses the elapsed real time as
normalization, while the green one is produced excluding the times spent doing
calibration, commissioning or maintenance, three activities that are
incompatible with Science-mode data taking.

##  4 Earthquakes

Earthquakes radiate energy through different types of seismic waves that are
commonly divided in "body" and "surface" waves, depending on the path followed
from the source to the receiver. Body waves that travel through the Earth are
usually detected first. The fastest are named P-waves and are compressional
longitudinal waves whose speed can reach 8 km/s. Then come the S-waves,
transverse shear waves whose velocity scales by a factor of $\sqrt{2}$ with
respect to P-waves. Surface waves are slower and their size dominates at large
epicentral distance since their amplitude scaling factor is 1/distance while
body waves scale with 1/distance2. Most relevant surface waves are Rayleigh
waves, that originate from P-wave and S-wave (with vertical polarization)
coupling at the Earth surface. The result is a wave with both longitudinal and
transversal components and a propagation speed up to a few km/s.

Since seismic waves excite buildings even at great distance, the Virgo
monitoring set at EGO includes local, regional and teleseismic earthquakes
since it was observed that all of them can induce large motion of the
interferometer elements. This can then saturate the control capability of the
feedback systems that keep Virgo at its nominal working point, leading to a
loss of control. Following each control loss (regardless of its origin: an
earthquake or another cause), data taking stops immediately and can only
restart after the completion of the semi-automated sequence that allows
restoring the Virgo global working point – during the O3 run, that procedure
took about 20 minutes on average [19]. But the time lost can be much longer in
case of a control loss due to an earthquake, in case the suspension normal
modes are excited by the seismic waves. In that case, one may have to wait up
to one hour after the event that the high-quality factor modes of the
suspensions are damped, before initiating the control acquisition procedure.
Since each control loss reduces the Virgo duty cycle, it is therefore
important to understand which fraction of these are due to earthquakes, and
what are the earthquakes that induce them.

Large earthquakes at local and regional scale do not happen very often, so the
type of earthquakes on which this analysis is focused is large earthquakes
that occur along the boundaries of the main tectonic plates. Most of them are
quite distant from EGO, meaning that a low-latency framework relying on data
from a variety of seismic stations worldwide could produce early warning
notices that would be received and processed ahead of the seismic waves
arrival. In that case, one could take preventive measures to try to mitigate
the effect of the ground shaking, with the goal of avoiding the control loss.
In the following, we describe the strategy implemented at Virgo during the O3
run, that relies on the Seismon framework [28, 29, 30] developed at LIGO – an
example of the existing teamwork among members of the global GW detector
network.

Furthermore, as explained below, the study has also highlighted another
contribution from much weaker earthquakes, quite close to EGO (the majority of
which occur on the Italian Apennines). Those have been more difficult to
identify as they do not lead to early warnings from Seismon and the frequency
of their seismic waves is much higher when they arrive at EGO: up to ${\sim}1$
Hz, whereas teleseism waves are in the frequency band $10-100$ mHz. In
addition, the proximity of their epicenters makes useless the use of warnings
that would always come too late. Thus, the only way to mitigate these
earthquakes is to understand how they impact the Virgo control system and what
could be done to strengthen it.

###  4.1 O3 Seismon setup at EGO

In addition to making the whole detector as robust as possible against the
passing of strong seismic waves, the only other leverage one can use to
mitigate the impact of earthquakes is to rely on early warnings provided by
worlwide arrays of seismometers.

Following initial tests done during the O2 run and the upgrade period that
followed, we ran at EGO during O3 an instance of the Seismon framework,
developed by LIGO to process earthquake early warnings provided by the US
Geological Survey (USGS) [31] and to compute information relevant for each
site of the LIGO-Virgo network. Namely, for each earthquake, Seismon
potentially predicts the arrival time of the different seismic waves (P-, S-
and surface), their amplitude at site and the probability of losing the
detector control in consequence of that earthquake That framework was split
into four consecutive steps, each associated with a server integrated within
the Virgo online data acquisition system (DAQ) used to steer and monitor the
detector.

  * •

Reception of the USGS alerts.

  * •

Processing of each alert by the Seismon framework.

  * •

Extraction of the subset of Seismon data pertinent to the EGO site and
provision to the Virgo online framework.

  * •

Local processing of these data.

In addition to producing a plot summarizing all information available from the
early warning, a loose cut is applied on magnitude and distance to estimate
whether the earthquake could be relevant, meaning that it could impact the
control of the Virgo detector. In that case, and if the warning was quick
enough to precede the arrival of the seismic wave on-site, an alarm would
latch on the main panel of the Virgo Detector Monitoring System [32, 33],
alerting the operator on duty in the control room.

In the nominal O3 control configuration, the two 3 km-long optical cavities
are kept in resonance by acting on the end mirror suspensions: their actuators
are the least noisy, at the price of a reduced correction range availability.
Actuators located at the level of the input mirror suspensions have higher
dynamics, while introducing slightly more noise. Thus, they can be used as
earthquake control mode (in short EQ mode) to try to maintain the Virgo
working point during periods of elevated seismic noise.

A smooth transition procedure, working both ways without losing the detector
control, has been developed to allow switching back and forth between end-
mirror and input-mirror actuations. During the O3 run, the procedure in use
was the following: once alerted by Seismon, the operator on duty would monitor
the optics suspension status and manually trigger the transition from nominal
mode to EQ mode when the test mass suspensions would start shaking
significantly. Once activated, that process would take a few tens of seconds
to complete. Then, either the detector would nevertheless lose its working
point (and the control acquisition procedure would have to be restarted from
the beginning), or the EQ mode control would be kept until the whole seismic
wave trains has passed by and the suspensions motion has been damped back to
levels low enough to allow resuming the nominal control mode.

Unrelated to earthquakes, the EQ mode was also found useful during periods of
high wind: gusts shake the building structures (walls and floors) and those
vibrations can couple to the suspensions, potentially causing control
corrections to saturate. However, since EQ mode was not validated for the
production of good quality data for physics analysis, this method was used
parsimoniously during most of O3 because corresponding data would have to be
discarded. A few weeks before the end of the run, the EQ mode got finally
qualified for regular data taking and later studies [34] showed that there was
no significant degradation of the Virgo sensitivity when switching to it.
Therefore, it was used more regularly from that time; the possibility to have
such a backup solution for O4 as well will be studied in the coming months.

###  4.2 Earthquakes impact during O3

The stronger and/or the closer to EGO the earthquake, the more likely it is to
impact the control of the Virgo detector. To study the impact of strong
regional earthquakes or teleseisms, the USGS warnings processed by Seismon are
sufficient (as they should include all such earthquakes). But it was soon
realized that some moderate earthquakes occurring at local and regional
distance (from few tens to few hundreds kilometers away from EGO), too weak to
generate a USGS alert and thus not processed by Seismon, could cause losses of
control of Virgo. To check if any of the control losses was caused by this
type of earthquakes, we queried [35] the INGV (Istituto Nazionale di Geofisica
e Vulcanologia) public earthquake catalogue [36] to download the list of
events that occurred during O3 in the Mediterranean region. This list partly
overlaps with the USGS one and duplicates were removed. All results presented
in the following are based on the whole set of earthquakes, assembled by
merging the USGS and INGV event lists.

The control of the Virgo detector is extremely complex. Therefore, finding out
how many earthquakes induced control losses during the O3 run required a
careful study of all control losses, documented below in A. An earthquake from
the list of USGS warnings is associated to a recorded control loss if the loss
occurs within the time range during which seismic waves were predicted to
arrive on-site according to Seismon and if the seismic activity around the
time of the control loss is significantly larger than its typical range of
variation. In case of concurring early warnings from different earthquakes
overlapping in time at EGO, the strongest is arbitrarily selected as the
reason for the control loss.

Estimating the strength of an earthquake when its seismic waves arrive at EGO
is not easy. Yet, this is a key point to address, first to reject quickly
warnings from harmless earthquakes and then to adjust the latency and level of
response for the crew in charge of steering the Virgo detector. During O3,
basic rectangular cuts in the magnitude-distance plane – e.g. if magnitude >
(…) or (distance < (…) km and magnitude > (…)) or etc. – were applied to the
live earthquake warnings received from USGS and processed by Seismon. During
the post-run analysis, the ranking

$\mathrm{ranking}=\frac{10^{\mathrm{magnitude}/2}}{\mathrm{distance[km]}}$ (1)

was introduced. While not complete – e.g. neither the hypocenter depth nor its
azimuth angle computed with respect to EGO are accounted for – this ranking
appears sound: the higher its value, the more likely the control loss.
Applying a (conservative) minimum cut at ranking = 0.02 allows to safely
remove more than half of the earthquakes to be analyzed.

Results shown below use the largest possible earthquake statistics, meaning
that one requires the Virgo detector to be fully controlled, but not
necessarily in Science mode. This looser requirement enlarges the dataset of
interest and hence the number of earthquake early warnings to be taken into
account.

Figure 11 highlights the epicentral distance and magnitude of the earthquakes
that led to a Virgo control loss. The top (bottom) row deals with the
earthquake magnitude (epicentral distance) while the right column displays the
ratio of the red and blue histograms shown on the left column. As expected,
the larger the earthquake magnitude, the more likely the control loss, with
the fraction of earthquakes leading to a control loss departing from 0 for
magnitude 6 and above. That fraction saturates to 1 (meaning that all events
causes a control loss) when magnitude exceeds 7.2. We also note that the
fraction is not null around magnitude 3: this reflects the control loss
consequence of some small local earthquakes recognizable also in the left side
histogram of Figure 11. The histogram ratio is much flatter for that other
variable, with the most significant bins reflecting the location of seismic
regions on the globe with respect to EGO, mainly the broad Mediterranean area
and the Ring of Fire (a region covering much of the rim of the Pacific Ocean
that is seismically very active).

Figure 11: Summary of the impact of earthquakes on the Virgo detector during
the O3 run. Left column: the blue (red) histogram shows all earthquakes (the
earthquakes that have induced a control loss); top: magnitude distribution;
bottom: distribution of the distance between EGO and the epicenter. Right
column: corresponding fraction as a function of the earthquake magnitude (top)
and distance (bottom). In all cases, the earthquakes that certainly could not
impact Virgo (ranking below 0.02) were excluded.

Figure 12 shows the population of earthquakes that caused a control loss (red
dots) in the two-dimensional plane epicentral distance vs. magnitude. These
earthquakes form the upper envelope of the scatter plot drawn, meaning they
are usually the earthquakes with largest magnitude for any distance. The
separation between red and green (earthquakes that did not cause a control
loss) dots is not perfect for at least two reasons. The first one is that the
control of the Virgo detector is complex enough that the actual level of
control (accuracy and stability) plays a role in whether or not the control is
lost for earthquakes at the limits of inducing a control loss. The second
reason is that our model could probably be improved by including other
earthquake warning parameters: two candidates would be the hypocenter depth
(the deeper the hypocenter, the lower the earthquake impact on the ground at
equivalent magnitude) and the azimuthal orientation of the epicenter with
respect to EGO.

Figure 12: Distribution of earthquakes in the plane distance-magnitude during
the O3 run. The earthquakes that caused a control loss (did not cause a
control loss) are represented with red (green) dots. The lack of points below
the main bulk of earthquakes is due to the ranking cut, set at 0.02.

Figures 13 and 14 show the location of the significant earthquakes that
occurred during 03 with the same color coding used in Figure 12. Their
distribution depicts the boundaries of the main tectonic plates and, as
discussed above, we can observe that the most harmful earthquakes for Virgo
are coming from the Mediterranean area (medium to large magnitudes but smaller
distances) and part of the Pacific Ring of Fire. The mid-Atlantic ridge and
the Asian portion of the Alpide earthquake belt did not produce many
earthquakes that impacted Virgo, possibly because of the limited statistics.
During the O3 run, the distribution of the earthquakes leading to control
loses was the following: ${\sim}15$% of close earthquakes; ${\sim}20$% from
other earthquakes in the Mediterranean area; and ${\sim}65$% from distant
earthquakes. We remark that this statistics has not an absolute meaning: the
O3 run took place during a quiet seismic period for Italy, compared to e.g.
2009 or 2016. This analysis will be updated in the future with data from the
O4 run.

Figure 13:  Location of the O3 earthquakes used in this study (ranking greater
than 0.02) The earthquakes that caused a Virgo control loss (did not cause a
control loss) are represented with red (green) dots. Figure 14: Zoom on the
Mediterranean area of the map shown in Fig. 13 above. It shows the earthquakes
nearby Virgo (whose site, EGO, is marked by a black cross) recorded during the
O3 run. The earthquakes that caused a control loss (did not cause a control
loss) are represented with red (green) dots.

Finally, Fig. 15 shows an example of the impact of a strong and distant
earthquake on the Virgo detector and how the early warning information was
used to change the control mode prior to the arrival of the strongest seismic
waves. This allowed the crew on duty to keep the working point of the
instrument by preventing the correction force (applied on mirror suspensions
to maintain resonance in the arm cavities) from saturating. Should that action
not have been performed, the control loss would have been unavoidable – as the
correction would have saturated around 22:27 UTC. The description of the
different stripcharts displayed is given below.

  * •

Top plot: variation of the index labelling the Virgo data taking
configuration: the Science mode corresponds to the value 1; other indices
shown here (-1, -7, -9) indicate different control configurations that are not
nominal and that were used to wait for the right moment to switch back to
Science data taking mode.

  * •

Second plot: stripchart of the BNS range versus time; the seismic waves
clearly make the BNS range go down and fluctuate more while they are passing
(see seismic activity variations recorded in the bottom plot, described
below); the BNS range recovers its steady value at the end of the plotted time
when the earthquake effect fades away.

  * •

Third plot: switch showing the times when the earthquake-resilient control
mode (’EQ-mode’) is turned on ($0\rightarrow 1$ transition) and later on off
($1\rightarrow 0$ transition) manually by the operator on-duty.

  * •

Fourth plot: For each second, maximum value of the correction applied on the
test masses to keep the Virgo arms in resonance. When the nominal control mode
is used, a control loss happens within two seconds at most after the time for
which the correction voltage222The mirror control is done by varying the
amount of current applied to actuators (pairs of coil-magnet): see Ref. [17]
for details. exceeds a 9.5 V threshold. This occurs a few times close to the
middle of the time range represented here but no control loss follows, as the
EQ-mode allows for larger corrections.

  * •

Bottom plot: seismic noise measured in three orthogonal directions (vertical
and along the two Virgo arms) using the dominant frequency range for
earthquakes recorded at teleseismic distance: 10 mHz $\rightarrow$ 100 mHz.

  * •

Finally, the vertical dashed lines common to all plots show the time of
important events. From left to right: the time at which the earthquake
occurred; the time at which the corresponding USGS warning had been received
and processed by the Seismon framework at EGO; the expected arrival time of
the seismic P-waves, S-waves and Rayleigh waves. For the latter, we use three
different arrival times that stem from different assumed velocities (5, 3.5
and 2 km/s respectively).

Figure 15: Example impact on the Virgo detector of a strong (magnitude greater
than 7) and distant (14,000 km away from EGO) earthquake, that occurred on May
06, 2019 at 21:19:37 UTC in Eastern Papua New Guinea. The description of the
different stripcharts is provided in the text.

###  4.3 Plans for O4

Work is in progress to build on the O3 experience and have a more performing,
better integrated, earthquake early warning framework for O4 (and beyond). The
plan is to run the latest version of Seismon with an improved prediction
capability for EGO, achieved by means of all the data collected during the O3
run. We are also exploring the possibility to use the INGV Early-Est system (a
framework for rapid location and seismic/tsunamigenic characterization of
earthquakes) [37, 38] as an additional source of warnings, complementary to
USGS. Tests are in progress to have this new live stream received at EGO and
integrated into the existing framework. The two sets of early warnings will
then be compared, in terms of latency and accuracy.

##  5 Bad weather

Through O3, the Virgo interferometer performed worst during days with adverse
meteorological conditions, namely high winds and intense sea activity. These
periods were generally associated with increased non-stationary noise in the
GW signal below about 100 Hz and with some difficulties in maintaining the
interferometer in its controlled state, resulting in reduced duty cycle. In
the following, we study the impact of the increased microseimic noise
associated to sea waves, then the influences of wind on BNS range, as well as
the effect of wind gusts on the global interferometer controls. Because of the
wind action on the sea surface, high winds and rough sea often occur together.
We use a statistical approach to disentangle their effects on the detector.

###  5.1 Impact of sea activity

Microseism amplitude at EGO increases by more than one order of magnitude
between calm and rough sea periods. For $10\%$ of the time during O3, ground
RMS velocity between 0.1 Hz and 1 Hz was above $4~{}\mathrm{\upmu m/s}$, as
shown in Fig. 5. This happened in particular in correspondence of the seasonal
change in the first part of O3b and for some periods of adverse weather
conditions in the first months of 2020. Periods of intense sea activity were
associated to larger than usual strain residual noise whose characteristics
and origin require further analysis.

####  5.1.1 Microseism impact on strain noise

Figure 16: Correlation between the low frequency noise in Virgo GW strain and
the microseism induced by the sea activity; top row for O3a and bottom for
O3b. Left: in blue the time series of the strain BLRMS in band
$[10,20]~{}\mathrm{Hz}$ and in red that of the BLRMS in band
$[0.1,1]~{}\mathrm{Hz}$ of a CEB seismometer, mostly influenced by the sea
activity. Right: 2D-histograms of the correlation between the two BLRMS, where
the colorscale counts, for every pixel in this map, how many data points have
the corresponding values of strain and microseism BLRMS. The annotation in the
top-right corner reports the value of the Pearson correlation coefficient $r$.

Periods of high sea activity were associated with larger strain residual noise
up to about 100 Hz. To characterize this effect, we made use of the _band-
limited_ RMS (BLRMS), defined for a generic signal, in a certain frequency
band $[f_{\mathrm{min}},f_{\mathrm{max}}]$, as:

$\mathrm{BLRMS}\big{(}t;[f_{\mathrm{min}},f_{\mathrm{max}}]\big{)}:=\sqrt{\int_{f_{\mathrm{min}}}^{f_{\mathrm{max}}}S(f;t)df}$
(2)

where $S(f;t)$ is an estimate of the signal _power spectral density_ (PSD)
referred to a time $t$.

In Fig. 16, we report, for the entire O3 run, in blue the BLRMS of the strain
in the band $[10,20]~{}\mathrm{Hz}$ and, in red, the CEB seismometer BLRMS in
the band $[0.1,1]~{}\mathrm{Hz}$. These have been estimated from (2), where
$S(f;t)$ is computed with the Welch’s method making use of strides of
$2048~{}\mathrm{seconds}$ and FFT length of $128~{}\mathrm{seconds}$,
overlapping by $50\%$ [39]. The correlation between the two curves is
apparent. In particular, when the microseism is intense, the peaks in the
strain BLRMS are almost everywhere coincident with those in the seismometer
BLRMS. This fact is also highlighted in the 2D-histograms on the right-hand
side of the same figure, where the Pearson correlation coefficient has been
computed for the two data taking periods, O3a (top) and O3b (bottom). In
general, we observe that, despite the “spikes” in correspondence of bad
weather conditions (in particular at the beginning of O3b and then during most
of Winter333That calendar season starts around day 50 of O3b and lasts almost
until the end of the data taking.), the induced strain noise at low frequency
has improved during O3 and can now be mostly attributed to microseism.

####  5.1.2 Microseism impact on glitch rates

Figure 17: Correlation between Virgo glitch rate and the sea induced
microseism during the O3 run. Left: the dashed gray line represents the daily
moving median of the glitch rate per minute recorded by Omicron [40] for
glitches with SNR $>$ 6.5 and frequency at peak in band
$[10,2048]~{}\mathrm{Hz}$, estimated over strides of $2048$ seconds. The blue
continuous line is the median rate referred to glitches with frequency at peak
in $[10,40]~{}\mathrm{Hz}$ band. The continuous red line is the BLRMS in band
$[0.1,1]~{}\mathrm{Hz}$ of a seismometer in the Virgo CEB. Right: 2D-histogram
of the glitch rate in band $[10,40]~{}\mathrm{Hz}$ and the microseism BLRMS,
where the colorscale counts, for every pixel in this map, how many data points
have the corresponding values of the rate and the microsiesm BLRMS. The
annotations in the top-right corners report the values of their Pearson
correlation coefficient $r$.

Besides an increase in the RMS value of the strain noise at low frequency,
microseisms induce short transients of power excess in this channel,
colloquially referred to as _glitches_. In Fig. 17 we report the minute rate
of these glitches during the entire O3 run. To reduce the – usually very large
– variability in their rate, we computed running daily medians. The gray
dashed line represents the time evolution of daily medians for glitches with
SNR $>6.5$ and frequency at peak in the band $[10,2048]~{}\mathrm{Hz}$, as
measured by the online Omicron pipeline [40]. The blue solid line is the
median minute rate of glitches with peak frequency in the
$[10,40]~{}\mathrm{Hz}$ band. These glitches accounted for about $30\%$ of the
total during O3a, and for almost $40\%$ in O3b, with peaks larger than $80\%$
in correspondence of periods of intense sea activity. This glitch rate is
highly correlated with microseism, represented in the left-hand side plot of
Fig. 17 by the solid red line of the running weekly median of the BLRMS in
band $[0.1,1]~{}\mathrm{Hz}$ of the CEB seismometer. On the right-hand side of
the same figure, we report the 2D-histogram of these two quantities and the
value of their Pearson coefficient ($r=0.91$).

####  5.1.3 Microseism and scattered light

Figure 18: Spectrograms of the light transmitted at the end of the arm
cavities and detected by photodiodes located behind, on suspended benches –
top plot: west arm, B8 photodiode, SWEB bench; bottom plot: north arm, B7
photodiode, SNEB bench. The typical pattern of scattered light noise (arches)
– both first order and second order (higher frequencies) – is visible. On the
SWEB plot, arch spacing and amplitude correspond to half the period of marine
microseism at Virgo (${\sim}3$ s) and a ground velocity of about 8 $\mu{\rm
m/s}$. The predictor for BENCH-MIRROR is shown in black, while the predictors
computed from mirror and bench motions are shown in blue and red,
respectively. The overlap shows that BENCH-MIRROR is the best predictor of
scattered light, closely matching the observed arches.

Glitches due to microseism often resemble arches in a time-frequency map, as
illustrated for example in Fig. 18. Arches are the typical signature of
scattered light (SL) noise processes, which is a major issue and topic of
investigation in the second generation GW detectors [12, 13, 14, 41, 42, 43].

A stray light beam bouncing off a moving surface adds coherently to the beam
main mode every time its optical path, $x(t)$, changes (increases or
decreases) by an integer wavelength. It follows that the frequency of the
strain noise is:

$f_{sc}(t)=\frac{2n|\dot{x}(t)|}{\lambda}$ (3)

where $\dot{x}(t)$ is the instantaneous relative velocity between the
interferometer beam and the scatterer, and $\lambda=1.064~{}\mu{\rm m}$ is the
Virgo laser wavelength. Equation 3 is referred to as predictor. In case the
scattered beam encounters a second reflective surface it can bounce back and
forth $n$ times along the same path before recombining, giving rise to higher
order noise arches, reaching out $n$-times larger frequencies.

In O3 the main sources of SL affecting the sensitivity were the suspended
optical benches placed beyond the end test masses in the terminal buildings
(SNEB, SWEB). In this case, the noise observed in the time-frequency domain is
well visible as power fluctuations in the cavity. The noise appears as a
series of arches, where the typical non-stationarity and non-linearity of the
noise is evident. Arch time spacing is the half-period of the oscillation of
the mirror-bench relative motion, and arch amplitude (i.e. the maximum
frequency extension of the induced strain noise) is
$f_{max}=(4\pi/\lambda)AFn$ where $A$ and $F$ are the amplitude and frequency
of the oscillation. If the frequency and amplitude of the oscillation are such
that $f_{max}>10$ Hz, the noise affects the GW detection frequency band.

Being those benches suspended and controlled [44], their motion induced by the
microseism was supposed to be attenuated enough to push the maximum frequency
of the arches below 10 Hz. Moreover, a control technique taking into account
the mirror-bench differential signals was implemented in order to reduce their
relative motion (BENCH-MIRROR), which is the quantity effectively responsible
of the noise coupling.

During O3, a malfunctioning was identified in the mechanical setting of the
West Bench suspension (SWEB) which caused its actual motion to be comparable
to the ground motion at the frequency of the main microseismic peak. Figure 18
shows the mirror contribution and the bench contribution to the arches
separately, for both North and West cavity, in two selected bad-weather
conditions. In the West arm power spectrogram, the typical pattern is visible:
the arches were entirely due to SWEB motion, and all the times the ground
motion exceeded a certain threshold during the run, these arches entered the
detector band. In the North arm power spectrogram, the arches were normally
much lower, and the contribution from the bench motion was of the same
magnitude as the mirror motion. It was even possible to find some special
conditions (the largest component of the ground motion centered at 0.2 Hz), in
which the mirror motion was prevalent (see Figure 18, bottom panel).

The issue concerning SWEB mechanics and control has been understood and cured
after O3. In O4, its residual motion is expected to be at least similar to the
one observed in O3 for SNEB. Further improvements in the control strategy will
be tested for both the mirror and the bench suspension.

####  5.1.4 Identification of scattered light culprits

Part of the effort regarding SL noise mitigation consists in the localisation
of SL sources, referred to as culprit, through data analysis. This can be a
difficult and time consuming operation in a km-long detector with many
possible sources of SL. Adaptive algorithms for time series analysis can be
used to this end, due to their ability to decompose non-linear and non-
stationary data into a set of oscillatory modes [45, 46, 47]. The methodology
described in [46] and based on the time varying filter empirical mode
decomposition (tvf-EMD) [48] adaptive algorithm is applied to the two data
segments shown in Fig. 18. SL noise couples with the differential motion of
the arm cavities (DARM, the Virgo longitudinal degree of freedom sensitive to
GW) time series, which is first low-passed and then decomposed using tvf-EMD
to extract its oscillatory modes, from which the instantaneous amplitude (IA)
is obtained using the Hilbert transform. Computing Equation 3 for a broad list
of position sensors and correlating with the IA of DARM’s oscillatory modes
allows to quickly identify the most correlated channel, i.e. the culprit. The
two data segment considered are

  * •

GPS: 1261098365 UTC - 2019/12/23 01:05:47 + 60s,

  * •

GPS: 1267253975 UTC - 2020/03/03 06:59:17 + 60s.

Obtained results are reported in Fig. 19, showing the predictors of the
culprit for the end benches, based on Equation 3, correlated with the IA of
DARM. The culprits are related to the BENCH-MIRROR channel in both cases. The
resulting values of correlation are $\rho=0.73$ for SWEB and $\rho=0.72$ for
SNEB. Since after low-passing the data the first two oscillatory modes of DARM
were found to be the most correlated with the same predictor, the sum of their
IA is considered and is shown in Fig. 19 for both cases, referred to as
_combo_. As a counter proof, in Fig. 18 the predictors of the culprits are
overlapped on the spectrograms of the WEB and NEB photodiodes. It can be seen
that they closely match the SL arches. In particular, for the SWEB case, the
mirror motion is small and the bench motion is mainly responsible for the
observed SL. For SNEB case, while the mirror motion is significant the BENCH-
MIRROR predictor, identified with adaptive analysis, better matches the arches
also in this case.

Figure 19: In red is the culprit’s predictor, i.e. Equation 3 for the relative
motion (diff) between the suspended end bench and the end mirror (BENCH-
MIRROR) of the West end (left) and North end (right). The sum of the IA of the
first two modes of DARM, extracted by tvf-EMD is shown in blue.

###  5.2 Impact of wind

Figure 20: Wind statistics as measured by the EGO weather station during the
O3 run. The left plot shows the joint distribution of the wind speed and
orientation, with the two black bars showing the directions of the two arms of
the Virgo detector. The right plot shows the complementary cumulative
distribution of the wind speed for each of the eight quadrants of the wind
rose.

Figure 20 summarizes the wind statistics recorded at EGO during the O3 run.
Wind is blowing more often from the East while the stronger winds are
predominantly coming from the West – the sea shore. The method described in
Sec. 3.2.1 can be applied to quantify the impact of the instantaneous wind
speed on the sensitivity. Figure 21 shows that the sensitivity is pretty much
unaffected until a wind speed of ${\sim}20-25$ km/h, while the detector gets
sensitive to larger speeds: the BNS range decrease exceeds ${\sim}4$ Mpc for a
wind speed of 50 km/h or above. Yet this variation is limited (about 10% of
nominal BNS range values during O3), meaning that the detector is quite robust
against wind. Another consequence of high-wind conditions is the need for the
Virgo global control system to use larger corrections to keep the instrument
at its nominal working point. And the larger these corrections, the more the
detector is vulnerable to additional disturbances that could make the
corrections saturate and lead to an almost immediate control loss.

Figure 21: Average variation of the BNS range around its local average, as a
function of the wind speed. In the Virgo DAQ, the BNS range and the wind speed
are updated every 4 and 2 seconds, respectively.

The effect of the wind speed is clearly visible on Fig. 22 that compares the
complementary cumulative distribution functions of the kilometric Fabry-Perot
cavity longitudinal corrections for different ranges of wind speed. Clearly,
the larger the wind speed, the higher the correction. On this plot, the
average wind speed and the maximum correction have been computed using non-
overlapping time windows of 30 seconds each. The largest displayed correction
range stops on purpose at 9 V because the actual physical correction saturates
at 9.5 V, a value that can be reached or even exceeded when there is a control
loss. As the control system has some small but non-zero internal latency, it
is not always clear whether the observed saturation is the cause of the
control loss or a consequence of it. Therefore, for a cumulative plot like the
one shown on Fig. 22, corrections above 9 V have been cut away to avoid
contamination from correction signals posterior to control losses.

Figure 22: O3 complementary cumulative distribution functions of the maximum
longitudinal corrections (in volts) keeping the Virgo arm cavities resonant
for different wind speed ranges. The mean wind speed and the maximal
corrections have been computed over 30 s time windows. The $x$-axis ends at 9
V, a bit below the saturation level of 9.5 V for that particular correction.

###  5.3 Disentangling sea activity and wind

Fig. 23 attempts to disentangle the impact of high microseism levels (due to
the nearby rough sea) and high wind, by looking at the O3 Virgo duty cycle as
a function of the microseism level for three different wind conditions: no cut
on wind speed (blue histogram); low wind speed (below 25 km/h, green); high
wind speed (above 25 km/h, red). One can see that in low wind conditions the
duty cycle is pretty much independent from microseismicity, whereas it is
lower and decreases more quickly when the wind level increases. Therefore, the
Virgo detector appears robust against microseism but more sensitive to wind.
Note that the extreme bins on the histograms plotted on Fig. 23 may have low
statistics compared to others (low wind and high microseism, or high wind and
low microseism are rare conditions): this explains why the duty cycles
reported there fluctuate significantly compared to neighboring bins.

Figure 23: Virgo duty cycle during the O3 run versus microseism activity, for
three different wind conditions: blue $\leftrightarrow$ no cut on wind speed;
green $\leftrightarrow$ low wind (speed below 25 km/h); red $\leftrightarrow$
high wind (speed above 25 km/h).

##  6 Other environment impacts

Additional sources of external noise have potential impact on the
interferometer. Hereafter we describe those sources that we have further
investigated during O3, namely: Schumann’s resonance magnetic fields,
lightning strikes and cosmic ray muons.

###  6.1 Magnetic noise

Figure 24: Amplitude spectral densities of indoor (blue curve) and outdoor
(red curve) magnetometers at EGO and at Sos Enattos mine in Sardinia (black
curve). The quiet Sos Enattos location shows evidence of Schumann resonances
peaked at approximately 8, 14, 21, 27 and 33 Hz.

Ambient magnetic fields can couple to GW interferometers, for example through
the magnetic actuators used for the control of the seismic isolation platforms
of optical components and of the test masses [12, 49]. Like gravitational
waves, electromagnetic (EM) waves travel at the speed of light, and, due to
their strength, could affect multiple detectors with time differences
compatible with those expected from some GW.

Magnetic fields that extend over the entire planet, such as the Schumann
resonances [50] (SR), or large-current lightning strikes, can limit the
sensitivity to GW signals correlated over multiple detectors [51, 52]. One
purpose of the EGO external magnetometers (see Sec. 2) is to monitor the level
of these global magnetic fields.

At Virgo, the external magnetic environment is much quieter than inside
experimental halls where stray magnetic fields are radiated by electric loads
and cables where large currents are circulating. Figure 24 compares inside and
outside magnetometer spectra recorded at Virgo during O3 and in the very quiet
environment inside the Sos Enattos mine in Sardinia [53]. The most intense
spectral noise features are narrow lines at the 50 Hz electric mains frequency
and its odd harmonics. The RMS amplitude of the 50 Hz line measured at Virgo
is of the order of 0.1 nT in the external location, while it is at least 50
times larger in any inside location.

Virgo external magnetometers detect the SR field. This consists of steady EM
waves that resonate inside the waveguide formed by the Earth surface and the
ionosphere, and which are excited by globe-wide lightning activity. The second
and third SR modes (peak frequency around 14 Hz and 21 Hz, respectively) are
visible above noise at almost any time, their median amplitude during O3 is a
few tenth of pT, their intensity follows a 24-hour modulation. The measured
daily modulation of the third SR mode is shown in Fig. 25. This modulation is
thought to be associated to temperature-driven variations in the height of the
ionosphere EM waveguide [54]. The first SR mode and those of order greater
than three, are often covered by anthropogenic magnetic noise. Figure 25 shows
that during the COVID-19 lockdown period from March to May 2020, the external
magnetic field median RMS in the low frequency region from 1 to 6 Hz reduced
by about 50$\%$ with respect to the reference period between December 2019 and
February 2020. At the same time, the magnetic field RMS amplitude between 18
Hz and 24 Hz around the $3^{rd}$ Schumann mode, did not change appreciably.

At EGO, anthropogenic external magnetic noise follows a daily modulation:
broad maxima during working hours and minima around 01:00 LT. This noise has
the form of short transients with intensity of $\approx 10$ pT extending from
DC up to approximately 20 Hz. We believe this noise is associated to train
transits along railway tracks at about 6 km distance from the site. The sudden
trunk-line change when a train passes from an electro-duct section to another
one creates stray currents and magnetic fields that are observed as magnetic
glitches at EGO. According to the measured coupling of ambient fields [12] we
estimate a negligible impact of Schumann’s and anthropogenic magnetic noise on
the sensitivity of the future Virgo upgrades. More relevant might be the
impact of the correlated Schumann noise on multiple interferometers, which is
under evaluation.

Figure 25:  Weekly averaged magnetic field band-limited RMS values computed in
two frequency bands: 1 to 6 Hz (dashed) and 18 to 24 Hz (solid). Magnetic
field intensity is measured externally of Virgo experimental buildings, in the
reference period between December 2019 and February 2020 (blue curves) and in
the period between March 15 and May 15 (red curves) which corresponds to
reduced anthropogenic activity within and outside of EGO because of the
COVID-19 pandemics.

###  6.2 Lightnings

Lightning strikes produce prompt EM waves and much slower air pressure waves
which induce vibrations of the ground and of the detector mechanical
components. There are studies of correlated lightnings noise between the Virgo
and LIGO sites [52] and at the KAGRA underground observatory [55].

The typical effect of the impact of a lightning strike occurring at
approximately 10 kilometers from the Virgo detector during O3 is illustrated
in Fig. 26. A distinctive feature of lightning strikes is a coincident short
transient noise in magnetometers located inside the 3 km-distant Virgo
experimental buildings (top graph of Fig. 26). The magnetic impulse is
followed by the slower sound shock wave detected by seismometers (middle graph
of Fig. 26). The bulk of displacement noise reaching the buildings is below 10
Hz.

The bottom graph of Fig. 26 illustrates the effect of the lightning in the GW
strain signal. In coincidence with the spike in magnetometers, we observe a
prompt broadband low-frequency noise and the onset of a 48 Hz narrow spectral
noise, with a minute-long decay time, leading to a ${\sim}30$% drop of the
live BNS range. This latter noise has been associated to one structural mode
of the West end test mass suspension, which gets excited because of the
coupling of ambient magnetic fields with the magnetic actuators located along
the suspension. Moreover, associated with the delayed acoustic and seismic
bursts of ambient noise reaching the experimental buildings, a broadband
strain noise shows up, extending up to about 100 Hz. This is likely due to
scattered light processes within the interferometer.

Data quality flags triggered by lightning strikes were produced during the O3
run; they proved useful in a test aiming at filtering out part of the false-
alarm triggers found by a real-time transient GW search [19]. Further studies
are planned during the O4 run preparation.

Figure 26: Impact on the Virgo environment and detector of one lightning
strike which occurred 6 to 10 km away from Virgo buidings on November 15, 2019
at 23:25:51 UTC. The spectrograms of a few relevant signals are shown. (Top) A
prompt magnetic transient is detected by magnetometers at the time of the
event, marked by the red vertical line. (Middle) A few tens of seconds later,
a seismic (and acoustic, not shown) transient is detected in the central
experimental area, marked by the black vertical line. The bottom spectrogram
shows the reconstructed GW strain during the same time interval. The red
vertical line marks the lightning strike occurence, the black, magenta and
white vertical lines mark the occurrence of seismic transients detected in the
CEB, NEB and WEB, respectively.

###  6.3 Cosmic muons

Ground-based GW detectors are constantly passed through by _muons_ , produced
by the interaction of cosmic rays with Earth’s atmosphere [56]. These
energetic charged particles can interact with the detector test masses and
constitute an additional source of noise, as addressed in the literature since
the first prototypes of resonant mass GW detectors [57, 58, 59, 60].

We report here the preliminary results on the first measurement of potential
effects of these muons on the Virgo detector noise. This study has been
carried out by means of about $17$ days, at the end of the O3b run, of joint
data acquisition of Virgo and a muon telescope designed by the IP2I laboratory
[61], installed in the CEB close to the beam splitter mirror. Two kind of
tests have been performed. In the first one, we have evaluated whether the
rate of muons in the correspondence of GW candidate events was larger than the
reference values of the period: we have found no statistical evidence of an
excess of muons in correspondence of these triggers. In the second test, we
have estimated the correlation of this rate with the rate of glitches in Virgo
noise. Figure 27 shows the time series corresponding to the rates of glitches
and muons, averaged on strides of 30 minutes. Here, a correlation is clearly
evident. This is actually not surprising, for the number of the muons arriving
at ground being highly dependent on air density and ultimately on parameters
like atmospheric pressure and temperature. These quantities are also witnesses
of the weather conditions, which in turn can determine an increase of the
detector noise, as we have commented in Sec. 5.1. Therefore, both the
variations of these rates share the same main cause, which explain their large
correlation. Once the effects of the atmospheric conditions are removed via a
regression analysis, the residuals exhibit no significant correlation.

Figure 27: Correlation between muon and glitch rates. The blue line represents
the time series of the average rate per minute of muons while the red one is
the time series of the rate per minute of glitches with $\mathrm{SNR}>4.5$ and
frequency at peak in $[10,4096]~{}\mathrm{Hz}$ as identified by the Omicron
pipeline [40]. Gaps in the latter correspond to periods when the detector was
not taking data.

##  7 Outlook and prospects for O4

The Virgo detector performances are affected by external environment
conditions; in particular, seismic noise, earthquakes, bad weather, magnetic
noise and lightnings have an impact on the detector sensitivity or duty cycle.
The main coupling mechanisms are: direct excitation of suspended mirrors,
vibration of experimental buildings, shaking of benches hosting auxiliary
optical systems, disturbances on critical electronic equipment, SL.

If the detector control system is able to manage the effect of a disturbance,
the interferometer can remain at its working point with a reduced sensitivity.
Otherwise the global control gets lost and the procedure to recover it has to
be started again from the beginning, thus impacting on the duty cycle – see A.

In this work we reported the results of the analysis of such events during the
O3 run. Thanks to the large amount of data collected, we were able to perform
a careful statistical analysis of the impact of several kinds of external
disturbances.

The results confirm that the Virgo detector is a very robust apparatus. The
sensitivity reduction due to anthropogenic seismic noise is very low: less
than 2% in terms of BNS range. Also the degradation due to the wind is
limited: it appears only for wind speeds larger than 25 km/h, reaching a
sensitivity reduction as large as 10% only for very high speed (larger than 50
km/h).

In these cases, the sensitivity reduction is due to an increased noise at low
frequency as well as to the appearance of short high frequency glitches. In
few cases, such kind of noise was indirectly originated by lightnings.

Both microseism and wind have an impact on the detector duty cycle, since the
increasing correction signals acting on the mirror during bad weather can
saturate, finally resulting to a control loss. It results that the Virgo
detector global control is more robust against microseism while it is less
effective against strong wind.

The analysis of control losses during O3 confirms that earthquakes are a
relevant source of these. The Seismon framework, useful to keep the detector
in a safe state to try to avoid loosing its control during such events, was
used during the whole O3 run and it is now being upgraded for the next
scientific run.

An upgrade of the EMS is in progress to better face the influence of external
disturbances: installation of a new lightning detector in the central area;
installation of two additional weather stations at the end buildings to
monitor local wind gusts; and the installation of more sensitive
accelerometers on locations prone to light scattering (viewports, external
optical benches, etc.).

These actions, together with several other upgrades of the Virgo detector,
already performed or presently in progress, will have a crucial role for the
success of the next scientific run O4, which is expected to start at the end
of 2022.

## Acknowledgements

The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica
Nucleare (INFN), the French Centre National de la Recherche Scientifique
(CNRS) and the Netherlands Organization for Scientific Research (NWO), for the
construction and operation of the Virgo detector and the creation and support
of the EGO consortium. The authors also gratefully acknowledge research
support from these agencies as well as by the Spanish Agencia Estatal de
Investigación, the Consellera d’Innovació, Universitats, Ciència i Societat
Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de
Catalunya, Spain, the National Science Centre of Poland and the European Union
– European Regional Development Fund; Foundation for Polish Science (FNP), the
Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of
Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS-FNRS),
Actions de Recherche Concertées (ARC) and Fonds Wetenschappelijk Onderzoek –
Vlaanderen (FWO), Belgium, the European Commission. The authors gratefully
acknowledge the support of the NSF, STFC, INFN, CNRS and Nikhef for provision
of computational resources.

We would like to thank all of the essential workers who put their health at
risk during the COVID-19 pandemic, without whom we would not have been able to
complete this work.

##  Appendix A Study of the control losses during O3

The Virgo detector needs to be controlled accurately in order to be sensitive
to gravitational-wave signals [17, 18]. Schematically, there is an automated
procedure [19] that brings the instrument from an initial state where the
optics and the laser are controlled independently one from another, to the
nominal state where the different optical cavities are jointly resonant and
the interferometer itself is used as a length etalon to control further the
laser frequency. That procedure typically takes about 15-20 minutes and
requires 1-2 attempts to complete. Then, the global control of the detector is
kept as long as possible, with feedback loops maintaining Virgo at its nominal
working point. When that control is lost for whatever reason, data taking
stops and the control acquisition procedure has to be started again. This
leads to a decrease of the instrument duty cycle and can cause transient
gravitational waves to be missed. Therefore, it is important to find out the
causes of the control losses and to use this information to improve the
feedback systems and make them more robust.

As explained in Sec. 4 above, a global study of the control losses was needed
to be able to extract those likely due to earthquakes. It was decided to focus
on the 601 control losses that occurred during O3 while the detector was
taking data in nominal conditions (Science mode), to be sure that no
particular human action was happening on the instrument at any of these times.
Related to the duration of the O3 run (about 11 months) and to the duty cycle
of the Virgo detector (about 75%), this corresponds to about 1 control loss
every 10 hours of data taking on average. And, in reality, uninterrupted data
taking stretches could be much longer as control losses usually cluster in
time when a particular problem impacts the detector.

The first part of the study was to define the time the control loss occurred
for each of these events. For that, we have used three different Virgo DAQ
channels.

  * •

Two fast channels, sampled at 10 kHz: ARM_POWER, latching when the power
stored in the kilometric arm cavities goes below some threshold, meaning that
they are not resonant anymore; DARK_FRINGE_SHUTTER, triggered when the fast
shutter protecting the dark fringe photodiodes from an excess of light [2]
closes.

  * •

One slow channel, sampled at 1 Hz: AUTOMATION_STATUS, monitoring the global
status of the detector, as seen by the automation process that steers the
instrument.

ARM_POWER | DARK_FRINGE_SHUTTER | AUTOMATION_STATUS | Total
---|---|---|---
14 | 559 | 28 | 601
Table 2: Number of control losses in Science mode witnessed first by each DAQ
channel used to time accurately control losses. As expected, the two fast
channels are by far those that detect a control loss first. Most of the time
the fast shutter protecting the dark fringe photodiodes closes before the arm
power loss gets large enough to trigger the other fast channel.

The time of a control loss is defined as the earliest time one of these three
switches flips from its nominal value to the value corresponding to an
uncontrolled detector. Most of the time, as expected, the fast channels are
the first ones to latch. And they do almost simultaneously, given that the
cavity resonance losses are all connected. Though, in practice, the dark
fringe shutter closes almost always before the cavity arm power has decreased
below its nominal threshold. In addition there are a few cases for which the
central automation system triggers first a shutdown of the detector global
control, either because it has detected an issue or because it has received a
manual abort request from the operator on duty. Table 2 shows the breakout of
witnesses for the O3 control losses that occurred while taking Science data.

Then, the selected strategy consists in testing several hypothesis in parallel
for each of these events – the main hypothesis investigated are listed in
Tabs. 3 and 4 and documented in the neighbouring text.

Various algorithms scanning the data around the control loss have thus been
developed, with the twofold goals of being

  * •

complete: to have as many control losses as possible tagged by at least one
control loss hypothesis;

  * •

selective: to find the right control loss origin as often as possible.

Achieving (close to) completeness requires testing many hypothesis, while a
profusion of algorithms could be detrimental to the selectivity of the method.
Therefore, the classification starts with a subset of hypothesis, those that,
when identified, certainly cause a control loss and are also very likely to be
the root cause of that particular event. Obvious examples in that category –
called sure in the following – are control losses induced manually by the
operator on duty, or hardware problems unambiguously identified by the real-
time monitoring system of the Virgo detector. These control loss hypothesis
are independent by definition and the associated algorithms should be
selective. This has been checked by processing the 601 O3 control losses
studied. All these events have been associated with at most one control loss
hypothesis belonging to the sure category: 24% with one, 76% with none.

Error | Manual | Hardware | Control software | PI | Earthquakes | Total
---|---|---|---|---|---|---
2 | 10 | 92 | 7 | 2 | 30 | 143 (24%)
Table 3: Sure causes for 143 O3 control losses in Science mode – see text for
details.

Table 3 provides details about the 143 control losses whose cause has been
tagged as sure, as described above. The dominant class is hardware problems,
mainly transient interruptions of the data flow coming from some suspensions
and causing feedback control systems to fail. The faulty components have been
identified and replaced during the post-O3 shutdown and upgrade phase.
Therefore, these problems are not expected to reoccur during the O4 run. Then,
earthquakes are the second most common source of control losses in the sure
category; about three times a month on average. Manual control losses induced
by the operator on shift follow: they are due to the need to switch from
nominal data taking to another task: weekly maintenance, regular calibration
or commissioning activity. In O4 and beyond, such control losses should no
longer occur as the procedure will be updated to require leaving Science mode
before manually aborting the control. In 7 cases (only 1% of the total control
losses) the source of the event could be traced to some software problem; 2
more cases were due to human errors.

Finally, two control losses are labelled as PI for parametric instabilities,
an optomechanical phenomenon due to the interaction between optical and
mechanical modes of the detector and that had been observed at LIGO in 2015
before finally being seen in Virgo as well in January 2020 [62]. If not
mitigated, a PI can make control systems saturate in a deterministic way
(meaning that the saturation will consistently reoccur as long as the detector
remains in a configuration favourable for its appearance and growth), thus
impacting the detector duty cycle. Moreover, it is impossible to predict
exactly what combinations of the instrument parameters will lead to a PI.
Therefore, a dedicated simulation framework has been developed to estimate the
susceptibility of Virgo to PIs during O3, for O4, and beyond [63].

| Fast
---
unlocks
| Actuation
---
saturation
| DARM
---
control
inaccuracy
| Power
---
loss in
sidebands
| Arm
---
power
asymmetry
| Likely
---
missing
data
| Automation
---
decision
Others | Total
173 | 85 | 77 | 22 | 4 | 10 | 23 |  | 64
---
(11%)
| 458
---
(76%)
Table 4:  Breakout by category of control losses not tagged as sure. 64 (about
11% of the total number of control losses recorded in Science mode during the
O3 Virgo run) control losses have not been accurately classified, either
because none of the tested hypothesis seemed to match the recorded data or
because too many hypothesis were found matching, making their classification
unconclusive. Further studies will be done when pre-O4 control losses data
become available, in order to make the current classification more complete.

Table 4 describes how the remaining control losses $({\sim}76\%)$ have been
classified. 11% of the total remain unclassified, either because none of the
hypothesis tested matched, or because too many did and there was no clear way
to find out which one was the root cause (if identified).

The largest category by far (29%) are the so-called fast unlocks, events that
are almost instantaneous and occur within the laser injection system, upstream
of the interferometer. Such control losses have been present for years, at
rates that strongly vary over time, ranging from crisis periods lasting some
hours to very quiet times. This past Summer, following detailled
investigations of the fast unlock characteristics, this problem has been
finally solved by installing [64] a pigtailed Electro-Optic-Modulator (EOM)
with a larger dynamic, and thus able to compensate the laser frequency
glitches that were found to be the cause of fast unlocks. They have not
reoccurred since this new EOM has been operated.

The next five categories are all related to the variety of feedback control
systems that are running in parallel to keep the whole detector at its nominal
working point. Improving the accuracy and the robustness of these systems
while making the instrument more complex and thus more sensitive to the
passing of gravitational wave is a permanent challenge, taken up during each
upgrade or commissioning phase.

The analysis of the O3 control losses has been made using two independent
software frameworks whose results have been compared: they have been found in
good agreement, in particular for the dominant control loss categories. With
the experience gained during O3, the goals for O4 are to improve the
monitoring of the control losses and to reduce the latency of their analysis.
A software framework similar to the Data Quality Reports (DQR) [19, 65, 66]
used to vet in real time the gravitational-wave transient candidates that are
significant enough to trigger a public alert is under development: here
control losses play the role of GW event candidates and the set of checks run
to assess the data quality is replaced by the test of the various hypotheses
for control loss. This upgraded tool will be available to improve the overall
performance of the instrument during the commissioning phase and associated
noise-hunting for the new, double-recycled Advanced Virgo interferometer.

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