Invariant Distribution of Promoter Activities in Escherichia coli

Cells need to allocate their limited resources to express a wide range of genes. To understand how Escherichia coli partitions its transcriptional resources between its different promoters, we employ a robotic assay using a comprehensive reporter strain library for E. coli to measure promoter activity on a genomic scale at high-temporal resolution and accuracy. This allows continuous tracking of promoter activity as cells change their growth rate from exponential to stationary phase in different media. We find a heavy-tailed distribution of promoter activities, with promoter activities spanning several orders of magnitude. While the shape of the distribution is almost completely independent of the growth conditions, the identity of the promoters expressed at different levels does depend on them. Translation machinery genes, however, keep the same relative expression levels in the distribution across conditions, and their fractional promoter activity tracks growth rate tightly. We present a simple optimization model for resource allocation which suggests that the observed invariant distributions might maximize growth rate. These invariant features of the distribution of promoter activities may suggest design constraints that shape the allocation of transcriptional resources

A Coarse-Grained Biophysical Model of E. coli and Its Application to Perturbation of the rRNA Operon Copy Number

We propose a biophysical model of Escherichia coli that predicts growth rate and an effective cellular composition from an effective, coarse-grained representation of its genome. We assume that E. coli is in a state of balanced exponential steadystate growth, growing in a temporally and spatially constant environment, rich in resources. We apply this model to a series of past measurements, where the growth rate and rRNA-to-protein ratio have been measured for seven E. coli strains with an rRNA operon copy number ranging from one to seven (the wild-type copy number). These experiments show that growth rate markedly decreases for strains with fewer than six copies. Using the model, we were able to reproduce these measurements. We show that the model that best fits these data suggests that the volume fraction of macromolecules inside E. coli is not fixed when the rRNA operon copy number is varied. Moreover, the model predicts that increasing the copy number beyond seven results in a cytoplasm densely packed with ribosomes and proteins. Assuming that under such overcrowded conditions prolonged diffusion times tend to weaken binding affinities, the model predicts that growth rate will not increase substantially beyond the wild-type growth rate, as indicated by other experiments. Our model therefore suggests that changing the rRNA operon copy number of wild-type E. coli cells growing in a constant rich environment does not substantially increase their growth rate. Other observations regarding strains with an altered rRNA operon copy number, such as nucleoid compaction and the rRNA operon feedback response, appear to be qualitatively consistent with this model. In addition, we discuss possible design principles suggested by the model and propose further experiments to test its validity

Prediction of Cellular Burden with Host--Circuit Models

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognised bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modelling paradigm can facilitate fast and robust design cycles in synthetic biology

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Volume III. DUNE far detector technical coordination

open966siAcknowledgments This document was prepared by the DUNE collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. The DUNE collaboration also acknowledges the international, national, and regional funding agencies supporting the institutions who have contributed to completing this Technical Design Report.The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay-these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- A nd dual-phase DUNE liquid argon TPC far detector modules. Volume III of this TDR describes how the activities required to design, construct, fabricate, install, and commission the DUNE far detector modules are organized and managed. This volume details the organizational structures that will carry out and/or oversee the planned far detector activities safely, successfully, on time, and on budget. It presents overviews of the facilities, supporting infrastructure, and detectors for context, and it outlines the project-related functions and methodologies used by the DUNE technical coordination organization, focusing on the areas of integration engineering, technical reviews, quality assurance and control, and safety oversight. Because of its more advanced stage of development, functional examples presented in this volume focus primarily on the single-phase (SP) detector module.openAbi B.; Acciarri R.; Acero M.A.; Adamov G.; Adams D.; Adinolfi M.; Ahmad Z.; Ahmed J.; Alion T.; Monsalve S.A.; Alt C.; Anderson J.; Andreopoulos C.; Andrews M.; Andrianala F.; Andringa S.; Ankowski A.; Antonova M.; Antusch S.; Aranda-Fernandez A.; Ariga A.; Arnold L.O.; Arroyave M.A.; Asaadi J.; Aurisano A.; Aushev V.; Autiero D.; Azfar F.; Back H.; Back J.J.; Backhouse C.; Baesso P.; Bagby L.; Bajou R.; Balasubramanian S.; Baldi P.; Bambah B.; Barao F.; Barenboim G.; Barker G.; Barkhouse W.; Barnes C.; Barr G.; Monarca J.B.; Barros N.; Barrow J.L.; Bashyal A.; Basque V.; Bay F.; Alba J.B.; Beacom J.F.; Bechetoille E.; Behera B.; Bellantoni L.; Bellettini G.; Bellini V.; Beltramello O.; Belver D.; Benekos N.; Neves F.B.; Berger J.; Berkman S.; Bernardini P.; Berner R.M.; Berns H.; Bertolucci S.; Betancourt M.; Bezawada Y.; Bhattacharjee M.; Bhuyan B.; Biagi S.; Bian J.; Biassoni M.; Biery K.; Bilki B.; Bishai M.; Bitadze A.; Blake A.; Siffert B.B.; Blaszczyk F.; Blazey G.; Blucher E.; Boissevain J.; Bolognesi S.; Bolton T.; Bonesini M.; Bongrand M.; Bonini F.; Booth A.; Booth C.; Bordoni S.; Borkum A.; Boschi T.; Bostan N.; Bour P.; Boyd S.; Boyden D.; Bracinik J.; Braga D.; Brailsford D.; Brandt A.; Bremer J.; Brew C.; Brianne E.; Brice S.J.; Brizzolari C.; Bromberg C.; Brooijmans G.; Brooke J.; Bross A.; Brunetti G.; Buchanan N.; Budd H.; Caiulo D.; Calafiura P.; Calcutt J.; Calin M.; Calvez S.; Calvo E.; Camilleri L.; Caminata A.; Campanelli M.; Caratelli D.; Carini G.; Carlus B.; Carniti P.; Terrazas I.C.; Carranza H.; Castillo A.; Castromonte C.; Cattadori C.; Cavalier F.; Cavanna F.; Centro S.; Cerati G.; Cervelli A.; Villanueva A.C.; Chalifour M.; Chang C.; Chardonnet E.; Chatterjee A.; Chattopadhyay S.; Chaves J.; Chen H.; Chen M.; Chen Y.; Cherdack D.; Chi C.; Childress S.; Chiriacescu A.; Cho K.; Choubey S.; Christensen A.; Christian D.; Christodoulou G.; Church E.; Clarke P.; Coan T.E.; Cocco A.G.; Coelho J.; Conley E.; Conrad J.; Convery M.; Corwin L.; Cotte P.; Cremaldi L.; Cremonesi L.; Crespo-Anadon J.I.; Cristaldo E.; Cross R.; Cuesta C.; Cui Y.; Cussans D.; Dabrowski M.; Motta H.D.; Peres L.D.S.; David Q.; Davies G.S.; Davini S.; Dawson J.; De K.; Almeida R.M.D.; Debbins P.; Bonis I.D.; Decowski M.; Gouvea A.D.; Holanda P.C.D.; Astiz I.L.D.I.; Deisting A.; Jong P.D.; Delbart A.; Delepine D.; Delgado M.; Dell'acqua A.; Lurgio P.D.; Neto J.R.D.M.; Demuth D.M.; Dennis S.; Densham C.; Deptuch G.; Roeck A.D.; Romeri V.D.; Vries J.D.; Dharmapalan R.; Dias M.; Diaz F.; Diaz J.; Domizio S.D.; Giulio L.D.; Ding P.; Noto L.D.; Distefano C.; Diurba R.; Diwan M.; Djurcic Z.; Dokania N.; Dolinski M.; Domine L.; Douglas D.; Drielsma F.; Duchesneau D.; Duffy K.; Dunne P.; Durkin T.; Duyang H.; Dvornikov O.; Dwyer D.; Dyshkant A.; Eads M.; Edmunds D.; Eisch J.; Emery S.; Ereditato A.; Escobar C.; Sanchez L.E.; Evans J.J.; Ewart E.; Ezeribe A.C.; Fahey K.; Falcone A.; Farnese C.; Farzan Y.; Felix J.; Fernandez-Martinez E.; Menendez P.F.; Ferraro F.; Fields L.; Filkins A.; Filthaut F.; Fitzpatrick R.S.; Flanagan W.; Fleming B.; Flight R.; Fowler J.; Fox W.; Franc J.; Francis K.; Franco D.; Freeman J.; Freestone J.; Fried J.; Friedland A.; Fuess S.; Furic I.; Furmanski A.P.; Gago A.; Gallagher H.; Gallego-Ros A.; Gallice N.; Galymov V.; Gamberini E.; Gamble T.; Gandhi R.; Gandrajula R.; Gao S.; Garcia-Gamez D.; Garcia-Peris M.A.; Gardiner S.; Gastler D.; Ge G.; Gelli B.; Gendotti A.; Gent S.; Ghorbani-Moghaddam Z.; Gibin D.; Gil-Botella I.; Girerd C.; Giri A.; Gnani D.; Gogota O.; Gold M.; Gollapinni S.; Gollwitzer K.; Gomes R.A.; Bermeo L.G.; Fajardo L.S.G.; Gonnella F.; Gonzalez-Cuevas J.; Goodman M.C.; Goodwin O.; Goswami S.; Gotti C.; Goudzovski E.; Grace C.; Graham M.; Gramellini E.; Gran R.; Granados E.; Grant A.; Grant C.; Gratieri D.; Green P.; Green S.; Greenler L.; Greenwood M.; Greer J.; Griffith C.; Groh M.; Grudzinski J.; Grzelak K.; Gu W.; Guarino V.; Guenette R.; Guglielmi A.; Guo B.; Guthikonda K.; Gutierrez R.; Guzowski P.; Guzzo M.M.; Gwon S.; Habig A.; Hackenburg A.; Hadavand H.; Haenni R.; Hahn A.; Haigh J.; Haiston J.; Hamernik T.; Hamilton P.; Han J.; Harder K.; Harris D.A.; Hartnell J.; Hasegawa T.; Hatcher R.; Hazen E.; Heavey A.; Heeger K.M.; Hennessy K.; Henry S.; Morquecho M.H.; Herner K.; Hertel L.; Hesam A.S.; Hewes J.; Pichardo A.H.; Hill T.; Hillier S.J.; Himmel A.; Hoff J.; Hohl C.; Holin A.; Hoppe E.; Horton-Smith G.A.; Hostert M.; Hourlier A.; Howard B.; Howell R.; Huang J.; Huang J.; Hugon J.; Iles G.; Iliescu A.M.; Illingworth R.; Ioannisian A.; Itay R.; Izmaylov A.; James E.; Jargowsky B.; Jediny F.; Jesus-Valls C.; Ji X.; Jiang L.; Jimenez S.; Jipa A.; Joglekar A.; Johnson C.; Johnson R.; Jones B.; Jones S.; Jung C.; Junk T.; Jwa Y.; Kabirnezhad M.; Kaboth A.; Kadenko I.; Kamiya F.; Karagiorgi G.; Karcher A.; Karolak M.; Karyotakis Y.; Kasai S.; Kasetti S.P.; Kashur L.; Kazaryan N.; Kearns E.; Keener P.; Kelly K.J.; Kemp E.; Ketchum W.; Kettell S.; Khabibullin M.; Khotjantsev A.; Khvedelidze A.; Kim D.; King B.; Kirby B.; Kirby M.; Klein J.; Koehler K.; Koerner L.W.; Kohn S.; Koller P.P.; Kordosky M.; Kosc T.; Kose U.; Kostelecky V.; Kothekar K.; Krennrich F.; Kreslo I.; Kudenko Y.; Kudryavtsev V.; Kulagin S.; Kumar J.; Kumar R.; Kuruppu C.; Kus V.; Kutter T.; Lambert A.; Lande K.; Lane C.E.; Lang K.; Langford T.; Lasorak P.; Last D.; Lastoria C.; Laundrie A.; Lawrence A.; Lazanu I.; Lazur R.; Le T.; Learned J.; Lebrun P.; Miotto G.L.; Lehnert R.; De Oliveira M.L.; Leitner M.; Leyton M.; Li L.; Li S.; Li S.; Li T.; Li Y.; Liao H.; Lin C.; Lin S.; Lister A.; Littlejohn B.R.; Liu J.; Lockwitz S.; Loew T.; Lokajicek M.; Lomidze I.; Long K.; Loo K.; Lorca D.; Lord T.; Losecco J.; Louis W.C.; Luk K.; Luo X.; Lurkin N.; Lux T.; Luzio V.P.; MacFarland D.; MacHado A.; MacHado P.; MacIas C.; MacIer J.; Maddalena A.; Madigan P.; Magill S.; Mahn K.; Maio A.; Maloney J.A.; Mandrioli G.; Maneira J.C.; Manenti L.; Manly S.; Mann A.; Manolopoulos K.; Plata M.M.; Marchionni A.; Marciano W.; Marfatia D.; Mariani C.; Maricic J.; Marinho F.; Marino A.D.; Marshak M.; Marshall C.; Marshall J.; Marteau J.; Martin-Albo J.; Martinez N.; Caicedo D.A.M.; Martynenko S.; Mason K.; Mastbaum A.; Masud M.; Matsuno S.; Matthews J.; Mauger C.; Mauri N.; Mavrokoridis K.; Mazza R.; Mazzacane A.; Mazzucato E.; McCluskey E.; McConkey N.; McFarland K.S.; McGrew C.; McNab A.; Mefodiev A.; Mehta P.; Melas P.; Mellinato M.; Mena O.; Menary S.; Mendez H.; Menegolli A.; Meng G.; Messier M.; Metcalf W.; Mewes M.; Meyer H.; Miao T.; Michna G.; Miedema T.; Migenda J.; Milincic R.; Miller W.; Mills J.; Milne C.; Mineev O.; Miranda O.G.; Miryala S.; Mishra C.; Mishra S.; Mislivec A.; Mladenov D.; Mocioiu I.; Moffat K.; Moggi N.; Mohanta R.; Mohayai T.A.; Mokhov N.; Molina J.A.; Bueno L.M.; Montanari A.; Montanari C.; Montanari D.; Zetina L.M.M.; Moon J.; Mooney M.; Moor A.; Moreno D.; Morgan B.; Morris C.; Mossey C.; Motuk E.; Moura C.A.; Mousseau J.; Mu W.; Mualem L.; Mueller J.; Muether M.; Mufson S.; Muheim F.; Muir A.; Mulhearn M.; Muramatsu H.; Murphy S.; Musser J.; Nachtman J.; Nagu S.; Nalbandyan M.; Nandakumar R.; Naples D.; Narita S.; Navas-Nicolas D.; Nayak N.; Nebot-Guinot M.; Necib L.; Negishi K.; Nelson J.K.; Nesbit J.; Nessi M.; Newbold D.; Newcomer M.; Newhart D.; Nichol R.; Niner E.; Nishimura K.; Norman A.; Northrop R.; Novella P.; Nowak J.A.; Oberling M.; Campo A.O.D.; Olivier A.; Onel Y.; Onishchuk Y.; Ott J.; Pagani L.; Pakvasa S.; Palamara O.; Palestini S.; Paley J.M.; Pallavicini M.; Palomares C.; Pantic E.; Paolone V.; Papadimitriou V.; Papaleo R.; Papanestis A.; Paramesvaran S.; Parke S.; Parsa Z.; Parvu M.; Pascoli S.; Pasqualini L.; Pasternak J.; Pater J.; Patrick C.; Patrizii L.; Patterson R.B.; Patton S.; Patzak T.; Paudel A.; Paulos B.; Paulucci L.; Pavlovic Z.; Pawloski G.; Payne D.; Pec V.; Peeters S.J.; Penichot Y.; Pennacchio E.; Penzo A.; Peres O.L.; Perry J.; Pershey D.; Pessina G.; Petrillo G.; Petta C.; Petti R.; Piastra F.; Pickering L.; Pietropaolo F.; Pillow J.; Plunkett R.; Poling R.; Pons X.; Poonthottathil N.; Pordes S.; Potekhin M.; Potenza R.; Potukuchi B.V.; Pozimski J.; Pozzato M.; Prakash S.; Prakash T.; Prince S.; Prior G.; Pugnere D.; Qi K.; Qian X.; Raaf J.; Raboanary R.; Radeka V.; Rademacker J.; Radics B.; Rafique A.; Raguzin E.; Rai M.; Rajaoalisoa M.; Rakhno I.; Rakotondramanana H.; Rakotondravohitra L.; Ramachers Y.; Rameika R.; Delgado M.R.; Ramson B.; Rappoldi A.; Raselli G.; Ratoff P.; Ravat S.; Razafinime H.; Real J.; Rebel B.; Redondo D.; Reggiani-Guzzo M.; Rehak T.; Reichenbacher J.; Reitzner S.D.; Renshaw A.; Rescia S.; Resnati F.; Reynolds A.; Riccobene G.; Rice L.C.; Rielage K.; Rigaut Y.; Rivera D.; Rochester L.; Roda M.; Rodrigues P.; Alonso M.R.; Rondon J.R.; Roeth A.; Rogers H.; Rosauro-Alcaraz S.; Rossella M.; Rout J.; Roy S.; Rubbia A.; Rubbia C.; Russell B.; Russell J.; Ruterbories D.; Saakyan R.; Sacerdoti S.; Safford T.; Sahu N.; Sala P.; Samios N.; Sanchez M.; Sanders D.A.; Sankey D.; Santana S.; Santos-Maldonado M.; Saoulidou N.; Sapienza P.; Sarasty C.; Sarcevic I.; Savage G.; Savinov V.; Scaramelli A.; Scarff A.; Scarpelli A.; Schaffer T.; Schellman H.; Schlabach P.; Schmitz D.; Scholberg K.; Schukraft A.; Segreto E.; Sensenig J.; Seong I.; Sergi A.; Sergiampietri F.; Sgalaberna D.; Shaevitz M.; Shafaq S.; Shamma M.; Sharma H.R.; Sharma R.; Shaw T.; Shepherd-Themistocleous C.; Shin S.; Shooltz D.; Shrock R.; Simard L.; Simos N.; Sinclair J.; Sinev G.; Singh J.; Singh V.; Sipos R.; Sippach F.; Sirri G.; Sitraka A.; Siyeon K.; Smargianaki D.; Smith A.; Smith A.; Smith E.; Smith P.; Smolik J.; Smy M.; Snopok P.; Nunes M.S.; Sobel H.; Soderberg M.; Salinas C.J.S.; Soldner-Rembold S.; Solomey N.; Solovov V.; Sondheim W.E.; Sorel M.; Soto-Oton J.; Sousa A.; Soustruznik K.; Spagliardi F.; Spanu M.; Spitz J.; Spooner N.J.; Spurgeon K.; Staley R.; Stancari M.; Stanco L.; Steiner H.; Stewart J.; Stillwell B.; Stock J.; Stocker F.; Stokes T.; Strait M.; Strauss T.; Striganov S.; Stuart A.; Summers D.; Surdo A.; Susic V.; Suter L.; Sutera C.; Svoboda R.; Szczerbinska B.; Szelc A.; Talaga R.; Tanaka H.; Oregui B.T.; Tapper A.; Tariq S.; Tatar E.; Tayloe R.; Teklu A.; Tenti M.; Terao K.; Ternes C.A.; Terranova F.; Testera G.; Thea A.; Thompson J.L.; Thorn C.; Timm S.; Tonazzo A.; Torti M.; Tortola M.; Tortorici F.; Totani D.; Toups M.; Touramanis C.; Trevor J.; Trzaska W.H.; Tsai Y.T.; Tsamalaidze Z.; Tsang K.; Tsverava N.; Tufanli S.; Tull C.; Tyley E.; Tzanov M.; Uchida M.A.; Urheim J.; Usher T.; Vagins M.; Vahle P.; Valdiviesso G.; Valencia E.; Vallari Z.; Valle J.W.; Vallecorsa S.; Berg R.V.; De Water R.G.V.; Forero D.V.; Varanini F.; Vargas D.; Varner G.; Vasel J.; Vasseur G.; Vaziri K.; Ventura S.; Verdugo A.; Vergani S.; Vermeulen M.A.; Verzocchi M.; De Souza H.V.; Vignoli C.; Vilela C.; Viren B.; Vrba T.; Wachala T.; Waldron A.V.; Wallbank M.; Wang H.; Wang J.; Wang Y.; Wang Y.; Warburton K.; Warner D.; Wascko M.; Waters D.; Watson A.; Weatherly P.; Weber A.; Weber M.; Wei H.; Weinstein A.; Wenman D.; Wetstein M.; While M.R.; White A.; Whitehead L.H.; Whittington D.; Wilking M.J.; Wilkinson C.; Williams Z.; Wilson F.; Wilson R.J.; Wolcott J.; Wongjirad T.; Wood K.; Wood L.; Worcester E.; Worcester M.; Wret C.; Wu W.; Wu W.; Xiao Y.; Yang G.; Yang T.; Yershov N.; Yonehara K.; Young T.; Yu B.; Yu J.; Zalesak J.; Zambelli L.; Zamorano B.; Zani A.; Zazueta L.; Zeller G.; Zennamo J.; Zeug K.; Zhang C.; Zhao M.; Zhivun E.; Zhu G.; Zimmerman E.D.; Zito M.; Zucchelli S.; Zuklin J.; Zutshi V.; Zwaska R.Abi B.; Acciarri R.; Acero M.A.; Adamov G.; Adams D.; Adinolfi M.; Ahmad Z.; Ahmed J.; Alion T.; Monsalve S.A.; Alt C.; Anderson J.; Andreopoulos C.; Andrews M.; Andrianala F.; Andringa S.; Ankowski A.; Antonova M.; Antusch S.; Aranda-Fernandez A.; Ariga A.; Arnold L.O.; Arroyave M.A.; Asaadi J.; Aurisano A.; Aushev V.; Autiero D.; Azfar F.; Back H.; Back J.J.; Backhouse C.; Baesso P.; Bagby L.; Bajou R.; Balasubramanian S.; Baldi P.; Bambah B.; Barao F.; Barenboim G.; Barker G.; Barkhouse W.; Barnes C.; Barr G.; Monarca J.B.; Barros N.; Barrow J.L.; Bashyal A.; Basque V.; Bay F.; Alba J.B.; Beacom J.F.; Bechetoille E.; Behera B.; Bellantoni L.; Bellettini G.; Bellini V.; Beltramello O.; Belver D.; Benekos N.; Neves F.B.; Berger J.; Berkman S.; Bernardini P.; Berner R.M.; Berns H.; Bertolucci S.; Betancourt M.; Bezawada Y.; Bhattacharjee M.; Bhuyan B.; Biagi S.; Bian J.; Biassoni M.; Biery K.; Bilki B.; Bishai M.; Bitadze A.; Blake A.; Siffert B.B.; Blaszczyk F.; Blazey G.; Blucher E.; Boissevain J.; Bolognesi S.; Bolton T.; Bonesini M.; Bongrand M.; Bonini F.; Booth A.; Booth C.; Bordoni S.; Borkum A.; Boschi T.; Bostan N.; Bour P.; Boyd S.; Boyden D.; Bracinik J.; Braga D.; Brailsford D.; Brandt A.; Bremer J.; Brew C.; Brianne E.; Brice S.J.; Brizzolari C.; Bromberg C.; Brooijmans G.; Brooke J.; Bross A.; Brunetti G.; Buchanan N.; Budd H.; Caiulo D.; Calafiura P.; Calcutt J.; Calin M.; Calvez S.; Calvo E.; Camilleri L.; Caminata A.; Campanelli M.; Caratelli D.; Carini G.; Carlus B.; Carniti P.; Terrazas I.C.; Carranza H.; Castillo A.; Castromonte C.; Cattadori C.; Cavalier F.; Cavanna F.; Centro S.; Cerati G.; Cervelli A.; Villanueva A.C.; Chalifour M.; Chang C.; Chardonnet E.; Chatterjee A.; Chattopadhyay S.; Chaves J.; Chen H.; Chen M.; Chen Y.; Cherdack D.; Chi C.; Childress S.; Chiriacescu A.; Cho K.; Choubey S.; Christensen A.; Christian D.; Christodoulou G.; Church E.; Clarke P.; Coan T.E.; Cocco A.G.; Coelho J.; Conley E.; Conrad J.; Convery M.; Corwin L.; Cotte P.; Cremaldi L.; Cremonesi L.; Crespo-Anadon J.I.; Cristaldo E.; Cross R.; Cuesta C.; Cui Y.; Cussans D.; Dabrowski M.; Motta H.D.; Peres L.D.S.; David Q.; Davies G.S.; Davini S.; Dawson J.; De K.; Almeida R.M.D.; Debbins P.; Bonis I.D.; Decowski M.; Gouvea A.D.; Holanda P.C.D.; Astiz I.L.D.I.; Deisting A.; Jong P.D.; Delbart A.; Delepine D.; Delgado M.; Dell'acqua A.; Lurgio P.D.; Neto J.R.D.M.; Demuth D.M.; Dennis S.; Densham C.; Deptuch G.; Roeck A.D.; Romeri V.D.; Vries J.D.; Dharmapalan R.; Dias M.; Diaz F.; Diaz J.; Domizio S.D.; Giulio L.D.; Ding P.; Noto L.D.; Distefano C.; Diurba R.; Diwan M.; Djurcic Z.; Dokania N.; Dolinski M.; Domine L.; Douglas D.; Drielsma F.; Duchesneau D.; Duffy K.; Dunne P.; Durkin T.; Duyang H.; Dvornikov O.; Dwyer D.; Dyshkant A.; Eads M.; Edmunds D.; Eisch J.; Emery S.; Ereditato A.; Escobar C.; Sanchez L.E.; Evans J.J.; Ewart E.; Ezeribe A.C.; Fahey K.; Falcone A.; Farnese C.; Farzan Y.; Felix J.; Fernandez-Martinez E.; Menendez P.F.; Ferraro F.; Fields L.; Filkins A.; Filthaut F.; Fitzpatrick R.S.; Flanagan W.; Fleming B.; Flight R.; Fowler J.; Fox W.; Franc J.; Francis K.; Franco D.; Freeman J.; Freestone J.; Fried J.; Friedland A.; Fuess S.; Furic I.; Furmanski A.P.; Gago A.; Gallagher H.; Gallego-Ros A.; Gallice N.; Galymov V.; Gamberini E.; Gamble T.; Gandhi R.; Gandrajula R.; Gao S.; Garcia-Gamez D.; Garcia-Peris M.A.; Gardiner S.; Gastler D.; Ge G.; Gelli B.; Gendotti A.; Gent S.; Ghorbani-Moghaddam Z.; Gibin D.; Gil-Botella I.; Girerd C.; Giri A.; Gnani D.; Gogota O.; Gold M.; Gollapinni S.; Gollwitzer K.; Gomes R.A.; Bermeo L.G.; Fajardo L.S.G.; Gonnella F.; Gonzalez-Cuevas J.; Goodman M.C.; Goodwin O.; Goswami S.; Gotti C.; Goudzovski E.; Grace C.; Graham M.; Gramellini E.; Gran R.; Granados E.; Grant A.; Grant C.; Gratieri D.; Green P.; Green S.; Greenler L.; Greenwood M.; Greer J.; Griffith C.; Groh M.; Grudzinski J.; Grzelak K.; Gu W.; Guarino V.; Guenette R.; Guglielmi A.; Guo B.; Guthikonda K.; Gutierrez R.; Guzowski P.; Guzzo M.M.; Gwon S.; Habig A.; Hackenburg A.; Hadavand H.; Haenni R.; Hahn A.; Haigh J.; Haiston J.; Hamernik T.; Hamilton P.; Han J.; Harder K.; Harris D.A.; Hartnell J.; Hasegawa T.; Hatcher R.; Hazen E.; Heavey A.; Heeger K.M.; Hennessy K.; Henry S.; Morquecho M.H.; Herner K.; Hertel L.; Hesam A.S.; Hewes J.; Pichardo A.H.; Hill T.; Hillier S.J.; Himmel A.; Hoff J.; Hohl C.; Holin A.; Hoppe E.; Horton-Smith G.A.; Hostert M.; Hourlier A.; Howard B.; Howell R.; Huang J.; Huang J.; Hugon J.; Iles G.; Iliescu A.M.; Illingworth R.; Ioannisian A.; Itay R.; Izmaylov A.; James E.; Jargowsky B.; Jediny F.; Jesus-Valls C.; Ji X.; Jiang L.; Jimenez S.; Jipa A.; Joglekar A.; Johnson C.; Johnson R.; Jones B.; Jones S.; Jung C.; Junk T.; Jwa Y.; Kabirnezhad M.; Kaboth A.; Kadenko I.; Kamiya F.; Karagiorgi G.; Karcher A.; Karolak M.; Karyotakis Y.; Kasai S.; Kasetti S.P.; Kashur L.; Kazaryan N.; Kearns E.; Keener P.; Kelly K.J.; Kemp E.; Ketchum W.; Kettell S.; Khabibullin M.; Khotjantsev A.; Khvedelidze A.; Kim D.; King B.; Kirby B.; Kirby M.; Klein J.; Koehler K.; Koerner L.W.; Kohn S.; Koller P.P.; Kordosky M.; Kosc T.; Kose U.; Kostelecky V.; Kothekar K.; Krennrich F.; Kreslo I.; Kudenko Y.; Kudryavtsev V.; Kulagin S.; Kumar J.; Kumar R.; Kuruppu C.; Kus V.; Kutter T.; Lambert A.; Lande K.; Lane C.E.; Lang K.; Langford T.; Lasorak P.; Last D.; Lastoria C.; Laundrie A.; Lawrence A.; Lazanu I.; Lazur R.; Le T.; Learned J.; Lebrun P.; Miotto G.L.; Lehnert R.; De Oliveira M.L.; Leitner M.; Leyton M.; Li L.; Li S.; Li S.; Li T.; Li Y.; Liao H.; Lin C.; Lin S.; Lister A.; Littlejohn B.R.; Liu J.; Lockwitz S.; Loew T.; Lokajicek M.; Lomidze I.; Long K.; Loo K.; Lorca D.; Lord T.; Losecco J.; Louis W.C.; Luk K.; Luo X.; Lurkin N.; Lux T.; Luzio V.P.; MacFarland D.; MacHado A.; MacHado P.; MacIas C.; MacIer J.; Maddalena A.; Madigan P.; Magill S.; Mahn K.; Maio A.; Maloney J.A.; Mandrioli G.; Maneira J.C.; Manenti L.; Manly S.; Mann A.; Manolopoulos K.; Plata M.M.; Marchionni A.; Marciano W.; Marfatia D.; Mariani C.; Maricic J.; Marinho F.; Marino A.D.; Marshak M.; Marshall C.; Marshall J.; Marteau J.; Martin-Albo J.; Martinez N.; Caicedo D.A.M.; Martynenko S.; Mason K.; Mastbaum A.; Masud M.; Matsuno S.; Matthews J.; Mauger C.; Mauri N.; Mavrokoridis K.; Mazza R.; Mazzacane A.; Mazzucato E.; McCluskey E.; McConkey N.; McFarland K.S.; McGrew C.; McNab A.; Mefodiev A.; Mehta P.; Melas P.; Mellinato M.; Mena O.; Menary S.; Mendez H.; Menegolli A.; Meng G.; Messier M.; Metcalf W.; Mewes M.; Meyer H.; Miao T.; Michna G.; Miedema T.; Migenda J.; Milincic R.; Miller W.; Mills J.; Milne C.; Mineev O.; Miranda O.G.; Miryala S.; Mishra C.; Mishra S.; Mislivec A.; Mladenov D.; Mocioiu I.; Moffat K.; Moggi N.; Mohanta R.; Mohayai T.A.; Mokhov N.; Molina J.A.; Bueno L.M.; Montanari A.; Montanari C.; Montanari D.; Zetina L.M.M.; Moon J.; Mooney M.; Moor A.; Moreno D.; Morgan B.; Morris C.; Mossey C.; Motuk E.; Moura C.A.; Mousseau J.; Mu W.; Mualem L.; Mueller J.; Muether M.; Mufson S.; Muheim F.; Muir A.; Mulhearn M.; Muramatsu H.; Murphy S.; Musser J.; Nachtman J.; Nagu S.; Nalbandyan M.; Nandakumar R.; Naples D.; Narita S.; Navas-Nicolas D.; Nayak N.; Nebot-Guinot M.; Necib L.; Negishi K.; Nelson J.K.; Nesbit J.; Nessi M.; Newbold D.; Newcomer M.; Newhart D.; Nichol R.; Niner E.; Nishimura K.; Norman A.; Northrop R.; Novella P.; Nowak J.A.; Oberling M.; Campo A.O.D.; Olivier A.; Onel Y.; Onishchuk Y.; Ott J.; Pagani L.; Pakvasa S.; Palamara O.; Palestini S.; Paley J.M.; Pallavicini M.; Palomares C.; Pantic E.; Paolone V.; Papadimitriou V.; Papaleo R.; Papanestis A.; Paramesvaran S.; Parke S.; Parsa Z.; Parvu M.; Pascoli S.; Pasqualini L.; Pasternak J.; Pater J.; Patrick C.; Patrizii L.; Patterson R.B.; Patton S.; Patzak T.; Paudel A.; Paulos B.; Paulucci L.; Pavlovic Z.; Pawloski G.; Payne D.; Pec V.; Peeters S.J.; Penichot Y.; Pennacchio E.; Penzo A.; Peres O.L.; Perry J.; Pershey D.; Pessina G.; Petrillo G.; Petta C.; Petti R.; Piastra F.; Pickering

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Supernova neutrino burst detection with the Deep Underground Neutrino Experiment

The Deep Underground Neutrino Experiment (DUNE), a 40-kton underground liquid argon time projection chamber experiment, will be sensitive to the electron-neutrino flavor component of the burst of neutrinos expected from the next Galactic core-collapse supernova. Such an observation will bring unique insight into the astrophysics of core collapse as well as into the properties of neutrinos. The general capabilities of DUNE for neutrino detection in the relevant few- to few-tens-of-MeV neutrino energy range will be described. As an example, DUNE's ability to constrain the νe spectral parameters of the neutrino burst will be considered

Volume III DUNE far detector technical coordination

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume III of this TDR describes how the activities required to design, construct, fabricate, install, and commission the DUNE far detector modules are organized and managed. This volume details the organizational structures that will carry out and/or oversee the planned far detector activities safely, successfully, on time, and on budget. It presents overviews of the facilities, supporting infrastructure, and detectors for context, and it outlines the project-related functions and methodologies used by the DUNE technical coordination organization, focusing on the areas of integration engineering, technical reviews, quality assurance and control, and safety oversight. Because of its more advanced stage of development, functional examples presented in this volume focus primarily on the single-phase (SP) detector module

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation