BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Leïla Haegel (University of Paris) DTSTART:20211115T160000Z DTEND:20211115T165000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/1 DESCRIPTION:Title: Probes of new physics during gravitational waves propagation - Leila H aegel\nby Leïla Haegel (University of Paris) as part of CMO-Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astrono my\n\n\nAbstract\nThe direct detection of gravitational waves opened an un precedented channel to probe fundamental physics. Proposed extensions of o ur current theories predict a dispersion of the gravitational waves during their propagation\, distorting the signals observed by ground-based inter ferometers compared to their predictions from general relativity. In this talk\, I present several analysis probing different alternative theories of gravitation. Using the multimessenger events consisting of gravitationa l waves and their electromagnetic counterpart\, extra dimensions and scala r-tensor theories are constrained from the comparison of the luminosity di stance inferred independently from both signals. Relying only on gravitati onal wave signals\, a large class of proposed theories (e.g. massive gravi ty) predict a frequency-dependent dispersion of the gravitational waves br eaking local CPT and/or Lorentz symmetry. Constraints on the corresponding effective field theories coefficients are obtained from the analysis of 3 1 events from the second LIGO-Virgo detections catalog.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Michael Coughlin (University of Minnesota) DTSTART:20211115T165000Z DTEND:20211115T174000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/2 DESCRIPTION:Title: Inference as a service for gravitational-wave astronomy\nby Michae l Coughlin (University of Minnesota) as part of CMO-Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\nAbstr act\nWe present a novel paradigm for the deployment of computing infrastru cture for the low-latency analyses of gravitational wave (GW) data. Here\, we specifically discuss two deep-learning algorithms\, 'DeepClean' and 'B BHNet'\, which are used for GW data denoising and compact binary source id entification respectively. Using “replayed” streams of the GW data of LIGO Hanford and Livingston from their third observing run\, we demonstrat e the subtraction of stationary and non-stationary noise sources followed by identification of candidates for the astrophysical transient detections at low latency. Depending on the computing platform used\, we show that i t is possible to achieve these at latencies of ~ a few hundreds of millise conds to a few seconds. Real-time delivery of gravitational-wave alerts is important for enabling rapid multi-messenger follow-up\, especially to ca pture short-lived electromagnetic counterparts. Additionally\, our impleme ntation offers seamless incorporation of hardware accelerators and enables the use of as-a-service computing\, which would be capable of meeting the future needs of gravitational-wave data analysis.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Massimiliano Razzano (University of Pisa) DTSTART:20211115T181000Z DTEND:20211115T190000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/3 DESCRIPTION:Title: Deep learning methods to investigate noise features in gravitational w ave detectors\nby Massimiliano Razzano (University of Pisa) as part of CMO-Detection and Analysis of Gravitational Waves in the era of Multi-Mes senger Astronomy\n\n\nAbstract\nGravitational waves have opened a new wind ow on the Universe and paved the way to multimessenger astronomy. Advanced LIGO and Advanced Virgo interferometers are probing an increasingly large r volume of space\, discovering more and more signals produced by the coal escence of compact binary systems. Characterizing these detectors and thei r noise is crucial to optimize the sensitivity. In particular\, glitches a re transient noise events impacting the data quality\, and their detection and classification is very important to improve the performance of the in terferometers. Deep learning techniques are a promising approach to recogn ize and classify glitches and to study noise in general. We will present h ow machine learning can help in investigating the time-frequency evolution of glitches and thus contribute to the low-latency characterization of gr avitational wave detectors.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Adam Coogan (Université de Montréal and Mila) DTSTART:20211115T190000Z DTEND:20211115T195000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/4 DESCRIPTION:Title: Observing and characterizing the dark matter environments of black hol e binaries with gravitational waves\nby Adam Coogan (Université de Mo ntréal and Mila) as part of CMO-Detection and Analysis of Gravitational W aves in the era of Multi-Messenger Astronomy\n\n\nAbstract\nGravitational wave measurements provide a new opportunity to determine whether dark matt er is truly a cold\, collisionless particle. Intermediate mass-ratio inspi rals (IMRIs) embedded in dark matter halos are particularly promising targ ets. These IMRIs can compress their dark halos to extreme densities as the y form\, leading to distinctive gravitational wave signals. In this talk I will show that future interferometers could observe and characterize thes e "dark dress" systems over large swaths of their parameter space. After e xplaining their waveform modeling\, I will map out which dark dresses coul d be detected\, distinguished from astrophysical IMRIs and accurately meas ured.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Sydney Otten (Radboud University & University of Amsterdam) DTSTART:20211115T220000Z DTEND:20211115T224500Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/5 DESCRIPTION:Title: (Conference Room San Felipe and Zoom)\nby Sydney Otten (Radboud Un iversity & University of Amsterdam) as part of CMO-Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\nAbstract : TBA\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Ryan Quitzow-James (Missouri University of Science and Technology) DTSTART:20211115T224500Z DTEND:20211115T233000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/6 DESCRIPTION:Title: Estimating glitch contaminated gravitational-wave signals using artifi cial neural networks with NNETFIX\nby Ryan Quitzow-James (Missouri Uni versity of Science and Technology) as part of CMO-Detection and Analysis o f Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\nAbstrac t\nInstrumental and environmental transient noise bursts in gravitational- wave detectors\, or glitches\, may impair astrophysical observations by ad versely affecting the sky localization and the parameter estimation of gra vitational-wave signals. Denoising of detector data is especially relevant during low-latency operations because electromagnetic follow-up of candid ate detections requires accurate\, rapid sky localization and inference of astrophysical sources. NNETFIX is a machine learning-based algorithm desi gned to remove glitches detected in coincidence with transient gravitation al-wave signals. NNETFIX uses artificial neural networks to estimate the p ortion of the data lost due to the presence of the glitch\, which allows t he recalculation of the sky localization of the astrophysical signal. The sky localization of the denoised signal may be significantly more accurate than the sky localization obtained from the original data or by excising the portion of the data impacted by the glitch. We test NNETFIX in simulat ed scenarios of binary black hole coalescence signals and discuss the pote ntial for its use in future low-latency LIGO-Virgo-KAGRA searches.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Gianfranco Bertone (University of Amsterdam) DTSTART:20211116T160000Z DTEND:20211116T165000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/7 DESCRIPTION:Title: Dark matter\, black holes\, and gravitational waves\nby Gianfranco Bertone (University of Amsterdam) as part of CMO-Detection and Analysis o f Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\nAbstrac t\nThe interplay between dark matter and black holes remains largely unexp lored. Dark matter can in principle be *made of* black holes\, as long as these are primordial\, i.e. they are formed in the very early universe. Da rk matter can also accumulate *around* black holes\, be them astrophysical or primordial\, and modify the rich phenomenology exhibited by these obj ects. After a brief overview of the status of dark matter searches\, I wi ll discuss the prospects for detecting primordial black holes\, or robustl y ruling them out as dark matter candidates. I will then discuss the pros pects for characterizing and identifying dark matter using gravitational w aves\, covering a wide range of dark matter candidate types and signals.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/7/ END:VEVENT BEGIN:VEVENT SUMMARY:Gabriele Vajente (California Institute of Technology) DTSTART:20211116T165000Z DTEND:20211116T174000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/8 DESCRIPTION:Title: Machine Learning and Gravitational Wave Detectors\nby Gabriele Vaj ente (California Institute of Technology) as part of CMO-Detection and Ana lysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\n Abstract\nThe use of machine learning techniques in the analysis of the da ta produced by gravitational wave detectors is a very active field of rese arch\, with many promising results. In this talk however\, I will discuss how machine learning could also be applied to the instrument science side of gravitational wave detectors\, to improve the sensitivity with noise su btraction or to improve the detector robustness with advanced control syst ems. This is a less mature field of research\, and results are only recent ly starting to appear.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/8/ END:VEVENT BEGIN:VEVENT SUMMARY:Pablo Cerdá-Durán (niversidad de Valencia) DTSTART:20211116T181000Z DTEND:20211116T190000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/9 DESCRIPTION:Title: (Conference Room San Felipe and Zoom)\nby Pablo Cerdá-Durán (niv ersidad de Valencia) as part of CMO-Detection and Analysis of Gravitationa l Waves in the era of Multi-Messenger Astronomy\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/9/ END:VEVENT BEGIN:VEVENT SUMMARY:Pablo Laguna (University of Texas at Austin) DTSTART:20211116T190000Z DTEND:20211116T195000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/10 DESCRIPTION:Title: Black Hole - Neutron Star Binary Mergers: The Imprint of Tidal Debris \nby Pablo Laguna (University of Texas at Austin) as part of CMO-Detec tion and Analysis of Gravitational Waves in the era of Multi-Messenger Ast ronomy\n\n\nAbstract\nDistinguishing black hole – neutron star binaries from binary black holes mergers for high mass ratios could be challenging because the neutron star coalesces with the black hole without experiencin g significant disruption. To investigate the transition of the behavior of a mixed binary merger into one like a black hole binary\, we present resu lts from a series of merger simulations for different mass ratios. We show how the degree of disruption of the neutron star impacts the inspiral and merger dynamics\, the properties of the final black hole\, the accretion disk formed from the circularization of the tidal debris\, the gravitation al waves\, and the strain spectrum and mismatches. The simulations use ini tial data constructed with a method that generalizes the Bowen-York initia l data for black hole punctures to the case of neutron stars.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/10/ END:VEVENT BEGIN:VEVENT SUMMARY:Vasileios Skliris (Cardiff University)) DTSTART:20211116T220000Z DTEND:20211116T224500Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/11 DESCRIPTION:Title: Real-Time Detection of Unmodeled Gravitational-Wave Transients Using Convolutional Neural Networks\nby Vasileios Skliris (Cardiff Universit y)) as part of CMO-Detection and Analysis of Gravitational Waves in the er a of Multi-Messenger Astronomy\n\n\nAbstract\nConvolutional Neural Network s (CNNs) have demonstrated potential for the real-time analysis of data fr om gravitational-wave detector networks for the specific case of signals f rom coalescing compact-object binaries such as black-hole binaries. Unfort unately\, training these CNNs requires a precise model of the target signa l\; they are therefore not applicable to a wide class of potential gravita tional-wave sources\, such as core-collapse supernovae and long gamma-ray bursts\, where unknown physics or computational limitations prevent the de velopment of comprehensive signal models. We demonstrate for the first tim e a CNN with the ability to detect generic signals -- those without a prec ise model -- with sensitivity across a wide parameter space. Our CNN has a novel structure that uses not only the network strain data but also the P earson cross-correlation between detectors to distinguish correlated gravi tational-wave signals from uncorrelated noise transients. We demonstrate t he efficacy of our CNN using data from the second LIGO-Virgo observing run . We show that it has sensitivity approaching that of the "gold-standard'' unmodeled transient searches currently used by LIGO-Virgo\, at extremely low (order of 1 second) latency and using only a fraction of the computing power required by existing searches\, allowing our models the possibility of true real-time detection of gravitational-wave transients associated w ith gamma-ray bursts\, core-collapse supernovae\, and other relativistic a strophysical phenomena.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/11/ END:VEVENT BEGIN:VEVENT SUMMARY:Jade Powell (Swinburne University of Technology) DTSTART:20211116T224500Z DTEND:20211116T233000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/12 DESCRIPTION:Title: The final core-collapse of pulsational pair instability supernovae\nby Jade Powell (Swinburne University of Technology) as part of CMO-Dete ction and Analysis of Gravitational Waves in the era of Multi-Messenger As tronomy\n\n\nAbstract\nIn this talk I will present 3D core-collapse supern ova simulations of massive pop III progenitor stars at the transition to t he pulsational pair instability regime. We simulate two progenitor models with initial masses of 85 and 100 solar masses with multiple equations of state. The 85 solar mass progenitor experiences a pair instability pulse c oincident with core collapse\, whereas the 100 solar mass progenitor has a lready gone through a sequence of four pulses 1\,500 years before collapse in which it ejected its hydrogen and helium envelope. The 85 solar mass m odels experience shock revival and then delayed collapse to a black hole d ue to ongoing accretion. We show the models have strong gravitational wave emission in the frequency band of current gravitational wave detectors. W e demonstrate how our understanding of the gravitational wave emission can aid in the detection of core-collapse supernovae and understanding the pa rameters of the astrophysical source.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/12/ END:VEVENT BEGIN:VEVENT SUMMARY:Bernhard Mueller (Monash University) DTSTART:20211117T160000Z DTEND:20211117T165000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/13 DESCRIPTION:Title: Magnetic Fields in Core-Collapse Supernovae and their Progenitors \nby Bernhard Mueller (Monash University) as part of CMO-Detection and Ana lysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\n Abstract\nMulti-dimensional simulations of core-collapse supernovae are es sential for providing waveforms that can assist the detection and interpre tation of gravitational waves from a prospective nearby explosion. An emer ging theme in core-collapse supernova modelling is the wider importance of magnetic fields. While magnetorotational explosions have long been invest igated as a scenario for rare hypernova explosions\, recent simulations su ggest that magnetic fields may play an important role in normal explosions as well. To better understand the role of magnetic fields both in normal and hyperenergetic explosions\, it is also imperative to revisit the inter play of convection\, magnetic fields and rotation during the pre-supernova evolution of massive stars. I will discuss progress on 3D magnetohydrodyn amic simulations of supernovae and their progenitors\, possible implicatio ns for hypernovae and magnetar formation\, and point to open issues in the current models\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/13/ END:VEVENT BEGIN:VEVENT SUMMARY:Elena Cuoco (European Gravitational Observatory) DTSTART:20211117T165000Z DTEND:20211117T174000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/14 DESCRIPTION:Title: Multimodal Analysis of Gravitational Wave Signals and Gamma-Ray Burst s from Binary Neutron Star Mergers\nby Elena Cuoco (European Gravitati onal Observatory) as part of CMO-Detection and Analysis of Gravitational W aves in the era of Multi-Messenger Astronomy\n\n\nAbstract\nA major boost in the understanding of the universe was given by the revelation of the fi rst coalescence event of two neutron stars (GW170817) and the observation of the same event across the entire electromagnetic spectrum. With third- generation gravitational wave detectors and the new astronomical faciliti es\, we expect many multi-messenger events of the same type. We anticipat e the need to analyse the data provided to us by such events not only to f ulfill the requirements of real-time analysis\, but also in order to decip her the event in its entirety through the information emitted in the diffe rent messengers using machine learning. What we propose is the application of a multimodal machine learning approach to characterize these events.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/14/ END:VEVENT BEGIN:VEVENT SUMMARY:Jess McIver (University of British Columbia) DTSTART:20211117T181000Z DTEND:20211117T190000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/15 DESCRIPTION:Title: New methods for gravitational-wave data analysis\nby Jess McIver (University of British Columbia) as part of CMO-Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\nAbstract\ nThe global network of ground-based gravitational wave (GW) detectors is e xpected to expand and increase in sensitivity by the middle of this decade . Effective characterization and calibration of near-future detectors will allow us to explore new physics and astrophysics\, including the origin o f detected GW sources and probes of cosmology. I will give an overview of recent efforts at UBC to develop new approaches to characterize the perfor mance of current and near-future detectors\, including novel metrics for d etector performance and characterization\, new methods for distinguishing between true GW signals and detector noise\, and improved techniques for G W signal reconstruction.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/15/ END:VEVENT BEGIN:VEVENT SUMMARY:Deirdre Shoemaker (University of Texas at Austin) DTSTART:20211117T190000Z DTEND:20211117T195000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/16 DESCRIPTION:Title: Brave new world of numerical relativity\nby Deirdre Shoemaker (Un iversity of Texas at Austin) as part of CMO-Detection and Analysis of Grav itational Waves in the era of Multi-Messenger Astronomy\n\n\nAbstract\nAft er decades of preparation\, the era of gravitational wave astronomy has be gun. The gravitational wave detectors\, LIGO and Virgo\, have published a catalog of 50 events of coalescing compact objects including black holes a nd neutron stars. I will present the role that numerical relativity played in the unveiling of the gravitational wave sky and anticipate how it migh t improve our understanding of gravity as the detectors improve..\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/16/ END:VEVENT BEGIN:VEVENT SUMMARY:Ik Siong Heng (University of Glasgow) DTSTART:20211118T160000Z DTEND:20211118T165000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/17 DESCRIPTION:Title: Astrophysics with joint analysis of multi-messenger observations\ nby Ik Siong Heng (University of Glasgow) as part of CMO-Detection and Ana lysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\n Abstract\nMulti-messenger observations of compact binary coalescences will enrich our understanding of the astrophysics behind such sources\, especi ally binary neutron star mergers. The joint analysis of gravitational wave data in conjunction with electromagnetic observations is a crucial ingred ient for multi-messenger astrophysics. This talk will present an overview of multi-messenger astrophysics research at the University of Glasgow\, in cluding the analysis of kilonova light curves for joint observations and a hierarchical Bayesian analysis\, with a machine learning augmented sample r (Nessai)\, for combining gravitational wave and gamma-ray observations t o identify plausible models for gamma-ray burst jet structures.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/17/ END:VEVENT BEGIN:VEVENT SUMMARY:Deep Chatterjee (U. Illinois Urbana-Champaign) DTSTART:20211118T165000Z DTEND:20211118T174000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/18 DESCRIPTION:Title: Application of machine learning in low-latency counterpart inference from gravitational waves\nby Deep Chatterjee (U. Illinois Urbana-Champ aign) as part of CMO-Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\nAbstract\nThe panchromatic observati ons of the electromagnetic (EM) counterpart of the binary neutron star (BN S) merger\, GW170817\, marked the dawn of multi-messenger EMGW astronomy. But it remains the only success story\, even after the LIGO/Virgo third ob serving run\, which reported 56 GW discoveries publicly. This shows that w hile we may have reached an era of routine GW astronomy\, the same for EMG W astronomy still awaits us. The first step is to develop low-latency data -products that aid the follow-up of interesting GW candidates. The low-lat ency nature of the problem\, taking into account physically motivated mode ls plus un-modeled or poorly understood sources of search biases makes the use of data-driven approaches and machine learning particularly suited fo r this problem. I will talk about such efforts using machine learning appr oaches both from the GW and EM standpoint for near real-time inference of EMGW counterparts.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/18/ END:VEVENT BEGIN:VEVENT SUMMARY:Greg Ashton (Royal Holloway\, University of London) DTSTART:20211118T180000Z DTEND:20211118T190000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/19 DESCRIPTION:Title: (Conference Room San Felipe and Zoom)\nby Greg Ashton (Royal Holl oway\, University of London) as part of CMO-Detection and Analysis of Grav itational Waves in the era of Multi-Messenger Astronomy\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/19/ END:VEVENT BEGIN:VEVENT SUMMARY:Javier M. Antelis (Embry-Riddle Aeronautical University) DTSTART:20211118T190000Z DTEND:20211118T195000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/20 DESCRIPTION:Title: Reduction of noise events in searches of gravitational wave bursts fr om core-collapse supernovae with machine learning\nby Javier M. Anteli s (Embry-Riddle Aeronautical University) as part of CMO-Detection and Anal ysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\n\nA bstract\nThe search of gravitational waves (GW) from core-collapse superno vae (CCSNe) relies on detection algorithms such as coherent WaveBurst (cWB ). False alarm rates and statistical significance might be affected by bli p and glitch noises embedded in the strain data\, which survive rejection tests. It is of interest then to detect and discard surviving noise events . This work presents the use of supervised machine learning (ML) methods\, in specific\, Linear Discriminant Analysis and Support Vector Machines\, to recognize between noise and signal events using a set of reconstruction parameters from cWB. We tested this ML follow-up method using strain data from the O3a run of advanced LIGO\, and CCSNe GW signals extracted from 3 D simulations. The ML model is learned using a dataset of noise and signal events extracted from a given on-source window\, and then it is used to i dentify and discard noise events in cWB analyses in different on-source wi ndows. Noise and signal reduction levels were assessed in single detector networks (L1 and H1) and two detector networks (L1H1). The results showed an effective enhancement of the statistical significance of cWB-based sear ches of GWs from CCSNe.\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/20/ END:VEVENT BEGIN:VEVENT SUMMARY:Christopher Messenger (Glasgow University) DTSTART:20211118T220000Z DTEND:20211118T224500Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/21 DESCRIPTION:Title: (Conference Room San Felipe and Zoom)\nby Christopher Messenger ( Glasgow University) as part of CMO-Detection and Analysis of Gravitational Waves in the era of Multi-Messenger Astronomy\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/21/ END:VEVENT BEGIN:VEVENT SUMMARY:Kendall Ackley (University of Warwick) DTSTART:20211118T224500Z DTEND:20211118T233000Z DTSTAMP:20260422T185530Z UID:CMO-21w5066/22 DESCRIPTION:Title: (Conference Room San Felipe and Zoom)\nby Kendall Ackley (Univers ity of Warwick) as part of CMO-Detection and Analysis of Gravitational Wav es in the era of Multi-Messenger Astronomy\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/CMO-21w5066/22/ END:VEVENT END:VCALENDAR