Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1903.04615

The US Program in Ground-Based Gravitational Wave Science: Contribution from the LIGO Laboratory

David Reitze·Rich Abbott·Carl Adams·Rana Adhikari·Nancy Aggarwal·Shreya Anand·Alena Ananyeva·Stuart Anderson·Stephen Appert·Koji Arai·Melody Araya·Stuart Aston·Juan Barayoga·Barry Barish·David Barker·Lisa Barsotti·Jeffrey Bartlett·Joseph Betzwieser·GariLynn Billingsley·Sebastien Biscans·Sylvia Biscoveanu·Kent Blackburn·Carl Blair·Ryan Blair·Brian Bockelman·Rolf Bork·Alyssa Bramley·Aidan Brooks·Sharon Brunett·Aaron Buikema·Craig Cahillane·Thomas Callister·Tom Carruthers·Filiberto Clara·Paul Corban·Michael Coughlin·Peter Couvares·Matthew Cowart·Dennis Coyne·Nicholas Demos·Fred Donovan·Jenne Driggers·Sheila Dwyer·Anamaria Effler·Robert Eisenstein·Todd Etzel·Matthew Evans·Tom Evans·Jon Feicht·Alvaro Fernandez-Galiana·Peter Fritschel·Valery Frolov·Michael Fyffe·Bubba Gateley·Joe Giaime·Dwayne Giardina·Evan Goetz·Sarah Gossan·Slawomir Gras·Philippe Grassia·Corey Gray·Anchal Gupta·Eric Gustafson·Les Guthman·Evan Hall·Jonathan Hanks·Joe Hanson·Raine Hasskew·Carl-Johan Haster·Matthew Heintze·Edgar Hernandez·Kathy Holt·Yiwen Huang·Tien Huynh-Dinh·Max Isi·Jeff Jones·Brittany Kamai·Jonah Kanner·Marie Kasprzack·Erik Katsavounidis·William Katzman·Keita Kawabe·Peter King·Jeffrey Kissel·Veronica Kondrashov·William Korth·Dan Kozak·Rahul Kumar·Michael Landry·Benjamin Lane·Robert Lanza·Michael Laxen·Albert Lazzarini·Yannick Lecoeuche·Ken Libbrecht·Ka-Lok Lo·Lionel London·Marc Lormand·Myron MacInnis·Georgia Mansell
View original source ↗Read source document ↗Export metadata ↓

Abstract

Recent gravitational-wave observations from the LIGO and Virgo observatories have brought a sense of great excitement to scientists and citizens the world over. Since September 2015,10 binary black hole coalescences and one binary neutron star coalescence have been observed. They have provided remarkable, revolutionary insight into the "gravitational Universe" and have greatly extended the field of multi-messenger astronomy. At present, Advanced LIGO can see binary black hole coalescences out to redshift 0.6 and binary neutron star coalescences to redshift 0.05. This probes only a very small fraction of the volume of the observable Universe. However, current technologies can be extended to construct "$3^\mathrm{rd}$ Generation" (3G) gravitational-wave observatories that would extend our reach to the very edge of the observable Universe. The event rates over such a large volume would be in the hundreds of thousands per year (i.e.tens per hour). Such 3G detectors would have a 10-fold improvement in strain sensitivity over the current generation of instruments, yielding signal-to-noise ratios of 1000 for events like those already seen. Several concepts are being studied for which engineering studies and reliable cost estimates will be developed in the next 5 years.

Explore related subjects

astro-ph.IMastro-ph.COgr-qc

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

David Reitze, Rich Abbott, Carl Adams, Rana Adhikari, Nancy Aggarwal, Shreya Anand, Alena Ananyeva, Stuart Anderson, Stephen Appert, Koji Arai, Melody Araya, Stuart Aston, Juan Barayoga, Barry Barish, David Barker, Lisa Barsotti, Jeffrey Bartlett, Joseph Betzwieser, GariLynn Billingsley, Sebastien Biscans, Sylvia Biscoveanu, Kent Blackburn, Carl Blair, Ryan Blair, Brian Bockelman, Rolf Bork, Alyssa Bramley, Aidan Brooks, Sharon Brunett, Aaron Buikema, Craig Cahillane, Thomas Callister, Tom Carruthers, Filiberto Clara, Paul Corban, Michael Coughlin, Peter Couvares, Matthew Cowart, Dennis Coyne, Nicholas Demos, Fred Donovan, Jenne Driggers, Sheila Dwyer, Anamaria Effler, Robert Eisenstein, Todd Etzel, Matthew Evans, Tom Evans, Jon Feicht, Alvaro Fernandez-Galiana, Peter Fritschel, Valery Frolov, Michael Fyffe, Bubba Gateley, Joe Giaime, Dwayne Giardina, Evan Goetz, Sarah Gossan, Slawomir Gras, Philippe Grassia, Corey Gray, Anchal Gupta, Eric Gustafson, Les Guthman, Evan Hall, Jonathan Hanks, Joe Hanson, Raine Hasskew, Carl-Johan Haster, Matthew Heintze, Edgar Hernandez, Kathy Holt, Yiwen Huang, Tien Huynh-Dinh, Max Isi, Jeff Jones, Brittany Kamai, Jonah Kanner, Marie Kasprzack, Erik Katsavounidis, William Katzman, Keita Kawabe, Peter King, Jeffrey Kissel, Veronica Kondrashov, William Korth, Dan Kozak, Rahul Kumar, Michael Landry, Benjamin Lane, Robert Lanza, Michael Laxen, Albert Lazzarini, Yannick Lecoeuche, Ken Libbrecht, Ka-Lok Lo, Lionel London, Marc Lormand, Myron MacInnis, Georgia Mansell. 2019-03-11. The US Program in Ground-Based Gravitational Wave Science: Contribution from the LIGO Laboratory. https://arxiv.org/abs/1903.04615

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Multi-Scale Contrastive Attention for Light-Curve Representation Learning

Current and next-generation time-domain surveys demand automated techniques capable of analyzing millions of light curves, observed in multiple filters, without relying on exhaustive human annotation or scarce spectroscopic follow-up. We present Astra-CLR, an attention-based, self-supervised contrastive learning framework which enables the representation of raw light curves into a highly discriminative latent space. Pre-trained on $\sim$2.1 million unlabeled Zwicky Transient Facility light curves, the framework utilizes partial light curves as input sequences to generate asymmetric, multi-scale temporal views (explicitly contrasting shorter sequences against longer ones) forcing the network to learn a robust "local-to-global" mapping strategy. Furthermore, we introduce a novel multi-view late fusion architecture that extends the model to efficiently handle longer light curves with larger numbers of observations while accommodating the different cadences associated with each filter. The discriminatory power of the resulting representations was evaluated by using them as input to a Multinomial Logistic Regression classifier, trained to identify 12 broad classes of variability. Final accuracy achieved $\sim 0.70$. When applying a label-efficient, partial top-layer fine-tuning strategy, the topological structure of the latent space is significantly refined, boosting results to $\sim$0.77. Astra-CLR is the first publicly available multi-filter time-series Transformer trained exclusively on real ZTF light curves. Results presented here demonstrate that it provides an ideal foundation for the development of end-to-end pipelines, taking into account color evolution and respecting the inhomogeneous nature of astronomical light curve sampling.

astro-ph.IM↗
arXiv2026

Combining astrometry with pulsar timing: the first joint analysis of very low frequency gravitational waves

The pHz to sub-nHz GW regime remains largely unexplored but is crucial for mapping the early inspiral stage of SMBHBs and probing early-Universe physics. Astrometry and pulsar timing offer orthogonal and deeply complementary secular observables to investigate this frequency band. We aim to present the first joint data analysis combining real astrometric proper motions with binary pulsar timing, to search for and constrain continuous gravitational waves (CWs) sourced by SMBHBs in the ultra-low-frequency regime ($10^{-12} \le f_{GW} \le10^{-9} Hz$). We employ a Bayesian model selection and upper limit estimation framework to combine apparent proper motion displacements of $\sim 1.5 \times 10^6$ quasars from the Gaia CRF3 catalog with the line-of-sight orbital period derivatives ($\dot{P}_b$) of 11 high-precision binary pulsars. To prevent spurious detections, we heavily model instrumental and astrophysical systematics: we propagate the Galactic potential uncertainty for pulsars via Monte Carlo simulations and perform a Vector Spherical Harmonics (VSH) decomposition up to the octupole order (l=3) for quasars. We find no statistically significant evidence for a CWs signal in the joint analysis ($\ln B_{joint} = -0.42 \pm 0.03$). In the absence of detection, we set the tightest constraints to date on CW strain in the pHz band, yielding a 95% upper limit of $h_0\le6.4x10^{-11}$ at a reference frequency of $f_{ref} = 4 \times 10^{-10}$ Hz. The combined dataset achieves full sky coverage and improves single-dataset upper limits by 20%-30%. Combining orthogonal observables successfully breaks spatial degeneracies intrinsic to isolated searches. Furthermore, forecasts from inj.-rec. indicate that with the extended temporal baseline and reduced uncertainties of the upcoming Gaia DR4, this joint framework is poised to break the $h_0 < 10^{-11}$ upper limit barrier for sub-nHz CWs.

astro-ph.IM↗
arXiv2026

Bayesian classification of astronomical spectra with class uncertainties

Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Aims: Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Methods: A CNN with simple architecture and about 20,000 parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.

astro-ph.IM↗