{"total":14,"items":[{"citing_arxiv_id":"2606.31364","ref_index":77,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Establishing Compactness as a Population Observable in Gravitational-Wave Astronomy","primary_cat":"gr-qc","submitted_at":"2026-06-30T08:58:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hierarchical analysis of GWTC-3 events measures effective compactness C_eff = 0.5^{+0.3}_{-0.1} consistent with black holes and limits low-compactness exotic merger rate to <0.7 Gpc^{-3} yr^{-1}.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.31304","ref_index":66,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A parametric signal plus noise inference framework for short duration non-Gaussian noise transients","primary_cat":"gr-qc","submitted_at":"2026-06-30T08:17:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Bilby-antiglitch jointly models astrophysical signals and quasi-physical glitches to recover true source properties from simulated gravitational wave data contaminated by loud non-Gaussian transients.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21310","ref_index":165,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection","primary_cat":"gr-qc","submitted_at":"2026-05-20T15:37:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"[164] L. Ruff, N. G¨ ornitz, L. Deecke, S. A. Siddiqui, R. A. Vandermeulen, A. Binder, E. M¨ uller, and M. Kloft, inProceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm¨ assan, Stockholm, Sweden, July 10-15, 2018, Proceedings of Machine Learning Research, edited by J. G. Dy and A. Krause (PMLR, 2018) pp. 4390-4399. [165] E. Muller, H. T. Janka, and A. Wongwathanarat, Astron. Astrophys.537, A63 (2012), arXiv:1106.6301 [astro-ph.SR]. [166] C. D. Ott, E. Abdikamalov, P. M¨ osta, R. Haas, S. Drasco, E. P. O'Connor, C. Reisswig, C. A. Meakin, and E. Schnetter, Astrophys. J.768, 115 (2013), arXiv:1210.6674 [astro-ph.HE]. [167] T. Kuroda, K. Kotake, K. Hayama, and T. Takiwaki,"},{"citing_arxiv_id":"2604.27734","ref_index":56,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Constraining Dipole Radiation with Multiband Gravitational Waves from Eccentric Binary Black Holes","primary_cat":"gr-qc","submitted_at":"2026-04-30T11:24:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Multiband observations of eccentric binary black holes can constrain dipole-radiation deviations from general relativity to |b| ≲ 10^{-7} for a GW231123-like event when combining one year of space-based data with ground-informed priors.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"suming a continuous one-year observation before binary coalescence and neglecting duty-cycle effects. The detec- tor noise models follow Ref. [23] for TianQin and Ref. [48] for LISA, while the corresponding orbital configurations are taken from Ref. [54] and Ref. [55], respectively. The full inference pipeline is implemented within the PyCBC Inference framework [56]. Posterior sampling is performed with nessai, a nested-sampling algorithm en- hanced by normalizing flows to improve sampling effi- ciency [57, 58]. For the long-duration space-based sig- nals considered in this work, direct likelihood evaluation with full-resolution waveforms would be computationally prohibitive. We therefore adopt a heterodyned likelihood"},{"citing_arxiv_id":"2604.21859","ref_index":100,"ref_count":2,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data","primary_cat":"astro-ph.HE","submitted_at":"2026-04-23T16:52:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"04239 [astro-ph.HE]. [97] D. Gerosa, S. Vitale, and E. Berti, Phys. Rev. Lett. 125, 101103 (2020), arXiv:2005.04243 [astro-ph.HE]. [98] R. Abbottet al.(LIGO Scientific, Virgo), Phys. Rev. Lett.125, 101102 (2020), arXiv:2009.01075 [gr-qc]. [99] R. Abbottet al.(LIGO Scientific, Virgo), Astrophys. J. Lett.900, L13 (2020), arXiv:2009.01190 [astro-ph.HE]. [100] S. L. Morton, S. Rinaldi, A. Torres-Orjuela, A. Derdzin- ski, M. P. Vaccaro, and W. Del Pozzo, Phys. Rev. D 108, 123039 (2023), arXiv:2310.16025 [gr-qc]. [101] R. C. Zhang, G. Fragione, C. Kimball, and V. Kalogera, Astrophys. J.954, 23 (2023), arXiv:2302.07284 [astro- ph.HE]. [102] I. M. Romero-Shaw, P. D. Lasky, and E. Thrane, As- trophys. J.940, 171 (2022), arXiv:2206."},{"citing_arxiv_id":"2604.11895","ref_index":73,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Novel ringdown tests of general relativity with black hole greybody factors","primary_cat":"gr-qc","submitted_at":"2026-04-13T18:00:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"GreyRing model based on greybody factors reproduces numerical relativity ringdown signals with mismatches of order 10^{-6} and enables a new post-merger consistency test of general relativity applied to GW250114.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"We assume nonprecessing binaries, so that±mmultipoles are related by˜hℓm(ω) = (−1)ℓ˜h∗ ℓ−m (−ω). We consider the GW250114-like numerical simulation SXS:BBH:3617, and perform injection-recovery tests with two independent models. We first generate injec- tions using the QNM model calibrated to the simula- tion and perform a standard QNM analysis withpycbc inference[73]. We inject QNMs with physical param- eters consistent with GW250114 in theℓ=m= 2multi- poleandrecovertheposteriordistributionsof(M, χ)(see Supplemental Material for details). Then, we perform an independent parameter estimation usingGreyRing, in which we inject a signal constructed from the same sim- ulation and recover it using only the frequency-domain"},{"citing_arxiv_id":"2604.08179","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GW231123: False Massive Graviton Signatures from Unmodeled Point-Mass Lensing","primary_cat":"gr-qc","submitted_at":"2026-04-09T12:35:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Unmodeled point-mass lensing produces a spurious nonzero graviton mass posterior in GW231123 that vanishes when lensing is included in the analysis.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"lens massM z L and the dimensionless impact parameter y. We adopt the same priors as in the LVK analysis of GW231123 for all standard source parameters, together with weakly informative priors on (M z L, y) for lensing hy- potheses [6, 26, 33]. Posterior sampling and evidence evaluation are per- formed usingPyCBCwith theDynestynested- sampling algorithm [37, 38], which simultaneously ex- plores the posterior distribution and evaluates the mul- tidimensional integral definingZ H. This setup provides consistent estimates of both posterior densities and ev- idences across all waveform hypotheses, and the sam- pling uncertainty in lnZ H is propagated directly into the quoted Bayes factors. To determine which waveform description is more"},{"citing_arxiv_id":"2603.06010","ref_index":120,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Accelerated Time-domain Analysis for Gravitational Wave Astronomy","primary_cat":"gr-qc","submitted_at":"2026-03-06T08:07:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Presents a practical fully time-domain end-to-end likelihood for gravitational-wave inference with structured linear algebra and GPU acceleration.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Manual, Volume 2A: Instruction Set Reference, Intel (2016), instruction-set reference including AVX and (on supporting CPUs) FMA instructions. [119] C. Lomont,Introduction to Intel®Advanced Vector Extensions, Tech. Rep. (Lawrence Livermore National Laboratory, 2011) mid-level overview of AVX, regis- ter widths, throughput considerations, and performance implications. [120]Floating Point and IEEE 754, NVIDIA (2024), clear overview of IEEE 754 rounding, and why FMA (single rounding) improves accuracy and performance. [121] A. Fog,Instruction Tables: Lists of instruction la- tencies, throughputs and micro-operation breakdowns for x86 CPUs(2025), practical measured through- put/latency data for AVX/FMA instructions across mi-"},{"citing_arxiv_id":"2510.04332","ref_index":125,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Biased parameter inference of eccentric, spin-precessing binary black holes","primary_cat":"gr-qc","submitted_at":"2025-10-05T19:29:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Eccentric BBH signals recovered with quasi-circular precessing models show biases in chirp mass and χ_p; Bayes factors favor eccentric aligned-spin models when both eccentricity and precession are present.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"tors, which can be calculated between recoveries with eccentric and quasi-circular models, are defined as: BE/C = p(⃗ s|hE) p(⃗ s|hC) (2) whereEandCcorrespond to eccentric and quasi-circular recoveries respectively, andh i enumerates the waveform approximants under consideration.1 D. Parameter Estimation To estimate parameters, we use thePyCBC Inference Toolkit[125] andbilby[126], and explore the parame- ter space that includes chirp mass (M), mass ratio (q), luminosity distance (d L), inclination angle (ι), time of coalescence (t c), phase of coalescence (ϕc), right ascen- sion (α), declination (δ), and polarization angle (ψ). For aligned spin recoveries, we use two additional parame- ters corresponding to thez-components of the spin vec-"},{"citing_arxiv_id":"2509.14849","ref_index":14,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences","primary_cat":"gr-qc","submitted_at":"2025-09-18T11:14:14+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"F-statistic framework analytically maximizes over distance and polarization to enable faster Bayesian inference of compact binary coalescences with a new evidence formulation that matches full frequency-domain results at lower cost.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.20996","ref_index":95,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Parameter inference of millilensed gravitational waves using neural spline flows","primary_cat":"gr-qc","submitted_at":"2025-05-27T10:31:21+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Neural spline flows perform fast posterior inference on 11-dimensional millilensed GW parameters with accuracy comparable to dynesty for most quantities and a 3-day to 0.8-second speedup.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2108.01045","ref_index":190,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run","primary_cat":"gr-qc","submitted_at":"2021-08-02T17:09:29+00:00","verdict":"ACCEPT","verdict_confidence":"HIGH","novelty_score":4.0,"formal_verification":"none","one_line_summary":"GWTC-2.1 adds eight new high-significance compact binary coalescence events to the prior catalog, extending the observed black hole mass range and including candidates inside the pair-instability mass gap.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"Out of the 8 new events presented in this section, GW190725 174728, GW190916 200658, GW190925 232845 and GW190926 050336 were also independently identified and analyzed as part of 3- OGC [17] using the PyCBC Inference package [189] and the IMRPhenomXPHM waveform model. We compare the inferred source properties for these events as pre- sented in 3-OGC [190] and, to minimize potential model systematic effects, the IMRPhenomXPHM analysis performed for GWTC-2.1 presented here. Overall, we find a broad agreement between the two analyses. While there are differences found in the two sets of posterior distributions, they appear consistent within expectations from the differing choices of the analysis configurations"},{"citing_arxiv_id":"2006.00714","ref_index":235,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue","primary_cat":"astro-ph.IM","submitted_at":"2020-06-01T04:46:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1811.02042","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy","primary_cat":"astro-ph.IM","submitted_at":"2018-11-05T21:35:34+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"variable unit prior minimum maximum m1,2 M⊙ uniform 5 100 a1,2 - uniform 0 0.8 θ1,2 rad. sin 0 π δφ, φJL rad. uniform 0 2 π dL Mpc comoving 10 2 5 × 103 ra rad. uniform 0 2 π dec rad. cos −π/2 π/2 ι rad. sin 0 π ψ rad. uniform 0 π φc rad. uniform 0 2 π LIGO detectors [53] in Hanford, Washington and Liv- ingston, Louisiana detected the coalescence of a binary black hole system [23]. The gravitational waves swept through the two detectors with a 6 .9+0.5 −0.4 ms time dif- ference which, when combined with polarization infor- mation, allowed for a sky-location reconstruction cover- ing an annulus of 590 deg 2 [23]. The initially-published masses of the colliding black holes were given as 36+5 −4 M⊙ and 29 +4 −4 M⊙ [1]. Subsequent analyses with more accu-"}],"limit":50,"offset":0}