{"total":19,"items":[{"citing_arxiv_id":"2606.29039","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural posterior estimation of Galactic Binary signals for the LISA mission","primary_cat":"astro-ph.IM","submitted_at":"2026-06-27T18:20:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28156","ref_index":130,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Joint inference of line-of-sight acceleration and orbital eccentricity in neutron-star--black-hole binaries","primary_cat":"gr-qc","submitted_at":"2026-06-26T14:52:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"All five NSBH events are consistent with zero line-of-sight acceleration; the joint posterior for GW200105_162426 disfavors both zero LOSA and zero eccentricity at 90% credibility.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02690","ref_index":101,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Speed and accuracy for long signals: Frequency-domain effective-one-body waveforms for compact binary coalescences","primary_cat":"gr-qc","submitted_at":"2026-06-01T18:00:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hybrid SPA-plus-FFT frequency-domain version of SEOBNRv5THM for quasi-circular spin-aligned BNS systems matches time-domain baseline accuracy while cutting computational cost for long signals.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28716","ref_index":89,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Eccentric and unbound compact binaries in the LIGO-Virgo-KAGRA catalog: parameter estimation and waveform systematics with SEOBNRv6EHM","primary_cat":"gr-qc","submitted_at":"2026-05-27T16:38:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SEOBNRv6EHM reduces parameter biases for eccentric binaries versus prior models and shows mild support for eccentricity in five catalog events plus comparable unbound fits for three high-mass events.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Gupte et al.,Eccentricity constraints disfavor single-single capture in nuclear star clusters as the origin of all LIGO-Virgo-KAGRA binary black holes,2603.29019. [88] M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno and B. Sch¨olkopf,Real-Time Gravitational Wave Science with Neural Posterior Estimation,Phys. Rev. Lett.127(2021) 241103 [2106.12594]. [89] M. Dax, S. R. Green, J. Gair, M. P ¨urrer, J. Wildberger, J. H. Macke et al.,Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference,Phys. Rev. Lett.130 (2023) 171403 [2210.05686]. [90] G. Ashton et al.,BILBY: A user-friendly Bayesian inference library for gravitational-wave astronomy,Astrophys. J. Suppl.241(2019) 27 [1811.02042]."},{"citing_arxiv_id":"2605.27499","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GenSBI: Generative Methods for Simulation-Based Inference in JAX","primary_cat":"cs.LG","submitted_at":"2026-05-26T17:59:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GenSBI delivers JAX-native implementations of generative SBI methods with transformer backbones and reports near-ideal calibration scores on standard benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21310","ref_index":127,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"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":"background","top_context_polarity":"background","context_text":"Lyu, H. Wang, Z. Cao, and Z. Ren, Com- mun. Phys.6, 212 (2023), arXiv:2207.07414 [gr-qc]. [125] M. Dax, S. R. Green, J. Gair, M. P¨ urrer, J. Wildberger, J. H. Macke, A. Buonanno, and B. Sch¨ olkopf, Phys. Rev. Lett.130, 171403 (2023), arXiv:2210.05686 [gr- qc]. [126] M. Saleemet al., Class. Quant. Grav.41, 195024 (2024), arXiv:2306.11366 [gr-qc]. [127] Y.-X. Wang, S.-J. Jin, T.-Y. Sun, J.-F. Zhang, and X. Zhang, Chin. Phys. C48, 125107 (2024), arXiv:2305.19003 [gr-qc]. [128] T.-Y. Sun, C.-Y. Xiong, S.-J. Jin, Y.-X. Wang, J.-F. Zhang, and X. Zhang, Chin. Phys. C48, 045108 (2024), arXiv:2312.08122 [gr-qc]. [129] C.-Y. Xiong, T.-Y. Sun, J.-F. Zhang, and X. Zhang, Phys. Rev. D111, 024019 (2025), arXiv:2405."},{"citing_arxiv_id":"2605.11274","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"End-to-End Population Inference from Gravitational-Wave Strain using Transformers","primary_cat":"gr-qc","submitted_at":"2026-05-11T21:54:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"an intermediate step and required fixing the catalog size in advance of training. In thisLetter, we introduceDingo-Pop, an end-to-end simulation-based inference (SBI) [ 32] framework for pop- ulation analysis that operates directly on GW strain data (Fig. 1, left). The strain data from individual events are first encoded into compact latent representations using aDingoembedding network [ 33]. These embeddings serve as tokens for a transformer encoder [ 34, 35] that accommodates variable catalog sizes. Finally, the trans- former output is mapped to a population posterior using a normalizing flow. To train, we simulate populations according to the likelihood (1) (Λ → {θ i} → {D i}), avoid- ing the expensive likelihood evaluations of HBA."},{"citing_arxiv_id":"2604.26581","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Normalizing flows for density estimation in multi-detector gravitational-wave searches","primary_cat":"astro-ph.HE","submitted_at":"2026-04-29T12:01:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Normalizing flows replace binned histograms for estimating multi-detector signal parameters in PyCBC, slashing storage by three orders of magnitude with under 0.05% sensitivity loss and up to 6.55% gains in specific cases.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[33] Federico Stachurski, Christopher Messenger, and Martin Hendry, \"Cosmological inference using gravitational waves and normalizing flows,\" Phys. Rev. D109, 123547 (2024), arXiv:2310.13405 [gr-qc]. [34] Kip S. Thorne, \"Gravitational Radiation,\" inThree Hundred Years of Gravitation, edited by Stephen W. Hawking and Werner Israel (Cambridge University Press, Cambridge, England, 1987) Chap. 9, pp. 330-458. [35] R. L. Forward, \"Wide Band Laser Interferometer Gravitational Radiation Experiment,\" Phys. Rev. D17, 379-390 (1978). [36] Stephen Fairhurst, \"Triangulation of gravitational wave sources with a network of detectors,\" New J. Phys. 11, 123006 (2009), [Erratum: New J.Phys. 13, 069602 (2011)], arXiv:0908.2356 [gr-qc]. [37] R. Abbottet al.(KAGRA, VIRGO, LIGO Scientific),"},{"citing_arxiv_id":"2604.14270","ref_index":36,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries","primary_cat":"gr-qc","submitted_at":"2026-04-15T18:00:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Science with Neural Posterior Estimation, Phys. Rev. Lett.127, 241103 (2021), arXiv:2106.12594 [gr-qc]. [35] M. Dax, S. R. Green, J. Gair, N. Gupte, M. P¨ urrer, V. Raymond, J. Wildberger, J. H. Macke, A. Buonanno, and B. Sch¨ olkopf, Real-time inference for binary neutron star mergers using machine learning, Nature639, 49 (2025), arXiv:2407.09602 [gr-qc]. [36] Q. Hu, J. Irwin, Q. Sun, C. Messenger, L. Suleiman, I. S. Heng, and J. Veitch, Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equa- tions of State, Astrophys. J. Lett.987, L17 (2025), arXiv:2412.03454 [gr-qc]. [37] Q. Hu, Hierarchical Subtraction with Neural Density Estimators as a General Solution to Overlapping Grav-"},{"citing_arxiv_id":"2604.13867","ref_index":96,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows","primary_cat":"gr-qc","submitted_at":"2026-04-15T13:30:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Mohanty and M. A. T. Chowdhury, Class. Quant. Grav.40, 125001 (2023), arXiv:2301.02398 [gr-qc]. [94] Y. Xu, M. Du, P. Xu, B. Liang, and H. Wang, Phys. Lett. B858, 139016 (2024), arXiv:2402.13091 [gr-qc]. [95] K. Mogushi, R. Quitzow-James, M. Cavagli` a, S. Kulka- rni, and F. Hayes, Mach. Learn. Sci. Tech.2, 035018 (2021), arXiv:2101.04712 [gr-qc]. [96] M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buo- nanno, and B. Sch¨ olkopf, Phys. Rev. Lett.127, 241103 (2021), arXiv:2106.12594 [gr-qc]. [97] M. Dax, S. R. Green, J. Gair, M. P¨ urrer, J. Wildberger, J. H. Macke, A. Buonanno, and B. Sch¨ olkopf, Phys. Rev. Lett.130, 171403 (2023), arXiv:2210.05686 [gr- qc]. [98] J. Langendorff, A. Kolmus, J. Janquart, and C."},{"citing_arxiv_id":"2604.12800","ref_index":83,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Spectroscopy of analogue black holes using simulation-based inference","primary_cat":"gr-qc","submitted_at":"2026-04-14T14:29:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Simulation-based inference reliably extracts physical parameters from noisy spectra of analogue black holes.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"of the National Academy of Sciences117, 30055 (2020), https://www.pnas.org/doi/pdf/10.1073/pnas.1912789117. [82] M. Deistler, J. Boelts, P. Steinbach, G. Moss, T. Moreau, M. Gloeckler, P. L. C. Rodrigues, J. Lin- hart, J. K. Lappalainen, B. K. Miller, P. J. Gon¸ calves, J.-M. Lueckmann, C. Schr¨ oder, and J. H. Macke, Simulation-based inference: A practical guide (2025), arXiv:2508.12939 [stat.ML]. [83] M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buo- nanno, and B. Sch¨ olkopf, Phys. Rev. Lett.127, 241103 (2021), arXiv:2106.12594 [gr-qc]. [84] M. Dax, S. R. Green, J. Gair, M. P¨ urrer, J. Wildberger, J. H. Macke, A. Buonanno, and B. Sch¨ olkopf, Phys. Rev. Lett.130, 171403 (2023), arXiv:2210.05686 [gr-qc]. [85] M. Dax, S. R. Green, J. Gair, N. Gupte, M."},{"citing_arxiv_id":"2604.11871","ref_index":125,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Not too close! Evaluating the impact of the baseline on the localization of binary black holes by next-generation gravitational-wave detectors","primary_cat":"gr-qc","submitted_at":"2026-04-13T18:00:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Baselines of 8-11 ms light travel time for two CE detectors provide a reasonable compromise for BBH sky localization, with third detectors eliminating multimodality for most or all events.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Renzini, P. S. Cole, C. Pacilio, M. Man- carella, and D. Gerosa, (2025), arXiv:2510.11861 [gr-qc]. [123] S. R. Green, C. Simpson, and J. Gair, Phys. Rev. D102, 104057 (2020), arXiv:2002.07656 [astro-ph.IM]. [124] M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Schölkopf, Phys. Rev. Lett.127, 241103 (2021), arXiv:2106.12594 [gr-qc]. [125] T. Islam, J. Roulet, and T. Venumadhav, (2022), arXiv:2210.16278 [gr-qc]. [126] J. Roulet, J. Mushkin, D. Wadekar, T. Venumadhav, B. Zackay, and M. Zaldarriaga, Phys. Rev. D110, 044010 (2024), arXiv:2404.02435 [gr-qc]. [127] D. Wadekar, T. Venumadhav, J. Roulet, A. K. Mehta, B. Zackay, J. Mushkin, and M. Zaldarriaga, Phys. Rev. D110, 044063 (2024), arXiv:2405."},{"citing_arxiv_id":"2604.06090","ref_index":158,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Posterior Predictive Checks for Gravitational-wave Populations: Limitations and Improvements","primary_cat":"gr-qc","submitted_at":"2026-04-07T17:03:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Maximum-likelihood-based posterior predictive checks detect model misspecification better than event-level versions for uncertain spin tilts, but current detector sensitivity limits their power; the Gaussian Component Spins model underpredicts high spin magnitudes and overpredicts anti-aligned tilts","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.18560","ref_index":67,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Inferring the population properties of galactic binaries from LISA's stochastic foreground","primary_cat":"astro-ph.HE","submitted_at":"2026-02-20T19:00:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.16879","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Accelerating parameter estimation for parameterized tests of general relativity with gravitational-wave observations","primary_cat":"gr-qc","submitted_at":"2025-11-21T01:08:52+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Relative binning accelerates TIGER parameterized GR tests by factors of 10-100 while recovering unbiased posteriors on simulated signals and real events like GW150914.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.12642","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Auto-encoder model for faster generation of effective one-body gravitational waveform approximations","primary_cat":"gr-qc","submitted_at":"2025-11-16T15:18:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Auto-encoder approximates SEOBNRv4 waveforms for four-parameter aligned-spin binaries, delivering 4 orders of magnitude speedup at median mismatch of 10^{-2}.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.20996","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"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":"2502.01093","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A practical Bayesian method for gravitational-wave ringdown analysis with multiple modes","primary_cat":"gr-qc","submitted_at":"2025-02-03T06:31:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"FIREFLY accelerates multi-mode GW ringdown analysis by analytically marginalizing QNM amplitudes and phases via Bayesian principles and importance sampling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2404.14286","ref_index":153,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA","primary_cat":"gr-qc","submitted_at":"2024-04-22T15:37:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Bayesian inference on LVK O1-O3 events with eccentric aligned-spin waveforms yields log10 Bayes factors of 1.77-4.75 favoring eccentricity for GW200129, GW190701 and GW200208_22, and >99.5% probability that at least one of 57 events is eccentric under an astrophysically motivated rate prior.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"dinary differential equations parameter estimation can be ex- pensive. For example, SEOBNRv4EHM takes O(100 − 700ms) per likelihood evaluation meaning inference can takeO(week) per event even parallelizing over 320 cores [24]. It is then very computationally expensive to analyze the catalog of GW events with such waveform model. We can instead use likelihood-free approaches to amortize the inference [153, 164, 165]. Here we use DINGO [153], which has been shown to achieve results with the same ac- curacy as standard samplers [115]. We pay the upfront cost of training a neural network for O(week), but then we can do inference on any events within the trained priors in O(hour). DINGO learns a mapping fd,S n : u → ϑϑϑ from a simple-base distribution p(u) = N(0,1)D to the complex target GW poste-"}],"limit":50,"offset":0}