arxiv: 2602.18560 · v2 · submitted 2026-02-20 · 🌌 astro-ph.HE · astro-ph.IM· gr-qc

Recognition: 2 theorem links

· Lean Theorem

Inferring the population properties of galactic binaries from LISA's stochastic foreground

Federico De Santi , Alessandro Santini , Alexandre Toubiana , Nikolaos Karnesis , Davide Gerosa

Authors on Pith no claims yet

Pith reviewed 2026-05-15 20:36 UTC · model grok-4.3

classification 🌌 astro-ph.HE astro-ph.IMgr-qc

keywords LISAgalactic binariesstochastic foregroundsimulation-based inferencepopulation inferencegravitational wavesneural posterior estimation

0 comments

The pith

LISA's stochastic gravitational-wave foreground encodes enough information to recover galactic binary population parameters including their total number.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a simulation-based method to extract population properties of galactic binaries straight from the reconstructed stochastic foreground that LISA will observe. It parametrizes sources in observable space by amplitude, frequency, and frequency derivative, then uses synthetic catalogs and a subtraction algorithm to train a neural estimator that maps foreground spectra back to those population parameters. If the approach works, analysts could obtain the total number of binaries and related statistics without first resolving every individual source. This matters because galactic binaries dominate LISA's signal and their collective properties carry information about binary formation and evolution in the Milky Way. The method also delivers a fast GPU version of the subtraction step as a practical byproduct.

Core claim

The stochastic foreground alone carries significant information about the Galactic binary population; a neural posterior estimator trained on spectra generated by a global-fit-inspired subtraction algorithm recovers the population parameters with good accuracy, including the total number of binaries.

What carries the argument

A neural posterior estimator trained to map reconstructed foreground spectra to population parameters in an observable-space parametrization of amplitude, frequency, and frequency derivative.

If this is right

Population parameters including total binary count can be recovered directly from the unresolved foreground.
The GPU-accelerated subtraction algorithm reduces computation time by roughly two orders of magnitude.
The framework provides a practical route toward joint inference that combines resolved and unresolved sources.
Foreground-based inference becomes feasible even when full global-fit analyses remain computationally expensive.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Future LISA pipelines could treat foreground population inference as an early, low-cost step before attempting source resolution.
The method could be tested on mock data sets that include additional noise sources or instrumental artifacts to check robustness.
Extending the parametrization to include higher-order derivatives might capture more subtle population features.

Load-bearing premise

The synthetic catalogs and foreground spectra generated via the global-fit-inspired subtraction algorithm faithfully represent the statistical properties of real LISA observations without introducing systematic biases.

What would settle it

Applying the trained estimator to actual LISA foreground data and obtaining population parameters that conflict with those derived from resolved sources or independent astrophysical models would falsify the claim.

Figures

Figures reproduced from arXiv: 2602.18560 by Alessandro Santini, Alexandre Toubiana, Davide Gerosa, Federico De Santi, Nikolaos Karnesis.

**Figure 2.** Figure 2: FIG. 2. Characteristic strain sensitivity as a function of selected population parameters. We show the predicted mean for the A [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: FIG. 3. Flowchart of our inference pipeline. The left column [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: FIG. 5. Training (blue) and validation (red) losses as a function [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: FIG. 6. Probability-probability plot obtained from 100 in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: FIG. 7. Inference results for the simulated catalog of Fig. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗

**Figure 8.** Figure 8: FIG. 8. Source catalog reconstruction from the (GPR) pos [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗

**Figure 9.** Figure 9: FIG. 9. Distribution of the relative error, [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗

**Figure 10.** Figure 10: FIG. 10. Inference on the astrophysical catalog of Ref. [ [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: FIG. 11 [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗

**Figure 12.** Figure 12: FIG. 12. Source catalog reconstruction from the posterior samples of Fig. [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗

**Figure 13.** Figure 13: FIG. 13. Example of the Gaussian copula applied to the joint [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗

**Figure 14.** Figure 14: FIG. 14. Execution time of the subtraction algorithm as a [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗

**Figure 15.** Figure 15: compare results obtained with the running mean and median PSD estimators introduced in Eqs. (D1)– (D2) considering the astrophysical catalog of Ref. [2]. Some related metrics are reported in Table III, together with similar results obtained using the simulation described in Sec. III C. The top panel of [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗

read the original abstract

Galactic binaries are expected to be the most numerous LISA sources and to produce a stochastic gravitational-wave foreground whose spectral shape encodes information about the underlying population. Extracting this information with standard hierarchical methods is challenging due to the high dimensionality of the problem and the computational cost of global-fit analyses. We present a simulation-based inference framework to measure the population properties of galactic binaries directly from the reconstructed foreground. Adopting an astrophysically agnostic parametrization in the observable space -- defined by signal amplitude, frequency, and frequency derivative -- we generate synthetic catalogs and foreground spectra using a global-fit-inspired subtraction algorithm. We then train a neural posterior estimator to map spectra to population parameters. We validate our method on simulated data and recover population parameters with good accuracy, including the total number of binaries. As a by-product, we present a GPU-accelerated version of the subtraction algorithm, which delivers a ~100X speed-up compared to previous implementations in the literature. Our results demonstrate that LISA's stochastic foreground alone carries significant information about the Galactic binary population and provide a practical step toward joint inference from resolved and unresolved sources.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper gives a workable SBI pipeline to extract galactic binary population parameters, including total count, from LISA foreground spectra using an agnostic observable-space parametrization and a fast subtraction routine.

read the letter

This paper shows how to pull population parameters for galactic binaries straight from LISA's stochastic foreground using simulation-based inference. The key advance is training a neural posterior estimator on spectra generated from an observable-space parametrization, with synthetic data made via a global-fit-inspired subtraction algorithm. They avoid the full hierarchical modeling mess by working directly in the observable space of amplitude, frequency, and frequency derivative. The GPU-accelerated subtraction routine gives a hundredfold speed-up, which is genuinely useful for generating the large training sets needed. On their simulated validation sets the recovery of parameters, including the total number of binaries, looks solid. The main limitation is that everything rests on how faithfully the simulations match reality. The subtraction step could introduce subtle biases if the global-fit assumptions don't hold perfectly for real data, and there's no test yet on data with realistic instrumental effects beyond the training distribution. The abstract claims good accuracy but leaves the details on uncertainties and generalization to the full text. This work is aimed at the LISA analysis community and anyone building population models for galactic binaries. It is a concrete step that makes the problem more tractable, so it deserves to go to peer review. The method is new enough and the implementation careful enough that referees should see it.

Referee Report

0 major / 3 minor

Summary. The paper presents a simulation-based inference framework to extract Galactic binary population properties (including total number) directly from LISA's stochastic foreground. Synthetic catalogs are generated in an observable-space parametrization (amplitude, frequency, frequency derivative); foreground spectra are produced with a global-fit-inspired subtraction algorithm; a neural posterior estimator is trained to map spectra to parameters; and the method is validated on simulated data, recovering parameters with good accuracy. A GPU-accelerated subtraction algorithm (~100X speedup) is provided as a by-product.

Significance. If the validation holds under realistic conditions, the work demonstrates that the unresolved foreground alone encodes usable information about the Galactic binary population, offering a computationally lighter alternative to full hierarchical global fits. The GPU-accelerated subtraction routine is a concrete practical contribution. Strengths include training and validation on independent simulations and the use of an astrophysically agnostic observable-space parametrization.

minor comments (3)

[Abstract] Abstract: the statement that parameters are recovered 'with good accuracy' should be accompanied by at least one quantitative metric (bias, coverage probability, or credible-interval width) and a brief note on checks for subtraction-induced biases or overfitting.
[Neural posterior estimation section] The description of the neural posterior estimator would benefit from explicit reporting of training/validation split sizes, any regularization or early-stopping criteria, and at least one diagnostic (e.g., posterior calibration plot or coverage test) on the held-out realizations.
[Results and figures] Figure captions and text should clarify whether the reported recovery includes the full posterior or only point estimates, and whether the subtraction algorithm's residual spectrum is used as the sole input or combined with other observables.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive evaluation of our manuscript and for recommending minor revision. We appreciate the recognition that the unresolved foreground encodes usable information about the Galactic binary population and that the GPU-accelerated subtraction routine constitutes a practical contribution.

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained simulation-based inference

full rationale

The paper generates synthetic catalogs and foreground spectra from population models via a subtraction algorithm, then trains a neural posterior estimator on those simulations to recover parameters from spectra. Validation occurs on held-out realizations, with no equations or steps where a fitted parameter is renamed as a prediction, no self-definitional loops, and no load-bearing self-citations that reduce the central claim to unverified inputs. The framework is externally falsifiable via simulation benchmarks and does not import uniqueness theorems or ansatzes from prior author work in a circular manner. This matches the standard non-circular structure of SBI pipelines.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method rests on standard domain assumptions in gravitational-wave data analysis and simulation-based inference; no new free parameters or invented entities are introduced beyond the neural-network training.

axioms (1)

domain assumption Synthetic foreground spectra generated from the observable-space parametrization and subtraction algorithm statistically match the properties of real LISA stochastic signals.
Invoked when training the neural estimator on simulated data.

pith-pipeline@v0.9.0 · 5510 in / 1207 out tokens · 25853 ms · 2026-05-15T20:36:54.550119+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We present a simulation-based inference framework to measure the population properties of galactic binaries directly from the reconstructed foreground. Adopting an astrophysically agnostic parametrization in the observable space—defined by signal amplitude, frequency, and frequency derivative—we generate synthetic catalogs and foreground spectra using a global-fit-inspired subtraction algorithm. We then train a neural posterior estimator to map spectra to population parameters.
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Our population model depends on eight dimensionless parameters Λ={αPL, αΓ, βΓ, µΓ, µ, σ, ϱ, Nb}

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

81 extracted references · 81 canonical work pages · 29 internal anchors

[1]

LISA Definition Study Report

M. Colpiet al., ESA-SCI-DIR-RP-002 (2024), arXiv:2402.07571 [astro-ph.CO]

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

Lamberts, S

A. Lamberts, S. Blunt, T. B. Littenberg, S. Garrison- Kimmel, T. Kupfer, and R. E. Sanderson, Mon. Not. R. Astron. Soc.490, 5888 (2019), arXiv:1907.00014 [astro- ph.HE]

work page arXiv 2019
[3]

Korol, V

V. Korol, V. Belokurov, C. J. Moore, and S. Toonen, Mon. Not. R. Astron. Soc.502, L55 (2021), arXiv:2010.05918 [astro-ph.GA]

work page arXiv 2021
[4]

Korol, N

V. Korol, N. Hallakoun, S. Toonen, and N. Karnesis, Mon. Not. R. Astron. Soc.511, 5936 (2022), arXiv:2109.10972 [astro-ph.HE]

work page arXiv 2022
[5]

Toubiana, N

A. Toubiana, N. Karnesis, A. Lamberts, and M. C. Miller, Astron. Astrophys.692, A165 (2024), arXiv:2403.16867 [astro-ph.SR]

work page arXiv 2024
[6]

The gravitational wave signal from the Galactic disk population of binaries containing two compact objects

G. Nelemans, L. R. Yungelson, and S. F. Porte- gies Zwart, Astron. Astrophys.375, 890 (2001), arXiv:astro-ph/0105221

work page internal anchor Pith review Pith/arXiv arXiv 2001
[7]

A. J. Ruiter, K. Belczynski, M. Benacquista, S. L. Lar- son, and G. Williams, Astrophys. J.717, 1006 (2010), arXiv:0705.3272 [astro-ph]

work page internal anchor Pith review Pith/arXiv arXiv 2010
[8]

Characterizing accreting double white dwarf binaries with the Laser Interferometer Space Antenna and Gaia

K. Breivik, K. Kremer, M. Bueno, S. L. Larson, S. Cough- lin, and V. Kalogera, Astrophys. J. Lett.854, L1 (2018), arXiv:1710.08370 [astro-ph.SR]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[9]

T. B. Littenberg, N. J. Cornish, K. Lackeos, and T. Rob- son, Phys. Rev. D101, 123021 (2020)

work page 2020
[10]

T. B. Littenberg and N. J. Cornish, Phys. Rev. D107, 063004 (2023), arXiv:2301.03673 [gr-qc]

work page arXiv 2023
[11]

M. L. Katz, N. Karnesis, N. Korsakova, J. R. Gair, and N. Stergioulas, Phys. Rev. D111, 024060 (2025), arXiv:2405.04690 [gr-qc]

work page arXiv 2025
[12]

S. H. Strub, L. Ferraioli, C. Schmelzbach, S. C. Stäh- ler, and D. Giardini, Phys. Rev. D110, 024005 (2024), arXiv:2403.15318 [gr-qc]

work page arXiv 2024
[13]

S. Deng, S. Babak, M. Le Jeune, S. Marsat, É. Plagnol, and A. Sartirana, Phys. Rev. D111, 103014 (2025), arXiv:2501.10277 [gr-qc]

work page arXiv 2025
[14]

Delfavero, K

V. Delfavero, K. Breivik, S. Thiele, R. O’Shaughnessy, and J. G. Baker, Astrophys. J.981, 66 (2025), arXiv:2409.15230 [gr-qc]

work page arXiv 2025
[15]

Georgousi, N

M. Georgousi, N. Karnesis, V. Korol, M. Pieroni, and N. Stergioulas, Mon. Not. R. Astron. Soc.519, 2552 (2022), arXiv:2204.07349 [astro-ph.GA]

work page arXiv 2022
[16]

A framework for LISA population inference

A. Toubiana and J. Gair, (2026), arXiv:2601.04168 [gr-qc]

work page internal anchor Pith review Pith/arXiv arXiv 2026
[17]

Srinivasan, E

R. Srinivasan, E. Barausse, N. Korsakova, and R. Trotta, Phys. Rev. D112, 103043 (2025), arXiv:2506.22543 [astro- ph.GA]

work page arXiv 2025
[18]

K.Cranmer, J.Brehmer,andG.Louppe,Proc.Natl.Acad. Sci. USA117, 30055 (2020), arXiv:1911.01429 [stat.ML]

work page arXiv 2020
[19]

Deistler, J

M. Deistler, J. Boelts, P. Steinbach, G. Moss, T. Moreau, M. Gloeckler, P. L. C. Rodrigues, J. Linhart, J. K. Lap- palainen, B. K. Miller, P. J. Gonçalves, J.-M. Lueckmann, C. Schröder, and J. H. Macke, (2025), arXiv:2508.12939 [stat.ML]

work page arXiv 2025
[20]

Alvey, U

J. Alvey, U. Bhardwaj, V. Domcke, M. Pieroni, and C. Weniger, Phys. Rev. D109, 083008 (2024), arXiv:2309.07954 [gr-qc]

work page arXiv 2024
[21]

Karnesis, S

N. Karnesis, S. Babak, M. Pieroni, N. Cornish, and T. Littenberg, Phys. Rev. D104, 043019 (2021), arXiv:2103.14598 [astro-ph.IM]

work page arXiv 2021
[22]

Santini, M

A. Santini, M. Muratore, J. Gair, and O. Hartwig, Phys. Rev. D112, 084050 (2025), arXiv:2507.06300 [gr-qc]

work page arXiv 2025
[23]

Hellström, M

L. Hellström, M. Giersz, A. Askar, A. Hypki, Y. Zhao, Y. Lu, S. Zhang, V. Vázquez-Aceves, and G. Wiktorowicz, Astron. Astrophys.702, A131 (2025), arXiv:2506.13122 [astro-ph.SR]

work page arXiv 2025
[24]

A. S. Rajamuthukumar, V. Korol, J. Stegmann, H. Preece, R. Pakmor, S. Justham, S. Toonen, and S. E. de Mink, Astron. Astrophys.704, A156 (2025), arXiv:2502.09607 [astro-ph.SR]

work page arXiv 2025
[25]

N. J. Cornish and T. B. Littenberg, Phys. Rev. D76, 083006 (2007), arXiv:0704.1808 [gr-qc]

work page internal anchor Pith review Pith/arXiv arXiv 2007
[26]

Long-term evolution of double white dwarf binaries accreting through direct impact

K. Kremer, J. Sepinsky, and V. Kalogera, Astrophys. J. 806, 76 (2015), arXiv:1502.06147 [astro-ph.SR]

work page internal anchor Pith review Pith/arXiv arXiv 2015
[27]

Zahn, Astron

J.-P. Zahn, Astron. Astrophys.57, 383 (1977)

work page 1977
[28]

J. R. Hurley, C. A. Tout, and O. R. Pols, Mon. Not. R. Astron. Soc.329, 897 (2002), arXiv:astro-ph/0201220 [astro-ph]

work page internal anchor Pith review Pith/arXiv arXiv 2002
[29]

J. E. Solheim, Publ. Astron. Soc. Pac.122, 1133 (2010)

work page 2010
[30]

K. J. Shen, Astrophys. J. Lett.805, L6 (2015), arXiv:1502.05052 [astro-ph.SR]

work page internal anchor Pith review Pith/arXiv arXiv 2015
[31]

D. Maoz, F. Mannucci, and G. Nelemans, Annu. Rev. Astron. Astrophys.52, 107 (2014), arXiv:1312.0628 [astro- ph.CO]

work page internal anchor Pith review Pith/arXiv arXiv 2014
[32]

K. J. Shen, D. Kasen, B. J. Miles, and D. M. Townsley, Astrophys. J.854, 52 (2018), arXiv:1706.01898 [astro- ph.HE]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[33]

Prospects for detection of detached double white dwarf binaries with Gaia, LSST and LISA

V. Korol, E. M. Rossi, P. J. Groot, G. Nelemans, S. Too- nen, and A. G. A. Brown, Mon. Not. R. Astron. Soc.470, 1894 (2017), arXiv:1703.02555 [astro-ph.HE]

work page internal anchor Pith review Pith/arXiv arXiv 2017
[34]

McMillan, A

J. McMillan, A. Ingram, C. Dashwood Brown, A. Igoshev, M. Middleton, G. Wiktorowicz, and S. Scaringi, (2026), arXiv:2602.11765 [astro-ph.HE]

work page arXiv 2026
[35]

T. R. Marsh, G. Nelemans, and D. Steeghs, Mon. Not. R. Astron. Soc.350, 113 (2004), arXiv:astro-ph/0312577 [astro-ph]

work page internal anchor Pith review Pith/arXiv arXiv 2004
[36]

Sberna, A

L. Sberna, A. Toubiana, and M. C. Miller, Astrophys. J. 908, 1 (2021), arXiv:2010.05974 [astro-ph.SR]

work page arXiv 2021
[37]

Predicting the binary black hole population of the Milky Way with cosmological simulations

A. Lamberts, S. Garrison-Kimmel, P. F. Hopkins, E.Quataert, J. S.Bullock, C.-A. Faucher-Giguère, A.Wet- zel, D. Kereš, K. Drango, and R. E. Sanderson, Mon. Not. R. Astron. Soc.480, 2704 (2018), arXiv:1801.03099 [astro- ph.GA]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[38]

D. Maoz, C. Badenes, and S. J. Bickerton, Astrophys. J. 751, 143 (2012), arXiv:1202.5467 [astro-ph.SR]

work page internal anchor Pith review Pith/arXiv arXiv 2012
[39]

The Merger Rate of Binary White Dwarfs in the Galactic Disk

C. Badenes and D. Maoz, Astrophys. J. Lett.749, L11 (2012), arXiv:1202.5472 [astro-ph.SR]

work page internal anchor Pith review Pith/arXiv arXiv 2012
[40]

The binary fraction, separation distribution, and merger rate of white dwarfs from SPY

D. Maoz and N. Hallakoun, Mon. Not. R. Astron. Soc. 467, 1414 (2017), arXiv:1609.02156 [astro-ph.SR]

work page internal anchor Pith review Pith/arXiv arXiv 2017
[41]

D. Maoz, N. Hallakoun, and C. Badenes, Mon. Not. R. Astron. Soc.476, 2584 (2018), arXiv:1801.04275 [astro- ph.SR]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[42]

Adamcewicz and E

C. Adamcewicz and E. Thrane, Mon. Not. R. Astron. Soc. 517, 3928 (2022), arXiv:2208.03405 [astro-ph.HE]

work page arXiv 2022
[43]

A. R. Wetzel, P. F. Hopkins, J.-h. Kim, C.-A. Faucher- Giguere, D. Keres, and E. Quataert, Astrophys. J. Lett. 827, L23 (2016), arXiv:1602.05957 [astro-ph.GA]

work page internal anchor Pith review Pith/arXiv arXiv 2016
[44]

LISA Science Study Team, ESA-L3-EST-SCI-RS001 www.cosmos.esa.int/documents/678316/1700384/SciRD.pdf (2018)

work page 2018
[45]

Buscicchio, F

R. Buscicchio, F. Pozzoli, D. Chirico, and A. Sesana, 20 (2025), arXiv:2511.03604 [astro-ph.IM]

work page arXiv 2025
[46]

Pozzoli, R

F. Pozzoli, R. Buscicchio, A. Klein, V. Korol, A. Sesana, and F. Haardt, Phys. Rev. D111, 063005 (2025), arXiv:2410.08274 [astro-ph.GA]

work page arXiv 2025
[47]

T.A.Prince, M.Tinto, S.L.Larson,andJ.W.Armstrong, Phys. Rev. D66, 122002 (2002), arXiv:gr-qc/0209039

work page internal anchor Pith review Pith/arXiv arXiv 2002
[48]

Hartwig and M

O. Hartwig and M. Muratore, Phys. Rev. D105, 062006 (2022), arXiv:2111.00975 [gr-qc]

work page arXiv 2022
[49]

De Santi and N

F. De Santi and N. Karnesis, doi.org/10.5281/zenodo.18710899, Fast LISA Sub- traction (2026)

work page doi:10.5281/zenodo.18710899 2026
[50]

M. L. Katz, doi.org/10.5281/zenodo.6500434, github.com/mikekatz04/gbgpu (2025)

work page doi:10.5281/zenodo.6500434 2025
[51]

M. L. Katz, C. Danielski, N. Karnesis, V. Korol, N. Tamanini, N. J. Cornish, and T. B. Littenberg, Mon. Not. R. Astron. Soc.517, 697 (2022), arXiv:2205.03461 [astro-ph.EP]

work page arXiv 2022
[52]

Tinto and S

M. Tinto and S. V. Dhurandhar, Living Rev. Relativ.8, 4 (2005), arXiv:gr-qc/0409034

work page arXiv 2005
[53]

Baghi, N

Q. Baghi, N. Karnesis, J.-B. Bayle, M. Besançon, and H. Inchauspé, J. Cosmology Astropart. Phys.04, 066 (2023), arXiv:2302.12573 [gr-qc]

work page arXiv 2023
[54]

Hartwig, M

O. Hartwig, M. Lilley, M. Muratore, and M. Pieroni, Phys. Rev. D107, 123531 (2023), arXiv:2303.15929 [gr-qc]

work page arXiv 2023
[55]

Reconstructing the spectral shape of a stochastic gravitational wave background with LISA,

C. Caprini, D. G. Figueroa, R. Flauger, G. Nardini, M. Peloso, M. Pieroni, A. Ricciardone, and G. Tasi- nato, J. Cosmology Astropart. Phys.11, 017 (2019), arXiv:1906.09244 [astro-ph.CO]

work page arXiv 2019
[56]

Flauger, N

R. Flauger, N. Karnesis, G. Nardini, M. Pieroni, A. Ric- ciardone, and J. Torrado, J. Cosmology Astropart. Phys. 01, 059 (2021), arXiv:2009.11845 [astro-ph.CO]

work page arXiv 2021
[57]

Pieroni and E

M. Pieroni and E. Barausse, J. Cosmology Astropart. Phys.07, 021 (2020), [Erratum: J. Cosmology Astropart. Phys. 09, E01 (2020)], arXiv:2004.01135 [astro-ph.CO]

work page arXiv 2020
[58]

C. E. Rasmussen and C. K. I. Williams,Gaussian Pro- cesses for Machine Learning(MIT, 2006)

work page 2006
[59]

M. D. McKay, R. J. Beckman, and W. J. Conover, Tech- nometrics21, 239 (1979)

work page 1979
[60]

S. R. Taylor and D. Gerosa, Phys. Rev. D98, 083017 (2018), arXiv:1806.08365 [astro-ph.HE]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[61]

A. G. d. G. Matthews, M. van der Wilk, T. Nickson, K. Fujii, A. Boukouvalas, P. León-Villagrá, Z. Ghahra- mani, and J. Hensman, J. Mach. Learn. Res.18, 1 (2017), arXiv:1610.08733 [stat.ML]

work page internal anchor Pith review Pith/arXiv arXiv 2017
[62]

van der Wilk, V

M. van der Wilk, V. Dutordoir, S. John, A. Artemev, V. Adam, and J. Hensman, arXiv:2003.01115 (2020)

work page arXiv 2003
[63]

Papamakarios, E

G. Papamakarios, E. Nalisnick, D. Jimenez Rezende, S. Mohamed, and B. Lakshminarayanan, J. Mach. Learn. Res.22, 1 (2021), arXiv:1912.02762 [stat.ML]

work page arXiv 2021
[64]

L. Dinh, J. Sohl-Dickstein, and S. Bengio, Interna- tional Conference on Learning Representations (2016), arXiv:1605.08803 [cs.LG]

work page internal anchor Pith review Pith/arXiv arXiv 2016
[65]

De Santi, M

F. De Santi, M. Razzano, F. Fidecaro, L. Muccillo, L. Pa- palini, and B. Patricelli, Phys. Rev. D109, 102004 (2024)

work page 2024
[66]

De Santi, github.com/fdesanti/hyperion (2024)

F. De Santi, github.com/fdesanti/hyperion (2024)

work page 2024
[67]

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]

work page arXiv 2021
[68]

D. P. Kingma and J. Ba, International Conference for Learning Representations (2017), arXiv:1412.6980 [cs.LG]

work page internal anchor Pith review Pith/arXiv arXiv 2017
[69]

The construction and use of LISA sensitivity curves

T. Robson, N. J. Cornish, and C. Liu, Class. Quantum Grav.36, 105011 (2019), arXiv:1803.01944 [astro-ph.HE]

work page internal anchor Pith review Pith/arXiv arXiv 2019
[70]

E. S. Phinney, (2001), arXiv:astro-ph/0108028 [astro- ph]

work page internal anchor Pith review Pith/arXiv arXiv 2001
[71]

P. J. Green, Biometrika82, 711 (1995)

work page 1995
[72]

Karnesis, M

N. Karnesis, M. L. Katz, N. Korsakova, J. R. Gair, and N. Stergioulas, Mon. Not. R. Astron. Soc.526, 4814 (2023), arXiv:2303.02164 [astro-ph.IM]

work page arXiv 2023
[73]

Whittle, J

P. Whittle, J. R. Stat. Soc. B15, 125 (1953)

work page 1953
[74]

Consequences of Disk Scale Height on LISA Confusion Noise from Close White Dwarf Binaries

M. Benacquista and K. Holley-Bockelmann, Astrophys. J. 645, 589 (2006), arXiv:astro-ph/0504135

work page internal anchor Pith review Pith/arXiv arXiv 2006
[75]

C. M. Fabbri, D. Gerosa, A. Santini, M. Mould, A. Toubiana, and J. Gair, Phys. Rev. D111, 104053 (2025), arXiv:2501.17233 [astro-ph.HE]

work page arXiv 2025
[76]

Rinaldi, A

S. Rinaldi, A. Toubiana, and J. R. Gair, J. Cosmology Astropart. Phys.12, 031 (2025), arXiv:2506.05153 [gr-qc]

work page arXiv 2025
[77]

A. G. Abacet al., (2025), arXiv:2508.18083 [astro- ph.HE]

work page internal anchor Pith review Pith/arXiv arXiv 2025
[78]

Sklar, IMS Lecture Notes Monogr

A. Sklar, IMS Lecture Notes Monogr. Ser.1, 1 (2009)

work page 2009
[79]

C. J. Moore, R. H. Cole, and C. P. L. Berry, Class. Quan- tum Grav.32, 015014 (2015), arXiv:1408.0740 [gr-qc]

work page internal anchor Pith review Pith/arXiv arXiv 2015
[80]

Armanoet al., Phys

M. Armanoet al., Phys. Rev. Lett.116, 231101 (2016)

work page 2016

Showing first 80 references.