pith. sign in

arxiv: 2604.21170 · v1 · submitted 2026-04-23 · 🌌 astro-ph.IM · astro-ph.CO

Joint Estimation of Properties of the Lunar Subsurface and Galactic Foregrounds with LuSEE-Night

Fatima Yousuf , Zack Li , Stuart D. Bale , David W. Barker , Jack Burns , Christian H. Bye , Hugo Camacho , Cristina-Maria Cordun
show 31 more authors
Johnny Dorigo Jones Adam Fahs Sonia Ghosh Keith Goetz Robert Grimm Sven Herrmann Joshua J. Hibbard Oliver Jeong Marc Klein-Wolt L\'eon V.E. Koopmans Joel Krajewski Corentin Louis Milan Maksimovi\'c Ryan McLean Raul A. Monsalve Arnur Nigmetov Paul O'Connor Aaron Parsons Michel Piat Marc Pulupa Rugved Pund David Rapetti Kaja M. Rotermund Benjamin Saliwanchik An\v{z}e Slosar Graham Speedie Nikolai Stefanov David Sundkvist Aritoki Suzuki Harish K. Vedantham Philippe Zarka
This is my paper

Pith reviewed 2026-05-08 14:15 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.CO
keywords LuSEE-Nightlunar subsurfacegalactic foregroundsBayesian inferencedielectric propertiesbeam calibrationradio telescope
0
0 comments X

The pith

Bayesian inference allows simultaneous estimation of lunar subsurface dielectric properties and galactic foreground parameters from LuSEE-Night simulations.

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

The paper demonstrates that a Bayesian pipeline can jointly recover both the unknown dielectric properties of the lunar subsurface at the LuSEE-Night landing site and the parameters of a galactic foreground model. This joint approach works because subsurface effects primarily alter the antenna beam near resonance while foreground variations affect the signal across the full frequency band. The subsurface reflections change the beam pattern at the 10-20 percent level, which is the dominant calibration obstacle for the low-frequency telescope scheduled to reach the lunar far side in 2027. If the separation holds, observers can avoid needing independent high-precision knowledge of either component before interpreting sky data.

Core claim

Simulations of the LuSEE-Night far-field beam for varying dielectric profiles of the lunar subsurface show that changes in subsurface properties have the largest impact around the antenna resonance, altering its amplitude, position, and width. In contrast, changes to the galactic foreground affect the data across the entire band. A Bayesian inference pipeline is applied to jointly estimate parameters of a galactic foreground model and the dielectric properties of the lunar subsurface, recovering both sets of parameters successfully.

What carries the argument

Bayesian inference pipeline that jointly fits galactic foreground model parameters and lunar subsurface dielectric profile to simulated beam responses, exploiting their distinct spectral signatures.

If this is right

  • Parameters of both the galaxy and subsurface can be estimated jointly without separate prior knowledge of either.
  • Subsurface variations dominate near resonance while foregrounds dominate broadband, enabling separation in the fit.
  • The method applies directly to the modeled LuSEE-Night landing site and its antenna resonance characteristics.
  • Accurate joint recovery reduces bias in interpreting low-frequency sky signals caused by subsurface reflections.

Where Pith is reading between the lines

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

  • The same pipeline could be tested on actual LuSEE-Night data after 2027 if the idealized models are extended to include real instrumental systematics.
  • Future lunar radio arrays might use this joint-fitting strategy to relax requirements on pre-launch subsurface characterization at candidate sites.
  • If separability weakens under more complex subsurface layering, targeted electromagnetic simulations could quantify the added uncertainty in beam modeling.

Load-bearing premise

Spectral variations for plausible subsurface and galaxy models have very different signatures that allow separation.

What would settle it

Apply the joint estimation to simulated data that includes realistic instrumental noise, calibration errors, or mismatched model assumptions and check whether input parameters are recovered within stated uncertainties.

Figures

Figures reproduced from arXiv: 2604.21170 by Aaron Parsons, Adam Fahs, An\v{z}e Slosar, Aritoki Suzuki, Arnur Nigmetov, Benjamin Saliwanchik, Christian H. Bye, Corentin Louis, Cristina-Maria Cordun, David Rapetti, David Sundkvist, David W. Barker, Fatima Yousuf, Graham Speedie, Harish K. Vedantham, Hugo Camacho, Jack Burns, Joel Krajewski, Johnny Dorigo Jones, Joshua J. Hibbard, Kaja M. Rotermund, Keith Goetz, L\'eon V.E. Koopmans, Marc Klein-Wolt, Marc Pulupa, Michel Piat, Milan Maksimovi\'c, Nikolai Stefanov, Oliver Jeong, Paul O'Connor, Philippe Zarka, Raul A. Monsalve, Robert Grimm, Rugved Pund, Ryan McLean, Sonia Ghosh, Stuart D. Bale, Sven Herrmann, Zack Li.

Figure 1
Figure 1. Figure 1: Model of LuSEE-Night instrument and lander in Ansys HFSS used to simulate antenna far-field beam pat￾tern. The instrument includes four 3m BeCu stacer antenna monopole elements arranged in a cross-dipole configuration. 2009) and the Chang’e 3 and 4 rover Lunar Penetrating Radars (J. Lai et al. 2019; L. Zhang et al. 2020). Additionally, several ground-based 21-cm experiments have also evaluated the effect o… view at source ↗
Figure 3
Figure 3. Figure 3: Simulated far-field linear gain patterns of a LuSEE-Night antenna monopole for six frequencies with subsurface parameters ϵr,top = 3.2, ϵr,bottom = 3.8, L = 0.5m. The simulated far-field beam patterns were created with Ansys HFSS, using the model shown in view at source ↗
Figure 4
Figure 4. Figure 4: Ground fraction of the simulated LuSEE-Night beam patterns over frequency. The ground fraction is calcu￾lated using Equation 1 and simulated LuSEE-Night antenna far-field beams for a range of values of the two dielectric re￾golith layers. The parameter values used to simulate these subsurface layers are described in view at source ↗
Figure 5
Figure 5. Figure 5: Model of the brightness temperature of the galac￾tic foregrounds at ν = 25 MHz for β = −2.5, γ = 0.1, k1 = 1.1, k2 = −10 as described in Equation 2 (top). Bright￾ness temperature of the pixel of the galactic foreground map at l = 270◦ , b = 0◦ over frequency ν (bottom). These galac￾tic foreground maps are convolved with the antenna beam patterns according to Equation 3 to simulate LuSEE-Night measurements view at source ↗
Figure 6
Figure 6. Figure 6: Simulated LuSEE-Night voltage power spectral density (PSD) measurements for the LuSEE-Night antenna monopoles. Each panel shows the PSD as a function of frequency and time for auto-correlations (diagonal) and cross-cor￾relations (off-diagonal) between the four monopoles (N, E, S, W), with real and imaginary components. The PSDs are derived from simulated correlated antenna temperature measurements using Eq… view at source ↗
Figure 7
Figure 7. Figure 7: Eigenvalues of the LuSEE-Night power spec￾tral denisty decompositions for varying subsurface (blue) and foreground (orange) parameters. The rapid decline indicates that the variance in both datasets is dominated by a small number of modes. (SVD) of the covariance matrix to identify the spec￾tral eigenmodes that describe the dominant directions of variation in the simulated data with respect to the foregrou… view at source ↗
Figure 8
Figure 8. Figure 8: The first four eigenmodes derived from the covariance matrices of the simulated spectra are shown for the subsurface parameter ensemble (bottom) and the foreground parameter ensemble (top). Each eigenmode describes the dominant directions of variation in the simulated power spectra. Foreground modes are smooth and dominated by large spectral slopes at lower frequencies, while subsurface modes exhibit more … view at source ↗
Figure 10
Figure 10. Figure 10: Comparing the PMC fit ( view at source ↗
Figure 11
Figure 11. Figure 11: Ratio of the residuals between the emulator and fiducial simulated LuSEE-Night voltage PSD measurements and the maximum value of the simulated LuSEE-Night voltage PSD measurements. We consider three cases of off-grid subsurface parameter vectors described in view at source ↗
read the original abstract

The Lunar Surface Electromagnetics Experiment (LuSEE-Night) is a joint NASA-DOE-ESA low-frequency radio telescope that will reach the lunar far side in 2027. The unknown dielectric properties of the subsurface at the LuSEE-Night landing site impose the most significant limitation for precision instrument calibration, as reflections from the lunar subsurface can change the primary beam at the 10-20% level. Simulations of these effects have provided insight and concern, showing that the lunar subsurface modeled as a lossy dielectric can absorb a large amount of the power of the sky signal. While this absorption may not strongly impact the signal-to-noise ratio in a sky-noise-dominated regime, it could complicate the beam pattern and make the signal more difficult to model and interpret. We have simulated the far-field properties of the LuSEE-Night beam for varying dielectric profiles of the lunar subsurface. We find that varying the properties of the lunar subsurface has the most significant impact around the antenna resonance, impacting its amplitude, position and width. Conversely, changing the properties of the foreground impacts the data across the band. We use a Bayesian inference pipeline to jointly estimate parameters of a galactic foreground model and dielectric properties of the lunar subsurface around the LuSEE-Night landing site and find that parameters of both the galaxy and subsurface properties can be estimated jointly. While the modeling is somewhat idealized, we believe that the results are largely robust owing to the fact that spectral variations for plausible subsurface and galaxy models have very different spectral signatures.

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.

Referee Report

2 major / 1 minor

Summary. The manuscript simulates the far-field beam of the LuSEE-Night lunar radio telescope under varying dielectric profiles of the subsurface and galactic foreground models. It applies a Bayesian inference pipeline to synthetic data and reports that parameters of both the galactic foreground and the lunar dielectric properties can be jointly recovered, attributing this to their distinct spectral signatures.

Significance. If the joint recovery is robust, the work would be significant for LuSEE-Night calibration by providing a pathway to simultaneously constrain instrument response and astrophysical signals. The forward modeling of subsurface-induced beam variations addresses a documented challenge in lunar low-frequency observations. The use of synthetic-data Bayesian fitting is a constructive approach, though its value depends on quantitative validation of parameter separability.

major comments (2)
  1. [Abstract and Bayesian results] Abstract and results on Bayesian recovery: the claim that galactic and subsurface parameters 'can be estimated jointly' is not accompanied by any reported posterior covariances, correlation coefficients, recovery fractions, or bias/variance statistics from the synthetic fits. Without these, the assertion that differing spectral signatures permit separation cannot be evaluated, especially given the idealized modeling.
  2. [Simulations] Simulations section: the analysis assumes specific dielectric profiles and foreground models with 'very different spectral signatures,' but provides no quantitative measure of signature orthogonality (e.g., Fisher information overlap or condition number of the parameter Fisher matrix) nor tests under modest extensions such as lossy layered profiles or beam uncertainties.
minor comments (1)
  1. [Abstract] The abstract states that subsurface variations impact amplitude, position, and width 'around the antenna resonance' but does not quote the LuSEE-Night frequency band or resonance frequency, which would aid context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful review and constructive suggestions. The comments highlight the need for stronger quantitative support of the joint recovery claims and additional analysis of the simulations. We address each point below and have revised the manuscript accordingly to improve the rigor of the presentation.

read point-by-point responses

  1. Referee: [Abstract and Bayesian results] Abstract and results on Bayesian recovery: the claim that galactic and subsurface parameters 'can be estimated jointly' is not accompanied by any reported posterior covariances, correlation coefficients, recovery fractions, or bias/variance statistics from the synthetic fits. Without these, the assertion that differing spectral signatures permit separation cannot be evaluated, especially given the idealized modeling.

    Authors: We agree that explicit reporting of posterior statistics would strengthen the manuscript. The Bayesian pipeline generates full posterior distributions, and the presented results show that input parameters are recovered within the reported uncertainties, supporting the claim of joint estimation. However, correlation coefficients, bias, and variance metrics were not included in the original submission. In the revised version, we will add a table of recovered parameters with uncertainties, biases, and the posterior correlation matrix between galactic foreground and subsurface dielectric parameters to allow direct evaluation of separability. revision: yes

  2. Referee: [Simulations] Simulations section: the analysis assumes specific dielectric profiles and foreground models with 'very different spectral signatures,' but provides no quantitative measure of signature orthogonality (e.g., Fisher information overlap or condition number of the parameter Fisher matrix) nor tests under modest extensions such as lossy layered profiles or beam uncertainties.

    Authors: The simulations demonstrate that subsurface variations primarily modify the beam response near resonance (affecting amplitude, center frequency, and width), whereas foreground changes impact the data across the full band; these distinct behaviors are the basis for the separability argument. We did not compute quantitative orthogonality metrics such as the Fisher matrix condition number, nor did we extend the models to lossy layered profiles or include beam uncertainties. These omissions reflect the scope of the current idealized study. In revision, we will add a brief quantitative discussion of parameter sensitivity based on the existing runs and explicitly note the limitations regarding more complex profiles. revision: partial

Circularity Check

0 steps flagged

No significant circularity in joint Bayesian estimation on synthetic data

full rationale

The paper simulates LuSEE-Night beam responses under varying lunar subsurface dielectric profiles and galactic foreground models, then applies Bayesian inference to recover the input parameters from the resulting synthetic observations. The claim that both sets of parameters can be jointly estimated rests on the observed difference in their spectral signatures across the band, which is an empirical feature of the chosen models rather than a definitional equivalence. No equation reduces the recovered posteriors to the simulation inputs by construction, the pipeline is falsifiable (degeneracies would appear if signatures overlapped), and no load-bearing self-citation or ansatz is invoked to force the result. This is a standard forward-modeling validation study.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard electromagnetic modeling assumptions and a foreground model whose functional form is not detailed here. No new physical entities are introduced.

free parameters (2)
  • dielectric profile parameters
    Varying dielectric constant and loss tangent with depth; values are explored in simulation but not enumerated.
  • galactic foreground parameters
    Parameters of the galactic foreground model fitted jointly.
axioms (2)
  • domain assumption Spectral signatures of subsurface reflections and galactic emission are sufficiently distinct to allow separation.
    Invoked in the final sentence of the abstract to justify robustness of the joint fit.
  • domain assumption The lunar subsurface can be modeled as a lossy dielectric stack.
    Used throughout the beam simulations described in the abstract.

pith-pipeline@v0.9.0 · 8300 in / 1358 out tokens · 92491 ms · 2026-05-08T14:15:38.458605+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

3 extracted references · 1 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    " id="W5M0MpCehiHzreSzNTczkc9d

    thebibliography [1] 20pt to REFERENCES 6pt =0pt \@twocolumntrue 12pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key o...

    work page doi:10.1016/j.icarus.2018.06.007 2017