Pith. sign in

REVIEW 3 major objections 7 minor 31 references

LDMX Phase II can discover light dark matter on the R=2.5 thermal target and tell dark-photon models apart from the 2D recoil spectrum.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.5

2026-07-31 12:25 UTC pith:S4XQIEPE

load-bearing objection Solid LDMX stats pipeline with real 5σ reach on R=2.5; the outer-product background is the main soft spot, mostly for Bayes factors and the R=2.2 edge cases. the 3 major comments →

arxiv 2607.24524 v1 pith:S4XQIEPE submitted 2026-07-27 hep-ph hep-ex

Light Dark Matter Discovery Potential and Model Selection at LDMX

Adam Berger , Riccardo Catena , Jan Conrad , Taylor R. Gray This is my paper
classification hep-ph hep-ex
keywords light dark matterLDMXdark photonthermal relic targetmissing momentumBayes factorparameter estimationhigher electromagnetic moments
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper asks whether the planned Light Dark Matter eXperiment can not only find light dark matter but also measure its properties and decide which dark-photon interaction is at work. Using a full likelihood on the two-dimensional recoil-electron energy and transverse-momentum distribution, with signal and background systematics, the authors project that Phase II (10^16 electrons on target) reaches 5σ discovery for complex scalar dark matter on the R=2.5 thermal relic curve under both low and high background assumptions, while the harder R=2.2 curve sits just below that threshold. When a signal is present at those strong benchmarks, both the dark-photon mass and the kinetic mixing are recovered inside their uncertainties by frequentist and Bayesian fits. Bayes factors then separate kinetic mixing from magnetic/electric dipole and charge-radius/anapole hypotheses, and the joint (E, p_T) analysis yields roughly an order of magnitude more discriminating power than energy alone. The same machinery is written so it can be dropped straight onto real LDMX data.

Core claim

At four thermal-relic benchmarks for complex scalar dark matter mediated by a dark photon, LDMX Phase II has projected 5σ discovery reach on the R=2.5 targets across N_BG=1 and 100, recovers both m_A' and ε within uncertainties there, and can statistically distinguish kinetic-mixing from higher electromagnetic-moment dark-photon models via Bayes factors, with the two-dimensional recoil analysis far stronger than energy-only.

What carries the argument

A binned two-dimensional Poisson likelihood in recoil energy and transverse momentum, with log-normal nuisance parameters on signal and background normalizations, evaluated by full toy pseudo-experiments (frequentist) and dynamic nested sampling (Bayesian evidence and posteriors).

Load-bearing premise

The background shape is built by multiplying independent one-dimensional energy and transverse-momentum distributions and normalizing them to two ad-hoc total counts rather than a full correlated Phase-II background model.

What would settle it

Apply the identical likelihood pipeline to real LDMX Phase-II data (or a high-fidelity full-detector simulation with correlated backgrounds) at the R=2.5 benchmarks and check whether the measured discovery significance, parameter contours, and Bayes factors match the projections.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A null Phase-II result excludes the R≳2.2 thermal targets over roughly 1 MeV–0.2 GeV in dark-photon mass.
  • An excess at the strong benchmarks yields ~10% relative uncertainties on both mediator mass and coupling.
  • The same framework can be reused for other missing-momentum experiments and other electron-coupled dark-sector models.
  • Adding a relic-density prior on the coupling lifts remaining shape degeneracies between kinetic mixing and charge-radius/anapole models.

Where Pith is reading between the lines

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

  • If real photo-nuclear backgrounds prove strongly E–p_T correlated, the quoted Bayes-factor gains from the two-dimensional analysis will shrink and may need re-optimization of the binning.
  • The hierarchy of model-separation power (2D ≫ energy-only > p_T-only) suggests that any future trigger or analysis cut that discards transverse-momentum information will measurably weaken dark-sector model selection.
  • Joint likelihoods with direct-detection or other accelerator data sets become straightforward once this LDMX likelihood is public, potentially closing the remaining R=2.2 window faster than either probe alone.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The manuscript builds a binned two-dimensional (recoil energy, transverse momentum) Poisson likelihood framework for LDMX Phase II (10^16 EOT), with log-normal normalization nuisances, and applies it to four benchmark points on the thermal relic target for complex scalar DM with a dark-photon mediator (R = 2.5 and 2.2, m_A' = 0.01 and 0.1 GeV), under two background normalizations (N_BG = 1 and 100). It reports projected 90% C.L. exclusion contours, median discovery significances from full toy pseudo-experiments (arguing correctly that Wilks asymptotics fail at these counts), frequentist and Bayesian parameter estimation for (m_A', epsilon), and Bayes-factor model comparison among kinetic mixing and four dark-electromagnetic-moment interaction models, finding that the R=2.5 benchmarks are discoverable at ≫5σ in both background scenarios and that the 2D analysis gives roughly an order of magnitude larger ln K than an energy-only analysis.

Significance. If the results hold, this is a useful and timely contribution for the light-DM community: it moves beyond counting-only sensitivity estimates to a complete statistical pipeline for LDMX, with concrete, falsifiable projections tied to stated relic-target benchmarks (R=2.5/2.2 at m_A' = 0.01, 0.1 GeV) and to two bracketing background scenarios. Particular strengths worth naming: (i) the explicit demonstration, via large toy ensembles (Appendix B), that the half-chi-square asymptotics fail at LDMX bin counts, with all near-threshold p-values therefore derived from pseudo-experiments; (ii) consistent frequentist (profile likelihood, CLs variant) and Bayesian (dynamic nested sampling, evidences) treatments with log-normal systematics and a hierarchical background-width hyperprior; (iii) the model-selection study showing the (E, |p_T|) analysis gains roughly an order of magnitude in ln K over energy-only, and the instructive misspecification-bias matrix of Fig. 12. The finding that benchmarks 1 and 3 are discoverable at ≫5σ in both background scenarios is likely robust; the R=2.2 threshold results are honestly presented.

major comments (3)
  1. [§2.2 (Background modelling) and Fig. 4] The two-dimensional background is constructed as the outer product of a 1D |p_T| photo-nuclear shape (Fig. 6 of [32]) and a 1D inclusive E distribution (Fig. 10 of [27]), assuming E and |p_T| are uncorrelated. This is acknowledged in footnote 4 as an approximation, but two specific problems deserve more than a footnote. (i) The outer product populates regions of the (E, |p_T|) plane that a single recoil electron cannot occupy — most obviously bins with |p_T| approaching or exceeding E, which violate the kinematic bound |p_T| <= E and the stated 40-degree acceptance cut sin^{-1}(|p_T|/E) < 40 deg. The manuscript does not state whether the 40-degree cut is imposed on the marginals before forming the outer product, on the product afterwards, or at all for the background; if applied after, the effective background normalization in the physical region is smaller than the quoted N_BG, and the
  2. [§5.2, Fig. 13] The relic-density-prior model discrimination, which the Conclusion highlights ('incorporating a relic density prior ... rendering some previously indistinguishable signal hypotheses statistically distinguishable'), is computed at R = 3 — the mass ratio the Introduction and §2.1 argue is now excluded by DAMIC-M/PandaX-4T electron-recoil constraints, which is the paper's stated motivation for adopting R = 2.5 and 2.2 benchmarks. The justification given (larger signal yields at R = 3) undercuts the result's relevance: the claim of practical interest is whether KM can be distinguished from C/A at the surviving benchmarks. Since at R = 2.5 the yields remain large (Table 4: S = 250 for KM, 5300 for C/A), the exercise should be repeatable at benchmark 3 with the R = 2.5 relic targets; the authors should either present that or clearly motivate why the R = 3 result is the one to quote. A secondar
  3. [§4.3, Table 2, and abstract/§6 wording] Two aspects of the low-yield parameter inference need tightening. (i) The 1-sigma and 2-sigma confidence regions in Fig. 6 and the half-interval widths in Table 2 are constructed under the asymptotic chi-square approximation with k = 2, yet Appendix B demonstrates that the asymptotic distributions fail in precisely this low-count regime, and for benchmarks 2 and 4 (S = 4 and 2) the approximation is not credible. The text concedes the resulting contours can be artifacts of averaging over 25 pseudo-experiments (the 'deceptively tight' BM4 contour), but Table 2 still reports single numbers without the ensemble variance, which the text says is 1-2 orders of magnitude larger than for benchmarks 1 and 3. The variance (or the per-realization interval coverage) should be reported in Table 2, or the BM2/BM4 entries marked as uninformative. (ii) Relatedly, the abstract states that 'both parameters
minor comments (7)
  1. [§3.2.1 vs §5.2] §3.2.1 states the median Bayes factor is taken over five pseudo-experiments, while §5.2 and the Fig. 11 caption state ten pseudo-data realizations. Please reconcile.
  2. [§3.2.1] §3.2.1 requires 'log K ≳ 10' as a stricter criterion than the Jeffreys K > 100 (ln K = 4.6); since Fig. 11 is labelled in ln K, clarify the logarithm base throughout and state the threshold consistently.
  3. [§4.2 and Appendix B] For benchmarks 1 and 3 the discovery significance is computed with the asymptotic formula of [11] and quoted as '≫5', which is appropriate; however, Appendix B (Fig. 15) displays specific asymptotic values such as Z_as = 58 sigma against toy distributions that the same figure shows are not asymptotic. Either remove the numerical Z_as labels or annotate them as unreliable by the paper's own demonstration.
  4. [§3.2 / §5.2] The Bayes factors are prior-dependent through the evidence; the log-uniform coupling prior spans epsilon in [10^{-20}, 1], i.e. 21 decades. A brief sensitivity statement (or a justification that the Occam factors cancel in the model pairs considered) would strengthen §5.2.
  5. [Fig. 4] Fig. 4 illustrates signal and background at m_A' = 1 GeV, epsilon = 10^{-3}, which is not one of the four benchmarks; please note in the caption that the point is illustrative.
  6. [Various] Typos and wording: 'couple strength' (abstract, should be 'coupling strength'); 'the explore the effect' (§2.2, should be 'to explore'); 'not longer fixed' (§3.1.3, should be 'no longer fixed'); 'Baye's factors' (§5.2); stray spaces in section headings ('F requentist', 'F ormalism', 'T est Statistic').
  7. [§4.3, Table 2] Table 2: for BM2 the quoted epsilon bias improves from 0.3 (N_BG=1) to 0.18 (N_BG=100); given the authors' own discussion of averaging artifacts for BM4, it would be worth noting whether this trend is physically meaningful or the same artifact.

Circularity Check

1 steps flagged

No significant circularity: standard LDMX sensitivity projections on external thermal targets and MadGraph/pseudo-data, not self-derived predictions.

specific steps
  1. other [§3.2.1; Fig. 13; cf. Fig. 1 of [25]]
    "To exploit the difference that does exist in the signal normalization of these model pairs, we add a prior on g_f that penalizes fit values far from the relic density curve. The prior π(g_f) is a normal distribution centred with its median on the g_f values of Fig. 1 of [25], with standard deviation σ_gf = 0.1. ... The previously indistinguishable model combination, KM and C/A, now have large Bayes factors"

    Minor and non-load-bearing: benchmarks already sit on the thermal target, and the optional g_f prior re-injects that same relic-target normalization (from overlapping-author ref. [25]) to split shape-degenerate KM vs C/A. This is transparent external-prior use for model comparison, not a derivation of discovery reach or parameter recovery; without it the paper still reports shape-based discrimination (KM vs M/E etc.) and the main frequentist claims.

full rationale

This is a projected exclusion/discovery and model-comparison study. Signal yields come from MadGraph dark-bremsstrahlung cross sections normalized to Phase-II EOT; backgrounds are taken from external LDMX collaboration shapes with ad hoc N_BG benchmarks; thermal (m_A', ε) points are imported from freeze-out calculations in the literature, not fitted to LDMX data. Frequentist toys and Bayesian nested sampling then evaluate test statistics, posteriors, and Bayes factors on independent pseudo-experiments. Recovering injected parameters and reporting ln K between DEM hypotheses is validation of the likelihood, not a prediction forced by construction. The only mild self-reference is optional: a relic-density prior on g_f (centered on values from the authors’ prior DEM paper) is added solely to break KM–C/A shape degeneracy in model comparison; main 5σ/exclusion/parameter-recovery claims do not depend on it. No self-definitional loop, no fitted-input-called-prediction of the central results, and no load-bearing uniqueness theorem imported from the authors.

Axiom & Free-Parameter Ledger

6 free parameters · 5 axioms · 0 invented entities

Claims rest on standard QFT dark-photon portals and thermal freeze-out targets from the literature, plus analysis choices (background factorization, N_BG benchmarks, 10% log-normal systematics, detector smearing parameters, α_D=0.5, R=2.2/2.5 benchmarks) that are fixed by hand rather than measured in this work. No new particle is invented here; DEM operators are imported. The load-bearing modeling axioms are the Poisson 2D likelihood, uncorrelated background construction, and the Phase-II exposure/selection.

free parameters (6)
  • α_D (dark fine-structure constant) = 0.5
    Fixed to 0.5 throughout to define thermal targets and signal rates; not fit to LDMX data.
  • N_BG overall background normalization benchmarks = 1 or 100
    Two discrete choices (1 and 100 expected background events) bracket unknown Phase-II background; central sensitivity claims depend on which is true.
  • σ_S, σ_B log-normal nuisance widths = 0.1 (and hyperprior σ=0.5)
    Set to 0.1 as representative ~10% MC/normalization uncertainty; Bayesian analysis further places a half-normal hyperprior on σ_B with shape σ=0.5.
  • Detector resolution parameters s,c,n and σ_pT curve = s=0.2, c=0.03, n=0
    Energy resolution s=0.2 GeV^{1/2}, c=0.03, n=0 and p_T resolution from LDMX design figures; smearing changes bin migrations and thus limits.
  • R = m_A'/m_DM benchmark ratios and four (m_A',ε) points = R∈{2.2,2.5}; four benchmark points in Fig. 1
    Chosen as R=2.5 and 2.2 at m_A'={0.01,0.1} GeV on thermal targets after direct-detection exclusions of R≳3; define all discovery/PE tables.
  • relic-density prior width σ_{g_f} on couplings for model comparison = σ_{g_f}=0.1
    Normal prior with σ=0.1 around relic-target g_f used to break KM–C/A normalization degeneracy in Bayes factors.
axioms (5)
  • standard math Binned observations in (E_e, |p_T|) are independent Poisson counts with mean S_ij(ε,m_A')θ_S + B_ij θ_B.
    Standard HEP likelihood; Eq. 3.2. Underpins all p-values, MLE, and posteriors.
  • domain assumption Complex scalar DM with dark photon kinetic mixing (and separately DEM operators) produces the observed relic via thermal freeze-out at the quoted (m_A',ε) targets for α_D=0.5.
    Benchmarks taken from prior freeze-out calculations [10] and micrOMEGAs+Boltzmann solver; discovery claims are relative to those targets (§2.1).
  • ad hoc to paper Two-dimensional background density factors as the product of a 1D |p_T| photo-nuclear shape and a 1D energy shape (uncorrelated E and p_T).
    Explicit modeling choice in §2.2 with authors noting it is an approximation pending full LDMX background characterization.
  • domain assumption Phase II delivers 10^16 EOT at 8 GeV with stated acceptance, E>50 MeV, |angle|<40°, optional E<3.16 GeV trigger cut, and vetoes that leave only the quoted residual background normalizations.
    Taken from LDMX design reports [9,27,28,32]; all absolute rates scale with this exposure and selection.
  • standard math Asymptotic χ^2 / half-χ^2 formulae are unreliable at low bin counts, so exclusion and near-5σ discovery must use toy pseudo-experiments.
    Authors correctly invoke and verify breakdown of Wilks/Cowan asymptotics (Appendix B); used to justify toy-based p-values.

pith-pipeline@v1.2.0-grok45-kimik3 · 32340 in / 4318 out tokens · 85750 ms · 2026-07-31T12:25:15.817938+00:00 · methodology

0 comments
read the original abstract

Light dark matter (DM) is a compelling scenario for the observed relic abundance, with accelerator-based searches as a powerful discovery strategy. The upcoming Light Dark Matter eXperiment (LDMX) is designed to probe light DM by measuring the energy and transverse momentum of recoil electrons in high-intensity electron-nucleus collisions. We evaluate the discovery potential of LDMX to light DM at four benchmark points along the thermal target for complex scalar DM mediated by a dark photon, for different background assumptions, and assess its model selection power at a representative benchmark. We find that LDMX has strong projected 5$\sigma$ discovery potential along the relic target across both background scenarios for certain benchmarks, and we further compute projected 90% C.L. exclusion limits assuming no measured signal events. The normalization and shape of the two-dimensional recoil electron distribution encodes the coupling and dark photon mass, respectively, enabling parameter inference in the event of a signal excess. We perform parameter estimation on simulated data and find that both parameters are recovered within their uncertainties along the relic target. We assess whether the data can distinguish between competing dark sector hypotheses, in particular, dark photons with additional higher electromagnetic moment interactions. We demonstrate that model comparison using the Bayes factor allows dark sector hypotheses to be statistically distinguished, with the two-dimensional analysis affording substantially greater discriminating power than the one-dimensional analysis. These results are obtained within a likelihood-based statistical framework, incorporating signal and background modelling with their associated systematic uncertainties and employing both frequentist and Bayesian methods. The framework is designed for direct application to real LDMX data.

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

31 extracted references · 22 linked inside Pith

  1. [1]

    Caputo and R

    A. Caputo and R. Essig,The Dark Photon: a 2026 Perspective, 3, 2026.arXiv:2603.08430

    Pith/arXiv arXiv 2026

  2. [2]

    Balan et al.,Resonant or asymmetric: the status of sub-GeV dark matter,JCAP01(2025) 053, [arXiv:2405.17548]

    S. Balan et al.,Resonant or asymmetric: the status of sub-GeV dark matter,JCAP01(2025) 053, [arXiv:2405.17548]. [3]DAMIC-M, K. Aggarwal et al.,Probing Benchmark Models of Hidden-Sector Dark Matter with DAMIC-M,Phys. Rev. Lett.135(2025), no. 7 071002, [arXiv:2503.14617]. [4]PandaX, M. Zhang et al.,Search for Light Dark Matter with 259 Days of Data in Panda...

    Pith/arXiv arXiv 2025

  3. [7]

    Krnjaic,Testing Thermal-Relic Dark Matter with a Dark Photon Mediator, arXiv:2505.04626

    G. Krnjaic,Testing Thermal-Relic Dark Matter with a Dark Photon Mediator, arXiv:2505.04626

    Pith/arXiv arXiv

  4. [8]

    Holdom,Two U(1)’s and Epsilon Charge Shifts,Phys

    B. Holdom,Two U(1)’s and Epsilon Charge Shifts,Phys. Lett. B166(1986) 196–198. [9]LDMX, T. Akesson et al.,LDMX - The Light Dark Matter eXperiment,arXiv:2508.11833

    arXiv 1986

  5. [10]

    Berlin, N

    A. Berlin, N. Blinov, G. Krnjaic, P. Schuster, and N. Toro,Dark Matter, Millicharges, Axion and Scalar Particles, Gauge Bosons, and Other New Physics with LDMX,Phys. Rev. D99 (2019), no. 7 075001, [arXiv:1807.01730]

    Pith/arXiv arXiv 2019

  6. [11]

    Cowan, K

    G. Cowan, K. Cranmer, E. Gross, and O. Vitells,Asymptotic formulae for likelihood-based tests of new physics,The European Physical Journal C71(feb, 2011)

    2011

  7. [12]

    Blinov, G

    N. Blinov, G. Krnjaic, and D. Tuckler,Characterizing dark matter signals with missing momentum experiments,Physical Review D103(feb, 2021)

    2021

  8. [13]

    Banerjee, R

    A. Banerjee, R. Catena, and T. R. Gray,Light Vector Dark Matter via a Magnetic Dipole Portal: Bridging Direct Detection and Fixed-Target Searches,arXiv:2511.23259

    Pith/arXiv arXiv

  9. [14]

    Fabbrichesi, E

    M. Fabbrichesi, E. Gabrielli, and G. Lanfranchi,The Dark Photon,arXiv:2005.01515

    Pith/arXiv arXiv 2005

  10. [15]

    E. C. G. Stueckelberg,Interaction energy in electrodynamics and in the field theory of nuclear forces,Helv. Phys. Acta11(1938) 225–244

    1938

  11. [16]

    Boehm and P

    C. Boehm and P. Fayet,Scalar dark matter candidates,Nucl. Phys. B683(2004) 219–263, [hep-ph/0305261]

    Pith/arXiv arXiv 2004

  12. [17]

    M. Ibe, S. Kobayashi, Y. Nakayama, and S. Shirai,Cosmological constraint on dark photon from Nef f,JHEP04(2020) 009, [arXiv:1912.12152]

    Pith/arXiv arXiv 2020

  13. [18]

    Sabti, J

    N. Sabti, J. Alvey, M. Escudero, M. Fairbairn, and D. Blas,Refined Bounds on MeV-scale Thermal Dark Sectors from BBN and the CMB,JCAP01(2020) 004, [arXiv:1910.01649]

    Pith/arXiv arXiv 2020

  14. [19]

    T. G. Rizzo,Kinetic Mixing and Portal Matter Phenomenology,Phys. Rev. D99(2019), no. 11 115024, [arXiv:1810.07531]. – 33 –

    Pith/arXiv arXiv 2019

  15. [20]

    Catena and T

    R. Catena and T. R. Gray,Spin-1 thermal targets for dark matter searches at beam dump and fixed target experiments,JCAP11(2023) 058, [arXiv:2307.02207]. [21]Planck, N. Aghanim et al.,Planck 2018 results. VI. Cosmological parameters,Astron. Astrophys.641(2020) A6, [arXiv:1807.06209]. [Erratum: Astron.Astrophys. 652, C4 (2021)]

    Pith/arXiv arXiv 2023

  16. [22]

    Gondolo and G

    P. Gondolo and G. Gelmini,Cosmic abundances of stable particles: Improved analysis,Nucl. Phys. B360(1991) 145–179

    1991

  17. [23]

    T. G. Rizzo,Toward UV models of kinetic mixing and portal matter. V. Indirect probes of the new physics scale,Phys. Rev. D109(2024), no. 5 055039, [arXiv:2312.00226]

    Pith/arXiv arXiv 2024

  18. [24]

    T. G. Rizzo,Toward UV models of kinetic mixing and portal matter. VI. A more complex dark matter sector?,Phys. Rev. D110(2024), no. 7 075037, [arXiv:2408.01296]

    Pith/arXiv arXiv 2024

  19. [25]

    Catena, T

    R. Catena, T. R. Gray, and T. Jerkvall,Production of dark photons through higher electromagnetic moments at LDMX: simulations and model discrimination,JHEP09(2025) 040, [arXiv:2502.13635]

    arXiv 2025

  20. [26]

    Belanger, F

    G. Belanger, F. Boudjema, A. Pukhov, and A. Semenov,micrOMEGAs 3: A program for calculating dark matter observables,Comput. Phys. Commun.185(2014) 960–985, [arXiv:1305.0237]. [27]LDMX, T. ˚Akesson et al.,Light Dark Matter eXperiment (LDMX),arXiv:1808.05219. [28]LDMX, T. ˚Akesson et al.,Sensitivity of an early dark matter search using the electromagnetic c...

    Pith/arXiv arXiv 2014

  21. [29]

    Liu and G

    Y.-S. Liu and G. A. Miller,Validity of the Weizs¨ acker-Williams approximation and the analysis of beam dump experiments: Production of an axion, a dark photon, or a new axial-vector boson, Phys. Rev. D96(2017), no. 1 016004, [arXiv:1705.01633]

    Pith/arXiv arXiv 2017

  22. [30]

    Alwall, R

    J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro,The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,JHEP07(2014) 079, [arXiv:1405.0301]

    Pith/arXiv arXiv 2014

  23. [31]

    Izaguirre, G

    E. Izaguirre, G. Krnjaic, P. Schuster, and N. Toro,Testing GeV-scale dark matter with fixed-target missing momentum experiments,Physical Review D91(may, 2015). [32]LDMX, T. ˚Akesson et al.,Photon-rejection power of the Light Dark Matter eXperiment in an 8 GeV beam,JHEP12(2023) 092, [arXiv:2308.15173]

    Pith/arXiv arXiv 2015

  24. [33]

    Lista,Practical Statistics for Particle Physicists, in2016 European School of High-Energy Physics, pp

    L. Lista,Practical Statistics for Particle Physicists, in2016 European School of High-Energy Physics, pp. 213–258, 2017.arXiv:1609.04150

    Pith/arXiv arXiv 2017

  25. [34]

    Conrad, O

    J. Conrad, O. Botner, A. Hallgren, and C. Perez de los Heros,Including systematic uncertainties in confidence interval construction for Poisson statistics,Phys. Rev. D67(2003) 012002, [hep-ex/0202013]

    Pith/arXiv arXiv 2003

  26. [35]

    J. S. Speagle,dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences,Mon. Not. Roy. Astron. Soc.493(2020), no. 3 3132–3158, [arXiv:1904.02180]

    Pith/arXiv arXiv 2020

  27. [36]

    Higson, W

    E. Higson, W. Handley, M. Hobson, and A. Lasenby,Dynamic nested sampling: an improved – 34 – algorithm for parameter estimation and evidence calculation,Stat. Comput.29(2018), no. 5 891–913, [arXiv:1704.03459]

    Pith/arXiv arXiv 2018

  28. [37]

    Jeffreys,The Theory of Probability

    H. Jeffreys,The Theory of Probability. Oxford Classic Texts in the Physical Sciences. 1939

    1939

  29. [38]

    J. O. Berger and L. R. Pericchi,Objective Bayesian methods for model selection: Introduction and comparison, inModel Selection(P. Lahiri, ed.), vol. 38 ofIMS Lecture Notes–Monograph Series, pp. 135–207. Institute of Mathematical Statistics, Beachwood, OH, 2001

    2001

  30. [39]

    A. L. Read,Presentation of search results: TheCL s technique,J. Phys. G28(2002) 2693–2704

    2002

  31. [40]

    Bechtle et al.,A proposal for the Lohengrin experiment to search for dark sector particles at the ELSA Accelerator,Eur

    P. Bechtle et al.,A proposal for the Lohengrin experiment to search for dark sector particles at the ELSA Accelerator,Eur. Phys. J. C85(2025), no. 5 600, [arXiv:2410.10956]. [41]DarkSHINE, J. Chen et al.,DarkSHINE Baseline Design Report: Physics Prospects and Detector Technologies,arXiv:2411.09345. – 35 –

    Pith/arXiv arXiv 2025