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On the dark matter origin of an LDMX signal

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A silicon-based direct detection experiment with 0.0013–1.9 kg-year exposure can statistically validate the dark matter origin of a future LDMX signal.

desk verdict A sensible strategy for validating the DM origin of an LDMX signal, but the headline threshold exposures rest on a chi-square test that is not calibrated as claimed. read the letter →

arxiv 2411.10216 v2 pith:ESVIBLBE submitted 2024-11-15 hep-ph astro-ph.COhep-ex

Riccardo Catena , Taylor R. Gray , Andreas Lund This is my paper · ORCID
classification hep-phastro-ph.COhep-ex
keywords darkmattersub-GeVLDMXfixed-targetexperimentdirectdetectionphotonsilicondetectorBayesianinference
topics Dark Matter
open problems Dark Matter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that a future positive signal at a fixed-target experiment such as LDMX cannot by itself establish that the new particle is dark matter; an invisible particle carrying beam energy could have a non-cosmological origin. To close that gap, the authors propose a four-step strategy: record the LDMX electron recoil-energy and transverse-momentum excess, operate a silicon direct-detection experiment with increasing exposure, extract a Bayesian posterior for the dark matter mass and coupling from the direct-detection data, and compare the resulting predicted LDMX distributions with the recorded ones in a chi-square test. Simulating complex scalar dark matter with a dark photon mediator, they find that the exposure needed to avoid wrongly rejecting a common dark matter origin grows with mass, from $0.0013\,\mathrm{kg\,year}$ at $m_\chi=4\,\mathrm{MeV}$ to $1.9\,\mathrm{kg\,year}$ at $m_\chi=25\,\mathrm{MeV}$. These exposures are within reach of current or near-future silicon detectors, so the strategy offers a concrete cross-check between fixed-target and direct searches.

What carries the argument

The machinery is a four-step analysis chain built on three components. First, Monte Carlo simulation of dark photon production in electron-tungsten scattering, $e^-W\to e^-W A'\to e^-W\chi\chi$, with $m_{A'}=3m_\chi$ and couplings on the relic-density line, produces the recorded LDMX signal in 30 bins of electron recoil energy and transverse momentum. Second, direct-detection data are simulated from the DM-electron scattering rate in silicon, expressed through the dielectric function and converted into electron-hole pair counts, and a Bayesian posterior for $(m_\chi,\sigma_e)$ is extracted from those counts. Third, the posterior is projected into predictions for the LDMX histograms, and the test statistic $\chi^2_{\rm TS}$ of Eq.~(4.1) compares predicted and recorded histograms; the threshold exposure $\xi_{\rm th}$ solves $\chi^2(\xi)=\chi^2_*$ at 95% C.L. The statistical engine is the chi-square test, while the physical engine is the complementary information carried by the $E_e$ and $P_T$ distributions.

What would settle it

Repeat the same four-step pipeline with simulated background events included at realistic levels, such as the inclusive single-electron background at LDMX and environmental or radioactive counts in a silicon detector. If the threshold exposure for the $m_\chi=10\,\mathrm{MeV}$ benchmark moves substantially from the quoted $\sim0.03\,\mathrm{kg\,year}$, or if $\chi^2(\xi)$ no longer crosses the 95% C.L. line within reachable exposures, the background-free estimate is falsified.

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Extended reading notes

Core claim

The central discovery is a quantitative threshold for validation: a silicon-based direct-detection experiment operating with exposure $\xi$ can assert or reject the dark matter origin of a hypothetical LDMX signal. The authors compute $\chi^2(\xi)$ from a test statistic comparing recorded and predicted $E_e$ and $P_T$ histograms, and define $\xi_{\rm th}$ as the exposure at which $\chi^2(\xi)$ crosses the 95% C.L. threshold $\chi^2_*$ under the null hypothesis of a common dark matter origin. They find that $\xi_{\rm th}$ increases with the dark matter mass, from $0.0013\,\mathrm{kg\,year}$ at $4\,\mathrm{MeV}$ to $1.9\,\mathrm{kg\,year}$ at $25\,\mathrm{MeV}$; at $10\,\mathrm{MeV}$ it is about $0.03\,\mathrm{kg\,year}$. They also show that when the LDMX and direct-detection signals are generated by different dark matter masses, the test rejects a common origin at 95% C.L. for essentially all exposures, provided the mass difference is large.

Load-bearing premise

The quantitative threshold exposures assume that both the LDMX excess and the direct-detection data consist purely of dark-matter events, with no ordinary background counts; any real background will change the required exposure.

Editorial extensions

If this is right

  • A future LDMX excess can be checked with exposures already in reach: $0.0013\,\mathrm{kg\,year}$ at $4\,\mathrm{MeV}$, roughly $0.03\,\mathrm{kg\,year}$ at $10\,\mathrm{MeV}$, and $1.9\,\mathrm{kg\,year}$ at $25\,\mathrm{MeV}$.
  • If the two signals share a dark matter origin, exposures below the threshold would lead one to wrongly reject that origin; the paper therefore defines a practical minimum exposure for a validation campaign.
  • The electron recoil energy and transverse momentum carry complementary information: $E_e$ contributes more when the dark matter masses are equal, while $P_T$ dominates when the underlying masses differ.
  • When the LDMX signal is produced by a different dark matter mass than the direct-detection signal, the chi-square test rejects a common origin at 95% C.L. for essentially all exposures if the mass difference is large, and for finite exposures if the masses are close.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if real backgrounds are added, the quoted threshold exposures are optimistic floors; the same pipeline would likely yield larger $\xi_{\rm th}$, particularly at low mass where the LDMX signal is small.
  • Editorial inference: the four-step posterior-predictive comparison is model-agnostic in structure, so it could be repurposed for other sub-GeV benchmark models or for comparing other fixed-target and direct-detection pairs.
  • Editorial inference: because the paper uses posterior means to build predictions, its stated insensitivity to astrophysical halo uncertainties may be fragile; a fully marginalized check over halo parameters would be a direct stress test of that claim.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes a four-step strategy to validate the dark matter interpretation of a future LDMX excess by combining LDMX data with next-generation silicon direct-detection (DD) data. The authors simulate an LDMX signal with MadGraph, simulate DD data with DarkELF for increasing exposures, perform Bayesian inference with MultiNest, use the resulting posterior to predict the LDMX electron recoil energy and transverse momentum distributions, and compare predictions with the simulated LDMX signal via a chi-square test. The central quantitative result is a threshold DD exposure, ξth, above which the test would not reject a common DM origin; the authors report ξth growing from 0.0013 kg-year at mχ = 4 MeV to 1.9 kg-year at mχ = 25 MeV. The paper also studies a cross-check in which the LDMX and DD data are generated from different DM masses, and shows that the test can often reject the wrong mass.

Significance. The proposed four-step strategy is a sensible and valuable proof-of-concept for cross-experiment validation of a sub-GeV DM signal, and the manuscript is clearly written. It uses standard public tools (MadGraph, DarkELF, MultiNest) and gives explicit equations for the likelihood, posterior, and test statistic, which aids reproducibility. The mass-mismatch study in Sec. 5 (Figs. 5-7) is a useful sanity check. However, the headline threshold exposures are not yet firmly established: the test statistic is not evaluated under the null distribution that is assumed, and the idealized no-background setup is not reflected in the unqualified abstract numbers. The paper is therefore best viewed as a methodology proposal whose quantitative outcome requires further calibration.

major comments (4)
  1. [Section 4, Eqs. (4.1)-(4.5)] The test statistic as evaluated does not have the claimed chi-square distribution under the null. Under the null in Eq. (4.2), if both datasets were independent Poisson counts with equal means, the denominator in Eq. (4.1) would be the variance of the difference; but in the actual evaluation (Eq. (4.4)), N_LDMX,i is fixed at the true mean and N_DD,i is a posterior mean, whose variance is not included in the denominator. Footnote 6 acknowledges that fixing the LDMX fluctuations makes chi^2 smaller than the true statistic, but the same issue applies to the DD posterior uncertainty, and no Monte Carlo calibration of chi^2_* under the null is provided. Since ξth is obtained by solving Eq. (4.3) with this uncalibrated chi^2_*, the quoted threshold exposures are likely underestimated, and their stated 95% C.L. meaning is not established. I recommend calibrating the threshold by simulating the full pipeline under the null, or using a statistic whose null distribution is known (for example, including Poisson fluctuations in both datasets and using the variance of N_LDMX,i - N_DD,i in the denominator).
  2. [Secs. 3.1 and 3.2] The simulated LDMX 'signal' and the DD data are generated as pure DM signals with no backgrounds, even though the LDMX signal is defined in Sec. 3.1 as an excess over inclusive single-electron backgrounds and the DD analysis uses only DM-induced electron-hole pairs. The abstract and conclusion report threshold exposures without this caveat. Real backgrounds at LDMX or in a silicon detector would change the effective counts entering Eq. (4.1) and could materially shift ξth. The authors should either add a background-injection study for at least one benchmark, or clearly state in the abstract and conclusions that the quoted thresholds apply to a background-free, proof-of-principle scenario.
  3. [Eq. (3.4) and Fig. 1 caption] The normalization of the LDMX signal is ambiguous. Eq. (3.4) defines NMG(mχ,ε) = ηLσee(mχ,ε), which for fixed luminosity and efficiency is the expected number of events; however, the caption of Fig. 1 states 'Simulations are performed with MadGraph assuming NMG = 10^4 events per simulation.' If NMG is fixed to 10^4 rather than computed from the luminosity and cross section, the LDMX histograms are not the expected event counts for the stated beam conditions, and the comparison between N_LDMX,i and N_DD,i in Eq. (4.1) is not on an absolute scale. The authors should clarify whether 10^4 is a Monte Carlo generation size used only for shape, and if so, explain how the rescaling in Eq. (3.6) restores the physical normalization. This is load-bearing for the absolute values of ξth.
  4. [Eq. (3.14)] The predicted LDMX histograms are approximated by evaluating N_i,MG at the posterior means mχ and ε, rather than by computing the actual posterior expectation ∫ dmχ dε f_DD N_i,MG. This 'plug-in' approximation is not generally valid for a nonlinear function, and it could bias the predicted histograms and hence χ2. The error estimate in Eq. (3.16) accounts for the spread of the posterior, but it does not correct a biased mean. The authors should justify this approximation for the ranges of mχ and ε considered, or test it by comparing the plug-in prediction with the full integral for at least one benchmark.
minor comments (6)
  1. [Header abstract vs. full-text abstract] The header abstract and the abstract in the full text give different numbers for the threshold exposure: the former quotes 0.012 kg-year at 4 MeV and 1 kg-year at 25 MeV, while the latter quotes 0.0013 kg-year and 1.9 kg-year. Please check and harmonize the numbers.
  2. [Fig. 4 and Sec. 5] The threshold exposure ξth is extracted from a fit A + B/ξ to the mean χ2(ξ) over 10 realizations, but no uncertainty on ξth is provided. Given the large scatter shown in Fig. 4, the statistical error on ξth could be substantial; please report an uncertainty or a range.
  3. [Sec. 4, Eq. (4.1)] The test statistic adds contributions from the recoil energy and transverse momentum distributions even though these are expected to be correlated with each other; the text acknowledges this but does not discuss the impact on the null distribution. A sentence explaining the expected effect, or a calibration check, would be helpful.
  4. [Sec. 5, Fig. 3] The text says χ2 is calculated for 10 different realisations of the DD data, but Eq. (4.4) uses posterior means; please clarify exactly which quantities vary between realisations and how the 10 values are combined.
  5. [Throughout] The phrase 'statistically dependent' is used to mean 'compatible with a common DM origin,' but a chi-square test can only reject or fail to reject the null. Consider using more standard terminology such as 'statistically compatible' (which the paper also uses) for clarity.
  6. [Eq. (3.11)] In Eq. (3.11), the symbol ω is used both for the deposited energy and for the average energy per electron-hole pair (3.6 eV); consider using a distinct symbol such as ω_pair to avoid confusion.

Circularity Check

0 steps flagged · score 1.0 of 10

No load-bearing circularity: the LDMX-versus-DD comparison is a genuine cross-check of two conditionally independent simulated datasets, and the few self-citations are not load-bearing.

full rationale

This paper is a Monte Carlo sensitivity study, not a derivation that reduces to its own inputs. The LDMX 'observed' histograms are generated from a benchmark point (m*_chi, eps*) using MadGraph, while the direct-detection datasets are independently Poisson-sampled from the DarkELF rate at the same benchmark. The posterior pdf obtained from the DD data is then used in Eq. (3.14) to predict LDMX histograms. Because the LDMX data are never used in the fit, the prediction is not a fitted input renamed as a prediction; the chi-square statistic in Eq. (4.1) compares two datasets that are independent conditional on the assumed dark matter model. The self-citations (e.g., Ref. [23] for linear response theory) support standard rate calculations implemented in DarkELF and do not carry the central quantitative claim. The acknowledged statistical caveat in footnote 6, that neglecting LDMX Poisson fluctuations makes the computed chi-square approach a smaller value than the true statistic, affects the calibration of the threshold exposure but is not a form of circularity. No load-bearing step is equivalent to its own input by construction.

Assumptions & free parameters 11 free parameters · 7 assumptions · 0 invented entities

The central result depends on a fixed dark-photon model, benchmark relations, and signal-only data. These are inputs from prior literature or by-hand choices, not derived or independently validated in this paper.

free parameters (11)
  • Benchmark DM mass and mixing (mχ*, ε*) = e.g. 10 MeV, 4.7e-5
    Chosen to give the correct relic density, read from Fig. 5 of Ref. [8]; the threshold exposure depends on these values.
  • Dark gauge coupling αD = 0.5
    Fixed by hand for all LDMX and DD simulations; a standard benchmark.
  • Mediator mass relation mA'/mχ = 3
    Assumed so the dark photon decays invisibly with branching ratio close to 1; no scan over this ratio.
  • Local DM density ρχ = 0.4 GeV/cm3
    DarkELF default; used to normalise DD rates.
  • Galactic velocity parameters (vesc, v0, ve) = 500, 220, 240 km/s
    Truncated Maxwell-Boltzmann defaults in DarkELF.
  • MultiNest priors = mχ in [1,100] MeV; σe in [1e-41,1e-35] cm2
    Log-uniform priors chosen for Bayesian inference; affect posterior width and hence predicted histograms.
  • Fit parameters A and B in χ2 = A + B/ξ = not quoted
    Used to define the threshold exposure by intersection with χ2*; A is nonzero partly because LDMX fluctuations are neglected.
  • LDMX efficiency η and integrated luminosity = η=0.5; 4e14 EOT
    Inputs to Eq. (3.4) for event counts; from LDMX literature.
  • Number of bins nb = 30
    Chosen as a trade-off between resolution and computational cost; affects chi-square degrees of freedom.
  • Silicon ionization parameters = ωgap=1.11 eV; ω=3.6 eV
    Used to convert deposited energy to electron-hole pair number Q.
  • Maximum Q in DD likelihood = 10
    nQ=10 electron-hole pairs; truncation is an analysis choice.
assumptions (7)
  • domain assumption The dark sector is described by a dark photon A' with kinetic mixing ε and complex scalar DM χ, Eq. (2.1).
    All simulations and predictions assume this specific model; no model-independent scan is performed.
  • domain assumption The dark matter has the correct thermal relic density through p-wave annihilation, fixing ε* (equivalently σe*) for each mass.
    Benchmarks are read from Fig. 5 of Ref. [8]; this is an external input, not derived here.
  • ad hoc to paper mA' = 3 mχ and αD = 0.5.
    Standard benchmark in LDMX literature, adopted without scan; the threshold exposure may depend on these choices.
  • domain assumption DarkELF's precomputed silicon dielectric function (from DFT/GPAW) accurately describes DM-electron scattering and screening.
    DD event rates are computed with DarkELF; the paper does not validate this against data.
  • domain assumption Tungsten can be modelled as an elementary spin-1/2 particle with the nuclear form factors of Eqs. (3.2)-(3.3).
    LDMX signal simulation relies on this approximation in MadGraph.
  • ad hoc to paper There are no backgrounds in either the LDMX signal or the DD data.
    Only DM-induced events are simulated; real experiments will have backgrounds that could change the required exposure.
  • standard math The chi-square statistic in Eq. (4.1) follows a chi-square distribution with n-1 or 2(n-1) degrees of freedom.
    Large-sample Poisson approximation; correlations between Ee and pT are neglected as stated in the footnote.

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Cite this review

Pith. "Pith review of On the dark matter origin of an LDMX signal." pith.science (2026). https://pith.science/paper/ESVIBLBE

@misc{pith2026241110216,
  author       = {Pith},
  title        = {Pith review of: On the dark matter origin of an LDMX signal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ESVIBLBE}},
  note         = {Machine review of arXiv:2411.10216}
}
abstract

Fixed target experiments where beam electrons are focused upon a thin target have shown great potential for probing new physics, including the sub-GeV dark matter (DM) paradigm. However, a signal in future experiments such as the light dark matter experiment (LDMX) would require an independent validation to assert its DM origin. To this end, we propose to combine LDMX and next generation DM direct detection (DD) data in a four-step analysis strategy, which we here illustrate with Monte Carlo simulations. In the first step, the hypothetical LDMX signal (i.e. an excess in the final state electron energy and transverse momentum distributions) is $\textit{recorded}$. In the second step, a DM DD experiment operates with increasing exposure to test the DM origin of the LDMX signal. Here, LDMX and DD data are simulated. In the third step, a posterior probability density function (pdf) for the DM model parameters is extracted from the DD data, and used to $\textit{predict}$ the electron recoil energy and transverse momentum distributions at LDMX. In the last step, $\textit{predicted}$ and $\textit{recorded}$ electron recoil energy and transverse momentum distributions are compared in a chi-square test. We present the results of this comparison in terms of a threshold exposure that a DD experiment has to operate with to assert whether $\textit{predicted}$ and $\textit{recorded}$ distributions $\textit{can}$ be statistically dependent. We find that this threshold exposure grows with the DM particle mass, $m_\chi$. It varies from 0.012 kg-year for a DM mass of $m_\chi=4$ MeV to 1 kg-year for $m_\chi=25$ MeV, which is or will soon be within reach.

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Forward citations

Cited by 1 Pith paper

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  1. Light Vector Dark Matter via a Magnetic Dipole Portal: Bridging Direct Detection and Fixed-Target Searches

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    A sub-GeV vector DM model with a magnetic dipole portal is studied at LDMX/NA64, but the claimed viable region relies on an unjustified thermal equilibrium assumption.

Reference graph

Works this paper leans on

28 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    Battaglieri et al.,US Cosmic Visions: New Ideas in Dark Matter 2017; Community Report, FERMILAB-CONF-17-282-AE-PPD-T (2017) [ 1707.04591]

    M. Battaglieri et al.,US Cosmic Visions: New Ideas in Dark Matter 2017; Community Report, FERMILAB-CONF-17-282-AE-PPD-T (2017) [ 1707.04591]

    arXiv 2017

  2. [2]

    Mitridate, T

    A. Mitridate, T. Trickle, Z. Zhang and K. M. Zurek,Snowmass white paper: Light dark matter direct detection at the interface with condensed matter physics, Phys. Dark Univ.40 (2023) 101221 [2203.07492]

    arXiv 2023

  3. [3]

    Balan et al.,Resonant or asymmetric: The status of sub-GeV dark matter, 2405.17548

    S. Balan et al.,Resonant or asymmetric: The status of sub-GeV dark matter, 2405.17548

    arXiv

  4. [4]

    Billard et al.,Direct detection of dark matter—APPEC committee report*, Rept

    J. Billard et al.,Direct detection of dark matter—APPEC committee report*, Rept. Prog. Phys. 85 (2022) 056201 [2104.07634]

    arXiv 2022

  5. [5]

    Fabbrichesi, E

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

    arXiv 2005

  6. [6]

    B. W. Lee and S. Weinberg,Cosmological Lower Bound on Heavy Neutrino Masses, Phys. Rev. Lett.39 (1977) 165

    work page 1977

  7. [7]

    deNiverville, C.-Y

    P. deNiverville, C.-Y. Chen, M. Pospelov and A. Ritz,Light dark matter in neutrino beams: production modelling and scattering signatures at MiniBooNE, T2K and SHiP, Phys. Rev. D 95 (2017) 035006 [1609.01770]

    arXiv 2017

  8. [8]

    Åkesson et al.,Light Dark Matter eXperiment (LDMX), 1808.05219

    LDMX collaboration, T. Åkesson et al.,Light Dark Matter eXperiment (LDMX), 1808.05219

    arXiv

Show all 28 references
  1. [9]

    Åkesson et al.,Current Status and Future Prospects for the Light Dark Matter eXperiment, in Snowmass 2021, 3, 2022,2203.08192

    T. Åkesson et al.,Current Status and Future Prospects for the Light Dark Matter eXperiment, in Snowmass 2021, 3, 2022,2203.08192

    2021 arXiv

  2. [10]

    Catena and T

    R. Catena and T. R. Gray,Spin-1 thermal targets for dark matter searches at beam dump and fixed target experiments, JCAP 11 (2023) 058 [2307.02207]

    2023 arXiv

  3. [11]

    S. Baum, R. Catena and M. B. Krauss,Impact of a XENONnT signal on LHC dijet searches, JHEP 07 (2019) 015 [1812.01594]

    2019 arXiv

  4. [12]

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

    B. Holdom,Two U(1)’s and Epsilon Charge Shifts, Phys. Lett.166B (1986) 196

    1986

  5. [13]

    K. S. Babu, C. F. Kolda and J. March-Russell,Implications of generalized Z - Z-prime mixing, Phys. Rev. D57 (1998) 6788 [hep-ph/9710441]. – 18 –

    1998 arXiv

  6. [14]

    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) 075001 [1807.01730]

    2019 arXiv

  7. [15]

    Boehm and P

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

    2004 arXiv

  8. [16]

    Boehm, X

    C. Boehm, X. Chu, J.-L. Kuo and J. Pradler,Scalar dark matter candidates revisited, Phys. Rev. D103 (2021) 075005 [2010.02954]

    2021 arXiv

  9. [17]

    Catena, D

    R. Catena, D. Cole, T. Emken, M. Matas, N. Spaldin, W. Tarantino et al.,Dark matter-electron interactions in materials beyond the dark photon model, JCAP 03 (2023) 052 [2210.07305]

    2023 arXiv

  10. [18]

    Alwall, M

    J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer and T. Stelzer,MadGraph 5 : Going Beyond, JHEP 06 (2011) 128 [1106.0522]

    2011 arXiv

  11. [19]

    Schuster, N

    P. Schuster, N. Toro and K. Zhou,Probing invisible vector meson decays with the NA64 and LDMX experiments, Phys. Rev. D105 (2022) 035036 [2112.02104]

    2022 arXiv

  12. [20]

    J. D. Bjorken, R. Essig, P. Schuster and N. Toro,New Fixed-Target Experiments to Search for Dark Gauge Forces, Phys. Rev. D80 (2009) 075018 [0906.0580]

    2009 arXiv

  13. [21]

    Knapen, J

    S. Knapen, J. Kozaczuk and T. Lin,python package for dark matter scattering in dielectric targets, Phys. Rev. D105 (2022) 015014 [2104.12786]

    2022 arXiv

  14. [22]

    J. J. Mortensen, A. H. Larsen, M. Kuisma, A. V. Ivanov, A. Taghizadeh, A. Peterson et al., Gpaw: An open python package for electronic structure calculations, The Journal of Chemical Physics 160 (2024)

    2024

  15. [23]

    Catena and N

    R. Catena and N. A. Spaldin,Linear response theory for light dark matter-electron scattering in materials, Phys. Rev. Res.6 (2024) 033230 [2402.06817]

    2024 arXiv

  16. [24]

    Essig, M

    R. Essig, M. Fernandez-Serra, J. Mardon, A. Soto, T. Volansky and T.-T. Yu,Direct Detection of sub-GeV Dark Matter with Semiconductor Targets, JHEP 05 (2016) 046 [1509.01598]

    2016 arXiv

  17. [25]

    Feroz, M

    F. Feroz, M. P. Hobson and M. Bridges,MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics, Mon. Not. Roy. Astron. Soc.398 (2009) 1601 [0809.3437]

    2009 arXiv

  18. [26]

    Essig, J

    R. Essig, J. Mardon and T. Volansky,Direct Detection of Sub-GeV Dark Matter, Phys. Rev. D85 (2012) 076007 [1108.5383]

    2012 arXiv

  19. [27]

    Hryczuk, E

    A. Hryczuk, E. Karukes, L. Roszkowski and M. Talia,Impact of uncertainties in the halo velocity profile on direct detection of sub-GeV dark matter, 2001.09156

    2001 arXiv

  20. [28]

    Arnquist et al.,First Constraints from DAMIC-M on Sub-GeV Dark-Matter Particles Interacting with Electrons, Phys

    DAMIC-M collaboration, I. Arnquist et al.,First Constraints from DAMIC-M on Sub-GeV Dark-Matter Particles Interacting with Electrons, Phys. Rev. Lett.130 (2023) 171003 [2302.02372]. – 19 –

    2023 arXiv

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