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Characterizing the Nature of the Unresolved Point Sources in the Galactic Center

T0 review · 2 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper argues that the failure of Non-Poissonian Template Fitting to recover an injected dark matter signal on Fermi data is expected whenever unresolved point sources are already present, and does not discredit the earlier…

desk verdict A careful simulation study that convincingly shows Leane & Slatyer's injection test does not by itself invalidate NPTF point-source evidence, though the real-data extrapolation rests on a single mismodeling proxy. read the letter →

arxiv 1908.10874 v2 pith:2WVMV5X3 submitted 2019-08-28 astro-ph.CO astro-ph.HEhep-ph

Laura J. Chang , Siddharth Mishra-Sharma , Mariangela Lisanti , Malte Buschmann , Nicholas L. Rodd , Benjamin R. Safdi This is my paper · ORCID
classification astro-ph.COastro-ph.HEhep-ph
keywords GalacticCenterExcessNon-PoissoniantemplatefittingFermi-LATdarkmatterannihilationmillisecondpulsarsgamma-raypointsourcessource-countdistributiondiffusebackgroundmismodeling
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 asks whether the statistical method known as Non-Poissonian Template Fitting (NPTF) can be trusted to distinguish dark matter from unresolved point sources in the gamma-ray excess at the Galactic Center. Using simulated Fermi-style maps, the authors find the method works cleanly when one component makes up the entire excess, but it can fail to split the flux correctly when dark matter and point sources are both present. The culprit is a degeneracy: in the ultra-faint limit, a population of unresolved sources is mathematically identical to smooth Poissonian emission. The paper uses this mechanism to explain why an earlier injection test, in which an artificial dark matter signal added to real Fermi data was misattributed to point sources, does not invalidate the original NPTF point-source claims. The authors conclude that diffuse mismodeling can make the failure worse, but that a true point-source signal still shows up robustly in their simulations.

What carries the argument

The load-bearing object is the source-count distribution $dN/dS$, the number of unresolved sources per unit flux, parameterized in the fits as a doubly broken power law. NPTF works through probability generating functions: a point-source template contributes non-Poissonian photon-count statistics through the average number of sources contributing $m$ photons per pixel, and the case $m=1$ is algebraically identical to a Poissonian template. That identity is the mechanism of the paper's main result—it makes an ultra-faint point-source population exactly degenerate with smooth dark-matter emission, so the fitted split between the two components is governed by priors and by the brighter sources that break the degeneracy. The paper also uses Bayes factors comparing models with and without the point-source template as the quantitative measure of whether a point-source signal is present.

What would settle it

Measure the spatial correlation function of the residuals left by fitting the p6v11 and Model F templates to the real Fermi data, and compare it with the residuals in the simulated maps. If the real residuals are much smoother, less clumpy, than the simulated mismatch, the mechanism that lets mismodeling masquerade as point sources would not apply on data, and the paper's explanation for the failed injection test would be undermined.

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Extended reading notes

Core claim

The paper's central claim is that Non-Poissonian Template Fitting (NPTF) remains a valid tool for finding unresolved point sources in the Galactic Center Excess, even though it cannot always separate those sources from a dark-matter signal. In simulations with a perfectly modeled background, the method correctly recovers a pure dark-matter excess and never mislabels it as point sources, and it recovers the point-source flux down to roughly one photon per source when the excess is pure point sources. When the excess is a mixture, the two hypotheses blur: a soft population of ultra-faint point sources is exactly degenerate with smooth Poissonian emission, so the fit can assign the whole excess to either component. Mismodeling the Galactic diffuse background makes this worse—it can create residual hotspots that are mistaken for point sources, and in a small fraction of realizations a pure dark-matter signal can be misidentified as point sources, though with weaker evidence than a true point-source population would give. From this the paper concludes that the failure of a dark-matter signal-injection test on real Fermi data is a natural consequence of the method's degeneracy, not a sign that the earlier NPTF detection of point sources is wrong.

Load-bearing premise

The simulated mismatch between two diffuse models (p6v11 and Model F) is assumed to stand in for the real, unknown mismodeling of the Fermi diffuse background, in both magnitude and spatial pattern.

Editorial extensions

If this is right

  • When the Galactic diffuse background is modeled perfectly, a Galactic Center Excess that is entirely dark matter is never misidentified as point sources, but an excess that is entirely point sources can be partially misattributed to dark matter.
  • For a mixed excess, the NPTF can assign the whole signal to one component, with the direction of the bias set by source brightness: soft point sources are more easily absorbed by the dark-matter template, and minority dark matter can be absorbed by the point-source template.
  • Diffuse mismodeling can turn a pure dark-matter signal into a false point-source detection in a minority of simulated realizations, but the inferred Bayes factors are always weaker than those for a genuine point-source population.
  • An artificial dark-matter signal injected on top of data that already contain unresolved point sources is naturally absorbed into the point-source template, especially under diffuse mismodeling, so signal-injection failures on real data do not by themselves indict the original NPTF analysis.

Reading between the lines

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

  • Editorial inference: Because the ultra-faint degeneracy is mathematical, no reweighting of the NPTF alone can separate smooth dark matter from an arbitrarily faint point-source population; additional information, such as a flux cutoff justified by pulsar surveys, is needed.
  • Editorial inference: Signal-injection tests on real data should be run alongside control injections into simulated maps that contain the same inferred point-source population, since the paper shows the test outcome depends mainly on what is already in the map.
  • Editorial inference: The same degeneracy suggests that wavelet-based point-source searches, which use spatial clustering rather than photon-count fluctuations, may partly break the ambiguity and could be combined with NPTF to constrain the faint end.
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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

2 major / 5 minor

Summary. This paper presents a systematic Monte Carlo study of the Non-Poissonian Template Fitting method applied to simulated Fermi-LAT maps of the Galactic Center. The authors construct simulated maps from Poissonian diffuse/background templates plus either dark-matter or point-source populations with hard or soft source-count benchmarks, generate 100 realizations per scenario, and measure how well NPTF recovers the injected DM/PS decomposition. The main findings are: a pure DM GCE is correctly identified when backgrounds are perfectly modeled; a pure soft-PS GCE can lose some flux to the DM template; mixed GCEs can be misassigned in either direction; under diffuse mismodeling (p6v11 maps analyzed with Model F) true PS populations still yield strong Bayes factors, while DM-only maps occasionally produce spurious PS evidence; and an injected DM signal on top of a PS GCE can be absorbed by the PS template, especially with mismodeling. The paper argues that this makes the Leane-Slatyer signal-injection failure a natural outcome and not, by itself, evidence against the original NPTF analysis.

Significance. The paper is a valuable and largely well-executed study. Its main strengths are the controlled injection protocol with 100 Monte Carlo realizations, the explicit derivation of the ultra-faint PS/Poissonian degeneracy from the generating function (Eq. 8), and the use of Bayes factors and full posterior distributions rather than single best fits. The central logical point — that a signal-injection test on data already containing a PS population is not a clean diagnostic of the original analysis — is sound and does not depend on the details of the diffuse mismodeling. The simulations are not circular, since the method is tested on data with known injections. If the results hold, they provide an important caution for interpreting both the original NPTF claim and the Leane-Slatyer critique. However, the quantitative statements about real-data robustness rest on the representativeness of the p6v11/Model F mismodeling pair, which is only partially tested.

major comments (2)
  1. [Sec. V A and Appendix B, Figs. B1-B2] The paper's inference that the p6v11-versus-Model F mismodeling is "a reasonable proxy" for real-data mismodeling is based on per-pixel residual histograms and smoothed residual maps, which do not constrain the small-scale spatial clustering that drives the non-Poissonian PS template response. Since the authors explicitly concede that "the spatial distribution of the residuals could be very different" (Sec. V A), the quantitative rates reported in Figs. 7 and 8 (e.g., the 35/100 and 7/100 realizations with ln(BF)>5 in the DM-only case) should not be read as predictions for real data without a spatial-clustering comparison. The central logical point about injection tests remains valid, but the robustness claim for real-data PS significance needs either a stronger proxy test, such as a two-point or wavelet statistic at the angular scales probed by NPTF, or a more limited statement of the conclusions.
  2. [Sec. II A (convergence criterion)] The paper discards NPTF scans when the Sb,1 posterior peaks at the lower boundary of its prior, but it never reports how many realizations are discarded in each scenario. Because this selection is made before computing recovery rates, Bayes-factor distributions, and statements such as "never" in Secs. IV and V, the reported frequencies are conditional on a criterion that preferentially removes exactly the ultra-faint, degenerate cases that are central to the paper's message. The authors should report the number of discarded scans per scenario and show that their conclusions are stable when the criterion is relaxed or when the Appendix C flux cutoff is used instead.
minor comments (5)
  1. [Sec. II B] The paper assumes a flat exposure map; while Ref. [47] provides a correction, a sentence quantifying the exposure variation in the ROI would help the reader assess the impact on source-count recovery.
  2. [Sec. IV C and Fig. 5] The injection tests are performed only for backgrounds that are 100% DM or 100% PS; since the real GCE may be a mixture, a sentence explaining why the mixed-case results in Sec. IV B would not qualitatively change the conclusions would strengthen the connection to Ref. [44].
  3. [Appendix B, Fig. B1] The residual maps are smoothed with a 1-degree Gaussian before display, which suppresses the small-scale structure relevant to the NPTF; reporting an unsmoothed map or a two-point statistic would make the comparison more transparent.
  4. [Sec. V A and Fig. 6] The statement that the recovered source-count functions for soft and hard PSs are "remarkably similar" under mismodeling is striking; a quantitative similarity measure would help the reader assess how much of the soft population is being hardened.
  5. [Sec. III] The notation "0.02 (34)%" is confusing and should be written as "0.02% (34%)" to distinguish the hard and soft cases.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central recovery and injection tests use simulated data with known ground truth, the Poisson/point-source degeneracy is derived from the likelihood equations, and self-citations are to public code and prior context rather than load-bearing evidence.

full rationale

The paper's central claims are benchmarked against simulated maps with known injected fluxes and source-count distributions, so they are not derived from the conclusions they are meant to test. The key degeneracy between ultra-faint point sources and Poissonian emission is derived from the NPTF formalism itself: Eq. (8) with m=1 reduces to the Poissonian generating function Eq. (7), and the text explicitly states that this means a Poissonian component can be thought of as single-photon sources with the same spatial distribution. This is a mathematical equivalence, not a fitted-input-as-prediction. The NPTF recovery tests (e.g., Figs. 2-10) compare reconstructed fluxes and source-count functions to known injected values, so they are self-contained. Self-citations to NPTFit [47] and to the earlier NPTF analysis [32] are used as tools and points of comparison, not as unverified premises that force the conclusions; the code is public and the simulated-injection tests would stand even if those references were absent. The one important caveat, explicitly conceded in Sec. V A, is that the magnitude of the p6v11-versus-Model F residuals is only a rough proxy for the real-data mismodeling and that the spatial distribution of residuals could be very different. That is an external-validity limitation, not circularity, because the simulation results with known injections are internally valid as demonstrations of method behavior. Therefore no circular step is present.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper uses existing NPTF formalism and simulation frameworks. The benchmark source-count parameters are chosen by hand to represent plausible PS populations. No new physical entities are introduced, and the central claims rest on the representativeness of the simulated scenarios.

free parameters (3)
  • Soft source-count benchmark parameters = Sb,1≈22, Sb,2≈0.2, n1=10, n2=1.9, n3=-0.8
    Chosen by hand to approximate the MSP luminosity function (Sec. III). The central results on PS/DM degeneracy are illustrated with these parameters; different choices could change quantitative recovery rates.
  • Hard source-count benchmark parameters = Sb,1≈15, n1≈9.5, n2≈-1
    Chosen to match the source-count function recovered in Ref [32]. Serves as the contrasting bright-source scenario.
  • Flux cutoff for source-count suppression (Appendix C) = corresponds to 1σ point-source significance
    Used in Appendix C to test the effect of removing ultra-faint sources from the model; the exact value is chosen by hand.
assumptions (4)
  • standard math The NPTF likelihood formalism with probability generating functions (Eqs. 7-9) correctly describes the photon statistics of Poissonian and non-Poissonian templates.
    Adopted from Ref [47,51,52]; not rederived in this paper.
  • domain assumption The Fermi PSF is well approximated by a double King function at 2 GeV, and a flat exposure map with the mean exposure is sufficient.
    Used to generate simulated maps (Sec. II B). The authors note non-uniform exposure corrections would not affect conclusions.
  • domain assumption The diffuse mismodeling between p6v11 and Model F is representative of the actual mismodeling on Fermi data.
    Sec V and Appendix B compare residuals and find them roughly commensurate, but acknowledge spatial structure may differ.
  • domain assumption DM and unresolved PSs both trace the NFW-squared spatial distribution.
    Footnote 10 notes the study could be repeated with a bulge template, so this is an assumption that could affect quantitative results.

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Cite this review

Pith. "Pith review of Characterizing the Nature of the Unresolved Point Sources in the Galactic Center." pith.science (2026). https://pith.science/paper/2WVMV5X3

@misc{pith2026190810874,
  author       = {Pith},
  title        = {Pith review of: Characterizing the Nature of the Unresolved Point Sources in the Galactic Center},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2WVMV5X3}},
  note         = {Machine review of arXiv:1908.10874}
}
read the original abstract

The Galactic Center Excess (GCE) of GeV gamma rays can be explained as a signal of annihilating dark matter or of emission from unresolved astrophysical sources, such as millisecond pulsars. Evidence for the latter is provided by a statistical procedure---referred to as Non-Poissonian Template Fitting (NPTF)---that distinguishes the smooth distribution of photons expected for dark matter annihilation from a "clumpy" photon distribution expected for point sources. In this paper, we perform an extensive study of the NPTF on simulated data, exploring its ability to recover the flux and luminosity function of unresolved sources at the Galactic Center. When astrophysical background emission is perfectly modeled, we find that the NPTF successfully distinguishes between the dark matter and point source hypotheses when either component makes up the entirety of the GCE. When the GCE is a mixture of dark matter and point sources, the NPTF may fail to reconstruct the correct contribution of each component. We further study the impact of mismodeling the Galactic diffuse backgrounds, finding that while a dark matter signal could be attributed to point sources in some outlying cases for the scenarios we consider, the significance of a true point source signal remains robust. Our work enables us to comment on a recent study by Leane and Slatyer (2019) that questions prior NPTF conclusions because the method does not recover an artificial dark matter signal injected on actual Fermi data. We demonstrate that the failure of the NPTF to extract an artificial dark matter signal can be natural when point sources are present in the data---with the effect further exacerbated by the presence of diffuse mismodeling---and does not on its own invalidate the conclusions of the NPTF analysis in the Inner Galaxy.

Figures

Figures reproduced from arXiv: 1908.10874 by the authors.

Figure 1
Figure 1. FIG. 1. Recovery of two benchmark source populations, both normalized to account for 100% of the GCE flux. The black points [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Differential source-count distributions (top panels) and cumulative flux distributions, integrated above a given threshold [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Comparison of the flux posterior (relative to the true injected flux) for the Galactic diffuse, NFW DM, and NFW [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Comparison of the flux posterior (relative to the true injected flux) for the Galactic diffuse, NFW DM, and NFW PS [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Same as the right-most panel of Fig. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. Bayes factors (BFs) characterizing the statistical preference for a model of the data that includes NFW PSs over a [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. Differential source-count distributions in the presence of diffuse mismodeling. The simulated data consists of the GCE, [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: FIG. 10. Differential source-count distributions recovered when the simulated data consists of the GCE, comprised entirely [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]

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Forward citations

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Reference graph

Works this paper leans on

69 extracted references · 17 canonical work pages · cited by 1 Pith paper

  1. [1]

    Goodenough and D

    L. Goodenough and D. Hooper, (2009), arXiv:0910.2998 [hep-ph]

    arXiv 2009

  2. [2]

    Hooper and L

    D. Hooper and L. Goodenough, Phys. Lett. B697, 412 (2011), arXiv:1010.2752 [hep-ph]

    arXiv 2011

  3. [3]

    Boyarsky, D

    A. Boyarsky, D. Malyshev, and O. Ruchayskiy, Phys. Lett. B705, 165 (2011), arXiv:1012.5839 [hep-ph]

    arXiv 2011

  4. [4]

    Hooper and T

    D. Hooper and T. Linden, Phys. Rev. D84, 123005 (2011), 1110.0006

    arXiv 2011

  5. [5]

    K. N. Abazajian and M. Kaplinghat, Phys. Rev. D86, 083511 (2012), [Erratum: Phys. Rev.D87,129902(2013)], 1207.6047

    arXiv 2012

  6. [6]

    Hooper and T

    D. Hooper and T. R. Slatyer, Phys. Dark Univ. 2, 118 (2013), 1302.6589

    arXiv 2013

  7. [7]

    Gordon and O

    C. Gordon and O. Macias, Phys. Rev. D88, 083521 (2013), [Erratum: Phys. Rev.D89,no.4,049901(2014)], 1306.5725

    arXiv 2013

  8. [8]

    Huang, A

    W.-C. Huang, A. Urbano, and W. Xue, (2013), 1307.6862

    arXiv 2013

Show all 69 references
  1. [9]

    Macias and C

    O. Macias and C. Gordon, Phys. Rev. D89, 063515 (2014), 1312.6671

    2014 arXiv

  2. [10]

    K. N. Abazajian, N. Canac, S. Horiuchi, and M. Kapling- hat, Phys. Rev. D90, 023526 (2014), 1402.4090

    2014 arXiv

  3. [11]

    Daylan, D

    T. Daylan, D. P. Finkbeiner, D. Hooper, T. Linden, S. K. N. Portillo, N. L. Rodd, and T. R. Slatyer, Phys. Dark Univ. 12, 1 (2016), 1402.6703

    2016 arXiv

  4. [12]

    Zhou, Y.-F

    B. Zhou, Y.-F. Liang, X. Huang, X. Li, Y.-Z. Fan, L. Feng, and J. Chang, Phys. Rev. D91, 123010 (2015), 1406.6948

    2015 arXiv

  5. [13]

    Calore, I

    F. Calore, I. Cholis, C. McCabe, and C. Weniger, Phys. Rev. D91, 063003 (2015), arXiv:1411.4647 [hep-ph]

    2015 arXiv

  6. [14]

    Calore, I

    F. Calore, I. Cholis, and C. Weniger, JCAP 1503, 038 (2015), 1409.0042

    2015 arXiv

  7. [15]

    K. N. Abazajian, N. Canac, S. Horiuchi, M. Kaplinghat, and A. Kwa, JCAP 1507, 013 (2015), 1410.6168

    2015 arXiv

  8. [16]

    Ajello et al

    M. Ajello et al. (Fermi-LAT), Astrophys. J. 819, 44 (2016), arXiv:1511.02938 [astro-ph.HE]. 21

    2016 arXiv

  9. [17]

    Linden, N

    T. Linden, N. L. Rodd, B. R. Safdi, and T. R. Slatyer, Phys. Rev. D94, 103013 (2016), arXiv:1604.01026 [astro- ph.HE]

    2016 arXiv

  10. [18]

    Karwin, S

    C. Karwin, S. Murgia, T. M. P. Tait, T. A. Porter, and P. Tanedo, Phys. Rev. D95, 103005 (2017), arXiv:1612.05687 [hep-ph]

    2017 arXiv

  11. [19]

    W. B. Atwood et al. (Fermi-LAT), Astrophys. J. 697, 1071 (2009), arXiv:0902.1089 [astro-ph.IM]

    2009 arXiv

  12. [20]

    K. N. Abazajian, JCAP 1103, 010 (2011), arXiv:1011.4275 [astro-ph.HE]

    2011 arXiv

  13. [21]

    Hooper, I

    D. Hooper, I. Cholis, T. Linden, J. Siegal-Gaskins, and T. Slatyer, Phys. Rev. D88, 083009 (2013), arXiv:1305.0830 [astro-ph.HE]

    2013 arXiv

  14. [22]

    Mirabal, Mon

    N. Mirabal, Mon. Not. Roy. Astron. Soc. 436, 2461 (2013), arXiv:1309.3428 [astro-ph.HE]

    2013 arXiv

  15. [23]

    Calore, M

    F. Calore, M. D. Mauro, and F. Donato, Astrophys. J. 796, 1 (2014), arXiv:1406.2706 [astro-ph.HE]

    2014 arXiv

  16. [24]

    Cholis, D

    I. Cholis, D. Hooper, and T. Linden, JCAP 1506, 043 (2015), arXiv:1407.5625 [astro-ph.HE]

    2015 arXiv

  17. [25]

    Petrovi´ c, P

    J. Petrovi´ c, P. D. Serpico, and G. Zaharijas, JCAP1502, 023 (2015), arXiv:1411.2980 [astro-ph.HE]

    2015 arXiv

  18. [26]

    Yuan and K

    Q. Yuan and K. Ioka, Astrophys. J. 802, 124 (2015), arXiv:1411.4363 [astro-ph.HE]

    2015 arXiv

  19. [27]

    R. M. O’Leary, M. D. Kistler, M. Kerr, and J. Dexter, (2015), arXiv:1504.02477 [astro-ph.HE]

    2015 arXiv

  20. [28]

    Ploeg, C

    H. Ploeg, C. Gordon, R. Crocker, and O. Macias, JCAP 1708, 015 (2017), arXiv:1705.00806 [astro-ph.HE]

    2017 arXiv

  21. [29]

    Eckner et al

    C. Eckner et al. , Astrophys. J. 862, 79 (2018), arXiv:1711.05127 [astro-ph.HE]

    2018 arXiv

  22. [30]

    Cholis, D

    I. Cholis, D. Hooper, and T. Linden, (2014), arXiv:1407.5583 [astro-ph.HE]

    2014 arXiv

  23. [31]

    R. T. Bartels, T. D. P. Edwards, and C. Weniger, Mon. Not. Roy. Astron. Soc. 481, 3966 (2018), arXiv:1805.11097 [astro-ph.HE]

    2018 arXiv

  24. [32]

    S. K. Lee, M. Lisanti, B. R. Safdi, T. R. Slatyer, and W. Xue, Phys. Rev. Lett. 116, 051103 (2016), 1506.05124

    2016 arXiv

  25. [33]

    Bartels, S

    R. Bartels, S. Krishnamurthy, and C. Weniger, Phys. Rev. Lett. 116, 051102 (2016), arXiv:1506.05104 [astro- ph.HE]

    2016 arXiv

  26. [34]

    Macias, C

    O. Macias, C. Gordon, R. M. Crocker, B. Coleman, D. Paterson, S. Horiuchi, and M. Pohl, Nat. Astron. 2, 387 (2018), arXiv:1611.06644 [astro-ph.HE]

    2018 arXiv

  27. [35]

    Macias, S

    O. Macias, S. Horiuchi, M. Kaplinghat, C. Gordon, R. M. Crocker, and D. M. Nataf, (2019), arXiv:1901.03822 [astro-ph.HE]

    2019 arXiv

  28. [36]

    Bartels, E

    R. Bartels, E. Storm, C. Weniger, and F. Calore, (2017), arXiv:1711.04778 [astro-ph.HE]

    2017 arXiv

  29. [37]

    T. D. Brandt and B. Kocsis, Astrophys. J. 812, 15 (2015), arXiv:1507.05616 [astro-ph.HE]

    2015 arXiv

  30. [38]

    Ajello et al

    M. Ajello et al. (Fermi-LAT), Submitted to: Astrophys. J. (2017), arXiv:1705.00009 [astro-ph.HE]

    2017 arXiv

  31. [39]

    Carlson, T

    E. Carlson, T. Linden, and S. Profumo, Phys. Rev. Lett. 117, 111101 (2016), 1510.04698

    2016 arXiv

  32. [40]

    Carlson, T

    E. Carlson, T. Linden, and S. Profumo, Phys. Rev. D94, 063504 (2016), 1603.06584

    2016 arXiv

  33. [41]

    H. A. Clark, P. Scott, R. Trotta, and G. F. Lewis, JCAP 1807, 060 (2018), arXiv:1612.01539 [astro-ph.HE]

    2018 arXiv

  34. [42]

    Calore, M

    F. Calore, M. Di Mauro, F. Donato, J. W. T. Hes- sels, and C. Weniger, Astrophys. J. 827, 143 (2016), arXiv:1512.06825 [astro-ph.HE]

    2016 arXiv

  35. [43]

    Fore- ground mismodeling and the point source explanation of the Fermi Galactic Center Excess,

    M. Buschmann, N. L. Rodd, B. R. Safdi, L. J. Chang, S. Mishra-Sharma, M. Lisanti, and O. Macias, “Fore- ground mismodeling and the point source explanation of the Fermi Galactic Center Excess,” in prep

  36. [44]

    R. K. Leane and T. R. Slatyer, (2019), arXiv:1904.08430 [astro-ph.HE]

    2019 arXiv

  37. [45]

    Lisanti, S

    M. Lisanti, S. Mishra-Sharma, N. L. Rodd, and B. R. Safdi, (2017), arXiv:1708.09385 [astro-ph.CO]

    2017 arXiv

  38. [46]

    L. J. Chang, M. Lisanti, and S. Mishra-Sharma, Phys. Rev. D98, 123004 (2018), arXiv:1804.04132 [astro- ph.CO]

    2018 arXiv

  39. [47]

    Mishra-Sharma, N

    S. Mishra-Sharma, N. L. Rodd, and B. R. Safdi, Astron. J. 153, 253 (2017), 1612.03173

    2017 arXiv

  40. [48]

    Acero et al

    F. Acero et al. (Fermi-LAT), Astrophys. J. Suppl. 218, 23 (2015), arXiv:1501.02003 [astro-ph.HE]

    2015 arXiv

  41. [49]

    (2019), arXiv:1902.10045 [astro-ph.HE]

    2019 arXiv

  42. [50]

    Lisanti, S

    M. Lisanti, S. Mishra-Sharma, L. Necib, and B. R. Safdi, Astrophys. J. 832, 117 (2016), arXiv:1606.04101 [astro- ph.HE]

    2016 arXiv

  43. [51]

    S. K. Lee, M. Lisanti, and B. R. Safdi, JCAP 1505, 056 (2015), arXiv:1412.6099 [astro-ph.CO]

    2015 arXiv

  44. [52]

    Malyshev and D

    D. Malyshev and D. W. Hogg, Astrophys. J. 738, 181 (2011), arXiv:1104.0010 [astro-ph.CO]

    2011 arXiv

  45. [53]

    Feroz, M

    F. Feroz, M. P. Hobson, and M. Bridges, Mon. Not. Roy. Astron. Soc. 398, 1601 (2009), arXiv:0809.3437 [astro- ph]

    2009 arXiv

  46. [54]

    Buchner, A

    J. Buchner, A. Georgakakis, K. Nandra, L. Hsu, C. Rangel, M. Brightman, A. Merloni, M. Salvato, J. Donley, and D. Kocevski, Astron. Astrophys. 564, A125 (2014), arXiv:1402.0004 [astro-ph.HE]

    2014 arXiv

  47. [55]

    Feroz, M

    F. Feroz, M. P. Hobson, E. Cameron, and A. N. Pettitt, (2013), arXiv:1306.2144 [astro-ph.IM]

    2013 arXiv

  48. [56]

    Feroz and M

    F. Feroz and M. P. Hobson, Mon. Not. Roy. Astron. Soc. 384, 449 (2008), arXiv:0704.3704 [astro-ph]

    2008 arXiv

  49. [57]

    Skilling, Bayesian Anal

    J. Skilling, Bayesian Anal. 1, 833 (2006)

    2006

  50. [58]

    Sup- plementary material for NPTFit,

    S. Mishra-Sharma, N. L. Rodd, and B. R. Safdi, “Sup- plementary material for NPTFit,” (2016)

    2016

  51. [60]

    M. Su, T. R. Slatyer, and D. P. Finkbeiner, Astrophys. J. 724, 1044 (2010), arXiv:1005.5480 [astro-ph.HE]

    2010 arXiv

  52. [61]

    J. F. Navarro, C. S. Frenk, and S. D. M. White, Astro- phys. J. 462, 563 (1996), astro-ph/9508025

    1996 arXiv

  53. [62]

    Ackermann et al

    M. Ackermann et al. (Fermi-LAT), Astrophys. J. 750, 3 (2012), arXiv:1202.4039 [astro-ph.HE]

    2012 arXiv

  54. [63]

    K. M. G´ orski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, Astro- phys. J. 622, 759 (2005), arXiv:astro-ph/0409513

    2005 arXiv

  55. [64]

    P´ erez and B

    F. P´ erez and B. E. Granger, Computing in Science and Engineering 9, 21 (2007)

    2007

  56. [65]

    J. D. Hunter, Computing In Science & Engineering 9, 90 (2007)

    2007

  57. [66]

    Johansson et al

    F. Johansson et al. , mpmath: a Python library for arbitrary-precision floating-point arithmetic (version 0.18) (2013), http://mpmath.org/

    2013

  58. [67]

    van der Walt, S

    S. van der Walt, S. C. Colbert, and G. Varoquaux, Computing in Science and Engineering 13, 22 (2011), arXiv:1102.1523 [cs.MS]

    2011 arXiv

  59. [68]

    McKinney, in Proceedings of the 9th Python in Sci- ence Conference, edited by S

    W. McKinney, in Proceedings of the 9th Python in Sci- ence Conference, edited by S. van der Walt and J. Mill- man (2010) pp. 51 – 56

    2010

  60. [69]

    SciPy: Open source scientific tools for Python,

    E. Jones, T. Oliphant, P. Peterson, et al., “SciPy: Open source scientific tools for Python,” (2001–)

    2001

  61. [70]

    −True NFW PS

    D. Foreman-Mackey, The Journal of Open Source Soft- ware 24 (2016), 10.21105/joss.00024. 22 Appendix A: Supplementary Figures In this Appendix, we provide a set of supplementary figures that are referenced and described in the main text. In Figs. A1–A7, PSs (when relevant) corr...

    2016 doi

Pith tools

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