Abstract
The Fermi bubbles are giant,
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Data availability
All data analysed in this study are publicly available. Fermi-LAT data are available from https://fermi.gsfc.nasa.gov/ssc/data/ and Gaia data are available from https://gea.esac.esa.int/archive/. The statistical pipeline, astrophysical templates and gamma-ray observations necessary to reproduce our main results are publicly available via Zenodo at https://doi.org/10.5281/zenodo.6210967.
Code availability
Fermi-LAT data used in our study were reduced and analysed using the standard FERMITOOLS V1.0.1 software package available from https://github.com/fermi-lat/Fermitools-conda/wiki. The performance of the Fermi-LAT was modelled with the P8R3_ULTRACLEANVETO_V2 instrument response functions. Spectral analysis and fitting were performed using custom MATHEMATICA code created by the authors, which is available from RMC upon reasonable request.
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Acknowledgements
R.M.C. acknowledges support from the Australian Government through the Australian Research Council under award number DP190101258 (shared with M.R.K.) and hospitality from the Virginia Institute of Technology, the Max-Planck Institut für Kernphysik and the GRAPPA Institute at the University of Amsterdam supported by the Kavli IPMU at the University of Tokyo. O.M. is supported by the GRAPPA Prize Fellowship and JSPS KAKENHI grant numbers JP17H04836, JP18H04340, JP18H04578 and JP20K14463. This work was supported by World Premier International Research Centre Initiative (WPI Initiative), MEXT, Japan. D.M. acknowledges support from the Australian Government through a Future Fellowship from the Australian Research Council, award number FT160100206. M.R.K. acknowledges support from the Australian Government through the Australian Research Council, award numbers DP190101258 (shared with R.M.C.) and FT180100375. The work of S.A. was supported by MEXT KAKENHI grant numbers JP20H05850 and JP20H05861. The work of S.H. is supported by the US Department of Energy Office of Science under award number DE-SC0020262 and NSF grant numbers AST-1908960 and PHY-1914409 and by the Japan Society for the Promotion of Science KAKENHI grant number JP22K03630. The work of D.S. is supported by the US Department of Energy Office of Science under award number DE-SC0020262. T.V. and A.R.D. acknowledge the support of the Australian Research Council’s Centre of Excellence for Dark Matter Particle Physics (CDM) CE200100008. A.J.R. acknowledges support from the Australian Government through the Australian Research Council under award number FT170100243. R.M.C. thanks E. Berkhuijsen, R. Beck, R. Ekers, M. Roth and T. Siegert for useful communications.
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R.M.C. initiated the project and led the spectral analysis and theoretical interpretation. O.M constructed the astrophysical templates, designed the analysis pipeline and performed the data analysis of the
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Extended data
Extended Data Fig. 1 The stellar density templates for the Sgr dSph used in this study.
Each map has been normalized, so the units are arbitrary; the color scale is logarithmic. Morphological differences among the templates are due to different stellar candidates (red clump or RR Lyrae), search algorithms, and search target (the dwarf remnant or the stream). Data sources are as follows: Model I, ref. 8; Model II, ref. 39; Model III, ref. 40; Model IV and Model V, ref. 41. Detailed descriptions of these templates are given in the S.I. sec. 2.
Extended Data Fig. 2 Goodness of fit computation for the best-fitting baseline + Sgr dSph model.
These use our preferred set of templates (first entry in Table 1). In each of the 15 panels, one for each of the energy bins in our analysis pipeline, the blue histograms show the distribution of - ln L values produced in 100 Monte Carlo trials where we use our pipeline to fit a mock data set produced by drawing photons from the same set of templates used in the fit; orange dashed vertical lines show the 68% confidence range of this distribution, and black dashed vertical lines show the mean. Under the hypothesis that our best-fitting model for the real Fermi observations is a true representation of the data, and that disagreements between the model and the data are solely the result of photon counting statistics, the log-likelihood values for our best-fitting model should be drawn from the distributions shown by the blue histograms. For comparison, the red vertical line shows the actual measured log likelihoods for our best fit. The fact that these measured values are well within the range spanned by the Monte Carlo trials indicates that we cannot rule out this hypothesis, indicating that our model is as good a fit to the data as could be expected given the finite number of photons that Fermi has observed.
Extended Data Fig. 3 Measured photon counts (left), best-fit baseline + Sgr dSph model (middle), and the fractional residuals (Data - Model)/Model (right).
The images were constructed by summing the corresponding energy bins over the energy ranges displayed on top of each panel: [0.5, 1.0] GeV, [1.0, 4.0] GeV, [4.0, 15.8] GeV, from top to bottom. The maps have been smoothed with Gaussian filters of radii 1. 0∘, 0. 8∘, and 0. 5∘ for each energy range displayed, respectively (where these angular scales are determined by the Fermi-LAT point spread function at the low-edge of the energy interval for the former two, while the latter is determined by the angular resolution of the gas maps). The spectrum of baseline + Sgr dSph model components shown here can be seen in Fig. ??. The 4FGL30
Extended Data Fig. 4 Results from our template mismatch tests.
Each of the coloured lines shows the results of a test where we generate synthetic data with one set of templates, and attempt to recover the Sgr dSph in those data using a different set. In the upper two panels, the horizontal axis shows the true, energy-integrated Sgr dSph photon flux in the synthetic data, while the vertical axis shows the value (with 1
Extended Data Fig. 5 Results of our rotation and translation tests.
Left: change in TS when repeating the analysis using the default baseline + Sgr dSph model, but with the Sgr dSph rotated about its centre by the indicated angle (blue points); TS values > 0 indicate an improved fit (dashed grey line), with TS = 46.1 corresponding to a 5
Extended Data Fig. 6 Sgr dSph spectra derived from template analysis using different Galactic diffuse emission models.
In all cases the spectrum shown is the flux averaged over the entire ROI, not the flux within the footprint of the Sgr dSph template. The fiducial model is our default choice (first entry in Table 1), while other lines correspond to alternate foregrounds - models 2D A (red), 2D B (black), and 2D C (blue) for the Galactic IC foreground, and models Interpolated (dark green) and GALPROP 3D-gas (light green) for the Galactic hadronic + bremsstrahlung foreground. The error bars display 1
Extended Data Fig. 7 Contribution of each template component to the γ -ray spectrum averaged over the entire ROI, for our default baseline + Sgr dSph model.
Components shown are as follows:
Supplementary information
Supplementary Information
Supplementary Figs. 1–3, Tables 1–3, text and references.
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Crocker, R.M., Macias, O., Mackey, D. et al. Gamma-ray emission from the Sagittarius dwarf spheroidal galaxy due to millisecond pulsars. Nat Astron 6, 1317–1324 (2022). https://doi.org/10.1038/s41550-022-01777-x
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DOI: https://doi.org/10.1038/s41550-022-01777-x
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