Jueid, A., Kip, J., Ruiz de Austri, R., & Skands, P. (2024). The Strong Force meets the Dark Sector: a robust estimate of QCD uncertainties for anti-matter dark matter searches. J. High Energy Phys., 02(2), 119–48pp.
Abstract: In dark-matter annihilation channels to hadronic final states, stable particles – such as positrons, photons, antiprotons, and antineutrinos – are produced via complex sequences of phenomena including QED/QCD radiation, hadronisation, and hadron decays. These processes are normally modelled by Monte Carlo (MC) event generators whose limited accuracy imply intrinsic QCD uncertainties on the predictions for indirect-detection experiments like Fermi-LAT, Pamela, IceCube or Ams-02. In this article, we perform a comprehensive analysis of QCD uncertainties, meaning both perturbative and nonperturbative sources of uncertainty are included – estimated via variations of MC renormalization-scale and fragmentation-function parameters, respectively – in antimatter spectra from dark-matter annihilation, based on parametric variations of the Pythia 8 event generator. After performing several retunings of light-quark fragmentation functions, we define a set of variations that span a conservative estimate of the QCD uncertainties. We estimate the effects on antimatter spectra for various annihilation channels and final-state particle species, and discuss their impact on fitted values for the dark-matter mass and thermally-averaged annihilation cross section. We find dramatic impacts which can go up to O(10%) for the annihilation cross section. We provide the spectra in tabulated form including QCD uncertainties and code snippets to perform fast dark-matter fits, in this github repository.
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Jueid, A., Kip, J., Ruiz de Austri, R., & Skands, P. (2023). Impact of QCD uncertainties on antiproton spectra from dark-matter annihilation. J. Cosmol. Astropart. Phys., 04(4), 068–15pp.
Abstract: Dark-matter particles that annihilate or decay can undergo complex sequences of processes, including strong and electromagnetic radiation, hadronisation, and hadron de-cays, before particles that are stable on astrophysical time scales are produced. Antiprotons produced in this way may leave footprints in experiments such as AMS-02. Several groups have reported an excess of events in the antiproton flux in the rigidity range of 10-20 GV. However, the theoretical modeling of baryon production is not straightforward and relies in part on phenomenological models in Monte Carlo event generators. In this work, we assess the impact of QCD uncertainties on the spectra of antiprotons from dark-matter annihila-tion. As a proof-of-principle, we show that for a two-parameter model that depends only on the thermally-averaged annihilation cross section ((o -v)) and the dark-matter mass (Mx), QCD uncertainties can affect the best-fit mass by up to ti 14% (with large uncertainties for large DM masses), depending on the choice of Mx and the annihilation channel (bb over bar or W+W-), and (o -v) by up to ti 10%. For comparison, changes to the underlying diffusion parameters are found to be within 1%-5%, and the results are also quite resilient to the choice of cosmic-ray propagation model. These findings indicate that QCD uncertainties need to be included in future DM analyses. To facilitate full-fledged analyses, we provide the spectra in tabulated form including QCD uncertainties and code snippets to perform mass interpolations and quick DM fits. The code can be found in this GitHub [1] repository.
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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Kim, J. S., Reuter, J., Rolbiecki, K., & Ruiz de Austri, R. (2016). A resonance without resonance: Scrutinizing the diphoton excess at 750 GeV. Phys. Lett. B, 755, 403–408.
Abstract: Motivated by the recent diphoton excesses reported by both ATLAS and CMS collaborations, we suggest that a new heavy spinless particle is produced in gluon fusion at the LHC and decays to a couple of lighter pseudoscalars which then decay to photons. The new resonances could arise from a new strongly interacting sector and couple to Standard Model gauge bosons only via the corresponding Wess-Zumino-Witten anomaly. We present a detailed recast of the newest 13 TeV data from ATLAS and CMS together with the 8 TeV data to scan the consistency of the parameter space for those resonances.
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Cabrera, M. E., Casas, J. A., Mitsou, V. A., Ruiz de Austri, R., & Terron, J. (2012). Histogram comparison tools for the search of new physics at LHC. Application to the CMSSM. J. High Energy Phys., 04(4), 133–27pp.
Abstract: We propose a rigorous and effective way to compare experimental and theoretical histograms, incorporating the different sources of statistical and systematic uncertainties. This is a useful tool to extract as much information as possible from the comparison between experimental data with theoretical simulations, optimizing the chances of identifying New Physics at the LHC. We illustrate this by showing how a search in the CMSSM parameter space, using Bayesian techniques, can effectively find the correct values of the CMSSM parameters by comparing histograms of events with multijets + missing transverse momentum displayed in the effective-mass variable. The procedure is in fact very efficient to identify the true supersymmetric model, in the case supersymmetry is really there and accessible to the LHC.
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