Amoroso, S., Caron, S., Jueid, A., Ruiz de Austri, R., & Skands, P. (2019). Estimating QCD uncertainties in Monte Carlo event generators for gamma-ray dark matter searches. J. Cosmol. Astropart. Phys., 05(5), 007–44pp.
Abstract: Motivated by the recent galactic center gamma-ray excess identified in the Fermi-LAT data, we perform a detailed study of QCD fragmentation uncertainties in the modeling of the energy spectra of gamma-rays from Dark-Matter (DM) annihilation. When Dark-Matter particles annihilate to coloured final states, either directly or via decays such as W(*) -> qq-', photons are produced from a complex sequence of shower, hadronisation and hadron decays. In phenomenological studies their energy spectra are typically computed using Monte Carlo event generators. These results have however intrinsic uncertainties due to the specific model used and the choice of model parameters, which are difficult to asses and which are typically neglected. We derive a new set of hadronisation parameters (tunes) for the PYTHIA 8.2 Monte Carlo generator from a fit to LEP and SLD data at the Z peak. For the first time we also derive a conservative set of uncertainties on the shower and hadronisation model parameters. Their impact on the gamma-ray energy spectra is evaluated and discussed for a range of DM masses and annihilation channels. The spectra and their uncertainties are also provided in tabulated form for future use. The fragmentation-parameter uncertainties may be useful for collider studies as well.
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Caron, S., Eckner, C., Hendriks, L., Johannesson, G., Ruiz de Austri, R., & Zaharijas, G. (2023). Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess. J. Cosmol. Astropart. Phys., 06(6), 013–56pp.
Abstract: The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.
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Otten, S., Caron, S., de Swart, W., van Beekveld, M., Hendriks, L., van Leeuwen, C., et al. (2021). Event generation and statistical sampling for physics with deep generative models and a density information buffer. Nat. Commun., 12(1), 2985–16pp.
Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
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van Beekveld, M., Beenakker, W., Caron, S., Kip, J., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Non-standard neutrino spectra from annihilating neutralino dark matter. SciPost Phys. Core, 6(1), 006–23pp.
Abstract: Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation.
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Strege, C., Bertone, G., Besjes, G. J., Caron, S., Ruiz de Austri, R., Strubig, A., et al. (2014). Profile likelihood maps of a 15-dimensional MSSM. J. High Energy Phys., 09(9), 081–59pp.
Abstract: We present statistically convergent profile likelihood maps obtained via global fits of a phenomenological Minimal Supersymmetric Standard Model with 15 free parameters (the MSSM-15), based on over 250M points. We derive constraints on the model parameters from direct detection limits on dark matter, the Planck relic density measurement and data from accelerator searches. We provide a detailed analysis of the rich phenomenology of this model, and determine the SUSY mass spectrum and dark matter properties that are preferred by current experimental constraints. We evaluate the impact of the measurement of the anomalous magnetic moment of the muon (g – 2) on our results, and provide an analysis of scenarios in which the lightest neutralino is a subdominant component of the dark matter. The MSSM-15 parameters are relatively weakly constrained by current data sets, with the exception of the parameters related to dark matter phenomenology (M-1, M-2, mu), which are restricted to the sub-TeV regime, mainly due to the relic density constraint. The mass of the lightest neutralino is found to be < 1.5TeV at 99% C.L., but can extend up to 3 TeV when excluding the g – 2 constraint from the analysis. Low-mass bino-like neutralinos are strongly favoured, with spin-independent scattering cross-sections extending to very small values, similar to 10(-20) pb. ATLAS SUSY null searches strongly impact on this mass range, and thus rule out a region of parameter space that is outside the reach of any current or future direct detection experiment. The best-fit point obtained after inclusion of all data corresponds to a squark mass of 2.3 TeV, a gluino mass of 2.1 TeV and a 130 GeV neutralino with a spin-independent cross-section of 2.4 x 10(-10) pb, which is within the reach of future multi-ton scale direct detection experiments and of the upcoming LHC run at increased centre-of-mass energy.
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van Beekveld, M., Beenakker, W., Caron, S., & Ruiz de Austri, R. (2016). The case for 100 GeV bino dark matter: a dedicated LHC tri-lepton search. J. High Energy Phys., 04(4), 154–26pp.
Abstract: Global fit studies performed in the pMSSM and the photon excess signal originating from the Galactic Center seem to suggest compressed electroweak supersymmetric spectra with a similar to 100 GeV bino-like dark matter particle. We find that these scenarios are not probed by traditional electroweak supersymmetry searches at the LHC. We propose to extend the ATLAS and CMS electroweak supersymmetry searches with an improved strategy for bino-like dark matter, focusing on chargino plus next-to-lightest neutralino production, with a subsequent decay into a tri-lepton final state. We explore the sensitivity for pMSSM scenarios with Delta m = m(NLSP) – m(LSF) similar to(5 – 50) GeV in the root s = 14 TeV run of the LHC. Counterintuitively, we find that the requirement of low missing transverse energy increases the sensitivity compared to the current ATLAS and CMS searches. With 300 fb(-1) of data we expect the LHC experiments to be able to discover these supersymmetric spectra with mass gaps down to Am 9 GeV for DM masses between 40 and 140 GeV. We stress the importance of a dedicated search strategy that targets precisely these favored pMSSM spectra.
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Caron, S., Kim, J. S., Rolbiecki, K., Ruiz de Austri, R., & Stienen, B. (2017). The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learning. Eur. Phys. J. C, 77(4), 257–25pp.
Abstract: A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets – each tested against 200 signal regions by ATLAS – have been used to train and validate SUSY-AI. The code is currently able to reproduce theATLAS exclusion regions in 19 dimensions with an accuracy of at least 93%. It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/.
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Caron, S., Casas, J. A., Quilis, J., & Ruiz de Austri, R. (2018). Anomaly-free dark matter with harmless direct detection constraints. J. High Energy Phys., 12(12), 126–24pp.
Abstract: Dark matter (DM) interacting with the SM fields via a Z-boson (Z-portal') remains one of the most attractive WIMP scenarios, both from the theoretical and the phenomenological points of view. In order to avoid the strong constraints from direct detection and dilepton production, it is highly convenient that the Z has axial coupling to DM and leptophobic couplings to the SM particles, respectively. The latter implies that the associated U(1) coincides with baryon number in the SM sector. In this paper we completely classify the possible anomaly-free leptophobic Z with minimal dark sector, including the cases where the coupling to DM is axial. The resulting scenario is very predictive and perfectly viable from the present constraints from DM detection, EW observables and LHC data (di-lepton, di-jet and mono-jet production). We analyze all these constraints, obtaining the allowed areas in the parameter space, which generically prefer mZ less than or similar to 500 GeV, apart from resonant regions. The best chances to test these viable areas come from future LHC measurements.
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van Beekveld, M., Caron, S., & Ruiz de Austri, R. (2020). The current status of fine-tuning in supersymmetry. J. High Energy Phys., 01(1), 147–41pp.
Abstract: In this paper, we minimize and compare two different fine-tuning measures in four high-scale supersymmetric models that are embedded in the MSSM. In addition, we determine the impact of current and future dark matter direct detection and collider experiments on the fine-tuning. We then compare the low-scale electroweak measure with the high-scale Barbieri-Giudice measure. We find that they reduce to the same value when the higgsino parameter drives the degree of fine-tuning. We also find spectra where the high-scale measure turns out to be lower than the low-scale measure. Depending on the high-scale model and fine-tuning definition, we find a minimal fine-tuning of 3-38 (corresponding to O(10-1)%) for the low-scale measure, and 63-571 (corresponding to O(1-0.1)%) for the high-scale measure. We stress that it is too early to conclude on the fate of supersymmetry, based only on the fine-tuning paradigm.
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Otten, S., Rolbiecki, K., Caron, S., Kim, J. S., Ruiz de Austri, R., & Tattersall, J. (2020). DeepXS: fast approximation of MSSM electroweak cross sections at NLO. Eur. Phys. J. C, 80(1), 12–9pp.
Abstract: We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for pp ->(chi) over tilde (+)(1)(chi) over tilde (-)(1), (chi) over tilde (0)(2)(chi) over tilde (0)(2) and (chi) over tilde (0)(2)(chi) over tilde (+/-)(1) as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below 0.5% allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of O(10(7)) from O(min) to O(mu s) per evaluation.
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