1,454 research outputs found

    Implications of XENON100 and LHC results for Dark Matter models

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    We perform a fit to the recent Xenon100 data and study its implications for Dark Matter scenarios. We find that Inelastic Dark Matter is disfavoured as an explana- tion to the DAMA/LIBRA annual modulation signal. Concerning the scalar singlet DM model, we find that the Xenon100 data disfavors its constrained limit. We study the CMSSM as well as the low scale phenomenological MSSM taking into account latest Tevatron and LHC data (1.1/fb) about sparticles and Bs \rightarrow {\mu}{\mu}. After the EPS 2011 conference, LHC excludes the "Higgs-resonance" region of DM freeze-out and Xenon100 disfavors the "well-tempered" bino/higgsino, realized in the "focus-point" region of the CMSSM parameter space. The preferred region shifts to heavier sparticles, higher fine-tuning, higher tan {\beta} and the quality of the fit deteriorates.Comment: v4: addendum included at the light of the Dark Matter and Higgs data presented during july 2012 by the Xenon100, ATLAS and CMS collaboration

    PPPC 4 DMőĹ\nu: A Poor Particle Physicist Cookbook for Neutrinos from DM annihilations in the Sun

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    We provide ingredients and recipes for computing neutrino signals of TeV-scale Dark Matter annihilations in the Sun. For each annihilation channel and DM mass we present the energy spectra of neutrinos at production, including: state-of-the-art energy losses of primary particles in solar matter, secondary neutrinos, electroweak radiation. We then present the spectra after propagation to the Earth, including (vacuum and matter) flavor oscillations and interactions in solar matter. We also provide a numerical computation of the capture rate of DM particles in the Sun. These results are available in numerical form.Comment: 27 pages, many figures. v2: a typo corrected in eq.(18). All results are available at http://www.marcocirelli.net/PPPC4DMID.htm

    MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

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    In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton-proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural networks optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark-antiquark pairs produced in proton-proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.Comment: 15 pages, 10 figure

    Progress towards an improved particle flow algorithm at CMS with machine learning

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    The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity. In recent years, the machine learned particle-flow (MLPF) algorithm, a graph neural network that performs PF reconstruction, has been explored in CMS, with the possible advantages of directly optimizing for the physical quantities of interest, being highly reconfigurable to new conditions, and being a natural fit for deployment to heterogeneous accelerators. We discuss progress in CMS towards an improved implementation of the MLPF reconstruction, now optimized using generator/simulation-level particle information as the target for the first time. This paves the way to potentially improving the detector response in terms of physical quantities of interest. We describe the simulation-based training target, progress and studies on event-based loss terms, details on the model hyperparameter tuning, as well as physics validation with respect to the current PF algorithm in terms of high-level physical quantities such as the jet and missing transverse momentum resolutions. We find that the MLPF algorithm, trained on a generator/simulator level particle information for the first time, results in broadly compatible particle and jet reconstruction performance with the baseline PF, setting the stage for improving the physics performance by additional training statistics and model tuning.Comment: 7 pages, 4 Figures, 1 Tabl

    Scalable neural network models and terascale datasets for particle-flow reconstruction

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    We study scalable machine learning models for full event reconstruction in high-energy electron-positron collisions based on a highly granular detector simulation. Particle-flow (PF) reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters or hits. We compare a graph neural network and kernel-based transformer and demonstrate that both avoid quadratic memory allocation and computational cost while achieving realistic PF reconstruction. We show that hyperparameter tuning on a supercomputer significantly improves the physics performance of the models. We also demonstrate that the resulting model is highly portable across hardware processors, supporting Nvidia, AMD, and Intel Habana cards. Finally, we demonstrate that the model can be trained on highly granular inputs consisting of tracks and calorimeter hits, resulting in a competitive physics performance with the baseline. Datasets and software to reproduce the studies are published following the findable, accessible, interoperable, and reusable (FAIR) principles.Comment: 19 pages, 7 figure

    A Bayesian estimation of the Milky Way's circular velocity curve using Gaia DR3

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    Our goal is to calculate the circular velocity curve of the Milky Way, along with corresponding uncertainties that quantify various sources of systematic uncertainty in a self-consistent manner. The observed rotational velocities are described as circular velocities minus the asymmetric drift. The latter is described by the radial axisymmetric Jeans equation. We thus reconstruct the circular velocity curve between Galactocentric distances from 5 kpc to 14 kpc using a Bayesian inference approach. The estimated error bars quantify uncertainties in the Sun's Galactocentric distance and the spatial-kinematic morphology of the tracer stars. As tracers, we used a sample of roughly 0.6 million stars on the red giant branch stars with six-dimensional phase-space coordinates from Gaia data release 3 (DR3). More than 99% of the sample is confined to a quarter of the stellar disc with mean radial, rotational, and vertical velocity dispersions of (35¬Ī18)‚ÄČkm/s(35\pm 18)\,\rm km/s, (25¬Ī13)‚ÄČkm/s(25\pm 13)\,\rm km/s, and (19¬Ī9)‚ÄČkm/s(19\pm 9)\,\rm km/s, respectively. We find a circular velocity curve with a slope of 0.4¬Ī0.6‚ÄČkm/s/kpc0.4\pm 0.6\,\rm km/s/kpc, which is consistent with a flat curve within the uncertainties. We further estimate a circular velocity at the Sun's position of vc(R0)=233¬Ī7‚ÄČkm/sv_c(R_0)=233\pm7\, \rm km/s and that a region in the Sun's vicinity, characterised by a physical length scale of ‚ąľ1‚ÄČkpc\sim 1\,\rm kpc, moves with a bulk motion of VLSR=7¬Ī7‚ÄČkm/sV_{LSR} =7\pm 7\,\rm km/s. Finally, we estimate that the dark matter (DM) mass within 14 kpc is log‚Ā°10MDM(R<14‚ÄČkpc)/M‚äô=(11.2‚ąí2.3+2.0)\log_{10}M_{\rm DM}(R<14\, {\rm kpc})/{\rm M_{\odot}}= \left(11.2^{+2.0}_{-2.3}\right) and the local spherically averaged DM density is ŌĀDM(R0)=(0.41‚ąí0.09+0.10)‚ÄČGeV/cm3=(0.011‚ąí0.002+0.003)‚ÄČM‚äô/pc3\rho_{\rm DM}(R_0)=\left(0.41^{+0.10}_{-0.09}\right)\,{\rm GeV/cm^3}=\left(0.011^{+0.003}_{-0.002}\right)\,{\rm M_\odot/pc^3}. In addition, the effect of biased distance estimates on our results is assessed

    Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

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    Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems
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