Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case

Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair of particles in a highly visual way. We set up an artificial neural network (ANN) to find out characteristic shapes of the spectra of the jets from heavy particle decays. By taking the Higgs jets and QCD jets as examples, we show that the ANN of the angular spectrum input has similar performance to existing taggers. In addition, some improvement is seen when additional extra radiations occur. Notably, the new algorithm automatically combines the information of the multi-point correlations in the jet.Comment: 18 pages, 12 figures, published in JHEP. A cut-based analysis is adde

Identifying a new particle with jet substructures

We investigate a potential of measuring properties of a heavy resonance X, exploiting jet substructure techniques. Motivated by heavy higgs boson searches, we focus on the decays of X into a pair of (massive) electroweak gauge bosons. More specifically, we consider a hadronic Z boson, which makes it possible to determine properties of X at an earlier stage. For $m_X$ of O(1) TeV, two quarks from a Z boson would be captured as a "merged jet" in a significant fraction of events. The use of the merged jet enables us to consider a Z-induced jet as a reconstructed object without any combinatorial ambiguity. We apply a conventional jet substructure method to extract four-momenta of subjets from a merged jet. We find that jet substructure procedures may enhance features in some kinematic observables formed with subjets. Subjet momenta are fed into the matrix element associated with a given hypothesis on the nature of X, which is further processed to construct a matrix element method (MEM)-based observable. For both moderately and highly boosted Z bosons, we demonstrate that the MEM with current jet substructure techniques can be a very powerful discriminator in identifying the physics nature of X. We also discuss effects from choosing different jet sizes for merged jets and jet-grooming parameters upon the MEM analyses.Comment: 36 pages, 11 figures, published in JHE

Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3

We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars from the Gaia DR3 catalog. Solving the collisionless Boltzmann equation with the assumption of approximate equilibrium, we calculate -- for the first time ever -- a model-free, unbinned, fully 3D map of the local acceleration and mass density fields within a 3 kpc sphere around the Sun. As our approach makes no assumptions about symmetries, we can test for signs of disequilibrium in our results. We find our results are consistent with equilibrium at the 10% level, limited by the current precision of the normalizing flows. After subtracting the known contribution of stars and gas from the calculated mass density, we find clear evidence for dark matter throughout the analyzed volume. Assuming spherical symmetry and averaging mass density measurements, we find a local dark matter density of $0.47\pm 0.05\;\mathrm{GeV/cm}^3$. We fit our results to a generalized NFW, and find a profile broadly consistent with other recent analyses.Comment: 19 pages, 13 figures, 3 table

The 750 GeV Diphoton Excess May Not Imply a 750 GeV Resonance

We discuss non-standard interpretations of the 750 GeV diphoton excess recently reported by the ATLAS and CMS Collaborations which do not involve a new, relatively broad, resonance with a mass near 750 GeV. Instead, we consider the sequential cascade decay of a much heavier, possibly quite narrow, resonance into two photons along with one or more invisible particles. The resulting diphoton invariant mass signal is generically rather broad, as suggested by the data. We examine three specific event topologies - the antler, the sandwich, and the 2-step cascade decay, and show that they all can provide a good fit to the observed published data. In each case, we delineate the preferred mass parameter space selected by the best fit. In spite of the presence of invisible particles in the final state, the measured missing transverse energy is moderate, due to its anti- correlation with the diphoton invariant mass. We comment on the future prospects of discriminating with higher statistics between our scenarios, as well as from more conventional interpretations.Comment: Discussion about the ATLAS Moriond EW2016 added. Matched to PRL accepted versio

GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs

Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires "upsampling" the star particles into individual stars following the same phase-space density. In this paper, we demonstrate that normalizing flows provide a viable upsampling method that greatly improves on conventionally-used kernel smoothing algorithms such as EnBiD. We demonstrate our flow-based upsampling technique, dubbed GalaxyFlow, on a neighborhood of the Solar location in two simulated galaxies: Auriga 6 and h277. By eye, GalaxyFlow produces stellar distributions that are smoother than EnBiD-based methods and more closely match the Gaia DR3 catalog. For a quantitative comparison of generative model performance, we introduce a novel multi-model classifier test. Using this classifier test, we show that GalaxyFlow more accurately estimates the density of the underlying star particles than previous methods.Comment: 17 pages, 11 figure

Monojet signatures from heavy colored particles: future collider sensitivities and theoretical uncertainties

In models with colored particle Q that can decay into a dark matter candidate X, the relevant collider process pp → QQ¯ → X X¯ + jets gives rise to events with significant transverse momentum imbalance. When the masses of Q and X are very close, the relevant signature becomes monojetlike, and Large Hadron Collider (LHC) search limits become much less constraining. In this paper, we study the current and anticipated experimental sensitivity to such particles at the High-Luminosity LHC at √s = 14 TeV with L = 3 ab−1 of data and the proposed High-Energy LHC at √s = 27 TeV with L = 15 ab−1 of data. We estimate the reach for various Lorentz and QCD color representations of Q. Identifying the nature of Q is very important to understanding the physics behind the monojet signature. Therefore, we also study the dependence of the observables built from the pp → QQ¯ + j process on Q itself. Using the state-of-theart Monte Carlo suites MadGraph5_aMC@NLO+Pythia8 and Sherpa, we find that when these observables are calculated at NLO in QCD with parton shower matching and multijet merging, the residual theoretical uncertainties are comparable to differences observed when varying the quantum numbers of Q itself. We find, however, that the precision achievable with NNLO calculations, where available, can resolve this dilemma

OPTIMASS: A Package for the Minimization of Kinematic Mass Functions with Constraints

Reconstructed mass variables, such as $M_2$, $M_{2C}$, $M_T^\star$, and $M_{T2}^W$, play an essential role in searches for new physics at hadron colliders. The calculation of these variables generally involves constrained minimization in a large parameter space, which is numerically challenging. We provide a C++ code, OPTIMASS, which interfaces with the MINUIT library to perform this constrained minimization using the Augmented Lagrangian Method. The code can be applied to arbitrarily general event topologies and thus allows the user to significantly extend the existing set of kinematic variables. We describe this code and its physics motivation, and demonstrate its use in the analysis of the fully leptonic decay of pair-produced top quarks using the $M_2$ variables.Comment: 39 pages, 12 figures, (1) minor revision in section 3, (2) figure added in section 4.3, (3) reference added and (4) matched with published versio