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Register a new userSearching for periodic signals in kinematic distributions using continuous wavelet transforms
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Many models of physics beyond the Standard Model include towers of particles whose masses follow an approximately periodic pattern with little spacing between them. These resonances might be too weak to detect individually, but could be discovered as a group by looking for periodic signals in kinematic distributions. The continuous wavelet transform, which indicates how much a given frequency is present in a signal at a given time, is an ideal tool for this. In this paper, we present a series of methods through which continuous wavelet transforms can be used to discover periodic signals in kinematic distributions. Some of these methods are based on a simple test statistic, while others make use of machine learning techniques. Some of the methods are meant to be used with a particular model in mind, while others are model-independent. We find that continuous wavelet transforms can give bounds comparable to current searches and, in some cases, be sensitive to signals that would go undetected by standard experimental strategies.
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Hadron production at low transverse momenta in semi-inclusive deep inelastic scattering can be described by transverse momentum dependent (TMD) factorization. This formalism has also been widely used to study the Drell-Yan process and back-to-back hadron pair production in $e^+e^-$ collisions. These processes are the main ones for extractions of TMD parton distribution functions and TMD fragmentation functions, which encode important information about nucleon structure and hadronization. One of the most widely used TMD factorization formalism in phenomenology formulates TMD observables in coordinate $b_perp$-space, the conjugate space of the transverse momentum. The Fourier transform from $b_perp$-space back into transverse momentum space is sufficiently complicated due to oscillatory integrands that it requires a careful and computationally intensive numerical treatment in order to avoid potentially large numerical errors. Within the TMD formalism, the azimuthal angular dependence is analytically integrated and the two-dimensional $b_perp$ integration reduces to a one-dimensional integration over the magnitude $b_perp$. In this paper we develop a fast numerical Hankel transform algorithm for such a $b_perp$-integration that improves the numerical accuracy of TMD calculations in all standard processes. Libraries for this algorithm are implemented in Python 2.7 and 3, C++, as well as FORTRAN77. All packages are made available open source.
We present a wavelet-based algorithm to identify dwarf galaxies in the Milky Way in ${it Gaia}$ DR2 data. Our algorithm detects overdensities in 4D position--proper motion space, making it the first search to explicitly use velocity information to search for dwarf galaxy candidates. We optimize our algorithm and quantify its performance by searching for mock dwarfs injected into ${it Gaia}$ DR2 data and for known Milky Way satellite galaxies. Comparing our results with previous photometric searches, we find that our search is sensitive to undiscovered systems at Galactic latitudes~$lvert brvert>20^{circ}$ and with half-light radii larger than the 50% detection efficiency threshold for Pan-STARRS1 (PS1) at (${it i}$) absolute magnitudes of =$-7<M_V<-3$ and distances of $32$ kpc $< D < 64$ kpc, and (${it ii}$) $M_V< -4$ and $64$ kpc $< D < 128$ kpc. Based on these results, we predict that our search is expected to discover $5 pm 2$ new satellite galaxies: four in the PS1 footprint and one outside the Dark Energy Survey and PS1 footprints. We apply our algorithm to the ${it Gaia}$ DR2 dataset and recover $sim 830$ high-significance candidates, out of which we identify a gold standard list of $sim 200$ candidates based on cross-matching with potential candidates identified in a preliminary search using ${it Gaia}$ EDR3 data. All of our candidate lists are publicly distributed for future follow-up studies. We show that improvements in astrometric measurements provided by ${it Gaia}$ EDR3 increase the sensitivity of this technique; we plan to continue to refine our candidate list using future data releases.
The sensitivity to dark matter signals at neutrino experiments is fundamentally challenged by the neutrino rates, as they leave similar signatures in their detectors. As a way to improve the signal sensitivity, we investigate a dark matter search strategy which utilizes the timing and energy spectra to discriminate dark matter from neutrino signals at low-energy, pulsed-beam neutrino experiments. This strategy was proposed in our companion paper arXiv:1906.10745, which we apply to potential searches at COHERENT, JSNS$^2$, and CCM. These experiments are not only sources of neutrinos but also high intensity sources of photons. The dark matter candidate of interest comes from the relatively prompt decay of a dark sector gauge boson which may replace a Standard-Model photon, so the delayed neutrino events can be suppressed by keeping prompt events only. Furthermore, prompt neutrino events can be rejected by a cut in recoil energy spectra, as their incoming energy is relatively small and bounded from above while dark matter may deposit a sizable energy beyond it. We apply the search strategy of imposing a combination of energy and timing cuts to the existing CsI data of the COHERENT experiment as a concrete example, and report a mild excess beyond known backgrounds. We then investigate the expected sensitivity reaches to dark matter signals in our benchmark experiments.
In recent years, evidence for lepton flavour universality violation beyond the Standard Model has been accumulated. In this context, a singly charged $SU(2)_L$ singlet scalar ($phi^pm$) is very interesting, as it can only have flavour off-diagonal couplings to neutrinos and charged leptons, therefore necessarily violating lepton flavour (universality). In fact, it gives a (necessarily constructive) tree-level effect in $elltoell^prime u u$ processes, while contributing to charged lepton flavour violating only at the loop-level. Therefore, it can provide a common explanation of the hints for new physics in $tautomu u u/tau(mu)to e u u$ and of the Cabibbo Angle Anomaly. Such an explanation predicts ${rm Br }[tauto egamma]$ to be of the order of a few times $10^{-11}$ while ${ rm Br}[tauto emumu]$ can be of the order of $10^{-9}$ for order one couplings and therefore in the reach of forthcoming experiments. Furthermore, we derive a {novel} coupling-independent lower limit on the scalar mass of $approx 200,$GeV by recasting LHC slepton searches. In the scenario preferred by low energy precision data, the lower limit is even strengthened to $approx300,$GeV, showing the complementary between LHC searches and flavour observables. Furthermore, we point out that this model can be tested by reinterpreting DM mono-photon searches at future $e^+e^-$ colliders.
The complex interplay of magnetohydrodynamics, gravity, and supersonic turbulence in the interstellar medium (ISM) introduces non-Gaussian structure that can complicate comparison between theory and observation. We show that the Wavelet Scattering Transform (WST), in combination with linear discriminant analysis (LDA), is sensitive to non-Gaussian structure in 2D ISM dust maps. WST-LDA classifies magnetohydrodynamic (MHD) turbulence simulations with up to a 97% true positive rate in our testbed of 8 simulations with varying sonic and Alfv{e}nic Mach numbers. We present a side-by-side comparison with two other methods for non-Gaussian characterization, the Reduced Wavelet Scattering Transform (RWST) and the 3-Point Correlation Function (3PCF). We also demonstrate the 3D-WST-LDA and apply it to classification of density fields in position-position-velocity (PPV) space, where density correlations can be studied using velocity coherence as a proxy. WST-LDA is robust to common observational artifacts, such as striping and missing data, while also sensitive enough to extract the net magnetic field direction for sub-Alfv{e}nic turbulent density fields. We include a brief analysis of the effect of point spread functions and image pixelization on 2D-WST-LDA applied to density fields, which informs the future goal of applying WST-LDA to 2D or 3D all-sky dust maps to extract hydrodynamic parameters of interest.
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