We develop a new machine learning algorithm, Via Machinae, to identify cold stellar streams in data from the Gaia telescope. Via Machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detec
t local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, Via Machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. We further apply an automated line-finding method based on the Hough transform to search for line-like features in patches of the sky. In this paper, we describe the Via Machinae algorithm in detail and demonstrate our approach on the prominent stream GD-1. A companion paper contains our identification of other known stellar streams as well as new stellar stream candidates from Via Machinae. Though some parts of the algorithm are tuned to increase sensitivity to cold streams, the Via Machinae technique itself does not rely on astrophysical assumptions, such as the potential of the Milky Way or stellar isochrones. This flexibility suggests that it may have further applications in identifying other anomalous structures within the Gaia dataset, for example debris flow and globular clusters.
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within
this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.
We develop a rigorous, semi-analytical method for maximizing any $bto ctau u$ observable in the full 20-real-dimensional parameter space of the dimension 6 effective Hamiltonian, given some fixed values of $R_{D^{(*)}}$. We apply our method to find t
he maximum allowed values of $F^L_{D^*}$ and $R_{J/psi}$, two observables which have both come out higher than their SM predictions in recent measurements by the Belle and LHCb collaborations. While the measurements still have large error bars, they add to the existing $R_{D^{(*)}}$ anomaly, and it is worthwhile to consider NP explanations. It has been shown that none of the existing, minimal models in the literature can explain the observed values of $F^L_{D^*}$ and $R_{J/psi}$. Using our method, we will generalize beyond the minimal models and show that there is no combination of dimension 6 Wilson operators that can come within $1sigma$ of the observed $R_{J/psi}$ value. By contrast, we will show that the observed value of $F^L_{D^*}$ can be achieved, but only with sizable contributions from tensor and mixed-chirality vector Wilson coefficients.
The $R_{D^{(*)}}$ anomalies are among the longest-standing and most statistically significant hints of physics beyond the Standard Model. Many models have been proposed to explain these anomalies, including the interesting possibility that right-hand
ed neutrinos could be involved in the $B$ decays. In this paper, we investigate future measurements at Belle II that can be used to tell apart the various new physics scenarios. Focusing on a number of $tau$ asymmetry observables (forward-backward asymmetry and polarization asymmetries) which can be reconstructed at Belle II, we calculate the contribution of the most general dimension 6 effective Hamiltonian (including right-handed neutrinos) to all of these asymmetries. We show that Belle II can use these asymmetries to distinguish between new-physics scenarios that use right- and left-handed neutrinos, and in most cases can likely distinguish the specific model itself.
The measured $B$-meson semi-leptonic branching ratios $R_{D}$ and $R_{D^*}$ have long-standing deviations between theory and experiment. We introduce a model which explains both anomalies through a single interaction by introducing a right-handed neu
trino as the missing energy particle. This interaction is mediated by a heavy charged vector boson ($W$) which couples only to right-handed quarks and leptons of the Standard Model through the mixing of these particles with new vector-like fermions. Previous $W$ models for the $R_{D^{(*)}}$ anomaly were strongly constrained from flavor changing neutral currents and direct collider searches for $Ztotautau$ resonances. We show that relying on right-handed fermion mixing enables us to avoid these constraints, as well as other severe bounds from electroweak precision tests and neutrino mixing.
We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) -- to build a highly effective boosted top tagger. Previous work (the DeepTop tagger of Kasieczka et al) has shown that a CNN-based top tagger can achi
eve comparable performance to state-of-the-art conventional top taggers based on high-level inputs. Here, we introduce a number of improvements to the DeepTop tagger, including architecture, training, image preprocessing, sample size and color pixels. Our final CNN top tagger outperforms BDTs based on high-level inputs by a factor of $sim 2$--3 or more in background rejection, over a wide range of tagging efficiencies and fiducial jet selections. As reference points, we achieve a QCD background rejection factor of 500 (60) at 50% top tagging efficiency for fully-merged (non-merged) top jets with $p_T$ in the 800--900 GeV (350--450 GeV) range. Our CNN can also be straightforwardly extended to the classification of other types of jets, and the lessons learned here may be useful to others designing their own deep NNs for LHC applications.
This manual describes the usage and structure of FormFlavor, a Mathematica-based tool for computing a broad list of flavor and CP observables in general new physics models. Based on the powerful machinery of FeynArts and FormCalc, FormFlavor calculat
es the one-loop Wilson coefficients of the dimension 5 and 6 Standard Model effective Lagrangian entirely from scratch. These Wilson coefficients are then evolved down to the low scale using one-loop QCD RGEs, where they are transformed into flavor and CP observables. The last step is accomplished using a model-independent, largely stand-alone package called FFObservables that is included with FormFlavor. The SM predictions in FFObservables include up-to-date references and accurate current predictions. Using the functions and modular structure provided by FormFlavor, it is straightforward to add new observables. Currently, FormFlavor is set up to perform these calculations for the general, non-MFV MSSM, but in principle it can be generalized to arbitrary FeynArts models. FormFlavor and an up-to-date manual can be downloaded from: http://formflavor.hepforge.org.
Null results from dark matter (DM) direct detection experiments and the 125 GeV Higgs both pose serious challenges to minimal supersymmetry. In this paper, we propose a simple extension of the MSSM that economically solves both problems: a dark secto
r consisting of a singlet and a pair of $SU(2)$ doublets. Loops of the dark sector fields help lift the Higgs mass to 125 GeV consistent with naturalness, while the lightest fermion in the dark sector can be viable thermal relic DM, provided that it is mostly singlet. The DM relic abundance is controlled by s-wave annihilation to tops and Higgsinos, leading to a tight relation between the relic abundance and the spin-dependent direct detection cross section. As a result, the model will be fully probed by the next generation of direct detection experiments. Finally we discuss the discovery potential at LHC Run II.
