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Abstract Massless chiral excitations can arise from the interactions between a fermion and an axion string, propagating along the string and allowing it to superconduct. The properties of these excitations, or zero modes, dictate how the string interacts with light and can thus have important phenomenological consequences. In this paper, we add a nowhere-vanishing Dirac mass for the fermion in the usual model of axion electrodynamics. We find that the zero modes exhibit an interesting phase structure in which they delocalize from the string’s core as the mass increases, up until a critical value past which they disappear. We study this structure from an analytic perspective, with explicit numerical solutions, and via anomaly inflow arguments. Finally, we derive the two-dimensional effective theory of the zero mode and its interactions with the four-dimensional gauge field and show how this effective theory breaks down as the zero modes delocalize.

Studies of speech rhythm have often used read speech rather than spontaneous speech in their comparisons. However, read speech has been shown to be perceptually different from spontaneous speech, which may be due to rhythmic differences between the two modes. To examine this, the effect of speaking mode (spontaneous or controlled) was assessed in a group of 82 Spanish-Catalan learners of English relative to a control group of 8 native English speakers. Results found strong rhythmic differences between the two modes, but minimal differences between the learners and native speakers. Additionally, Mahalanobis distance analyses revealed that non-native speakers differed significantly more from the native control group in the spontaneous condition than the controlled condition.

Abstract When the Froggatt-Nielsen mechanism is used to explain the Standard Model flavor hierarchy, new physics couplings are also determined by the horizontal symmetry. However, additional symmetries or dynamics in the UV can sometimes lead to a departure from this naïve scaling for the new physics couplings. We show that an effective way to keep track of these changes is by using the new spurions of the U(3)5 global flavor symmetry, where we parameterize extra suppression or enhancement factors, referred to as wrinkles, using the same power counting parameter as in the original Froggatt-Nielsen model. As a concrete realization, we consider two flavor spurions of the S 1 leptoquark, and demonstrate that wrinkles can be used to make an enhanced value of BR B + → K + ν ν ¯ $$ \textrm{BR}\left({B}^{+}\to {K}^{+}\nu \overline{\nu}\right) $$ consistent with other flavor observables. We also present example UV models that realize wrinkles, and comment on choosing consistent charges in ordinary Froggatt-Nielsen models without the typical monotonicity condition.

Abstract Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain types of data (background) but not others (signals). In this paper, we study some challenges associated with variational autoencoders, such as the dependence on hyperparameters and the metric used, in the context of anomalous signal (top and W) jets in a QCD background. We find that the hyperparameter choices strongly affect the network performance and that the optimal parameters for one signal are non-optimal for another. In exploring the networks, we uncover a connection between the latent space of a variational autoencoder trained using mean-squared-error and the optimal transport distances within the dataset. We then show that optimal transport distances to representative events in the background dataset can be used directly for anomaly detection, with performance comparable to the autoencoders. Whether using autoencoders or optimal transport distances for anomaly detection, we find that the choices that best represent the background are not necessarily best for signal identification. These challenges with unsupervised anomaly detection bolster the case for additional exploration of semi-supervised or alternative approaches.

Abstract One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the top-quark mass parameter. We compare four different methodologies for top-quark mass measurement: a classical histogram fit similar to one commonly used in experiment augmented by soft-drop jet grooming; a 2D profile likelihood fit with a nuisance parameter; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.

Abstract A statistically significant excess of gamma rays has been reported and robustly confirmed in the Galactic Center over the past decade. Large local dark matter densities suggest that this Galactic Center Excess (GCE) may be attributable to new physics, and indeed it has been shown that this signal is well-modelled by annihilations dominantly into b b ¯ $$ b\overline{b} $$ with a WIMP-scale cross section. In this paper, we consider Majorana dark matter annihilating through a Higgs portal as a candidate source for this signal, where a large CP-violation in the Higgs coupling may serve to severely suppress scattering rates. In particular, we explore the phenomenology of two UV completions, a singlet-doublet model and a doublet-triplet model, and map out the available parameter space which can give a viable signal while respecting current experimental constraints.

Abstract Mixing of axion fields is widely used to generate EFTs with phenomenologically advantageous features, such as hierarchies between axion couplings to different gauge fields and/or large effective field ranges. While these features are strongly constrained by periodicity for models with only a single axion, mixing has been used in the literature (sometimes incorrectly) to try to evade some of these constraints. In this paper, we ask whether it is possible to use axion mixing to generate an EFT of axions that evades these constraints by flowing to a theory of a non-compact scalar in the IR. We conclude that as long as the light axion is exactly massless, it will inherit the periodicity and associated constraints of the UV theory. However, by giving the light axion a mass, we can relax these constraints with effects proportional to the axion mass squared, including non-quantized couplings and the realignment of monodromy to a light axion with a larger field range. To show this, we consider various examples of axions mixing with other axions or with non-compact scalar fields, and work in a basis where coupling quantization is manifest. This basis makes it clear that in the case where an axion is eaten through the Higgs or Stückelberg mechanism, the light axion does not have a large effective field range, in contrast to some recent claims in the literature. Additionally, we relate our results about axion EFTs to a well-known fact about gauge theory: that QFTs with compact gauge groups in the UV flow to QFTs with compact gauge groups in the IR, and make this correspondence precise in the 2+1 dimensional case.

In this short paper we present a so far unnoticed syntactic constraint on contrastive focus reduplication (CR), a phenomenon that restricts the semantics of the reduplicated element to a prototypical meaning (but has other uses as well): The CR construction cannot be modified. In the case of nominal CR, this means that adjectival modification is blocked, as in *black COFFEE-coffee. We present data from English and German to support our claim and highlight how earlier accounts fail to capture the syntactic restriction. We then provide a sketch of an analysis couched with a Cartographic framework, tentatively proposing that CR results either (i) from cyclical movement of phrasal material through the specifiers of functional projections into the specifier of a focus phrase or (ii) by directly moving phrasal material into the specifier of a focus phrase.

Abstract Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods for the purpose of classifying jets according to their electric charge. We find that both neural networks that incorporate distance within the jet as an input and boosted decision trees including radial distance information can provide significant improvement in jet charge extraction over current methods. Specifically, convolutional, recurrent, and recursive networks can provide the largest improvement over traditional methods, in part by effectively utilizing distance within the jet or clustering history. The advantages of using a fixed-size input representation (as with the CNN) or a small input representation (as with the RNN) suggest that both convolutional and recurrent networks will be essential to the future of modern machine learning at colliders.

It has been observed that cross-linguistically the core posture verbs ‘sit’/‘stand/‘lie’ can extend their meaning beyond the literal sense encoding posture or spatial orientation (see Newman 2002 for an overview). In the cognitive literature, the conceptual background of these extensions has been discussed, but up to now, there has been no discussion of the non-literal senses in the theoretical linguistic literature, including how the different senses are disambiguated. This paper supplements the cognitive descriptions of posture verbs, presenting data from an independent corpus study and proposing a formal analysis. The in-depth investigation of one English posture verb, ‘sit’, yields an empirical generalization that contributes to the discussion surrounding non-literal meaning.