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Proton-proton collisions at the LHC generate a high-intensity collimated beam of neutrinos in the forward (beam) direction, characterised by energies of up to several TeV. The recent observation of LHC neutrinos by FASER$$\nu $$$\nu$and SND@LHC signifies that this previously overlooked particle beam is now available for scientific investigation. Here we quantify the impact that neutrino deep-inelastic scattering (DIS) measurements at the LHC would have on the parton distributions (PDFs) of protons and heavy nuclei. We generate projections for DIS structure functions for FASER$$\nu $$$\nu$and SND@LHC at Run III, as well as for the FASER$$\nu $$$\nu$2, AdvSND, and FLArE experiments to be hosted at the proposed Forward Physics Facility (FPF) operating concurrently with the High-Luminosity LHC (HL-LHC). We determine that up to one million electron-neutrino and muon-neutrino DIS interactions within detector acceptance can be expected by the end of the HL-LHC, covering a kinematic region inxand$$Q^2$$$Q^2$overlapping with that of the Electron-Ion Collider. Including these DIS projections in global (n)PDF analyses, specifically PDF4LHC21, NNPDF4.0, and EPPS21, reveals a significant reduction in PDF uncertainties, in particular for strangeness and the up and down valence PDFs. We show that LHC neutrino data enable improved theoretical predictions for core processes at the HL-LHC, such as Higgs and weak gauge boson production. Our analysis demonstrates that exploiting the LHC neutrino beam effectively provides CERN with a “Neutrino-Ion Collider” without requiring modifications in its accelerator infrastructure.

 

The quantum chemistry community has developed analytic forces for approximate electronic excited states to enable walking on excited state potential energy surfaces (PES). One can thereby computationally characterize excited state minima and saddle points. Always implicit in using this machinery is the fact that an excited state PES only exists within the realm of the Born-Oppenheimer approximation, where the nuclear and electronic degrees of freedom separate. This work demonstrates through ab initio calculations and simple nonadiabatic dynamics that some excited state minimum structures are fantastical: they appear to exist as stable configurations only as a consequence of the PES construct, rather than being physically observable. Each fantastical structure exhibits an unphysically high predicted harmonic frequency and associated force constant. This fact can serve as a valuable diagnostic of when an optimized excited state structure is non-observable. The origin of this phenomenon can be attributed to the coupling between different electronic states. As PESs approach one another, the upper surface can form a minimum that is very close to a near-touching point. The force constant, evaluated at this minimum, relates to the strength of the electronic coupling rather than to any characteristic excited state vibration. Nonadiabatic dynamics results using a Landau-Zener model illustrate that fantastical excited state structures have extremely short lifetimes on the order of a few femtoseconds. Their appearance in a calculation signals the presence of a nearby conical intersection through which the system will rapidly cross to a lower surface.

 

Despite the ubiquitous presence of tactile actuators (tactors) in mobile devices, there is a continuing need for more advanced tactors that can cover the entire frequency range of human tactile perception. Broadband tactors can increase information transmission and enrich sensory experience. The engineering challenges are multifold in that the ideal tactors should exhibit an effective bandwidth of at least 300 Hz, small form factor, robustness, power efficiency and low cost. For wearable applications, there are the additional challenges of ease of mounting and maintaining adequate skin contact during body movements. We propose an approach to interleave narrowband tactile stimuli to achieve broadband effects, taking advantage of the limited spatial resolution of the skin on the torso and limbs. Three psychophysical experiments were conducted to assess the validity of this approach. Participants performed pairwise discriminations of two broadband stimuli delivered using one or two tactors. The broadband stimuli consisted of one mid-frequency and one high-frequency component delivered through one tactor by mixing the two components, or through two tactors (one component per tactor). The first two experiments revealed extraneous cues such as localization and mutual masking of mid- and high-frequency components that were subsequently eliminated in the third experiment. Results from 12 participants confirmed that performance on pairwise comparisons was below the discrimination threshold, confirming that broadband haptic effects can be achieved through narrowband tactors placed within the skin’s two-point limen. 

Accurately predicting the ridership of public-transit routes provides substantial benefits to both transit agencies, who can dispatch additional vehicles proactively before the vehicles that serve a route become crowded, and to passengers, who can avoid crowded vehicles based on publicly available predictions. The spread of the coronavirus disease has further elevated the importance of ridership prediction as crowded vehicles now present not only an inconvenience but also a public-health risk. At the same time, accurately predicting ridership has become more challenging due to evolving ridership patterns, which may make all data except for the most recent records stale. One promising approach for improving prediction accuracy is to fine-tune the hyper-parameters of machine-learning models for each transit route based on the characteristics of the particular route, such as the number of records. However, manually designing a machine-learning model for each route is a labor-intensive process, which may require experts to spend a significant amount of their valuable time. To help experts with designing machine-learning models, we propose a neural-architecture and feature search approach, which optimizes the architecture and features of a deep neural network for predicting the ridership of a public-transit route. Our approach is based on a randomized local hyper-parameter search, which minimizes both prediction error as well as the complexity of the model. We evaluate our approach on real-world ridership data provided by the public transit agency of Chattanooga, TN, and we demonstrate that training neural networks whose architectures and features are optimized for each route provides significantly better performance than training neural networks whose architectures and features are generic. 

Current wearable haptic display technology is limited by the lack of broadband tactors capable of delivering rich haptic effects across the entire perceptible frequency range. Audio speakers are often used in laboratory studies as broadband tactors, but it is difficult to attach them to skin and maintain contact during movement. Commercially-available narrowband tactors are small, low in cost and power efficient. We investigate the idea of interleaving narrowband tactile stimuli to achieve broadband effects. Twelve participants performed pairwise discrimination of two stimulus alternatives using two broadband tactors. One alternative was a broadband vibration composed of the sum of a mid- and a high-frequency vibration, delivered by a single tactor. The other alternative consisted of the mid-frequency component delivered by one tactor and the high-frequency by the other. The upper arm was chosen for stimulation because the tactors can be placed within the two-point limen of the skin. The sensitivity index results were significantly below 1.0, the criterion for discrimination threshold, thereby confirming that broadband haptic effects can be achieved by placing narrowband tactors with mid and high resonant frequencies within the skin’s spatial resolution. We provide guidelines and examples of applying our findings to the design of wearable haptic displays. 

Public transit agencies struggle to maintain transit accessibility with reduced resources, unreliable ridership data, reduced vehicle capacities due to social distancing, and reduced services due to driver unavailability. In collaboration with transit agencies from two large metropolitan areas in the USA, we are designing novel approaches for addressing the afore-mentioned challenges by collecting accurate real-time ridership data, providing guidance to commuters, and performing operational optimization for public transit. We estimate rider-ship data using historical automated passenger counting data, conditional on a set of relevant determinants. Accurate ridership forecasting is essential to optimize the public transit schedule, which is necessary to improve current fixed lines with on-demand transit. Also, passenger crowding has been a problem for public transportation since it deteriorates passengers’ wellbeing and satisfaction. During the COVID-19 pandemic, passenger crowding has gained importance since it represents a risk for social distancing violations. Therefore, we are creating optimization models to ensure that social distancing norms can be adequately followed while ensuring that the total demand for transit is met. We will then use accurate forecasts for operational optimization that includes (a) proactive fixed-line schedule optimization based on predicted demand, (b) dispatch of on-demand micro-transit, prioritizing at-risk populations, and (c) allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). Finally, this paper presents some initial results from our project regarding the estimation of ridership in public transit.