Optimisation of the SHiP Beam Dump Facility with generative surrogate models

The SHiP experiment is a proposed fixed target experiment at the CERN SPS to search for new particles. To operate optimally, the experiment should feature a zero background environment. The residual muons flying from the target are one of the largest sources of the background. To remove them from the detector acceptance, a dedicated muon shield magnet is introduced in the experiment. The shield should be optimised to deliver the best physics performance at the lowest cost. The optimisation procedure is very computationally costly and, thus, requires ded- icated methods. This thesis comprises of a detailed description of a new machine learning method for the optimisation, comparisons to existing techniques, and the application of the method to optimising the muon shield magnet. In addition, the set of technological and simulation problems affecting the optimisation is discussed in details. Finally, the set of requirements for the muon shield prototype design and verification is presented.Open Acces

Graph Neural Networks for Link Prediction with Subgraph Sketching

Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to count triangles (the backbone of most LP heuristics) and because they can not distinguish automorphic nodes (those having identical structural roles). Both expressiveness issues can be alleviated by learning link (rather than node) representations and incorporating structural features such as triangle counts. Since explicit link representations are often prohibitively expensive, recent works resorted to subgraph-based methods, which have achieved state-of-the-art performance for LP, but suffer from poor efficiency due to high levels of redundancy between subgraphs. We analyze the components of subgraph GNN (SGNN) methods for link prediction. Based on our analysis, we propose a novel full-graph GNN called ELPH (Efficient Link Prediction with Hashing) that passes subgraph sketches as messages to approximate the key components of SGNNs without explicit subgraph construction. ELPH is provably more expressive than Message Passing GNNs (MPNNs). It outperforms existing SGNN models on many standard LP benchmarks while being orders of magnitude faster. However, it shares the common GNN limitation that it is only efficient when the dataset fits in GPU memory. Accordingly, we develop a highly scalable model, called BUDDY, which uses feature precomputation to circumvent this limitation without sacrificing predictive performance. Our experiments show that BUDDY also outperforms SGNNs on standard LP benchmarks while being highly scalable and faster than ELPH.Comment: 29 pages, 19 figures, 6 appendice

Search for New Physics with the SHiP experiment at CERN

The SHiP Collaboration has proposed a general-purpose experimental facility operating in beam dump mode at the CERN SPS accelerator with the aim of searching for light, long-lived exotic particles. The detector system aims at measuring the visible decays of hidden sector particles to both fully reconstructible final states and to partially reconstructible final states with neutrinos, in a nearly background free environment. In addition to that, it can detect light dark matter via its scattering and study tau neutrino physics. Using a high-intensity beam of 400 GeV protons, the experiment is capable of integrating $2 \times 10^{20}$ protons in five years, which allows probing dark photons, dark scalars, axion-like particles and heavy neutral leptons with GeV-scale masses at sensitivities that exceed by orders of magnitude those of existing and projected experiments. The sensitivity to heavy neutrinos will allow for the first time to probe, in the mass range between the kaon and the charm meson mass, a coupling range for which baryogenesis and the magnitude of the active neutrino masses can be explained. The sensitivity to light dark matter reaches well below the elastic scalar dark matter relic density limits in the range from a few MeV/c$^2$ up to 200 MeV/c$^2$

Geometric Analysis of Sun-Assisted Lunar Transfer Trajectories in the Planar Bicircular Four-Body Model

This research presents a geometric analysis of Sun-assisted low-energy lunar transfers and several convenient tools that enable the systematic trajectory design in the framework of the planar bicircular restricted four-body problem. By analogy with the patched conic approximation approach for high-energy transfers, a Sun-assisted low-energy trajectory is divided into three legs. Two interior legs, departing and arriving, are located inside the Earth–Moon region of prevalence and designed in the Earth–Moon circular restricted three-body problem, whereas the exterior leg lies outside the region of prevalence and is calculated in the Earth–Moon–Sun bicircular restricted four-body model. The whole trajectory is obtained by smoothly patching the three legs on the boundary of the region of prevalence. The arrival conditions are met by targeting a specific point in the L2 lunar gateway. The interior legs are easily adjustable to the four-body dynamics. The database of planar lunar transfer trajectories can be used to select an initial guess for the multiple-shooting procedure of designing a three-dimensional Sun-assisted lunar transfer in high-fidelity dynamical models

Measurement of the muon flux for the SHiP experiment

We report the results of the measurement of the muon flux emanating from the SHiP target at the CERN SPS. A replica of the SHiP target followed by a $2.4~\rm{m}$ iron hadron absorber was installed in the H4 400 GeV/c proton beamline. To measure the momentum spectrum, a spectrometer consisting of drift tubes and resistive plate chambers (RPCs) was placed around the Goliath magnet. During a three week period a dataset for analysis corresponding to $3.27 \times 10^{11}$ protons on target (POT) was recorded. This amounts to approximatively $1\%$ of a SHiP spill. The amount of accumulated data allows us to make a validation of the results from our Pythia and Geant4 based Monte Carlo (FairShip)