SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work

The LHC Phenomenology of Vectorlike Confinement

We investigate in detail the LHC phenomenology of "vectorlike confinement", where the Standard Model is augmented by a new confining gauge interaction and new light fermions that carry vectorlike charges under both the Standard Model and the new gauge group. If the new interaction confines at the TeV scale, this framework gives rise to a wide range of exotic collider signatures such as the production of a vector resonance that decays to a pair of collider-stable charged massive particles (a "di-CHAMP" resonance), to a pair of collider-stable massive colored particles (a "di-R-hadron resonance), to multiple photons, $W$s and $Z$s via two intermediate scalars, and/or to multi-jet final states. To study these signals at the LHC, we set up two benchmark models: one for the di-CHAMP and multi-photon signals, and the other for the di-R-hadron and multijet signals. For the di-CHAMP/multi-photon model, Standard Model backgrounds are negligible, and we show that a full reconstruction of the spectrum is possible, providing powerful evidence for vectorlike confinement. For the di-R-hadron/multijet model, we point out that in addition to the di-R-hadron signal, the rate of the production of four R-hadrons can also be sizable at the LHC. This, together with the multi-jet signals studied in earlier work, makes it possible to single out vectorlike confinement as the underlying dynamics.Comment: 32 pages, 28 figures. Several typos fixed, one paragraph added elaborating choice of benchmarks. Version accepted by JHEP

Cosmological Phase Transitions and their Properties in the NMSSM

We study cosmological phase transitions in the Next-to-Minimal Supersymmetric Standard Model (NMSSM) in light of the Higgs discovery. We use an effective field theory approach to calculate the finite temperature effective potential, focusing on regions with significant tree-level contributions to the Higgs mass, a viable neutralino dark matter candidate, 1-2 TeV stops, and with the remaining particle spectrum compatible with current LHC searches and results. The phase transition structure in viable regions of parameter space exhibits a rich phenomenology, potentially giving rise to one- or two-step first-order phase transitions in the singlet and/or $SU(2)$ directions. We compute several parameters pertaining to the bubble wall profile, including the bubble wall width and $\Delta\beta$ (the variation of the ratio in Higgs vacuum expectation values across the wall). These quantities can vary significantly across small regions of parameter space and can be promising for successful electroweak baryogenesis. We estimate the wall velocity microphysically, taking into account the various sources of friction acting on the expanding bubble wall. Ultra-relativistic solutions to the bubble wall equations of motion typically exist when the electroweak phase transition features substantial supercooling. For somewhat weaker transitions, the bubble wall instead tends to be sub-luminal and, in fact, likely sub-sonic, suggesting that successful electroweak baryogenesis may indeed occur in regions of the NMSSM compatible with the Higgs discovery.Comment: 49 pages + 2 appendices, 6 figures. v2: Minor corrections; matches version published in JHE

Occlusion resistant learning of intuitive physics from videos

To reach human performance on complex tasks, a key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation. This ability, often referred to as intuitive physics, has recently received attention and several methods were proposed to learn these physical rules from video sequences. Yet, most of these methods are restricted to the case where no, or only limited, occlusions occur. In this work we propose a probabilistic formulation of learning intuitive physics in 3D scenes with significant inter-object occlusions. In our formulation, object positions are modeled as latent variables enabling the reconstruction of the scene. We then propose a series of approximations that make this problem tractable. Object proposals are linked across frames using a combination of a recurrent interaction network, modeling the physics in object space, and a compositional renderer, modeling the way in which objects project onto pixel space. We demonstrate significant improvements over state-of-the-art in the intuitive physics benchmark of IntPhys. We apply our method to a second dataset with increasing levels of occlusions, showing it realistically predicts segmentation masks up to 30 frames in the future. Finally, we also show results on predicting motion of objects in real videos

Rethinking benchmark dates in international relations

International Relations has an ‘orthodox set’ of benchmark dates by which much of its research and teaching is organized: 1500, 1648, 1919, 1945 and 1989. This article argues that International Relations scholars need to question the ways in which these orthodox dates serve as internal and external points of reference, think more critically about how benchmark dates are established, and generate a revised set of benchmark dates that better reflects macro-historical international dynamics. The first part of the article questions the appropriateness of the orthodox set of benchmark dates as ways of framing the discipline’s self-understanding. The second and third sections look at what counts as a benchmark date, and why. We systematize benchmark dates drawn from mainstream International Relations theories (realism, liberalism, constructivism/English School and sociological approaches) and then aggregate their criteria. The fourth section of the article uses this exercise to construct a revised set of benchmark dates which can widen the discipline’s theoretical and historical scope. We outline a way of ranking benchmark dates and suggest a means of assessing recent candidates for benchmark status. Overall, the article delivers two main benefits: first, an improved heuristic by which to think critically about foundational dates in the discipline; and, second, a revised set of benchmark dates which can help shift International Relations’ centre of gravity away from dynamics of war and peace, and towards a broader range of macro-historical dynamics

Fermion Dark Matter with Scalar Triplet at Direct and Collider Searches

Fermion dark matter (DM) as an admixture of additional singlet and doublet vector like fermions provides an attractive and allowed framework by relic density and direct search constraints within TeV scale, although limited by its discovery potential at the Large Hadron Collider (LHC). An extension of the model with scalar triplet can yield neutrino masses and provide some cushion to the direct search constraint of the DM through pseudo-Dirac mass splitting. This in turn, allow the model to live in a larger region of the parameter space and open the door for detection at LHC, even if slightly. The model however can see an early discovery at International Linear Collider (ILC) without too much of fine-tuning. The complementarity of LHC, ILC and direct search prospect of this framework is studied in this paper.Comment: 55 pages, 28 figures, version accepted in PR