First Search for the Associated Production of a Higgs Boson with a Single Top Quark

The production of the Higgs boson in association with a single top quark is sensitive to the relative sign of the coupling parameters describing its interaction with fermions and gauge bosons. The tHq production mode therefore provides an good handle on the Yukawa coupling Yt. The first searches for single-top + Higgs in the H>bbbar, gamma gamma, tau+tau- and W+W- decay channels are presented, using the full 8 TeV dataset recorded with the CMS detector. Special emphasis is put on the analyses' peculiarities and their dominating systematic uncertainties, and a combination of all individual channels is performed. The analyses are optimized for a scenario of Yt=-1, which is enhanced by a factor of 13 with respect to the Standard Model production rate. The observed combined upper exclusion limit is 2.8 times the cross section of this exotic scenario (2.0 expected).Comment: Preprint of the proceedings for the contribution to the LHCP2015 conference, St. Petersburg, Russi

Search for H→bbˉ\text{H}\to \text{b}\bar{\text{b}} in association with single top quarks as a test of Higgs couplings

The associated production of Higgs boson and single top quark is of particular interest since it is senstive to the relative sign of the Higgs boson coupling to gauge bosons and the Yukawa coupling $y$ to fermions. The presented analysis is setting upper production limits on a model with $y_\text{t}=-1$, which has an enhanced cross section compared to the standard model expectation. For this it focusses on the Higgs boson decaying to a pair of b quarks and uses the full dataset of $pp$ collisions recorded with the CMS detector in 2012. It reports an upper limit on 7.57 times the expected cross section, with an expected sensitivity of 5.14. This translates into the exclusion of associated tHq production with $y_\text{t}=-1$-like characteristics with a cross section smaller than 1.77\,pb

tWH associated production at the LHC

We study Higgs boson production in association with a top quark and a $W$ boson at the LHC. At NLO in QCD, $tWH$ interferes with $t\bar t H$ and a procedure to meaningfully separate the two processes needs to be employed. In order to define $tWH$ production for both total rates and differential distributions, we consider the diagram removal and diagram subtraction techniques that have been previously proposed for treating intermediate resonances at NLO, in particular in the context of $tW$ production. These techniques feature approximations that need to be carefully taken into account when theoretical predictions are compared to experimental measurements. To this aim, we first critically revisit the $tW$ process, for which an extensive literature exists and where an analogous interference with $t \bar t$ production takes place. We then provide robust results for total and differential cross sections for $tW$ and $tWH$ at 13 TeV, also matching short-distance events to a parton shower. We formulate a reliable prescription to estimate the theoretical uncertainties, including those associated to the very definition of the process at NLO. Finally, we study the sensitivity to a non-Standard-Model relative phase between the Higgs couplings to the top quark and to the $W$ boson in $tWH$ production.Comment: v3: expanded some discussions in the text, improved some plots (results unchanged

Search for the associated production of a single top quark and a Higgs boson in the H to bbbar decay channel at 8 and 13 TeV with the CMS experiment

A search for the associated production of a single top quark and a Higgs boson is performed with 8 and 13 TeV data recorded with the CMS detector. Upper limits on the allowed cross sections are quoted

Predicting Properties of Oxide Glasses Using Informed Neural Networks

Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young's modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models.Comment: 25 page

Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models

Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose RS3L, a novel simulation-based SSL strategy that employs a method of re-simulation to drive data augmentation for contrastive learning. By intervening in the middle of the simulation process and re-running simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how R3SL pre-training enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.Comment: 24 pages, 9 figure

Autoencoders for real-time SUEP detection

Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns (SUEP), at the Large Hadron Collider: the production of dark quarks in proton–proton collisions leading to a dark shower and the high-multiplicity production of dark hadrons. The final experimental signature is spherically symmetric energy deposits by an anomalously large number of soft Standard Model particles with a transverse energy of O(100) MeV. Assuming Yukawa-like couplings of the scalar portal state, the dominant production mode is gluon fusion, and the dominant background comes from multi-jet QCD events. We have developed a deep learning-based Anomaly Detection technique to reject QCD jets and identify any anomalous signature, including SUEP, in real-time in the High-Level Trigger system of experiments like the Compact Muon Solenoid at the Large Hadron Collider. A deep convolutional neural autoencoder network has been trained using QCD events by taking transverse energy deposits in the inner tracker, electromagnetic calorimeter, and hadron calorimeter sub-detectors as 3-channel image data. Due to the sparse nature of the data, only ∼0.5% of the total ∼ 300 k image pixels have nonzero values. To tackle this challenge, a nonstandard loss function, the inverse of the so-called Dice Loss, is exploited. The trained autoencoder with learned spatial features of QCD jets can detect 40% of the SUEP events, with a QCD event mistagging rate as low as 2%. The model inference time has been measured using the Intel® Core TM i5-9600KF processor and found to be ∼ 20ms, which perfectly satisfies the High-Level Trigger system’s latency of O(10$^2$ ) ms. Given the virtue of the unsupervised learning of the autoencoders, the trained model can be applied to any new physics model that predicts an experimental signature anomalous to QCD jets

Triggering Dark Showers with Conditional Dual Auto-Encoders

Auto-encoders (AEs) have the potential to be effective and generic tools for new physics searches at colliders, requiring little to no model-dependent assumptions. New hypothetical physics signals can be considered anomalies that deviate from the well-known background processes generally expected to describe the whole dataset. We present a search formulated as an anomaly detection (AD) problem, using an AE to define a criterion to decide about the physics nature of an event. In this work, we perform an AD search for manifestations of a dark version of strong force using raw detector images, which are large and very sparse, without leveraging any physics-based pre-processing or assumption on the signals. We propose a dual-encoder design which can learn a compact latent space through conditioning. In the context of multiple AD metrics, we present a clear improvement over competitive baselines and prior approaches. It is the first time that an AE is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as a performant, model-independent algorithm to deploy, e.g., in the trigger stage of LHC experiments such as ATLAS and CMS.Comment: 25 pages, 7 figures, and 11 table

Global DNA hypomethylation prevents consolidation of differentiation programs and allows reversion to the embryonic stem cell state.

DNA methylation patterns change dynamically during mammalian development and lineage specification, yet scarce information is available about how DNA methylation affects gene expression profiles upon differentiation. Here we determine genome-wide transcription profiles during undirected differentiation of severely hypomethylated (Dnmt1⁻/⁻) embryonic stem cells (ESCs) as well as ESCs completely devoid of DNA methylation (Dnmt1⁻/⁻;Dnmt3a⁻/⁻;Dnmt3b⁻/⁻ or TKO) and assay their potential to transit in and out of the ESC state. We find that the expression of only few genes mainly associated with germ line function and the X chromosome is affected in undifferentiated TKO ESCs. Upon initial differentiation as embryoid bodies (EBs) wild type, Dnmt1⁻/⁻ and TKO cells downregulate pluripotency associated genes and upregulate lineage specific genes, but their transcription profiles progressively diverge upon prolonged EB culture. While Oct4 protein levels are completely and homogeneously suppressed, transcription of Oct4 and Nanog is not completely silenced even at late stages in both Dnmt1⁻/⁻ and TKO EBs. Despite late wild type and Dnmt1⁻/⁻ EBs showing a much higher degree of concordant expression, after EB dissociation and replating under pluripotency promoting conditions both Dnmt1⁻/⁻ and TKO cells, but not wild type cells rapidly revert to expression profiles typical of undifferentiated ESCs. Thus, while DNA methylation seems not to be critical for initial activation of differentiation programs, it is crucial for permanent restriction of developmental fate during differentiation