Pileup Mitigation with Machine Learning (PUMML)

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.Comment: 20 pages, 8 figures, 2 tables. Updated to JHEP versio

Learning to Classify from Impure Samples with High-Dimensional Data

A persistent challenge in practical classification tasks is that labeled training sets are not always available. In particle physics, this challenge is surmounted by the use of simulations. These simulations accurately reproduce most features of data, but cannot be trusted to capture all of the complex correlations exploitable by modern machine learning methods. Recent work in weakly supervised learning has shown that simple, low-dimensional classifiers can be trained using only the impure mixtures present in data. Here, we demonstrate that complex, high-dimensional classifiers can also be trained on impure mixtures using weak supervision techniques, with performance comparable to what could be achieved with pure samples. Using weak supervision will therefore allow us to avoid relying exclusively on simulations for high-dimensional classification. This work opens the door to a new regime whereby complex models are trained directly on data, providing direct access to probe the underlying physics.Comment: 6 pages, 2 tables, 2 figures. v2: updated to match PRD versio

ABCDisCo: Automating the ABCD Method with Machine Learning

The ABCD method is one of the most widely used data-driven background estimation techniques in high energy physics. Cuts on two statistically-independent classifiers separate signal and background into four regions, so that background in the signal region can be estimated simply using the other three control regions. Typically, the independent classifiers are chosen "by hand" to be intuitive and physically motivated variables. Here, we explore the possibility of automating the design of one or both of these classifiers using machine learning. We show how to use state-of-the-art decorrelation methods to construct powerful yet independent discriminators. Along the way, we uncover a previously unappreciated aspect of the ABCD method: its accuracy hinges on having low signal contamination in control regions not just overall, but relative to the signal fraction in the signal region. We demonstrate the method with three examples: a simple model consisting of three-dimensional Gaussians; boosted hadronic top jet tagging; and a recasted search for paired dijet resonances. In all cases, automating the ABCD method with machine learning significantly improves performance in terms of ABCD closure, background rejection and signal contamination.Comment: 37 pages, 12 figure

Tumor‐stroma interactions differentially alter drug sensitivity based on the origin of stromal cells

Due to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear, however, what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor microenvironment. Using a large‐scale coculture assay optimized to measure drug‐induced cell death, we identify tumor–stroma interactions that modulate drug sensitivity. Our data show that the chemo‐insensitivity typically associated with aggressive subtypes of breast cancer is not observed if these cells are grown in 2D or 3D monoculture, but is manifested when these cells are cocultured with stromal cells, such as fibroblasts. Furthermore, we find that fibroblasts influence drug responses in two distinct and divergent manners, associated with the tissue from which the fibroblasts were harvested. These divergent phenotypes occur regardless of the drug tested and result from modulation of apoptotic priming within tumor cells. Our study highlights unexpected diversity in tumor–stroma interactions, and we reveal new principles that dictate how fibroblasts alter tumor drug responses

Phase 1b safety study of farletuzumab, carboplatin and pegylated liposomal doxorubicin in patients with platinum-sensitive epithelial ovarian cancer

Farletuzumab is a humanized monoclonal antibody that binds to folate receptor alpha, over-expressed in epithelial ovarian cancer (EOC) but largely absent in normal tissue. Previously, carboplatin plus pegylated liposomal doxorubicin showed superior progression-free survival and an improved therapeutic index compared with carboplatin/paclitaxel in relapsed platinum-sensitive EOC. This study assessed safety of farletuzumab/carboplatin/pegylated liposomal doxorubicin in women with platinum-sensitive recurrent EOC

Phylogenetically independent behavior mediating geographic distributions suggests habitat is a strong driver of phenotype in crangonyctid amphipods

It is unclear if geographic distributions of animals are behaviorally mediated or simply maintained by ecologically-driven deleterious effects on fitness. Furthermore, it is not well known how behaviors that may affect geographic distributions and responses to environmental stressors evolve. To explore this, we examined behavioral and physiological reactions to light in six species of amphipods in the family Crangonyctidae collected from a variety of subterranean and epigean habitats. Stark differences between epigean and subterranean habitats occupied by different crangonyctid species allowed this clade to serve as an appropriate model system for studying the link between habitat and phenotype. We sampled habitats in or adjacent to the Edwards Aquifer in central Texas and collected two epigean and four stygobiontic species. We examined respiratory and behavioral responses to light in all study species. We found that similarities in behavioral and physiological responses to light between species were only weakly correlated with genetic relatedness but were correlated with habitat type. However, the breadth of variation in phenotype was found to be correlated with phylogenetic relationships, suggesting that population level trait evolution likely involves interactions between standing population level variation and strength of selection. Our findings suggest that natural selection via environmental conditions may outweigh history of common ancestry when predicting phenotypic similarities among species, and that behavioral and physiological phenotypes may mediate the evolution of biogeographic distributions

Bubbles from Nothing

Within the framework of flux compactifications, we construct an instanton describing the quantum creation of an open universe from nothing. The solution has many features in common with the smooth 6d bubble of nothing solutions discussed recently, where the spacetime is described by a 4d compactification of a 6d Einstein-Maxwell theory on S^2 stabilized by flux. The four-dimensional description of this instanton reduces to that of Hawking and Turok. The choice of parameters uniquely determines all future evolution, which we additionally find to be stable against bubble of nothing instabilities.Comment: 19 pages, 6 figure

Decay of flux vacua to nothing

We construct instanton solutions describing the decay of flux compactifications of a $6d$ gauge theory by generalizing the Kaluza-Klein bubble of nothing. The surface of the bubble is described by a smooth magnetically charged solitonic brane whose asymptotic flux is precisely that responsible for stabilizing the 4d compactification. We describe several instances of bubble geometries for the various vacua occurring in a $6d$ Einstein-Maxwell theory namely, AdS_4 x S^2, R^{1,3} x S^2, and dS_4 x S^2. Unlike conventional solutions, the bubbles of nothing introduced here occur where a {\em two}-sphere compactification manifold homogeneously degenerates.Comment: 31 pages, 15 figure