Data driven background estimation in HEP using Generative Adversarial Networks

Data-driven methods are widely used to overcome shortcomings of Monte Carlo simulations (lack of statistics, mismodeling of processes, etc.) in experimental high energy physics. A precise description of background processes is crucial to reach the optimal sensitivity for a measurement. However, the selection of the control region used to describe the background process in a region of interest biases the distribution of some physics observables, rendering the use of such observables impossible in a physics analysis. Rather than discarding these events and/or observables, we propose a novel method to generate physics objects compatible with the region of interest and properly describing the correlations with the rest of the event properties. We use a generative adversarial network (GAN) for this task, as GANs are among the best generator models for various applications. We illustrate the method by generating a new misidentified photon for the $\gamma + \mathrm{jets}$ background of the $\mathrm{H}\to\gamma\gamma$ analysis at the CERN LHC, and demonstrate that this GAN generator is able to produce a coherent object correlated with the different properties of the rest of the event

Reconstruction of electromagnetic showers in calorimeters using Deep Learning

The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or Atlas experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used to reconstruct the energy and position of these particles from the showers they induce in the electromagnetic calorimeter. Despite their accuracy and efficiency, these methods still suffer from several limitations, such as low-energy background and limited capacity to reconstruct close-by particles. This paper introduces an innovative machine-learning technique to measure the energy and position of photons and electrons based on convolutional and graph neural networks, taking the geometry of the CMS electromagnetic calorimeter as an example. The developed network demonstrates a significant improvement in resolution both for photon energy and position predictions compared to the algorithm used in CMS. Notably, one of the main advantages of this new approach is its ability to better distinguish between multiple close-by electromagnetic showers

ISAR imaging Based on the Empirical Mode Decomposition Time-Frequency Representation

International audienceThis work proposes an adaptation of the Empirical Mode Decomposition Time-Frequency Distribution (EMD-TFD) to non-analytic complex-valued signals. Then, the modified version of EMD-TFD is used in the formation of Inverse Synthetic Aperture Radar (ISAR) image. This new method, referred to as NSBEMD-TFD, is obtained by extending the Non uniformly Sampled Bivariate Empirical Mode Decomposition (NSBEMD) to design a filter in the ambiguity domain and to clean the Time-Frequency Distribution (TFD) of signal. The effectiveness of the proposed scheme of ISAR formation is illustrated on synthetic and real signals. The results of our proposed methods are compared to other Time-Frequency Representation (TFR) such as Spectrogram, Wigner-Ville Distribution (WVD), Smoothed Pseudo Wigner-Ville Distribution (SPWVD) or others methods based on EMD

A Proposal for Non-Intrusive Namespaces in OCaml

International audienceWe present a work-in-progress about adding namespaces to OCaml. Inspired by other lan-guages such as Scala or C++, our aim is to de-sign and formalize a simple and non-intrusive namespace mechanism without complexifying the core language. Namespaces in our ap-proach are a simple way to define libraries while avoiding name clashes. They are also meant to simplify the build process, clarify-ing and reducing (to zero whenever possible) the responsibility of external tools

MoccaDB - an integrative database for functional, comparative and diversity studies in the Rubiaceae family

<p>Abstract</p> <p>Background</p> <p>In the past few years, functional genomics information has been rapidly accumulating on Rubiaceae species and especially on those belonging to the <it>Coffea </it>genus (coffee trees). An increasing number of expressed sequence tag (EST) data and EST- or genomic-derived microsatellite markers have been generated, together with Conserved Ortholog Set (COS) markers. This considerably facilitates comparative genomics or map-based genetic studies through the common use of orthologous loci across different species. Similar genomic information is available for e.g. tomato or potato, members of the Solanaceae family. Since both Rubiaceae and Solanaceae belong to the Euasterids I (lamiids) integration of information on genetic markers would be possible and lead to more efficient analyses and discovery of key loci involved in important traits such as fruit development, quality, and maturation, or adaptation. Our goal was to develop a comprehensive web data source for integrated information on validated orthologous markers in Rubiaceae.</p> <p>Description</p> <p>MoccaDB is an online MySQL-PHP driven relational database that houses annotated and/or mapped microsatellite markers in Rubiaceae. In its current release, the database stores 638 markers that have been defined on 259 ESTs and 379 genomic sequences. Marker information was retrieved from 11 published works, and completed with original data on 132 microsatellite markers validated in our laboratory. DNA sequences were derived from three <it>Coffea </it>species/hybrids. Microsatellite markers were checked for similarity, <it>in vitro </it>tested for cross-amplification and diversity/polymorphism status in up to 38 Rubiaceae species belonging to the Cinchonoideae and Rubioideae subfamilies. Functional annotation was provided and some markers associated with described metabolic pathways were also integrated. Users can search the database for marker, sequence, map or diversity information through multi-option query forms. The retrieved data can be browsed and downloaded, along with protocols used, using a standard web browser. MoccaDB also integrates bioinformatics tools (CMap viewer and local BLAST) and hyperlinks to related external data sources (NCBI GenBank and PubMed, SOL Genomic Network database).</p> <p>Conclusion</p> <p>We believe that MoccaDB will be extremely useful for all researchers working in the areas of comparative and functional genomics and molecular evolution, in general, and population analysis and association mapping of Rubiaceae and Solanaceae species, in particular.</p

A Mouse Model for Chikungunya: Young Age and Inefficient Type-I Interferon Signaling Are Risk Factors for Severe Disease

Chikungunya virus (CHIKV) is a re-emerging arbovirus responsible for a massive outbreak currently afflicting the Indian Ocean region and India. Infection from CHIKV typically induces a mild disease in humans, characterized by fever, myalgia, arthralgia, and rash. Cases of severe CHIKV infection involving the central nervous system (CNS) have recently been described in neonates as well as in adults with underlying conditions. The pathophysiology of CHIKV infection and the basis for disease severity are unknown. To address these critical issues, we have developed an animal model of CHIKV infection. We show here that whereas wild type (WT) adult mice are resistant to CHIKV infection, WT mouse neonates are susceptible and neonatal disease severity is age-dependent. Adult mice with a partially (IFN-α/βR+/−) or totally (IFN-α/βR−/−) abrogated type-I IFN pathway develop a mild or severe infection, respectively. In mice with a mild infection, after a burst of viral replication in the liver, CHIKV primarily targets muscle, joint, and skin fibroblasts, a cell and tissue tropism similar to that observed in biopsy samples of CHIKV-infected humans. In case of severe infections, CHIKV also disseminates to other tissues including the CNS, where it specifically targets the choroid plexuses and the leptomeninges. Together, these data indicate that CHIKV-associated symptoms match viral tissue and cell tropisms, and demonstrate that the fibroblast is a predominant target cell of CHIKV. These data also identify the neonatal phase and inefficient type-I IFN signaling as risk factors for severe CHIKV-associated disease. The development of a permissive small animal model will expedite the testing of future vaccines and therapeutic candidates

Chikungunya Virus Infection

Chikungunya virus (CHIKV) is an alphavirus transmitted by mosquitoes, mostly Aedes aegypti and Aedes albopictus. After half a century of focal outbreaks of acute febrile polyarthralgia in Africa and Asia, the disease unexpectedly spread in the past decade with large outbreaks in Africa and around the Indian Ocean and rare autochthonous transmission in temperate areas. This emergence brought new insights on its pathogenesis, notably the role of the A226V mutation that improved CHIKV fitness in Ae. albopictus and the possible CHIKV persistence in deep tissue sanctuaries for months after infection. Massive outbreaks also revealed new aspects of the acute stage: the high number of symptomatic cases, unexpected complications, mother-to-child transmission, and low lethality in debilitated patients. The follow-up of patients in epidemic areas has identified frequent, long-lasting, rheumatic disorders, including rare inflammatory joint destruction, and common chronic mood changes associated with quality-of-life impairment. Thus, the globalization of CHIKV exposes countries with Aedes mosquitoes both to brutal outbreaks of acute incapacitating episodes and endemic long-lasting disorders

Type I IFN controls chikungunya virus via its action on nonhematopoietic cells

Chikungunya virus (CHIKV) is the causative agent of an outbreak that began in La Réunion in 2005 and remains a major public health concern in India, Southeast Asia, and southern Europe. CHIKV is transmitted to humans by mosquitoes and the associated disease is characterized by fever, myalgia, arthralgia, and rash. As viral load in infected patients declines before the appearance of neutralizing antibodies, we studied the role of type I interferon (IFN) in CHIKV pathogenesis. Based on human studies and mouse experimentation, we show that CHIKV does not directly stimulate type I IFN production in immune cells. Instead, infected nonhematopoietic cells sense viral RNA in a Cardif-dependent manner and participate in the control of infection through their production of type I IFNs. Although the Cardif signaling pathway contributes to the immune response, we also find evidence for a MyD88-dependent sensor that is critical for preventing viral dissemination. Moreover, we demonstrate that IFN-α/β receptor (IFNAR) expression is required in the periphery but not on immune cells, as IFNAR−/−→WT bone marrow chimeras are capable of clearing the infection, whereas WT→IFNAR−/− chimeras succumb. This study defines an essential role for type I IFN, produced via cooperation between multiple host sensors and acting directly on nonhematopoietic cells, in the control of CHIKV

Chikungunya Virus Infection during Pregnancy, Réunion, France, 2006

Except for prenatal hospital admissions for those infected, this virus had no effect on outcomes

Search for the standard model Higgs boson in tau final states

We present a search for the standard model Higgs boson using hadronically decaying tau leptons, in 1 inverse femtobarn of data collected with the D0 detector at the Fermilab Tevatron ppbar collider. We select two final states: tau plus missing transverse energy and b jets, and tau+ tau- plus jets. These final states are sensitive to a combination of associated W/Z boson plus Higgs boson, vector boson fusion and gluon-gluon fusion production processes. The observed ratio of the combined limit on the Higgs production cross section at the 95% C.L. to the standard model expectation is 29 for a Higgs boson mass of 115 GeV.Comment: publication versio