Towards recognizing the light facet of the Higgs Boson

The Higgs boson couplings to bottom and top quarks have been measured and agree well with the Standard Model predictions. Decays to lighter quarks and gluons, however, remain elusive. Observing these decays is essential to complete the picture of the Higgs boson interactions. In this work, we present the perspectives for the 14 TeV LHC to observe the Higgs boson decay to gluon jets assembling convolutional neural networks, trained to recognize abstract jet images constructed embodying particle flow information, and boosted decision trees with kinetic information from Higgs-strahlung $ZH\to \ell^+\ell^- + gg$ events. We show that this approach might be able to observe Higgs to gluon decays with a significance of around $2.4\sigma$ improving significantly previous prospects based on cut-and-count analysis. An upper bound of $BR(H\to gg)\leq 1.74\times BR^{SM}(H\to gg)$ at 95\% confidence level after 3000 fb$^{-1}$ of data is obtained using these machine learning techniques.Comment: 17 pages, 7 figures and 4 tables. Version accepted for publication in Machine Learning Science and Technolog

Developing an app to interpret chest X-rays to support the diagnosis of respiratory pathology with artificial intelligence

Background: Medical images, including results from X-rays, are an integral part of medical diagnosis. Their interpretation requires an experienced radiologist. One of the main problems in developing countries is access to timely medical diagnosis. Lack of investment in health care infrastructure, geographical isolation and shortage of trained specialists are common obstacles to providing adequate health care in many areas of the world. In this work we show how to build and deploy a Deep Learning computer vision application for the classification of 14 common thorax disease using X-rays images. Methods: We make use of the FAST.AI and pytorch framework to create and train the DenseNet-121 model to classify the X-ray images from the ChestX-ray14 data set which contains 112,120 frontal-view X-ray images of 30,805 unique patients. After training and validate our model we create a web-app using Heroku, this web-app can be accessed by any mobile device with internet connection. Results: We obtained 70% for detecting pneumothorax for the one-vs-all task. Meanwhile, for the multilabel-multiclass task we are able to achieve state-of-the-art accuracy with fewer epochs, reducing drastically the training time of the model. We also demonstrate the feature localization of our model by using the Grad-CAM methodologies, feature which can be useful for early diagnostic of dangerous illnesses. Conclusions: In this work we present our study of the use of machine learning techniques to identify diseases using X-ray information. We have used the new framework of Fast.AI, and imported the resulting model to an app which can be tested by any user. The app has an intuitive interface where the user can upload an image and obtain a likelihood for the given image be classified as one of the 14 labeled diseases. This classification could assist diagnosis by medical providers and broaden access to medical services to remote areas.publishe

Phenomenology of vector-like leptons with Deep Learning at the Large Hadron Collider

In this paper, a model inspired by Grand Unification principles featuring three generations of vector-like fermions, new Higgs doublets and a rich neutrino sector at the low scale is presented. Using the state-of-the-art Deep Learning techniques we perform the first phenomenological analysis of this model focusing on the study of new charged vector-like leptons (VLLs) and their possible signatures at CERN’s Large Hadron Collider (LHC). In our numerical analysis we consider signal events for vector-boson fusion and VLL pair production topologies, both involving a final state containing a pair of charged leptons of different flavor and two sterile neutrinos that provide a missing energy. We also consider the case of VLL single production where, in addition to a pair of sterile neutrinos, the final state contains only one charged lepton. We propose a novel method to identify missing transverse energy vectors by comparing the detector response with Monte-Carlo simulated data. All calculated observables are provided as data sets for Deep Learning analysis, where a neural network is constructed, based on results obtained via an evolutive algorithm, whose objective is to maximise either the accuracy metric or the Asimov significance for different masses of the VLL. Taking into account the effect of the three analysed topologies, we have found that the combined significance for the observation of new VLLs at the high-luminosity LHC can range from 5.7σ, for a mass of 1.25 TeV, all the way up to 28σ if the VLL mass is 200 GeV. We have also shown that by the end of the LHC Run-III a 200 GeV VLL can be excluded with a confidence of 8.8 standard deviations. The results obtained show that our model can be probed well before the end of the LHC operations and, in particular, providing important phenomenological information to constrain the energy scale at which new gauge symmetries emergent from the considered Grand Unification picture can be manifest.publishe

On interplay between flavour anomalies and neutrino properties

A minimal extension of the Standard Model (SM) featuring two scalar leptoquarks, an SU(2) doublet with hypercharge 1/6 and a singlet with hypercharge 1/3, is proposed as an economical benchmark model for studies of an interplay between flavour physics and properties of the neutrino sector. The presence of such type of leptoquarks radiatively generates neutrino masses and offers a simultaneous explanation for the current B-physics anomalies involving $b \to c \ell \nu_\ell$ decays. The model can also accommodate both the muon magnetic moment and the recently reported $W$ mass anomalies, while complying with the most stringent lepton flavour violating observables.Comment: Accepted versio

Gravitational-Wave Signatures of Chiral-Symmetric Technicolor

A chiral-symmetric technicolor model successfully reconciles the tension between electroweak precision tests and traditional technicolor models. Focusing on its simplest realization preserving the conventional Higgs mechanism, we study its primordial gravitational wave signatures originating from first order phase transitions in the early Universe. We found that abundant phase transition patterns arise from a physically viable parameter space. Besides, we have also found gravitational wave signals possibly visible by future experiments, such as LISA, BBO and u-DECIGO. Our results stress the importance of gravitational wave detectors in exploring new physics complementary to ground colliders in the multi-messenger astronomy era.Comment: 9 pages, 6 figures, 3 table

Phenomenology of a flavored multiscalar Branco-Grimus-Lavoura-like model with three generations of massive neutrinos

In this paper, we present several possible anomaly free implementations of the Branco-Grimus-Lavoura (BGL) model with two Higgs doublets and one singlet scalar. The model also includes three generations of massive neutrinos that get their mass via a type-I seesaw mechanism. A particular anomaly free realization, which we dub νBGL-1 scenario, is subjected to an extensive phenomenological analysis, from the perspective of flavor physics and collider phenomenology.publishe

Space-time distribution of the ichthyofauna from Saco da Fazenda estuary, Itajaí, Santa Catarina, Brazil

Copyright © 2009 Coastal Education and Research Foundation (CERF).A Ictiofauna do estuário Saco da Fazenda foi estudada mensalmente entre julho de 2003 e junho de 2004 em quatro áreas definidas em função das características fisiográficas e da representatividade do estuário nesta região. Foram capturados 4502 exemplares, distribuídos em 42 espécies, 35 gêneros e 21 famílias. Engraulidae foram os peixes mais abundantes, onde Cetengraulis edentulus dominou nas capturas. As espécies de ocorrência ocasional, representadas, principalmente por indivíduos juvenis, predominaram nas amostragens. As maiores abundâncias ocorreram durante os meses de verão e outono, em contraste com as elevadas biomassas na primavera-outono; sendo que a área IV diferenciou-se das demais, por contribuir com as maiores capturas. Os índices de riqueza, diversidade e equitabilidade, apresentaram padrões semelhantes de flutuação, com valores elevados nos meses de primavera e verão. O índice de Jaccard revelou uma maior similaridade na composição da ictiofauna entre as áreas II e IV, enquanto a menor ocorreu entre I e IV, provavelmente devido às diferentes áreas destes locais.ABSTRACT: From July 2003 to June 2004, the physiographic characteristics of the ichthyofauna of the estuary of Saco da Fazenda were studied in four defined areas representative of the estuary. A total of 4502 individuals were captured, with 42 species, 35 genera, and 21 families. Engraulidae were the most abundant fish, and Cetengraulis edentulus dominated the captures. The species of occasional occurrence prevailed in the samplings and were represented mainly by juvenile individuals. The highest abundances occurred during the months of summer and autumn, in contrast with high biomasses in the spring and autumn; area IV contributed the largest captures. The richness indexes, diversity, and equitability presented similar flotation patterns, with high values in spring and summer. The Jaccard index revealed a greater similarity in the composition of the ichthyofauna in areas II and IV, while the lowest happened between I and IV, which is probably due to the different sizes of these areas. This paper clearly shows the relevance of this estuary, albeit strongly impacted, for recruitment of small fish mainly during summer and autumn months

Assessing the Influence of Reversed Items and Force-Choice on the Work and Meaning Inventory [Evaluación de la influencia de los ítems invertidos y de elección forzosa en el Inventario de trabajo significativo]

AbstractResponse biases are issues in inventories in positive organizational psychology. The study aims to control the response bias in the assessment of meaning of work through two methods: reversed key items and forced-choice format. The sample consisted of 351 professionals; women constituted 60.0 % of the sample. The participants answered two versions of the instrument for meaning of work: Likert-type items and forced-choice. For both versions, the unifactorial model was the most appropriate for the data available. The results indicate that the random intercepts model fit the Likert data (CFI = .92), as well as the forced-choice model (CFI = .97). Besides, the latent dimension of the forced-choice version did not correlate with acquiescence index (r < .08; p > .05), and approximately 20 % of the variance of the items might be due to the method (Likert or forced-choice). The present study illustrates the importance of response bias control in self-report instruments. ResumenLos sesgos de respuesta son problemas en los inventarios de la psicología organizacional positiva. El estudio tiene como objetivo controlar el sesgo de respuesta en la eva­luación del trabajo significativo a través de dos métodos: ítems clave invertidos y formato de elección forzosa. La muestra estuvo formada por 351 profesionales; las muje­res constituyeron el 60.0 % de la muestra. Los participan­tes respondieron dos versiones del instrumento de signifi­cado del trabajo: ítems tipo Likert y elección forzosa. Para ambas versiones, el modelo unifactorial fue el más apro­piado para los datos disponibles. Los resultados indican que el modelo de intersecciones aleatorias se ajusta a los datos Likert (CFI = .92), así como al modelo de elección forzada (CFI = .97). Además, la dimensión latente de la versión de elección forzada no se correlacionó con el ín­dice de aquiescencia (r < .08; p > .05), y aproximada­mente el 20 % de la varianza de los ítems podría deberse al método (Likert o forzado). elección). El presente estu­dio ilustra la importancia del control del sesgo de res­puesta en los instrumentos de autoinforme