scholarly journals Towards the ultimate differential SMEFT analysis

2020 ◽  
Vol 2020 (9) ◽  
Author(s):  
Shankha Banerjee ◽  
Rick S. Gupta ◽  
Joey Y. Reiness ◽  
Satyajit Seth ◽  
Michael Spannowsky

Abstract We obtain SMEFT bounds using an approach that utilises the complete multi-dimensional differential information of a process. This approach is based on the fact that at a given EFT order, the full angular distribution in the most important electroweak processes can be expressed as a sum of a fixed number of basis functions. The coefficients of these basis functions — the so-called angular moments — and their energy dependance, thus form an ideal set of experimental observables that encapsulates the complete multi-dimensional differential information of the process. This approach is generic and the observables constructed allow to avoid blind directions in the SMEFT parameter space. While this method is applicable to many of the important electroweak processes, as a first example we study the pp → V(ℓℓ)h(bb) process (V ≡ Z/W±, ℓℓ ≡ ℓ+ℓ−/ℓ±ν), including QCD NLO effects, differentially. We show that using the full differential data in this way plays a crucial role in simultaneously and maximally constraining the different vertex structures of the Higgs coupling to gauge bosons. In particular, our method yields bounds on the $$ {hV}_{\mu \nu}{V}^{\mu \nu},{hV}_{\mu \nu}{\tilde{V}}^{\mu \nu} $$ hV μν V μν , hV μν V ˜ μν and $$ hVff\left( ff\equiv f\overline{f}/f\overline{f}^{\prime}\right) $$ hVff ff ≡ f f ¯ / f f ¯ ′ couplings, stronger than projected bounds reported in any other process. This matrix-element-based method can provide a transparent alternative to complement machine learning techniques that also aim to disentangle correlations in the SMEFT parameter space.

Author(s):  
Padmavathi .S ◽  
M. Chidambaram

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.


2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  

Author(s):  
Anantvir Singh Romana

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.


Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.


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