Related papers: Signal mixture estimation for degenerate heavy Hig…
Charged Higgs phenomenology and detection chance at future linear colliders through the production process $e^{-}e^{+} \ra H^{+}H^{-} \ra \tau^{+}\nu \tau \bar{\nu}$ is studied. Both charged Higgs bosons are considered to decay to $\tau\nu$…
Future GW observatories, such as the Einstein Telescope (ET), are expected to detect gravitational wave signals, some of which are likely to overlap with each other. This overlap may lead to misidentification as a single GW event,…
Vector boson fusion proposed initially as an alternative channel for finding heavy Higgs has now established itself as a crucial search scheme to probe different properties of the Higgs boson or for new physics. We explore the merit of…
We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this method on the final state of a Higgs boson decaying to two…
The observed Higgs boson signals to-date could be due to having two quasi-degenerate 125 GeV scalar states in Nature. This kind of scenario tallies well with the predictions from the Next-to-Minimal Supersymmetric Standard Model (NMSSM). We…
Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we…
We propose a new renormalization scheme in the two Higgs doublet models with a softly-broken $Z_2$ symmetry and CP-conservation in the Higgs sector. In this scheme, counterterms for mixing angles of the Higgs bosons are determined by using…
Signature of heavier charged Higgs boson, much above the top quark mass, is investigated at the LHC Run 2 experiments, following its decay mode via top and bottom quark focusing on both hadronic and leptonic signal final states. The generic…
The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of…
This document is prepared for the LCWS2016 conference proceedings. It reviews the current results on the Higgs CP property measurements from both ATLAS and CMS experiments in the Higgs to diboson decays, and in the Vector Boson Fusion…
A precise measurement of the Higgs boson couplings to bottom and top quarks is of paramount importance during the upcoming LHC runs. We present a comprehensive analysis for the Higgs production process in association with a…
We study the production and decay of a coupled system of mixed neutral MSSM Higgs bosons in exclusive double-diffractive processes at the LHC, including non-vanishing CP phases in the soft supersymmetry-breaking gaugino masses and…
We present a novel machine learning framework tailored to detect massive black hole binaries observed by spaceborne gravitational wave detectors like the Laser Interferometer Space Antenna (LISA) and predict their future merger times. The…
In this paper, we address the problem of how to robustly train a ConvNet for regression, or deep robust regression. Traditionally, deep regression employs the L2 loss function, known to be sensitive to outliers, i.e. samples that either lie…
We explore new physics scenarios which are optimally probed through precision Higgs measurements rather than direct collider searches. Such theories consist of additional electroweak charged or singlet states which couple directly to or mix…
Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. To achieve this, deep learning methods need to be promoted from the level of mere associations to being able…
We present a dedicated graph neural network (GNN)-based methodology for the extraction of the Higgs boson signal strength $\mu$, incorporating systematic uncertainties. The architecture features two branches: a deterministic GNN that…
This study estimates cognitive effort based on functional near-infrared spectroscopy data and performance scores using a hybrid DeepNet model. The estimation of cognitive effort enables educators to modify material to enhance learning…
The analog signals generated in the read-out electronics of radiation detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital…
This paper explores different strategies for enhancing sensitivity to new heavy resonances that decay into two or more Higgs bosons. This is achieved using two neural network architectures: an unsupervised autoencoder for anomaly detection…