A renormalizable supersymmetric SO(10) model

A realistic grand unified model has never been constructed in the literature due to three major difficulties: the seesaw mechanism without spoiling gauge coupling unification, the doublet-triplet splitting and the proton decay suppression. We propose a renormalizable supersymmetric SO(10) model with all these difficulties solved naturally by imposing an extra discrete symmetry.Comment: 12 page

Probing the CP-even Higgs Sector via H3→H2H1H_3\to H_2H_1 in the Natural NMSSM

After the discovery of a Standard Model (SM) like Higgs boson, naturalness strongly favors the next to the Minimal Supersymmetric SM (NMSSM). In this letter, we point out that the most natural NMSSM predicts the following CP-even Higgs $H_i$ sector: (A) $H_2$ is the SM-like Higgs boson with mass pushed-upward by a lighter $H_1$ with mass overwhelmingly within $[m_{H_2}/2,m_{H_2}]$; (B) $m_{H_3}\simeq 2\mu/\sin2\beta\gtrsim300$ GeV; (C) $H_3$ has a significant coupling to top quarks and can decay to $H_1H_2$ with a large branching ratio. Using jet substructure we show that all the three Higgs bosons can be discovered via $gg\to H_3 \to H_1H_2\to b\bar b \ell\nu jj$ at the 14 TeV LHC. Especially, the LEP-LHC scenario with $H_1\simeq98$ GeV has a very good discovery potential.Comment: 5 pages, 2 figures. Some typos corrected and reference adde

Determining physical properties of star-forming regions using conditional invertible neural network

Star formation is one of the most fundamental subjects in astronomy where astronomers have been seeking answers to key questions: how efficiently stars form and how newly born stars affect their surroundings. Our understanding of star formation relies mostly on the observations of star-forming regions. However, it is a non-trivial task to interpret the observations because diverse physical processes are non-linearly coupled so the observational data are highly degenerate. Additionally, the ever-expanding volume of observational data in recent days necessitates a new method that analyses large amounts of data more quickly and effectively. In this thesis, we introduce deep learning-based tools we have developed to efficiently and effectively interpret massive data of observed star-forming regions. We adopt the conditional invertible neural network (cINN) architecture specialised in solving the inverse problem of degenerate systems. We introduce the cINNs developed for cloud-scale observations and cINNs for individual star-scale observations. Our networks are very useful tools that can consistently and quickly analyse large amounts of data. We evaluate the performance of the networks, demonstrating that our networks predict physical properties accurately and precisely

Soft Methodology for Cost-and-error Sensitive Classification

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms. We also demonstrate that the methodology can be extended for considering the weighted error rate instead of the original error rate. This extension is useful for tackling unbalanced classification problems.Comment: A shorter version appeared in KDD '1

Experimentally reducing the quantum measurement back-action in work distributions by a collective measurement

In quantum thermodynamics, the standard approach to estimate work fluctuations in unitary processes is based on two projective measurements, one performed at the beginning of the process and one at the end. The first measurement destroys any initial coherence in the energy basis, thus preventing later interference effects. In order to decrease this back-action, a scheme based on collective measurements has been proposed in~[PRL 118, 070601 (2017)]. Here, we report its experimental implementation in an optical system. The experiment consists of a deterministic collective measurement on identically prepared two qubits, encoded in the polarisation and path degree of a single photon. The standard two projective measurement approach is also experimentally realized for comparison. Our results show the potential of collective schemes to decrease the back-action of projective measurements, and capture subtle effects arising from quantum coherence.Comment: 9 pages, 4 figure

Measurement of Grüneisen parameter of tissue by photoacoustic spectrometry

The Grüneisen parameter of tissue is a constitutive parameter in photoacoustic tomography. Here, we applied photoacoustic spectrometry (PAS) to directly measure the Grüneisen parameter. In our PAS system, laser pulses at wavelengths between 460 and 1600 nm were delivered to tissue samples, and photoacoustic signals were detected by a 20 MHz flat water-immersion ultrasonic transducer. By fitting photoacoustic spectra to light absorption spectra, we found that the Grüneisen parameter was 0.73 for porcine subcutaneous fat tissue, and 0.15 for oxygenated bovine red blood cells at room temperature (24°C)

A renormalizable supersymmetric SO(10) model with natural doublet-triplet splitting

We propose a renormalizable supersymmetric SO(10) model where the doublet-triplet splitting problem is solved using the Dimopoulos-Wilczek mechanism. An unwanted coupling is forbidden through a filter sector. To suppress proton decay without spoiling gauge coupling unification, there is a problem in the weak doublets which requires further improvements.Comment: 14 Pages, 1 figure