Looking for cosmic neutrino background

Since the discovery of neutrino oscillation in atmospheric neutrinos by the Super-Kamiokande experiment in 1998, study of neutrinos has been one of exciting fields in high-energy physics. All the mixing angles were measured. Quests for (1) measurements of the remaining parameters, the lightest neutrino mass, the CP violating phase(s), and the sign of mass splitting between the mass eigenstates m3 and m1, and (2) better measurements to determine whether the mixing angle θ23 is less than π/4, are in progress in a well-controlled manner. Determining the nature of neutrinos, whether they are Dirac or Majorana particles is also in progress with continuous improvement. On the other hand, although the ideas of detecting cosmic neutrino background have been discussed since 1960s, there has not been a serious concerted effort to achieve this goal. One of the reasons is that it is extremely difficult to detect such low energy neutrinos from the Big Bang. While there has been tremendous accumulation of information on Cosmic Microwave Background since its discovery in 1965, there is no direct evidence for Cosmic Neutrino Background. The importance of detecting Cosmic Neutrino Background is that, although detailed studies of Big Bang Nucleosynthesis and Cosmic Microwave Background give information of the early Universe at ~a few minutes old and ~300 k years old, respectively, observation of Cosmic Neutrino Background allows us to study the early Universe at ~1 s old. This article reviews progress made in the past 50 years on detection methods of Cosmic Neutrino Background

Background Study on nu_e Appearance from a nu_mu Beam in Very Long Baseline Neutrino Oscillation Experiments with a Large Water Cherenkov Detector

There is a growing interest in very long baseline neutrino oscillation experimentation using accelerator produced neutrino beam as a machinery to probe the last three unmeasured neutrino oscillation parameters: the mixing angle theta_13, the possible CP violating phase delta_CP and the mass hierarchy, namely, the sign of delta-m^2_32. Water Cherenkov detectors such as IMB, Kamiokande and Super-Kamiokande have shown to be very successful at detecting neutrino interactions. Scaling up this technology may continue to provide the required performance for the next generation of experiments. This report presents the latest effort to demonstrate that a next generation (> 100 kton) water Cherenkov detector can be used effectively for the rather difficult task of detecting nu_e events from the neutrino oscillation nu_mu -> nu_e despite the large expected potential background resulting from pi^0 events produced via neutral current interactions.Comment: 13 pages. typo in uncertainty in conclusion fixe

Genetic analysis of TP53 in childhood myelodysplastic syndrome and juvenile myelomonocytic leukemia

信州大学博士（医学）・学位論文・平成23年3月31日授与（甲第886号)・齋藤章治ArticleLEUKEMIA RESEARCH. 35(12):1578-1584 (2011)journal articl

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales. Archives of Virology (2021) 166:3567–3579. https://doi.org/10.1007/s00705-021-05266-wIn March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.This work was supported in part through Laulima Government Solutions, LLC prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272201800013C. J.H.K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC under Contract No. HHSN272201800013C. This work was also supported in part with federal funds from the National Cancer Institute (NCI), National Institutes of Health (NIH), under Contract No. 75N91019D00024, Task Order No. 75N91019F00130 to I.C., who was supported by the Clinical Monitoring Research Program Directorate, Frederick National Lab for Cancer Research. This work was also funded in part by Contract No. HSHQDC-15-C-00064 awarded by DHS S&T for the management and operation of The National Biodefense Analysis and Countermeasures Center, a federally funded research and development center operated by the Battelle National Biodefense Institute (V.W.); and NIH contract HHSN272201000040I/HHSN27200004/D04 and grant R24AI120942 (N.V., R.B.T.). S.S. acknowledges partial support from the Special Research Initiative of Mississippi Agricultural and Forestry Experiment Station (MAFES), Mississippi State University, and the National Institute of Food and Agriculture, US Department of Agriculture, Hatch Project 1021494. Part of this work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001030), the UK Medical Research Council (FC001030), and the Wellcome Trust (FC001030).S

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

Looking for cosmic neutrino background

Since the discovery of neutrino oscillation in atmospheric neutrinos by the Super-Kamiokande experiment in 1998, study of neutrinos has been one of exciting fields in high-energy physics. All the mixing angles were measured. Quests for (1) measurements of the remaining parameters, the lightest neutrino mass, the CP violating phase(s), and the sign of mass splitting between the mass eigenstates m3 and m1, and (2) better measurements to determine whether the mixing angle θ23 is less than π/4, are in progress in a well-controlled manner. Determining the nature of neutrinos, whether they are Dirac or Majorana particles is also in progress with continuous improvement. On the other hand, although the ideas of detecting cosmic neutrino background have been discussed since 1960s, there has not been a serious concerted effort to achieve this goal. One of the reasons is that it is extremely difficult to detect such low energy neutrinos from the Big Bang. While there has been tremendous accumulation of information on Cosmic Microwave Background since its discovery in 1965, there is no direct evidence for Cosmic Neutrino Background. The importance of detecting Cosmic Neutrino Background is that, although detailed studies of Big Bang Nucleosynthesis and Cosmic Microwave Background give information of the early Universe at ~a few minutes old and ~300 k years old, respectively, observation of Cosmic Neutrino Background allows us to study the early Universe at ~1 s old. This article reviews progress made in the past 50 years on detection methods of Cosmic Neutrino Background

Maximum likelihood reconstruction of water Cherenkov events with deep generative neural networks

Large water Cherenkov detectors have shaped our current knowledge of neutrino physics and nucleon decay, and will continue to do so in the foreseeable future. These highly capable detectors allow for directional and topological, as well as calorimetric information to be extracted from signals on their photosensors. The current state-of-the-art approach to water Cherenkov reconstruction relies on maximum-likelihood estimation, with several simplifying assumptions employed to make the problem tractable. In this paper, we describe neural networks that produce probability density functions for the signals at each photosensor, given a set of inputs that characterizes a particle in the detector. The neural networks we propose allow for likelihood-based approaches to event reconstruction with significantly fewer assumptions compared to traditional methods, and are thus expected to improve on the current performance of water Cherenkov detectors.Large water Cherenkov detectors have shaped our current knowledge of neutrino physics and nucleon decay, and will continue to do so in the foreseeable future. These highly capable detectors allow for directional and topological, as well as calorimetric information to be extracted from signals on their photosensors. The current state-of-the-art approach to water Cherenkov reconstruction relies on maximum-likelihood estimation, with several simplifying assumptions employed to make the problem tractable. In this paper, we describe neural networks that produce probability density functions for the signals at each photosensor, given a set of inputs that characterizes a particle in the detector. The neural networks we propose allow for likelihood-based approaches to event reconstruction with significantly fewer assumptions compared to traditional methods, and are thus expected to improve on the current performance of water Cherenkov detectors