Confronting dark matter with the diphoton excess from a parent resonance decay

A diphoton excess with an invariant mass of about 750 GeV has been recently reported by both ATLAS and CMS experiments at LHC. While the simplest interpretation requires the resonant production of a 750 GeV (pseudo)scalar, here we consider an alternative setup, with an additional heavy parent particle which decays into a pair of 750 GeV resonances. This configuration improves the agreement between the 8 TeV and 13 TeV data. Moreover, we include a dark matter candidate in the form of a Majorana fermion which interacts through the 750 GeV portal. The invisible decays of the light resonance help to suppress additional decay channels into Standard Model particles in association with the diphoton signal. We realise our hierarchical framework in the context of an effective theory, and we analyse the diphoton signal as well as the consistency with other LHC searches. We finally address the interplay of the LHC results with the dark matter phenomenology, namely the compatibility with the relic density abundance and the indirect detection bounds.Comment: 11 figures, 2 tables, 29 page

The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning

A key research question at the Large Hadron Collider (LHC) is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: It requires the time consuming generation of scattering events, the simulation of the detector response, the event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiment. In the BSM-AI project we attack this challenge with a new approach. Machine learning tools are thought to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets - each tested with 200 signal regions by ATLAS - have been used to train and validate SUSY-AI. The code is currently able to reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at least 93 percent. It has been validated further within the constrained MSSM and a minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded at http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/

DeepXS: Fast approximation of MSSM electroweak cross sections at NLO

We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for $pp\to\tilde{\chi}^+_1\tilde{\chi}^-_1,$ $\tilde{\chi}^0_2\tilde{\chi}^0_2$ and $\tilde{\chi}^0_2\tilde{\chi}^\pm_1$ as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below $0.5\,\%$ allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of $\mathcal{O}(10^7)$ from $\mathcal{O}(\rm{min})$ to $\mathcal{O}(\mu\rm{s})$ per evaluation.Comment: 7 pages, 3 figure

Identifying WIMP dark matter from particle and astroparticle data

One of the most promising strategies to identify the nature of dark matter consists in the search for new particles at accelerators and with so-called direct detection experiments. Working within the framework of simplified models, and making use of machine learning tools to speed up statistical inference, we address the question of what we can learn about dark matter from a detection at the LHC and a forthcoming direct detection experiment. We show that with a combination of accelerator and direct detection data, it is possible to identify newly discovered particles as dark matter, by reconstructing their relic density assuming they are weakly interacting massive particles (WIMPs) thermally produced in the early Universe, and demonstrating that it is consistent with the measured dark matter abundance. An inconsistency between these two quantities would instead point either towards additional physics in the dark sector, or towards a non-standard cosmology, with a thermal history substantially different from that of the standard cosmological model.Comment: 24 pages (+21 pages of appendices and references) and 14 figures. v2: Updated to match JCAP version; includes minor clarifications in text and updated reference

Evolution of late Cenozoic Antarctic Ice on the Central Basin of the Ross Sea, Antarctica

第6回極域科学シンポジウム[OG] 地圏11月16日（月）　国立極地研究所３階セミナー

Genes and environment underlying lung health

Chronic Obstructive Pulmonary Disease (COPD) is een chronische vernauwing van de luchtwegen en/of aantasting van het elastische longweefsel en wordt veroorzaakt door een abnormale reactie op de inademing van schadelijke stoffen, zoals tabaksrook. Roken wordt gezien als de belangrijkste risicofactor voor de ontwikkeling van COPD, echter 25-45% van alle COPD patiënten heeft nooit gerookt. Daarnaast lijkt genetische gevoeligheid een belangrijke rol te spelen. In dit proefschrift onderzochten we individuen uit twee cohorten uit de algemene populatie, LifeLines en Vlagtwedde-Vlaardingen. We vonden dat zien dat passief roken en beroepsblootstelling aan stoffen, gassen, dampen en pesticiden is geassocieerd met een lagere longfunctie niveau, een versnelde afname van longfunctie en een hogere prevalentie van grote en kleine luchtwegobstructie. Daarnaast identificeerden we verschillende factoren die individuele gevoeligheid voor deze blootstelling beïnvloeden, zoals actief roken en verschillende genetische varianten in nog niet eerder gevonden genen, zoals PCDH9, GALNT13, PDE4D, TMEM176A en NOS1. Verder onderzoek zal zich moeten richten op de biologische functie van de nieuw gevonden genetische varianten, en de onderliggende biologische mechanismen via welke deze genetische varianten uiteindelijk kunnen leiden tot de ontwikkeling van COPD. Deze kennis kan in de toekomst bijdragen aan de ontwikkeling van nieuwe behandelmethoden voor COPD. Tenslotte kunnen interventies gericht op het voorkomen van blootstelling aan tabaksrook en werkgerelateerde stoffen bijdragen aan betere gezondheid van de longen en uiteindelijk leiden tot een lagere COPD prevalentie