Learning new physics from an imperfect machine

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.

A search for heavy long-lived staus in the LHCb detector at √s = 7 and 8 TeV

Thi Viet Nga La

The Large Hadron Collider (LHC) has been producing pp collisions at 7 and 8 TeV since 2010 and promises a new era of discoveries in particle physics. One of its experiments, the Large Hadron Collider beauty (LHCb) experiment, was constructed to study CP violation in the B meson system. In addition to B physics, new Physics beyond the Standard Model can also be searched for at this single-arm forward spectrometer. With the different sub-detectors and the high resolution of the tracking system, the LHCb detector has the ability to search for heavy, long-lived and charged particles, which are predicted by extensions of the Standard Model. One of these extensions, the minimal Gauge Mediated Supersymmetry Breaking (mGMSB), proposes such a particle, named stau (τ~) - the SUSY bosonic counterpart of the heavy lepton tau (τ). The theory proposes that the staus may be pair-produced in pp collisions or in the decays of heavier particles, and have only electromagnetic interactions with the atoms of the medium like the muons. Therefore, we expect that at the energy of the LHC these particles can be produced if they do exist and that we have a chance to discover them at LHCb, as well as at the other experiments of the LHC. This thesis is dedicated to the search for stau pairs produced in pp collisions at the centre-of-mass energies √s = 7 and 8 TeV in the LHCb detector. For this purpose, we generated the stau pairs with seven different particle masses ranging from 124 to 309 GeV/c2 and simulated their path through the LHCb detector, as well as their muon background from the decays Z0, γ∗ → μ+μ−. Based on the results from the simulation, a set of cuts are then defined to select the stau pairs. Some muon pairs at high energies will also pass the selection cuts. Thus, to separate the stau pairs from the muon pairs, the Neural Network technique has been used. A first Neural Network has been used to distinguish the stau tracks from the muon tracks using their signals left in the sub-detectors: the VELO silicon detector, the electromagnetic calorimeter, the hadron calorimeter and the RICH detectors. Then, two methods to select the stau pairs have been developed: the first one is based on the product of the two responses from the first Neural Network (NN1) for the two tracks, the second one employs a second Neural Network to separate the stau pairs from the muon pairs by using the above product of the two NN1 responses and the invariant mass of pair. Finally, a favourable region for the staus finding has been defined and the expected numbers of stau and muon pairs in this region have been evaluated. The training of the Neural Network has been achieved with the Monte Carlo variables, then the trained Neural Network has been used to classify the data. The data used in our work were collected by the LHCb experiment in 2011 and 2012 and correspond to integrated luminosities of 1 fb−1 at √s = 7 TeV and of 2 fb−1 at √s = 8 TeV. No significant excess of signal has been observed. Upper limits at 95% CL on the cross section for stau pair production in pp collisions at √s = 7 and 8 TeV have been computed by using the profile likelihood method, which is derived from the well known Feldman and Cousins method.

EPFL2013

A facility to search for hidden particles at the CERN SPS: the SHiP physics case

Kyrylo Bondarenko, Alexey Boyarsky, Marco Drewes, Shintaro Eijima, Oleg Ruchayskiy, Lesya Shchutska, Anurag Tripathi

This paper describes the physics case for a new fixed target facility at CERN SPS. The SHiP (search for hidden particles) experiment is intended to hunt for new physics in the largely unexplored domain of very weakly interacting particles with masses below the Fermi scale, inaccessible to the LHC experiments, and to study tau neutrino physics. The same proton beam setup can be used later to look for decays of tau-leptons with lepton flavour number non-conservation, tau -> 3 mu and to search for weakly-interacting sub-GeV dark matter candidates. We discuss the evidence for physics beyond the standard model and describe interactions between new particles and four different portals-scalars, vectors, fermions or axion-like particles. We discuss motivations for different models, manifesting themselves via these interactions, and how they can be probed with the SHiP experiment and present several case studies. The prospects to search for relatively light SUSY and composite particles at SHiP are also discussed. We demonstrate that the SHiP experiment has a unique potential to discover new physics and can directly probe a number of solutions of beyond the standard model puzzles, such as neutrino masses, baryon asymmetry of the Universe, dark matter, and inflation.

Institute of Physics2016

Cosmology intertwined: A review of the particle physics, astrophysics, and cosmology associated with the cosmological tensions and anomalies

Richard Irving Anderson, Hsin-Yu Chen, Frédéric Courbin, Fabio Finelli, Mikhail Ivanov, Melissa Joseph, Suresh Kumar, Julien Lesgourgues, Florian Niedermann, Emre Ozulker

The standard Lambda Cold Dark Matter (Lambda CDM) cosmological model provides a good description of a wide range of astrophysical and cosmological data. However, there are a few big open questions that make the standard model look like an approximation to a more realistic scenario yet to be found. In this paper, we list a few important goals that need to be addressed in the next decade, taking into account the current discordances between the different cosmological probes, such as the disagreement in the value of the Hubble constant H-0, the sigma(8)-S-8 tension, and other less statistically significant anomalies. While these discordances can still be in part the result of systematic errors, their persistence after several years of accurate analysis strongly hints at cracks in the standard cosmological scenario and the necessity for new physics or generalisations beyond the standard model. In this paper, we focus on the 5.0 sigma tension between the Planck CMB estimate of the Hubble constant H-0 and the SH0ES collaboration measurements. After showing the H-0 evaluations made from different teams using different methods and geometric calibrations, we list a few interesting new physics models that could alleviate this tension and discuss how the next decade's experiments will be crucial. Moreover, we focus on the tension of the Planck CMB data with weak lensing measurements and redshift surveys, about the value of the matter energy density Omega(m), and the amplitude or rate of the growth of structure (sigma(8), f sigma(8)). We list a few interesting models proposed for alleviating this tension, and we discuss the importance of trying to fit a full array of data with a single model and not just one parameter at a time. Additionally, we present a wide range of other less discussed anomalies at a statistical significance level lower than the H-0-S-8 tensions which may also constitute hints towards new physics, and we discuss possible generic theoretical approaches that can collectively explain the non-standard nature of these signals. Finally, we give an overview of upgraded experiments and next-generation space missions and facilities on Earth that will be of crucial importance to address all these open questions. (C) 2022 The Author(s). Published by Elsevier B.V.