Samuel May

In many analyses in high energy physics, attempts are made to remove the effects of detector smearing in data by techniques referred to as "unfolding" histograms, thus obtaining estimates of the true values of histogram bin contents. Such unfolded histograms are then compared to theoretical predictions, either to judge the goodness of fit of a theory, or to compare the abilities of two or more theories to describe the data. When doing this, even informally, one is testing hypotheses. However, a more fundamentally sound way to test hypotheses is to smear the theoretical predictions by simulating detector response and then comparing to the data without unfolding; this is also frequently done in high energy physics, particularly in searches for new physics. One can thus ask: to what extent does hypothesis testing after unfolding data materially reproduce the results obtained from testing by smearing theoretical predictions? We argue that this "bottom-line-test" of unfolding methods should be studied more commonly, in addition to common practices of examining variance and bias of estimates of the true contents of histogram bins. We illustrate bottom-line-tests in a simple toy problem with two hypotheses.

Washington letter Get access Samuel J. May Samuel J. May Search for other works by this author on: Oxford Academic Google Scholar Notes and Queries, Volume s2-VIII, Issue 197, 8 October 1859, Page 289, https://doi.org/10.1093/nq/s2-VIII.197.289b Published: 08 October 1859

Advanced machine learning (ML) methods are increasingly used in CMS physics analyses to maximize the sensitivity of a wide range of measurements. The landscape is diverse in terms of both methods and applications. Deep learning methods, from recurrent long short-term memory (LSTM) architectures for classification tasks to deep autoencoders for data quality monitoring, have greatly improved the physics results delivered from the CMS experiment. Algorithms are developed both for collaboration-wide use as well as for individual physics analyses. Results from CMS, such as the measurement of the Higgs boson’s properties in the diphoton decay channel, exploit a variety of ML algorithms to reduce uncertainties on measurements.

Measurements of single top and rare top quark production may be sensitive to theories of physics beyond the standard model (SM), including those in which the energy scale of new physics is beyond the energies directly accessible at the Large Hadron Collider. Such models may be observable through signatures like forbidden SM interactions or deviations of the top quark's couplings from the SM predictions. An overview of recent searches from the ATLAS and CMS Collaborations is presented, with a focus on flavor-changing neutral currents.

Author(s): May, Samuel James | Advisor(s): Yagil, Avraham; Wuerthwein, Frank | Abstract: This dissertation presents the first observation of Higgs boson production in association with a top quark-antiquark pair in the diphoton decay channel, with a significance of 6.6 standard deviations. The measurement is performed with a dataset of 13 TeV proton-proton collisions recorded by the Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC), corresponding to an integrated luminosity of 137 \fbinv.

Top quark production can probe physics beyond the standard model (SM) in different ways.The Effective Field Theory (EFT) framework allows searching for beyond the SM (BSM) effects in a model independent way.CMS experiment is pioneering EFT measurements that move towards using full potential of the data in the most global way possible.Searches for flavour-changing neutral currents (FCNC) and anomalous top quark interactions are also being pursued in CMS which are complementary to the EFT approach.This talk reviews the current limits on FCNC searches in the top sector, and EFT interpretations.