We study three-flavor QCD in a uniform magnetic field using chiral perturbation theory (χPT). We construct the vacuum free energy density, quark condensate shifts induced by the magnetic field and the renormalized magnetization to <math><mi>O</mi><mo>(</mo><msup><mrow><mi>p</mi></mrow><mrow><mn>6</mn></mrow></msup><mo>)</mo></math> in the chiral expansion. We find that the calculation of the free energy is greatly simplified by cancellations among two-loop diagrams involving charged mesons. In comparing our results with recent <math><mn>2</mn><mo>+</mo><mn>1</mn></math>-flavor lattice QCD data, we find that the light quark condensate shift at <math><mi>O</mi><mo>(</mo><msup><mrow><mi>p</mi></mrow><mrow><mn>6</mn></mrow></msup><mo>)</mo></math> is in better agreement than the shift at <math><mi>O</mi><mo>(</mo><msup><mrow><mi>p</mi></mrow><mrow><mn>4</mn></mrow></msup><mo>)</mo></math>. We also find that the renormalized magnetization, due to its small-ness, possesses large uncertainties at <math><mi>O</mi><mo>(</mo><msup><mrow><mi>p</mi></mrow><mrow><mn>6</mn></mrow></msup><mo>)</mo></math> due to the uncertainties in the low-energy constants.

United States

Prabal Adhikari

Norway

Inga Strümke

Vacuum free energy, quark condensate shifts and magnetization in three-flavor chiral perturbation theory to In a Uniform Magnetic Field

We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two different models, XGBoost and a deep neural network, to exploit correlations between observables and compare this approach to the traditional cut-and-count method. We consider different methods to analyze the models’ output, finding that a template fit generally performs better than a simple cut. By means of a Shapley decomposition, we gain additional insight into the relationship between event kinematics and the machine learning model output. We consider a supersymmetric scenario with a metastable sneutrino as a concrete example, but the methodology can be applied to a much wider class of models.

Daniel Alvestad

Nikolai Fomin

South Korea

Jörn Kersten

Steffen Maeland

Beyond cuts in small signal scenarios

If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a $$\sim 20\%$$ <math><mrow><mo>∼</mo><mn>20</mn><mo>%</mo></mrow></math>  improvement in the estimate uncertainty.

United Kingdom

Anders Kvellestad

Signal mixture estimation for degenerate heavy Higgses using a deep neural network

We consider a gaugino-mediated supersymmetry breaking scenario where in addition to the gauginos the Higgs fields couple directly to the field that breaks supersymmetry. This yields non-vanishing trilinear scalar couplings in general, which can lead to large mixing in the stop sector providing a sufficiently large Higgs mass. Using the most recent release of F eyn H iggs , we show the implications on the parameter space. Assuming a gravitino LSP, we find allowed points with a neutralino, sneutrino or stau NLSP. We test these points against the results of Run 1 of the LHC, considering in particular searches for heavy stable charged particles.