5 papers
Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches
Jack Y. Araz, Michael Spannowsky
Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it. A raw anomaly score has no calibrated meaning, a mod…
Continuous-variable ADAPT-VQE for bosonic lattice models
Dimitrios Athanasakos, Gloria Tejedor-GarcÃa, Jack Y. Araz +4
We present a continuous-variable adaptive variational quantum eigensolver (CV-ADAPT-VQE). As concrete examples, we consider the ground-state preparation for (i) the Bose-Hubbard mo…
New insights into the puzzle through Top-Bottom synergies
Jack Y. Araz, Christoph Englert, Matthew Kirk +1
Anomalies in the non-leptonic and decays may be an indication of physics beyond the Standard Model,…
Reinterpretation and preservation of data and analyses in HEP
Jon Butterworth, Sabine Kraml, Harrison Prosper +145
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes…
Communicating Likelihoods with Normalising Flows
Jack Y. Araz, Anja Beck, Méril Reboud +2
We present a machine-learning-based workflow to model an unbinned likelihood from its samples. A key advancement over existing approaches is the validation of the learned likelihoo…