240 citations · 261 across the 13 of their papers we have counts for
14 papers
Cross-Detector Transfer Learning with Parnassus: From ALEPH to SLD
Ya-Feng Lo, Chi Lung Cheng, Benjamin Nachman
Parnassus is a fast detector-simulation and reconstruction framework that maps truth-level particles directly to reconstructed particles. In this work, we investigate cross-detecto…
Differentiable Parametric Simulation and Reconstruction Models in Parnassus
Abdelrahman Elabd, Eilam Gross, Dmitrii Kobylianskii +2
Parnassus is a framework for fast detector simulation and reconstruction, directly mapping truth-level particles onto reconstructed objects. Such models can be built from deep gene…
Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
Chi Lung Cheng, Julia Gonski, Runze Li +5
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detect…
Unfolding with a Wasserstein Loss
Katy Craig, Benjamin Faktor, Benjamin Nachman
Data unfolding -- the removal of noise or artifacts from measurements -- is a fundamental task across the experimental sciences. Of particular interest are applications in physics,…
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Biwei Dai, Po-Wen Chang, Wahid Bhimji +15
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allow…
Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer
Jonathan Renusch
The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This ta…
Searching for Anomalies with Foundation Models
Vinicius Mikuni, Benjamin Nachman
Foundation models have the potential to extend the discovery reach for anomaly detection searches. When studying the large OmniLearned foundation model on data from the CMS experim…
Weakly Supervised Anomaly Detection in Events with a Higgs Boson and Exotic Physics
Chi Lung Cheng, Sarah Demers, Sascha Diefenbacher +3
We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this meth…
Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li +5
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of…
Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
Rikab Gambhir, Radha Mastandrea, Benjamin Nachman +1
We present the first study of anti-isolated Upsilon decays to two muons () in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-bas…
Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
Radha Mastandrea, Benjamin Nachman, Tilman Plehn
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and sho…
Incorporating Physical Priors into Weakly-Supervised Anomaly Detection
Chi Lung Cheng, Gup Singh, Benjamin Nachman
We propose a new machine-learning-based anomaly detection strategy for comparing data with a background-only reference (a form of weak supervision). The sensitivity of previous str…
Non-resonant Anomaly Detection with Background Extrapolation
Kehang Bai, Radha Mastandrea, Benjamin Nachman
Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. Non-resonant new physics sc…
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…