Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference
Fuente:
arXiv
Saved in:
| Main Authors: | Chatterjee, Atrideb, Choudhury, Arghya, Mitra, Sourav, Mondal, Arpita, Mondal, Subhadeep |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bilinear R-parity violating supersymmetry under the light of neutrino oscillation, higgs and flavor data
by: Choudhury, Arghya, et al.
Published: (2023)
by: Choudhury, Arghya, et al.
Published: (2023)
Markov Chain Monte Carlo analysis to probe trilinear $R$-parity violating SUSY scenarios and possible LHC signatures
by: Choudhury, Arghya, et al.
Published: (2024)
by: Choudhury, Arghya, et al.
Published: (2024)
Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
by: Choudhury, Arghya, et al.
Published: (2024)
by: Choudhury, Arghya, et al.
Published: (2024)
Status of R-parity violating SUSY
by: Choudhury, Arghya, et al.
Published: (2024)
by: Choudhury, Arghya, et al.
Published: (2024)
Improving sensitivity of trilinear RPV SUSY searches using machine learning at the LHC
by: Choudhury, Arghya, et al.
Published: (2023)
by: Choudhury, Arghya, et al.
Published: (2023)
Slepton searches in the trilinear RPV SUSY scenarios at the HL-LHC and HE-LHC
by: Choudhury, Arghya, et al.
Published: (2023)
by: Choudhury, Arghya, et al.
Published: (2023)
CNN on `Top': In Search of Scalable & Lightweight Image-based Jet Taggers
by: Baruah, Rajneil, et al.
Published: (2025)
by: Baruah, Rajneil, et al.
Published: (2025)
Probing sub-TeV Higgsinos aided by a ML-based top tagger in the context of Trilinear RPV SUSY
by: Baruah, Rajneil, et al.
Published: (2024)
by: Baruah, Rajneil, et al.
Published: (2024)
Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference
by: Alvarez, Ezequiel, et al.
Published: (2026)
by: Alvarez, Ezequiel, et al.
Published: (2026)
Transforming Simulation to Data Without Pairing
by: Gendreau-Distler, Eli, et al.
Published: (2025)
by: Gendreau-Distler, Eli, et al.
Published: (2025)
Stabilizing Neural Likelihood Ratio Estimation
by: Acosta, Fernando Torales, et al.
Published: (2025)
by: Acosta, Fernando Torales, et al.
Published: (2025)
Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
by: Dreyer, Etienne, et al.
Published: (2024)
by: Dreyer, Etienne, et al.
Published: (2024)
Stay Positive: Neural Refinement of Sample Weights
by: Nachman, Benjamin, et al.
Published: (2025)
by: Nachman, Benjamin, et al.
Published: (2025)
Frequentist Uncertainties on Neural Density Ratios with wifi Ensembles
by: Benevedes, Sean, et al.
Published: (2025)
by: Benevedes, Sean, et al.
Published: (2025)
Signal model parameter scan using Normalizing Flow
by: Saito, Masahiko, et al.
Published: (2024)
by: Saito, Masahiko, et al.
Published: (2024)
Unifying Simulation and Inference with Normalizing Flows
by: Du, Haoxing, et al.
Published: (2024)
by: Du, Haoxing, et al.
Published: (2024)
Jet Image Tagging Using Deep Learning: An Ensemble Model
by: Bassa, Juvenal, et al.
Published: (2025)
by: Bassa, Juvenal, et al.
Published: (2025)
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
by: Backes, Mathias, et al.
Published: (2022)
by: Backes, Mathias, et al.
Published: (2022)
Revisiting the Electroweakino Sector of the Baryon Number Violating MSSM at the HL-LHC with Deep Neural Networks
by: Barman, Rahool Kumar, et al.
Published: (2026)
by: Barman, Rahool Kumar, et al.
Published: (2026)
Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flows
by: Valsecchi, Davide, et al.
Published: (2026)
by: Valsecchi, Davide, et al.
Published: (2026)
Accelerating HEP simulations with Neural Importance Sampling
by: Deutschmann, Nicolas, et al.
Published: (2024)
by: Deutschmann, Nicolas, et al.
Published: (2024)
Graph theory inspired anomaly detection at the LHC
by: Araz, Jack Y., et al.
Published: (2025)
by: Araz, Jack Y., et al.
Published: (2025)
FlexCAST: Enabling Flexible Scientific Data Analyses
by: Nachman, Benjamin, et al.
Published: (2025)
by: Nachman, Benjamin, et al.
Published: (2025)
Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
by: Gambhir, Rikab, et al.
Published: (2025)
by: Gambhir, Rikab, et al.
Published: (2025)
Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties
by: Benato, Lisa, et al.
Published: (2025)
by: Benato, Lisa, et al.
Published: (2025)
Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
by: Chan, Jay
Published: (2026)
by: Chan, Jay
Published: (2026)
Seeing Double: Calibrating Two Jets at Once
by: Gambhir, Rikab, et al.
Published: (2024)
by: Gambhir, Rikab, et al.
Published: (2024)
Accelerating Resonance Searches via Signature-Oriented Pre-training
by: Li, Congqiao, et al.
Published: (2024)
by: Li, Congqiao, et al.
Published: (2024)
Why Is Attention Sparse In Particle Transformer?
by: Legge, Timothy, et al.
Published: (2025)
by: Legge, Timothy, et al.
Published: (2025)
Look everywhere effects in anomaly detection
by: Hein, Marie, et al.
Published: (2025)
by: Hein, Marie, et al.
Published: (2025)
Resummed Distribution Functions: Making Perturbation Theory Positive and Normalized
by: Gambhir, Rikab, et al.
Published: (2025)
by: Gambhir, Rikab, et al.
Published: (2025)
Accurate and robust methods for direct background estimation in resonant anomaly detection
by: Das, Ranit, et al.
Published: (2024)
by: Das, Ranit, et al.
Published: (2024)
stanhf: HistFactory models in the probabilistic programming language Stan
by: Fowlie, Andrew
Published: (2025)
by: Fowlie, Andrew
Published: (2025)
Non-resonant Anomaly Detection with Background Extrapolation
by: Bai, Kehang, et al.
Published: (2023)
by: Bai, Kehang, et al.
Published: (2023)
Tools for Unbinned Unfolding
by: Milton, Ryan, et al.
Published: (2025)
by: Milton, Ryan, et al.
Published: (2025)
Automatizing the search for mass resonances using BumpNet
by: Arguin, Jean-Francois, et al.
Published: (2025)
by: Arguin, Jean-Francois, et al.
Published: (2025)
On the Statistical Interpretation of Discoveries in LHC Data
by: Chekanov, S. V., et al.
Published: (2026)
by: Chekanov, S. V., et al.
Published: (2026)
Searching for Anomalies with Foundation Models
by: Mikuni, Vinicius, et al.
Published: (2026)
by: Mikuni, Vinicius, et al.
Published: (2026)
Effective Lagrangian Morphing
by: Balasubramanian, Rahul, et al.
Published: (2022)
by: Balasubramanian, Rahul, et al.
Published: (2022)
Kitchen Sink Anomaly Detection
by: Das, Ranit, et al.
Published: (2026)
by: Das, Ranit, et al.
Published: (2026)
Similar Items
-
Bilinear R-parity violating supersymmetry under the light of neutrino oscillation, higgs and flavor data
by: Choudhury, Arghya, et al.
Published: (2023) -
Markov Chain Monte Carlo analysis to probe trilinear $R$-parity violating SUSY scenarios and possible LHC signatures
by: Choudhury, Arghya, et al.
Published: (2024) -
Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
by: Choudhury, Arghya, et al.
Published: (2024) -
Status of R-parity violating SUSY
by: Choudhury, Arghya, et al.
Published: (2024) -
Improving sensitivity of trilinear RPV SUSY searches using machine learning at the LHC
by: Choudhury, Arghya, et al.
Published: (2023)