An implementation of neural simulation-based inference for parameter estimation in ATLAS
Kaydedildi:
| Yayımlandı: | arXiv.org (Dec 2, 2024), p. n/a |
|---|---|
| Yazar: | |
| Baskı/Yayın Bilgisi: |
Cornell University Library, arXiv.org
|
| Konular: | |
| Online Erişim: | Citation/Abstract Full text outside of ProQuest |
| Etiketler: |
Etiket eklenmemiş, İlk siz ekleyin!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3139000063 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3139000063 | ||
| 045 | 0 | |b d20241202 | |
| 100 | 1 | |a ATLAS Collaboration | |
| 245 | 1 | |a An implementation of neural simulation-based inference for parameter estimation in ATLAS | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 2, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a neural simulation-based inference framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses. | |
| 653 | |a Simulation | ||
| 653 | |a Quarks | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Neural networks | ||
| 653 | |a Higgs bosons | ||
| 653 | |a Histograms | ||
| 653 | |a Confidence intervals | ||
| 653 | |a Parameter sensitivity | ||
| 653 | |a Statistical methods | ||
| 653 | |a Statistical inference | ||
| 653 | |a Large Hadron Collider | ||
| 653 | |a Uncertainty analysis | ||
| 653 | |a Leptons | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Couplings | ||
| 773 | 0 | |t arXiv.org |g (Dec 2, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3139000063/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.01600 |