Name: Modern Machine Learning and Particle Physics
Brand: arxiv.org
SKU: arxiv-org-115
Availability: OnlineOnly

critic

Modern Machine Learning and Particle Physics

preview

Please join the and ourgenerousin supporting arXiv during our giving campaign September 23-27. 100% of your contribution will fundimprovements and new initiatives to benefit arXiv's global scientific community.[secure site, no need to create account]arXiv:2103.12226 (hep-ph)Over the past five years, modern machine learning has been quietlyrevolutionizing particle physics. Old methodology is being outdated andentirely new ways of thinking about data are becoming commonplace. This articlewill review some aspects of the natural synergy between modern machine learningand particle physics, focusing on applications at the Large Hadron Collider. Asampling of examples is given, from signal/background discrimination tasksusing supervised learning to direct data-driven approaches. Some comments onpersistent challenges and possible future directions for the field are includedat the end.Subjects:Cite as: (or for this version)From: Matthew Schwartz [][v1]Mon, 22 Mar 2021 23:22:33 UTC (1,656 KB)Current browse context: hep-phloading...Bibliographic Tools()Code()Related Papers()()About arXivLabsarXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.Have an idea for a project that will add value for arXiv's community? and .|()

Please join the and ourgenerousin supporting arXiv during our giving campaign September 23-27. 100% of your contribution will fundimprovements and new initiatives to benefit arXiv's global scientific community.[secure site, no need to create account]arXiv:2103.12226 (hep-ph)Over the past five years, modern machine learning has been quietlyrevolutionizing particle physics. Old methodology is being outdated andentirely new ways of thinking about data are becoming commonplace. This articlewill review some aspects of the natural synergy between modern machine learningand particle physics,...

public

arxiv.org

Credible

Speculation

Anecdotal Evidence

Generalization

Stacking the Deck

Appeal to Authority

Straw Man

Slippery Slope

Emotional Appeal

Red Herring

False Dilemma

Illogical

Moral Equivalency

Ad Hominem

Correlation w/o Causation

Semantics

Begging the Question

Faulty Analogy

Non Sequitur

Bandwagon

Dogmatism

Failing Occam's Razor

Biased

Pure Opinion

Hit Piece

Racial Bias

National Bias

Religious Bias

Financial Incentive

Political Agenda

Gender Bias

Misused Image/Video

Study Misinterpreted

Mistake

Misused Term

Science Misrepresented

Factual Error

Satire

Surface Level

Lack of Reliable Sources

Sensational

Not Credible

Investigative

Balanced

Well Sourced

Great Context

sports

entertainment

international

business

technology

science

culture

politics

media

health

Modern Machine Learning and Particle Physics

No article scores yet.

CRITIC REVIEWS

PUBLIC REVIEWS