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Record W7023925658

Probes of new physics at the intensity and energy frontiers

2018· dissertation· en· W7023925658 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2018
Typedissertation
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsnot available
FundersInstitut Périmètre de physique théoriqueNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaNational Science Foundation
KeywordsNucleofectionFusible alloyGloomHyporeflexiaSubpoenaLiquation
DOInot available

Abstract

fetched live from OpenAlex

In this thesis, we review the basics of phenomenology in particle physics at neutrino beam dump experiments and hadron colliders. We then consider the phenomenology of various new particles, with masses between 1 MeV and 1 TeV, at the intensity and energy frontiers. We perform sensitivity analyses for physics beyond the standard model at particle colliders (LEP and LHC) and a number of past and future neutrino beam dump experiments (SHiP, DUNE, LSND, MicroBooNE, MiniBooNE and SBND). In particular, we motivate searches for new heavy neutral leptons in single photon events at neutrino and collider experiments (and also via supernova cooling), millicharged particles in single electron events at neutrino experiments, lepton flavor violating scalars via standard model induced mixed flavor neutrino trident production at neutrino experiments, and colored scalar doublets at colliders in events with many jets, soft leptons and low missing energy. In the process, we set novel new bounds on the parameters of these theories and propose powerful new searches that can be performed. We also motivate the construction of a new detector at the LHC called milliQan, and perform a full Geant4 simulation to calculate its projected sensitivity for millicharged particles. The milliQan experiment has since been approved and is currently undergoing construction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.180
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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