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Record W2247426959

Active Targets for Experiments with Rare Isotopes

2014· article· en· W2247426959 on OpenAlexaboutno aff
I. Wiedenhoever

Bibliographic record

VenueBulletin of the American Physical Society · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear physicsProtonDetectorPhysicsBeam (structure)Nuclear engineeringOpticsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Experimental studies of un-bound nuclear states and nuclear reaction rates relevant for astrophysical processes are an important area of research with rare isotope beams. Both topics require the development of specialized experimental methods to study resonant reactions. The so-called active target approach, where the target material becomes part of the detection process, promises to combine high yields from thicker targets and low background with high resolution. This presentation will describe the implementation of the active-target technique in the ANASEN detector, which was developed by researchers from Louisiana State University and Florida State University. ANASEN was used in a number of stable and rare iosotope experiments in α– and proton scattering, as well as (α, p) and (d, p) reactions at FSU’s in-flight radioactive beam facility RESOLUT. ANASEN also was used to perform the first experiment, proton scattering off a 37K beam at the ReA3 facility. Another active-target detector with a very different approach is found in the Active Target Time-Projection Chamber, which was developed by a collaboration between researchers from MSU, the University of Notre Dame, Western Michigan University, LLNL, LBNL, and St. Mary’s University (Canada). First experiments with an AT-TPC prototype have been reported [1]. The talk will summarize the results from the first experiments with these systems, describe further development and future research projects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.240
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2014
Admission routes1
Has abstractyes

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