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Record W2336583268

First year of data taking at high energy for MoEDAL

2016· article· en· W2336583268 on OpenAlexaboutno aff
Cern Bulletin

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

MoEDAL - the LHC experiment designed to search for highly ionising avatars of new physics, such as magnetic monopoles or massive pseudo-stable charged particles - has collected data from p-p and Pb-Pb collisions at high energy. A unique feature of the experiment is its membership, which includes a high school. Visualisation of the MoEDAL array in the LHCb cavern. (Photo: MoEDAL Collaboration) MoEDAL’s physics programme is investigating more than 34 scenarios that could yield potentially revolutionary insights into such fundamental questions as: are there extra dimensions or new symmetries? Where does the mechanism for the generation of mass originate from? Does magnetic charge exist? What is the nature of dark matter? How did the Big Bang develop? MoEDAL employs a number of unconventional methodologies, specially tuned to the prospect of discovery physics. The largely passive MoEDAL detector, deployed at Point 8 on the LHC ring, works in two ways. First, it acts like a giant camera, comprised of nuclear track detectors - analysed offline by ultra-fast scanning microscopes - sensitive only to new physics. Second, it is uniquely able to trap highly ionising particle messengers of physics beyond the Standard Model - for example, the magnetic monopole - for further study. Last but not least, MoEDAL's radiation environment is monitored by a state-of-the-art real-time TimePix pixel detector array. View of the MoEDAL experiment, located at Point 8 on the LHC ring. (Photo: MoEDAL Collaboration.) The full MoEDAL detector was installed in the winter of 2014 and started to take data officially in the spring of 2015. “In parallel to completing the installation of our detectors, we are analysing the first data from 2012 taken by test detectors, which were exposed to proton-proton and heavy-ion collisions,” says James Pinfold, physicist from the University of Alberta and the Spokesperson for the MoEDAL collaboration. “Our first physics result paper, based on data taken with a prototype trapping detector deployed for 8 TeV centre-of-mass energy collisions, is in its final stages of preparation. We now have our first year of proton-proton data taken at 13 TeV and Pb-Pb collisions and we look forward to the restart of the LHC in March.” Members of the MoEDAL experiment at the Royal Society Summer Exhibition “on shift” at the first soirée. From left to right: Arttu Rajantie (Imperial College London), Edward Gillman (Imperial College London), Mairi Sakellariadou (King's College London), James Pinfold (University of Alberta), Anna Evans (Simon Langton School), Caitlin Cooke (Simon Langton School) and Becky Parker (Simon Langton School). (Photo: MoEDAL Collaboration.) MoEDAL’s sensitivity is complementary to that of the other LHC detectors, extending the discovery horizon of the LHC using detectors that are insensitive to all Standard Model particles and immune to a background of fake signals. Importantly, MoEDAL is the first collider detector to be able to provide a permanent record of discovery physics that can be examined time and again. The MoEDAL collaboration has tripled in size since its final approval in 2010 and now includes 66 physics groups from over 20 institutes on four continents. Alongside established research institutes and universities, the collaboration can also count on the unique contribution of some 20 students from the Simon Langton Grammar School in the UK. “The Langton School students are renowned for their expertise with the TimePix detector, which is one of the key detectors of our apparatus,” confirms Pinfold. In 2015, the Royal Society of London chose to feature CERN’s MoEDAL experiment and its “Monopole Quest” at its Summer Science Exhibition. “Visitors took part in a number of activities,” recalls Pinfold. “They could design their own monopole detector, take part in the Citizen Science project to search online for monopole tracks in exposed MoEDAL plastic nuclear track detectors, and test MoEDAL trapping volumes for captured monopoles. In addition, thanks to an app developed by the students of the Langton School, they could visualise a Dirac monopole and investigate radioactivity on their cell phones using a MoEDAL TimePix Chip.” A truly amazing experience for the thousands of people who visited the exhibit!

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.166
GPT teacher head0.362
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes1
Has abstractyes

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