Machine learning enabled measurements of astrophysical ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> ) reactions with the SECAR recoil separator
Bibliographic record
Abstract
The synthesis of heavy elements in supernovae is affected by low-energy <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mrow> <a:mo>(</a:mo> <a:mi>n</a:mi> <a:mo>,</a:mo> <a:mi>p</a:mi> <a:mo>)</a:mo> </a:mrow> </a:math> and <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"> <b:mrow> <b:mo>(</b:mo> <b:mi>p</b:mi> <b:mo>,</b:mo> <b:mi>n</b:mi> <b:mo>)</b:mo> </b:mrow> </b:math> reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of a nucleus significantly. We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:mrow> <c:mo>(</c:mo> <c:mi>p</c:mi> <c:mo>,</c:mo> <c:mi>n</c:mi> <c:mo>)</c:mo> </c:mrow> </c:math> reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. A new measurement of the <d:math xmlns:d="http://www.w3.org/1998/Math/MathML"> <d:mrow> <d:mmultiscripts> <d:mi>Fe</d:mi> <d:mprescripts/> <d:none/> <d:mn>58</d:mn> </d:mmultiscripts> <d:mo>(</d:mo> <d:mi>p</d:mi> <d:mo>,</d:mo> <d:mi>n</d:mi> <d:mo>)</d:mo> <d:mmultiscripts> <d:mi>Co</d:mi> <d:mprescripts/> <d:none/> <d:mn>58</d:mn> </d:mmultiscripts> </d:mrow> </d:math> reaction in inverse kinematics with a <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"> <e:mrow> <e:mn>3.66</e:mn> <e:mo>±</e:mo> <e:mn>0.12</e:mn> </e:mrow> </e:math> MeV/nucleon <f:math xmlns:f="http://www.w3.org/1998/Math/MathML"> <f:mmultiscripts> <f:mi>Fe</f:mi> <f:mprescripts/> <f:none/> <f:mn>58</f:mn> </f:mmultiscripts> </f:math> beam (corresponding to <g:math xmlns:g="http://www.w3.org/1998/Math/MathML"> <g:mrow> <g:mn>3.69</g:mn> <g:mo>±</g:mo> <g:mn>0.12</g:mn> </g:mrow> </g:math> MeV proton energy in normal kinematics) yielded a cross-section of <h:math xmlns:h="http://www.w3.org/1998/Math/MathML"> <h:mrow> <h:mn>20.3</h:mn> <h:mo>±</h:mo> <h:mn>6.3</h:mn> </h:mrow> </h:math> mb and served as a proof of principle experiment for the new technique demonstrating its effectiveness in achieving the required performance criteria. This novel approach paves the way for studying astrophysically important <i:math xmlns:i="http://www.w3.org/1998/Math/MathML"> <i:mrow> <i:mo>(</i:mo> <i:mi>p</i:mi> <i:mo>,</i:mo> <i:mi>n</i:mi> <i:mo>)</i:mo> </i:mrow> </i:math> reactions on unstable nuclei produced at FRIB.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".