HOLISMOKES
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: Observational
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.079
- Threshold uncertainty score
- 0.856
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.183 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Modeling the mass distributions of strong gravitational lenses is often necessary in order to use them as astrophysical and cosmological probes. With the large number of lens systems (≳10 5 ) expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional Markov chain Monte Carlo techniques that are time consuming. We train a convolutional neural network (CNN) on images of galaxy-scale lens systems to predict the five parameters of the singular isothermal ellipsoid (SIE) mass model (lens center x and y , complex ellipticity e x and e y , and Einstein radius θ E ). To train the network we simulate images based on real observations from the Hyper Suprime-Cam Survey for the lens galaxies and from the Hubble Ultra Deep Field as lensed galaxies. We tested different network architectures and the effect of different data sets, such as using only double or quad systems defined based on the source center and using different input distributions of θ E . We find that the CNN performs well, and with the network trained on both doubles and quads with a uniform distribution of θ E > 0.5″ we obtain the following median values with 1 σ scatter: Δ x = (0.00 −0.30 +0.30 )″, Δ y = (0.00 −0.29 +0.30 )″, Δ θ E = (0.07 −0.12 +0.29 )″, Δ e x = −0.01 −0.09 +0.08 , and Δ e y = 0.00 −0.09 +0.08 . The bias in θ E is driven by systems with small θ E . Therefore, when we further predict the multiple lensed image positions and time-delays based on the network output, we apply the network to the sample limited to θ E > 0.8″. In this case the offset between the predicted and input lensed image positions is (0.00 −0.29 +0.29 )″ and (0.00 −0.31 +0.32 )″ for the x and y coordinates, respectively. For the fractional difference between the predicted and true time-delay, we obtain 0.04 −0.05 +0.27 . Our CNN model is able to predict the SIE parameter values in fractions of a second on a single CPU, and with the output we can predict the image positions and time-delays in an automated way, such that we are able to process efficiently the huge amount of expected galaxy-scale lens detections in the near future.
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.
The record
- Venue
- Astronomy and Astrophysics
- Topic
- Galaxies: Formation, Evolution, Phenomena
- Field
- Physics and Astronomy
- Canadian institutions
- not available
- Funders
- Lawrence Berkeley National LaboratoryJapan Society for the Promotion of ScienceSmithsonian Astrophysical ObservatoryUniversity of Colorado BoulderInstituto de Astrofísica de CanariasOffice of ScienceMax-Planck-Institut für AstronomieMax-Planck-Institut für AstrophysikMax-Planck-GesellschaftMinistério da Ciência, Tecnologia e InovaçãoCabinet Office, Government of JapanAcademia SinicaDeutsche ForschungsgemeinschaftUniversity of OxfordYork UniversityCarnegie Institution for ScienceUniversidad Nacional Autónoma de MéxicoSpace Telescope Science InstituteLeibniz-GemeinschaftUniversity of Notre DameYale UniversityStrongCarnegie Mellon UniversityPrinceton UniversityAlfred P. Sloan FoundationUniversity of WashingtonJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahToray Science FoundationHigh Energy Accelerator Research OrganizationUniversity of TokyoOhio State UniversityJapan Science and Technology AgencyU.S. Department of EnergySmithsonian InstitutionNational Astronomical Observatory of JapanNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityNational Aeronautics and Space AdministrationMinistry of Education, Culture, Sports, Science and Technology
- Keywords
- GalaxyGravitational lensMarkov chain Monte CarloLens (geology)Convolutional neural networkMass distributionEllipsoidEinstein radiusWeak gravitational lensingOffset (computer science)
- Has abstract in OpenAlex
- yes