Searching for Wide Companions and Identifying Circum(sub)stellar Disks through PSF Fitting of Spitzer/IRAC Archival Images
Bibliographic record
Abstract
Abstract Direct imaging surveys have discovered wide-orbit planetary-mass companions that challenge existing models of both star and planet formation, but their demographics remain poorly sampled. We have developed an automated binary companion point-spread function (PSF) fitting pipeline to take advantage of Spitzer's infrared sensitivity to planetary-mass objects and circum(sub)stellar disks, measuring photometry across the four Infrared Array Camera (IRAC) channels of 3.6, 4.5, 5.8, and 8.0 μm. We present PSF fitting photometry of archival Spitzer/IRAC images for 11 young, low-mass (M ∼ 0.044–0.88 M ⊙; K3.5–M7.5) members of three nearby star-forming regions (Chameleon, Taurus, and Upper Scorpius; d ∼ 150 pc; τ ∼ 1–10 Myr) that host confirmed or candidate faint companions at ρ = 1.″68–7.″31. We recover all system primaries, six confirmed, and two candidate low-mass companions in our sample. We also measure nonphotospheric [3.6]–[8.0] colors for three of the system primaries, four of the confirmed companions, and one candidate companion, signifying the presence of circumstellar or circum(sub)stellar disks. We furthermore report the confirmation of a ρ = 4.″66 (540 au) companion to [SCH06] J0359+2009 which was previously identified as a candidate via imaging over five years ago, but was not studied further. Based on its brightness (M [3.6] = 8.53 mag), we infer the companion mass to be M = 20 ± 5 M Jup given the primary’s model-derived age of 10 Myr. Our framework is sensitive to companions with masses less than 10 M Jup at separations of ρ = 300 au in nearby star-forming regions, opening up a new regime of parameter space that has yet to be studied in detail, discovering planetary-mass companions in their birth environments and revealing their circum(sub)stellar disks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".