Preliminary census of galaxies in the LISA localization volume – I. Searching for LISA candidate massive black hole binary merger hosts using Sloan Digital Sky Survey photometry
Bibliographic record
Abstract
ABSTRACT With the launch of the Laser Interferometer Space Antenna (LISA), we will be able to estimate the sky position, luminosity distance (dL), chirp mass, and mass ratio for detected merging massive black hole binary (MBHB) systems. LISA’s uncertainties on these estimates will evolve over time, and enable electromagnetic (EM) follow-up observations as early as a month from coalescence. In this paper, we create a framework that takes simulated LISA parameter estimates for sky localization and dL for an MBHB and performs a census of matching EM galaxies, or candidate host galaxies. We used this framework to investigate these parameter estimates for simulated MBHB systems with masses of $3\times 10^{5}$, $3\times 10^{6}$, and $1\times 10^{7}$ M$_{\odot }$ at redshifts of 0.3 and 0.5 and used these parameters to select matching galaxies from archival Sloan Digital Sky Survey (SDSS) photometry. We found that the number of candidate host galaxies for a simulated MBHB system at a redshift of 0.3 and 1 h from coalescence ranged from tens to thousands. After coalescence, we found that our census numbers dropped to zero for all systems when considering median constraints most likely due to survey limitations. For an MBHB with mass $3\times 10^{6}$ M$_{\odot }$ at 1 h from coalescence, increasing the redshift from 0.3 to 0.5 or varying the sky position within the SDSS footprint resulted in the number of EM counterparts increasing by approximately a factor of 2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".