Optical and near-infrared s-process abundance scales in the Galactic disc

Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: We're looking at the paper: "Optical and near-infrared s-process abundance scales in the Galactic disc".

Jocelyn: Here's the gist of the paper. The gist The SDSS-V pipeline expands intrinsic Nd variations by a scaling factor of 5.54 relative to the stable optical baseline, which is driven by baseline compression in the APOGEE H-band where molecular forest growth depresses the local continuum Data and Methodology The study investigates a discrepancy between optical and near-infrared (NIR) s-process abundance scales, specifically for Neodymium (Nd), using a direct cross-match between the optical Gaia-ESO Survey (DR5) and NIR SDSS-V/MWM (DR19).

Vera: The researchers utilized two major spectroscopic datasets: the fifth internal data release of the Gaia-ESO Survey (GES DR5) and the nineteenth data release of the Sloan Digital Sky Survey (SDSS-V/MWM DR19).

Jocelyn: The Gaia-ESO Survey primarily focused on OCs, dedicating approximately 30% of its 340 nights to these targets to provide a homogeneous and unbiased sample.

Vera: For high-precision chemical analysis, the data reduction and spectral analysis were performed using a multi-pipeline strategy internally homogenised by the survey working groups.

Title and authors: Vera: So we’re looking at this paper, "Optical and near-infrared s-process abundance scales in the Galactic disc." It addresses a real problem astronomers have been seeing where how they measure neutron capture elements like Neodymium, or Nd, in optical surveys doesn't match what they see in near-infrared surveys.

Jocelyn: So it's not some physical trend across the Galaxy; it’s an artifact of how the data is being processed in those infrared bands when dealing with high metallicities, where those molecules get dense.

Jocelyn: So for someone just listening to the show, it means that when you look at these massive near-infrared datasets from SDSS-V or APOGEE, you can use them for studying structure formation and chemical enrichment as long as you apply this scaling factor of five point five four relative to the optical baseline.

Jocelyn: It gives us a much cleaner way to interpret those infrared measurements for Nd and other weak neutron capture features, because now we know what the systematic error is.

The paper's summary: Vera: So, this paper is basically about how we measure the abundance of Neodymium, or Nd, in two different ways—using optical surveys versus near-infrared ones—and showing that they don't match up without some serious adjustment.

Jocelyn: Right. They’re comparing the Gaia-ESO Survey data with the SDSS-V MWM data to see where those scales diverge when we talk about s-process elements like Nd.

Subrahmanyan: The main finding is that this divergence isn't just random noise in the measurements, it’s caused by something happening in how the light is being measured. Specifically, they found a scaling factor of five point five four applied to the intrinsic variations in Nd when you look at those near-infrared SDSS-V results compared to the stable optical baseline.

Vera: That scaling factor is what they use to bridge that gap, and they explain *why* it’s there. It comes down to a baseline compression effect happening in the APOGEE H-band, where the molecular forest—those carbon and oxygen molecules—grows at high metallicities and it depresses the local continuum.

Jocelyn: So because that continuum is depressed, when you run the abundance determination engine in those near-infrared pipelines, it gets forced to inflate its derived abundances just to get a mathematical fit. It’s line-blanketing causing the inflation.

Subrahmanyan: Exactly. They show this isn't some big physical trend sweeping across the Galaxy; it's an artifact of how those specific infrared measurements are processed when they hit high metallicities, where those molecules get dense.

Vera: And to fix this, they developed an empirical calibration framework. It’s a tool that lets researchers convert those near-infrared abundances into equivalent optical standards, which brings them back in line with the optical data we already trust so much.

Jocelyn: That correction is important because it directly fixes a problem in Galactic chemical evolution modeling. It eliminates what they call that contradictory negative radial

Nd/Fe: gradient of minus zero point zero five two dex per kiloparsec that some recent near-infrared surveys had reported.

Subrahmanyan: That negative gradient was pulling the models in one direction, while the optical high-resolution Gaia-ESO survey showed a rising trend. This correction aligns them perfectly with that rising optical trend, which is what we expect physically.

Jocelyn: That’s right. The paper confirms that the scale expansion is a spectroscopic artifact driven by line-blanketing, not some hidden physical process we haven't modeled yet.

Vera: It sets up a roadmap for future high-resolution spectroscopic surveys by showing that automated infrared pipelines have to be anchored firmly to established optical baselines when analyzing these kinds of elements.

Jocelyn: Next time we talk about chemical evolution models, we’ll need to make sure this calibration tool is applied consistently across all the data sources.

The paper's improvements: Vera: They propose an improvement to the AI system that can do automated cross-survey reconciliation of chemical abundance scales by applying an empirical calibration framework to correct systematic biases between different spectroscopic surveys like Gaia-ESO and SDSS-V/MWM DR19.

Jocelyn: That means we can use AI to automatically apply a correction when comparing data from one survey to another, which should make the numbers much more consistent across the board.

Subrahmanyan: And they also suggest that the AI system can detect and quantify spectroscopic artifacts in near-infrared data, specifically identifying baseline compression caused by molecular forest growth at high metallicities that artificially inflates derived abundances for weak lines like Nd II.

Vera: That’s a big deal because it means we can use those infrared datasets with more confidence now that we know exactly what's causing the artificial inflation in the first place.

Jocelyn: They also suggest an improved AI system to develop a calibration tool that converts near-infrared abundances into equivalent optical standards using this formula,

Nd/H: cor equals zero point one eight one times

Nd/H: S DS S DR19 plus zero point one zero one.

Subrahmanyan: That formula is a concrete way to do the conversion, and it sounds like a practical step toward making these different datasets truly compatible.

Vera: And they suggest that this improved AI system can automatically correct contradictory radial abundance gradients in Galactic chemical evolution models by applying that scaling factor of five point five four to SDSS-V results, aligning them with optical high-resolution standards and eliminating the negative gradient of minus zero point zero five two dex per kiloparsec.

Jocelyn: That directly tackles one of the biggest problems in modeling how the Galaxy has built up its elements over time. If that gradient is fixed, our models for chemical enrichment should look much more realistic.

Subrahmanyan: So, they’re not just reporting an artifact; they’re providing a practical method—a roadmap for future high-resolution spectroscopic surveys—on how to handle these weak neutron capture features consistently.

Vera: The limitation they mention is that this method is empirical, so it relies on the calibration framework being accurate across all the different stellar populations they are looking at.

Jocelyn: So while this fixes the current discrepancy, we still need to keep looking for any physical processes that might be driving these variations in the first place.

Subrahmanyan: It confirms that we can use those massive near-infrared datasets for studying chemical enrichment and structure formation, provided you use this correction to ensure consistency across all regimes.

Conclusion: Vera: So, to wrap up on "Optical and near-infrared s-process abundance scales in the Galactic disc," this paper confirms that what we see is largely an artifact of line-blanketing in the infrared data, not a deep physical trend in the Galaxy.

Jocelyn: Right. They proved that by applying this empirical calibration framework, you can reconcile those contradictory radial

Nd/Fe: gradients we’ve been seeing between optical and near-infrared surveys.

Subrahmanyan: From a theoretical standpoint, it means our models of s-process yields in the disk have to account for this systematic shift when extrapolating from one wavelength regime to another.

Vera: It basically gives us a unified way to use these massive near-infrared datasets while keeping them consistent with the high-resolution optical benchmarks we rely on so much.

Jocelyn: I think it’s a big deal because it validates using the infrared data for chemical evolution studies, provided you use this correction to ensure consistency across all the different survey regimes.

Subrahmanyan: If we can reliably use these large datasets for structure formation studies, that opens up new avenues for connecting stellar chemistry directly to how gas flows and forms stars in the Milky Way.

Vera: It’s a concrete finding because it shows exactly where the errors are coming from, which is vital when you’re trying to constrain things like nucleosynthesis yields.

Jocelyn: Right.