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BOSS-CLAM infers Teff, logg, [Fe/H], and [α/M] from low-resolution BOSS spectra via a generative forward model, producing a clean catalog of 915,514 stars.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-01 04:23 UTC pith:Y6BX2BNN

load-bearing objection Solid, useful generative pipeline with a large public catalog, but the headline precision and low-metallicity accuracy rest on partly in-sample validation and an untested scale extrapolation. the 4 major comments →

arxiv 2607.22822 v2 pith:Y6BX2BNN submitted 2026-07-24 astro-ph.SR astro-ph.GAastro-ph.IM

BOSS-CLAM: Utilizing a Constrained Linear Absorption Model to Infer Stellar Parameters from BOSS Spectra

Ilija Medan , Andrew R. Casey , Alexander P. Ji , Jonah M. Otto , Kayvon Sharifi , Zachary Way , Madeleine McKenzie , Natalie R. Myers
show 8 more authors
Keivan G. Stassun Peter J. Smith Andrew Tkachenko Vedant Chandra Michael R. Blanton Peter M. Frinchaboy Guy S. Stringfellow Sean Morrison
This is my paper
classification astro-ph.SR astro-ph.GAastro-ph.IM
keywords BOSS-CLAMstellar parametersNMFgenerative spectral modelSDSS-V BOSSmetallicityalpha abundanceGalactic archaeology
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

BOSS-CLAM is a generative, forward-modeling pipeline that estimates effective temperature, surface gravity, iron abundance, and alpha-element abundance from the relatively low-resolution optical BOSS spectra collected by SDSS-V. The model represents each continuum-normalized spectrum as a non-negative combination of learned absorption basis vectors, and optimizes a smooth polynomial mapping from stellar labels to the combination weights in the same fit that learns the basis. Training labels are stitched from four sources to cover cool dwarfs through hot OB stars, and the paper reports parameters for 1,708,214 DR20 spectra, with a clean subset of 915,514 sources. The authors argue this approach avoids the attenuation bias of discriminative models, and validate the results on open and globular clusters, wide binaries, and duplicate observations from two telescopes. If correct, it provides a public catalog an order of magnitude larger than current optical SDSS-V parameter sets, usable for Galactic archaeology and chemical tagging.

Core claim

The paper's central claim is that stellar labels can be inferred from BOSS spectra by forward modeling rather than classification: learned NMF basis vectors encode absorption features, and a quadratic polynomial maps Teff, logg, [Fe/H], and [α/M] to non-negative weights, with the mapping and basis optimized jointly on a multi-source label set. Because the model generates spectra from labels rather than regressing labels from spectra, the noise in the low-resolution spectrum enters only the variance, not the bias, of inferred parameters. The paper further claims that this yields homogeneous abundances from cool M dwarfs through hot OB stars, recovers cluster abundances across a wide metallici

What carries the argument

Non-negative Matrix Factorization (NMF): spectra are decomposed as 1 − WH, where H are non-negative basis absorption spectra and W are non-negative weights. The weights are generated from standardized labels through a quadratic feature map with a learned coefficient matrix and a softplus nonlinearity. The same objective optimizes the basis, the mapping, a per-pixel scatter term, and the labels themselves, with a label-regularization term that allows imperfect or partially missing labels (hot stars have no [Fe/H] or [α/M] labels). This joint optimization is what lets the method adapt to lower resolution and imperfect continuum normalization.

Load-bearing premise

The catalog's abundance scale is stitched by empirical corrections onto an infrared-derived label scale, and for [Fe/H] < −1.5 the correction is an untested constant extrapolation; if that scale transfer fails, the low-metallicity and alpha-abundance results are biased without any flag being triggered.

What would settle it

Compare BOSS-CLAM [Fe/H] and [α/M] with high-resolution optical abundances for a sample of metal-poor giants with [Fe/H] < −1.5. If the constant offset used below −1.5 is wrong, the difference should trend with [Fe/H] and exceed the claimed ~0.15 dex scatter; the paper reports no such external check in that regime.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the scale transfer holds, BOSS-CLAM delivers roughly 915k stars with trustworthy Teff, logg, [Fe/H], and [α/M] in a single homogeneous catalog, an order-of-magnitude expansion for optical SDSS-V stellar parameters.
  • The claimed abundance precision at SNR 10—σ[Fe/H] ≈ 0.15 dex and σ[α/M] ≈ 0.06 dex—means faint, low-SNR BOSS targets become usable for population studies, extending Galactic archaeology to fainter stars.
  • Cluster validation indicates the pipeline avoids the systematic metallicity biases seen in a discriminative neural-net baseline, particularly at the metal-poor end.
  • The trained generative model can synthesize BOSS-like spectra for arbitrary stellar labels, enabling construction of mock surveys and testing of selection functions.
  • Recovery of known thin/thick disk chemical sequences and Magellanic Cloud chemistry supports use of the catalog for chemical tagging and disk-structure studies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extension: because the model generates spectra from labels, the same architecture could be retrained on other R≈2000 surveys, turning heterogeneous label sets into a homogeneous catalog without waiting for a single high-resolution survey to cover the whole HR diagram.
  • Extension: the constant extrapolation below [Fe/H] = −1.5 is the point most worth stress-testing; a dedicated high-resolution optical sample of metal-poor giants would either confirm the extrapolation or reveal a low-metallicity bias that the current flagging system would not catch.
  • Extension: the reported [α/M]–[Fe/H] degeneracy for alpha-rich stars suggests that adding carbon and nitrogen labels, as the paper notes in passing, might also tighten the alpha-abundance estimates, and this is a concrete testable improvement.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper presents BOSS-CLAM, a generative forward-modeling pipeline that infers Teff, logg, [Fe/H], and [α/M] from continuum-normalized SDSS-V BOSS spectra. The model maps stellar labels to NMF basis weights through a quadratic polynomial (P=15) and jointly optimizes the spectral decomposition, the label-to-weight mapping, and a per-pixel scatter term during training; at inference the labels are optimized with fixed spectral model. Training labels are drawn from four sources: ASPCAP, BOSS-MINESweeper, wide binaries, and a hot-star validation sample. The pipeline is applied to 1,708,214 DR20 spectra, with a recommended clean catalog of 915,514 sources. Validation includes open and globular clusters, wide binaries, APO/LCO repeatability, and comparisons against eight external catalogs. The paper claims accurate abundances across a wide HR range and wide metallicity range, with σ[Fe/H]≈0.15 dex and σ[α/M]≈0.06 dex at SNR=10.

Significance. If the accuracy claims hold, BOSS-CLAM is a major community resource: it provides a public, validated, order-of-magnitude larger stellar-parameter catalog for the SDSS-V BOSS sample, with release of the pipeline, trained model, and catalog. The methodological core is sound and unusually well specified: Eq. (6) gives the full training objective, the generative formulation mitigates attenuation bias relative to discriminative models, and the validation design includes genuinely external benchmarks (wide binaries, APO/LCO repeatability, cross-survey comparisons). The explicit flagging system based on covariance correlations and targeting information is a useful contribution. The main weakness is that the absolute abundance scale at low metallicity — a load-bearing part of the abstract's 'wide range of metallicity' claim — rests on an extrapolated correction.

major comments (4)
  1. [§2.2 and §5.1 / Eq. (1)] The low-metallicity abundance scale is set by an extrapolation that is not independently validated. The ASPCAP 'Nominal' training sample deliberately removes [Fe/H]<-1.5 and logg<3.5 (§2.1), so the quadratic correction in Eq. (1) is fitted only for [Fe/H]>-1.5; below -1.5 the correction is frozen at Δ[Fe/H](-1.5). The M92 validation (§5.1 and Fig. 10) is not fully external because the BOSS-MINESweeper VAC (Chandra et al. 2026) supplies both the corrected training labels and the literature cluster abundances used in Fig. 11. Agreement with M92 therefore demonstrates internal consistency with the training scale, not absolute accuracy at [Fe/H]≈-2.3. Please add an external high-resolution low-metallicity comparison (e.g., GALAH or other optical high-res samples) or explicitly restrict the abstract's accuracy claim in the low-metallicity regime.
  2. [§4.1] The DESI comparison shows a metallicity offset that the authors attribute to 'a difference in abundance measurements in the optical and infrared' or to model differences. This bears directly on the choice to train BOSS-CLAM on infrared-derived ASPCAP labels for optical BOSS spectra. If optical and infrared abundance scales differ, the ASPCAP-based zero point is not automatically transferable to BOSS spectra. The paper should quantify this risk, for example by reporting the DESI offset in [Fe/H] and [α/M] as a function of SNR and stellar type, and by checking whether an independent optical high-resolution sample (GALAH) shows the same offset pattern in the same regime.
  3. [§5.1 / Fig. 11] The cluster validation does not quantify the [α/M] systematics that the text acknowledges ('BOSS-CLAM can under- or over-estimate the value relative to the literature for all clusters'). Since [α/M] is a primary product and the wide-binary test quotes σ≈0.06 dex at SNR=10, the cluster comparison should report per-cluster mean offsets and RMS for both [Fe/H] and [α/M]. Without a numerical summary, the claim of 'homogeneous, accurate abundances' is not fully supported for [α/M].
  4. [§3.1/3.2 and Fig. 3] The train/test split is not clearly specified. The text says seven stars per bin are selected into the training set, but then states 'we run this inference step on all of the data from our four groups' when describing the comparison in Fig. 3. If Fig. 3 includes stars used in training, the quoted scatter and MAD are optimistic. Please state explicitly which stars were held out and report held-out-only metrics.
minor comments (4)
  1. [§2.1] The prose says 'removing stars where the spectrum fit and [Fe/H] were flagged as bad' but the filter list includes `flag_bad = False` and `fe_h_flags = 0`. Clarify what `flag_bad` refers to (ASPCAP fit vs. spectrum-level flag).
  2. [Eq. (6)] The per-pixel scatter is denoted s_m in the equation but s_λ in the text. Use a single notation for clarity.
  3. [Fig. 9 caption] The bottom panel's bar heights are described as counts of stars with each flag bit set, but the caption should state explicitly that a star can contribute to multiple bars if it has multiple flags.
  4. [Abstract / §5.1] The abstract's 'accurate abundances across a wide range of metallicity' is stronger than what the cluster test demonstrates, given the [α/M] systematics acknowledged in §5.1. Consider softening to 'precise and internally homogeneous' or adding a quantitative statement.

Circularity Check

2 steps flagged

Wide-binary precision headline is in-sample: the same binaries that define the low-mass training labels yield the quoted sigma[Fe/H] and sigma[alpha/M].

specific steps
  1. fitted input called prediction [Section 2.3 (training labels) and Section 5.2 / Figure 13 (wide-binary validation)]
    "To add data for the low-mass dwarfs in the training set, we utilize the wide binaries from K. El-Badry et al. (2021). ... With this, we then transfer the [Fe/H] and [alpha/M] from the ASPCAP parameters of the primary to the BOSS spectrum of the secondary. ... To probe this, we use the wide binaries from K. El-Badry et al. (2021) that meet the same quality cuts as in Section 2.3. ... implying that sigma[Fe/H] ~ 0.15 dex and sigma[alpha/M] ~ 0.06 at SNR = 10."

    The exact wide-binary catalog used to construct low-mass dwarf training labels (secondary [Fe/H]/[alpha/M] defined as the primary's ASPCAP values) is then used as the validation sample for the quoted precision. No holdout or exclusion is described, so the scatter in Figure 13 measures the model's ability to reproduce label assignments it was trained on, not an independent external accuracy estimate. The abstract's headline precision is therefore partly in-sample.

  2. fitted input called prediction [Section 5.1, Figure 10 (M67 cluster validation)]
    "The APOGEE training set, including M67, recovers the effects from diffusion processes near the turn-off, resulting in a shift of <= -0.1 dex (D. Souto et al. 2019), similar to what is seen here."

    M67 is acknowledged to be inside the APOGEE/ASPCAP training set that supplies the Nominal BOSS-CLAM labels. Using M67 as a validation cluster therefore partly checks in-sample reproduction of the training label scale rather than purely external accuracy. The intra-cluster scatter test retains some value, but the cluster's absolute metallicity agreement is not fully independent of the training data.

full rationale

The core BOSS-CLAM derivation is not definitionally circular: the NMF mapping Theta, basis H, and labels are jointly optimized against continuum-normalized BOSS spectra through the spectrum likelihood plus label regularization (Eq. 6), and the catalog is additionally compared with genuinely external surveys (GALAH, DESI, Gaia, LAMOST) and with APO/LCO repeatability. However, the headline precision claim in the abstract comes from the Section 5.2 wide-binary test, and that test uses the same El-Badry et al. (2021) binaries that supplied the low-mass dwarf training labels in Section 2.3, with secondary abundances defined by transfer from the primary. Because no holdout is described, the quoted sigma[Fe/H] ~ 0.15 dex and sigma[alpha/M] ~ 0.06 dex are in-sample scatter estimates. The M67 check is a milder version of the same issue, since the paper itself notes M67 is in the APOGEE training set. The low-metallicity extrapolation in Eq. (1) is a genuine accuracy risk rather than a circularity: the constant correction below [Fe/H] = -1.5 is untested, but the M92 comparison uses an external literature value (J.-W. Lee 2023), so that concern belongs to correctness risk, not to definitional reduction. Overall, the central precision claim is partially circular, while independent content remains in the cross-survey and repeatability checks.

Axiom & Free-Parameter Ledger

5 free parameters · 7 axioms · 0 invented entities

The central claim rests on the fidelity of four heterogeneous label sources placed on a common scale by two fitted corrections, and on the quadratic NMF-weight mapping capturing stellar physics across the HR diagram. No new physical entities are introduced; the 160 NMF basis vectors are data-driven representations. The model contains free capacity (K=160, P=15) that is fit to training data, and the cluster validation partially overlaps training labels (M67 acknowledged).

free parameters (5)
  • MINESweeper→ASPCAP [Fe/H] scale polynomial = -0.0204·[Fe/H]² - 0.0937·[Fe/H] - 0.119; constant below -1.5
    Eq. 1: fitted offset to merge the two label sources onto one scale. The catalog inherits this fitted scale, and the constant extrapolation below [Fe/H]=−1.5 is untested.
  • MINESweeper→ASPCAP [α/M] offset = 0.106
    A constant subtracted from BOSS-MINESweeper [α/M] values, fitted to align scales (Section 2.2). Not derived from physics.
  • SNR-scaling fit for abundance scatter = A=0.479, B=0.486 ([Fe/H]); A=0.157, B=0.411 ([α/M])
    Wide-binary scatter fit σ=A·SNR^-B. The quoted precision σ[Fe/H]≈0.15 dex at SNR=10 is this fit, not an independent measurement.
  • NMF basis count K and label weight λ_ℓ = K=160; λ_ℓ=1
    Hand-chosen hyperparameters (Section 3.1) that set mapping capacity and label-regularization strength; both affect inferred labels.
  • Training-set sampling scheme = 40 bins per axis; 7 stars per bin; 80% of hot stars
    Hand-chosen sampling (Section 3.1) determines the NMF basis and mapping coverage, thus the relative weighting of disk, dwarf, and giant regimes in training.
axioms (7)
  • domain assumption Continuum-normalized stellar spectra are representable as F̂ = 1 − WH with non-negative absorption basis H and non-negative weights W (Eq. 5).
    Core modeling assumption of BOSS-CLAM, carried from CLAM (Casey et al. 2026); absorption is purely additive after continuum normalization.
  • domain assumption ASPCAP labels measured in near-infrared APOGEE spectra remain valid labels for the same stars observed in optical BOSS spectra.
    Training transfers ASPCAP labels to BOSS spectra (Section 2.1). The DESI comparison (Section 4.1) hints that optical and infrared abundance scales may differ.
  • domain assumption A quadratic polynomial in four standardized labels (P=15 features) maps labels to NMF weights smoothly over the full HR diagram.
    Section 3.1: the Θ mapping is quadratic. The M dwarf underperformance suggests limited expressiveness or coverage in that regime.
  • domain assumption Wide binary components share identical [Fe/H] and [α/M], allowing primary-to-secondary label transfer after astrometric and photometric quality cuts.
    Section 2.3: standard common-origin assumption used to create low-mass dwarf training labels.
  • domain assumption Mann et al. (2015, 2019) photometric relations provide unbiased Teff and logg for the M dwarf training labels.
    Used for the low-mass dwarf training set (Section 2.3).
  • domain assumption Residual continuum-normalization errors are absorbed by the NMF latent representation rather than biasing inferred labels.
    Section 3 states NMF captures residual variation; Section 4.2 concedes poor continuum normalization can change inferred metallicity without being flagged.
  • standard math The per-pixel Gaussian likelihood with learned scatter s_m (Eq. 6) is a valid noise model for BOSS spectra.
    Standard likelihood construction; the log-variance term prevents trivial inflation of scatter.

pith-pipeline@v1.3.0-alltime-deepseek · 25931 in / 19593 out tokens · 183176 ms · 2026-08-01T04:23:02.943757+00:00 · methodology

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read the original abstract

Large spectroscopic surveys require robust pipelines capable of inferring stellar parameters over a wide range of the Hertzsprung-Russell (HR) diagram from data of varying quality. SDSS-V is one such survey, where the data from the lower-resolution, optical BOSS spectrograph will provide a large dataset covering a wide range of Galactic stellar populations. To better analyze these data, we present BOSS-CLAM, a generative, forward modeling pipeline for inferring effective temperature ($T_\mathrm{eff}$), surface gravity ($\log g$), metallicity ($[\mathrm{Fe/H}]$), and $\alpha-$abundance ($[\alpha/\mathrm{M}]$) from continuum-normalized BOSS spectra. BOSS-CLAM maps stellar labels to Non-negative Matrix Factorization (NMF) basis vector weights via a polynomial mapping jointly optimized with the spectral decomposition, which provides a more flexible framework for working with the lower-resolution BOSS data. Additionally, training labels are drawn from four complementary sources (ASPCAP, BOSS-MINESweeper, wide binaries, and a hot star validation sample), which enables coverage from cool M dwarfs through hot OB stars, and across a wide range of metallicity. We infer parameters for 1,708,214 BOSS spectra, with a recommended clean catalog of 915,514 sources. Validation against open and globular clusters demonstrates homogeneous, accurate abundances across a wide range of metallicity. Wide binary tests yield abundance uncertainties of $\sigma_{[\mathrm{Fe/H}]} \approx 0.15$ dex and $\sigma_{[\alpha/\mathrm{M}]} \approx 0.06$ dex at SNR = 10. Finally, we demonstrate that the BOSS-CLAM catalog recovers known chemical structure of the Milky Way disk and is well-suited for Galactic archaeology, chemical tagging, and stellar population modeling. The pipeline, trained model, and catalog are publicly released as part of SDSS-V DR20.

Figures

Figures reproduced from arXiv: 2607.22822 by Alexander P. Ji, Andrew R. Casey, Andrew Tkachenko, Guy S. Stringfellow, Ilija Medan, Jonah M. Otto, Kayvon Sharifi, Keivan G. Stassun, Madeleine McKenzie, Michael R. Blanton, Natalie R. Myers, Peter J. Smith, Peter M. Frinchaboy, Sean Morrison, Vedant Chandra, Zachary Way.

Figure 1
Figure 1. Figure 1: Difference between the stellar parameters of the labels used in this work (True; Section 2), and the stellar parameters from BOSSNet (top row) and BOSS-CLAM (bottom row). Such methods are sensitive to differences between the observed and synthetic spectra. These differences be￾come increasingly important at lower resolutions, where broad spectral features and continuum variations can dominate the fit. Addi… view at source ↗
Figure 2
Figure 2. Figure 2: Kiel diagram (left column) and [α/M] vs. [Fe/H] (right column) for the data sources used for the training set, as described in Section 2. The only data source not included in this figure is for the hot stars with 7000 < Teff < 53000 K. These are not included as there are no [Fe/H] or [α/M] data for these objects [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Inference results for the testing set from the BOSS-CLAM pipeline. On all plots, the x-axis shows the true parameters and the y-axis is the BOSS-CLAM parameters. The black dashed line is the one-to-one relation. The title for each panel gives the scatter and median absolute deviation between the true and inferred parameters. In all cases, a good agreement is observed between the true and inferred parameter… view at source ↗
Figure 4
Figure 4. Figure 4: Example spectrum fits from the inference step for different classes of stars in the training sample. In each section, the top panel shows the observed (black) and best fit forward modeled (red) spectra, the second panel is the residual, and the bottom four panels are the forward model spectrum derivatives with respect to each parameter. The right of each section shows a zoom-in on interesting spectral fits… view at source ↗
Figure 5
Figure 5. Figure 5: Kiel diagram showing the inferred parameters from the BOSS-CLAM. The left column shows the number counts and the right the median [Fe/H]. The top row shows the Kiel diagram for all parameters that meet our quality cuts (flag bad = False for number counts and clam flags = 0 for the median [Fe/H]) and the bottom row for spectra that also have SNR > 40. in the inferred parameters, we find a degeneracy with [α… view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of [α/M] vs. [Fe/H] using the inferred parameters from the BOSS-CLAM. The left panel shows the distribution for all parameters that meet our quality cuts (clam flags = 0), the middle panel for spectra that also have SNR > 40, and the right panel for spectra that also have SNR > 40 and are in the program mwm magcloud. panel; [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Derivative of forward modeled BOSS-CLAM spectra with respective to [α/M] for a Solar-like star (top row). The derivative is relative to [α/M] = 0 dex. The bottom rows show regions of the normalized spectra where we see the largest changes in the derivative, as shown in the top row. Each color corresponds to the normalized spectrum with varying [α/M]. Teff and log g correlation. The former are failures of o… view at source ↗
Figure 8
Figure 8. Figure 8: A comparison of the BOSS-CLAM stellar parameters to those measured from 8 other methods. Columns are grouped into four parameters: effective temperature, surface gravity, metallicity, and α-abundance. Each row corresponds to a different catalog which is labeled on the far left. In cases where a catalog does not provide α-abundance, we leave the plot empty. A one-to-one dashed line is plotted and the compar… view at source ↗
Figure 9
Figure 9. Figure 9: Top Row: Kiel diagram (left) and distribution of [α/M] vs. [Fe/H] (right) for inferred parameters from the BOSS-CLAM with flag bad = True. Bottom Row: Bar graph showing the counts of stars within various SDSS-V programs that have the various clam flags bits set. This only shows the ten most numerous programs. Also, we should note that a star can be within multiple programs and have multiple flags set. valu… view at source ↗
Figure 10
Figure 10. Figure 10: Kiel diagram for M67 (top row) and M92 (bottom row) using the BOSS-CLAM parameters (left column) and BOSSNet (right panel). The scatter points are colored by [Fe/H] with the colorbar centered on [Fe/H] = 0.02 for M67 (U. Heiter et al. 2014) and [Fe/H] = −2.282 for M92 (J.-W. Lee 2023). The inlay shows the distribution of the [Fe/H] values from each pipeline, and the red dashed line is again the approximat… view at source ↗
Figure 11
Figure 11. Figure 11: Cluster member stars plotted in the [Fe/H] versus [α/M] plane for several globular and open clusters. Colored stars show the mean [Fe/H] and [α/M] value from the literature, with the error bars showing the 1σ error. For the globular cluster literature abundances, we use the list from V. Chandra et al. (2026), which was collated from a variety of sources, including: J. C. Roediger et al. (2014) (47 Tuc), I… view at source ↗
Figure 12
Figure 12. Figure 12: Residual BOSS-CLAM [Fe/H] (left) and [α/M] (right) relative to the respective cluster median values are shown against log g for 10 open clusters from the E. L. Hunt & S. Reffert (2024) catalog. Black error bars denote the 16th, median, and 84th percentiles in log g bins of width 0.3 from 3.5 to 5. The legend indicates the number of members with log g > 3.3 and membership probability ≥ 0.8 with BOSS-CLAM p… view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of abundances from the BOSS-CLAM for wide binary pairs that pass the quality cuts outlined in Section 2.3. The top row shows the difference in [Fe/H] and [α/M] as a function of the difference in temperature between the primary and secondary. The bottom row shows the difference in [Fe/H] and/or [α/M] as a function of SNR of the secondary. Only binaries with |∆Teff | < 500 K are included in the b… view at source ↗
Figure 14
Figure 14. Figure 14: Comparison of parameters from the BOSS-CLAM for 719 stars with spectra taken at both APO and LCO. The points are color-coded by the minimum SNR from the two spectra (cut at SNR to better show the dynamic range). The title above each plot shows the scatter and median absolute deviation between the parameters derived from each spectrum. formalize the systematic uncertainties. This can be done through e.g. a… view at source ↗
Figure 15
Figure 15. Figure 15: Distribution of [α/M] and [Fe/H] using the BOSS-CLAM parameters for different bins of Galactic radius (R) and height (z), similar to the figure from M. R. Hayden et al. (2015). These Galactic coordinates are calculated using the photogeometric distances from C. A. L. Bailer-Jones et al. (2021). The cyan lines show the approximate location of the high￾and low-α sequences from the APOGEE DR19 data. model us… view at source ↗
Figure 16
Figure 16. Figure 16: BOSS-CLAM forward modeled spectra for Teff = 5772 K, log g = 4.44 dex and [α/M]= 0, for vari￾ous values of [Fe/H]. The top panel is zoomed in on the Mg triplet region, and the bottom panel on the Ca triplet; two metallicity indicators in the BOSS wavelength range that are prominent in Solar-type stars. used as standards, and cool M dwarfs. In future itera￾tions, the training labels in these regimes will b… view at source ↗

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  1. The Twentieth Data Release of the Sloan Digital Sky Survey: First All-Sky BOSS Spectra, eROSITA-SDSS-V Mapper Coordinated Observations, and a Preview of the Local Volume Mapper

    astro-ph.GA 2026-07 accept novelty 6.0

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