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REVIEW 3 major objections 6 minor 103 references

A jet-aware spectral fitting pipeline corrects SDSS's systematic misclassification of blazars and recovers the known BL Lac–FSRQ population split.

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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T0 review · deepseek-v4-flash

2026-08-01 00:34 UTC pith:PC2FJ4FF

load-bearing objection Useful pipeline and catalogue, but the headline redshift improvement is softer than it looks—check the 16 3FHL-disagreeing STAR cases before trusting the η claim. the 3 major comments →

arxiv 2607.26175 v1 pith:PC2FJ4FF submitted 2026-07-28 astro-ph.HE

Spectroscopic Analysis of Fermi-detected Blazars using SDSS-V

Mohammed Iddrisu Nlowie , James Aird , Eli Kasai , Amy L. Rankine , Paloma Guetzoyan , Scott Anderson , Catarina Aydar , William N. Brandt
show 12 more authors
Joel R. Brownstein Xinyu Dai Lorena Hern\'andez-Garc\'ia Paola Rodr\'iguez Hidalgo Pranavi Hiremath Anton Koekemoer Andrea Merloni Sean Morrison Mara Salvato Donald P. Schneider Axel Schwope Benny Trakhtenbrot
This is my paper
classification astro-ph.HE
keywords blazarsBL Lacertae objectsflat-spectrum radio quasarsSDSS-Vspectral fittingredshiftequivalent widthgamma-ray sources
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.

This paper claims that the SDSS automated spectroscopic pipeline systematically misclassifies blazars because its template library lacks a non-thermal jet continuum. The authors develop a multi-component fitting pipeline that models a power-law jet alongside galaxy, QSO, and emission-line templates, and apply it to 746 Fermi-detected sources with SDSS-V spectra. They report that 707 sources are well-fitted, with 59.4% best described by a Power-law+Galaxy model (BL Lac candidates) and 37.6% by Power-law+QSO or Power-law+Lines models (FSRQ candidates). Independent WISE infrared colours confirm that 96.6% of sources the SDSS pipeline called stars are actually blazars. The new classifications recover the established redshift and gamma-ray luminosity separation between BL Lacs and FSRQs, and the equivalent-width analysis shows the |EW|=5 Angstrom boundary is a population-level trend rather than a sharp dichotomy.

Core claim

The paper's central claim is that a physically motivated, multi-component spectral fitting approach—explicitly including a non-thermal power-law jet component alongside host galaxy and QSO/emission-line templates—can reliably classify and estimate redshifts for Fermi-detected blazars that the standard SDSS pipeline misidentifies as stars, galaxies, or ordinary quasars. Applied to 746 optical counterparts of 4FGL-DR4 gamma-ray sources with SDSS-V spectra, the pipeline yields 707 well-fitted blazar candidates. The model selection recovers the established cosmological separation: BL Lac candidates have median redshift z=0.360 and median gamma-ray luminosity 1.39e45 erg/s, while FSRQ candidates

What carries the argument

The central mechanism is a six-family model selection framework where each observed spectrum is fit with single-component models (Galaxy, QSO, Power-law) and multi-component models (Power-law+Galaxy, Power-law+QSO, Power-law+Line-only). The power-law component represents non-thermal synchrotron jet emission with a curvature parameter, and the multi-component fits are evaluated on a logarithmic redshift grid with a chi-squared likelihood, marginalized over templates, refined locally, and selected via the corrected Akaike Information Criterion (AICc). The fractional contribution of the power-law component to the total optical flux (jet fraction) provides a continuous measure of jet dominance.

Load-bearing premise

The redshift validation assumes that the 3FHL spectroscopic redshifts are reliable reference values for the 111-source subset, and specifically that when the new pipeline disagrees with 3FHL for the 16 SDSS-STAR sources, the 3FHL value is the unreliable one.

What would settle it

A targeted spectroscopic follow-up of the 16 SDSS-STAR sources where the new pipeline disagrees with 3FHL would settle the redshift accuracy question. If the 3FHL redshifts are confirmed for a majority of these, the claimed 10.4% reduction in catastrophic failures would be an overstatement. Alternatively, if the new redshifts are confirmed, the 3FHL compilation for this subset is demonstrably unreliable.

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

If this is right

  • If the pipeline is right, SDSS-V spectra can be used to classify and redshift blazars even when jet emission overwhelms thermal features, enabling population studies without requiring new observations.
  • The recovered BL Lac/FSRQ separation in redshift and gamma-ray luminosity confirms the model-based classification is physically meaningful and can be used to update Fermi source classifications, reducing the fraction of uncertain-type blazars.
  • The finding that many BL Lac candidates show emission lines with |EW|>5 Angstroms suggests the traditional BL Lac/FSRQ dichotomy is oversimplified; a continuous jet-fraction and equivalent-width description may better represent the blazar population.
  • The 10.4% reduction in catastrophic redshift failures over the SDSS pipeline, if general, implies that archival SDSS spectra of gamma-ray sources can yield improved redshifts for a significant fraction of objects.
  • The pipeline is directly scalable to the full SDSS-V footprint and future large spectroscopic surveys, offering a foundation for blazar population studies in larger samples.

Where Pith is reading between the lines

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

  • A direct consequence the paper leaves implicit: the misclassification rate among SDSS 'STAR' labels in gamma-ray samples implies that other archival samples selected by optical classification alone may have substantial blazar contamination, and cross-matching with WISE colours could rescue them.
  • The continuous jet-fraction description suggests a testable extension: multi-epoch spectroscopy of the hybrid sources (with both emission and absorption) could reveal whether their classification shifts with jet activity, as expected if some are masquerading BL Lacs or transitional objects.
  • The paper's redshift validation depends on the 3FHL reference being trustworthy for the 111-source subset; an independent redshift campaign targeting the 16 SDSS-STAR sources where the new pipeline disagrees with 3FHL would directly settle the reliability question.
  • The fact that PL+Lines sources have jet fractions interpreted as upper limits suggests that a more complete model including a separate accretion-disk continuum component could break the degeneracy and refine the jet/disk decomposition.

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

3 major / 6 minor

Summary. The paper presents a new multi-component spectral fitting pipeline for blazar classification and redshift estimation, applied to 746 optical counterparts of Fermi/4FGL-DR4 sources with SDSS-V spectroscopy. The pipeline fits power-law jet continuum with galaxy, QSO, or emission-line templates, selects among six model families via AICc, and classifies 707 well-fitted sources into BL Lac candidates (PL+Galaxy, 59.4%) and FSRQ candidates (PL+QSO/Lines, 37.6%). The authors report that WISE colours confirm 96.6% of the 88 SDSS stellar misclassifications as extragalactic blazars, that the new redshift estimates reduce catastrophic failures by 10.4% relative to the SDSS pipeline on a 111-source 3FHL validation sample (η=0.387 vs 0.432), and that the traditional |EW|=5 Å boundary is reproduced only at the population level, with a large hybrid population. The paper also derives median redshifts and gamma-ray luminosities for the two classes (z=0.360 vs 1.026; L_gamma=1.39e45 vs 4.23e46 erg/s).

Significance. If the quantitative claims hold, the paper delivers a valuable, publicly applicable tool for blazar spectroscopy in SDSS-V and a large classified sample with independent WISE and 3FHL cross-checks. The pipeline explicitly addresses a real, documented failure mode of SDSS automated classification, and the AICc model-selection framework with externally validated WISE colours is a solid foundation. The population-level EW analysis and the identification of a substantial hybrid population are interesting and could inform future blazar taxonomy. However, the paper's headline redshift-improvement claim rests on an asymmetric catastrophic-failure metric and on dismissing 16 of the 21 SDSS-STAR validation sources as unreliable 3FHL redshifts without independent verification; this is the load-bearing element that needs strengthening.

major comments (3)
  1. [§5.2, Eq. (18)–(19)] The claimed 10.4% reduction in catastrophic failures depends on an asymmetric definition. Eq. (19) counts every SDSS CLASS=STAR as a failure regardless of the actual redshift, which is reasonable for an extragalactic sample. However, for the 21 SDSS-STAR sources in the 3FHL validation set, the paper counts only 5 as recovered and assumes the 16 remaining disagreements are due to unreliable 3FHL redshifts. If those 16 are instead counted as catastrophic failures of the new pipeline (as they would be under the paper's own |Δz|/(1+z)>0.15 criterion), η_fit becomes (43+16)/111 ≈ 0.53, which is worse than the SDSS η=0.432. The statement that 'the reliability of the 3FHL reference redshifts for this specific subset cannot be taken for granted' is not a substitute for a provenance check. The abstract and conclusions advertise the improvement; this needs to be re-evaluated with a symmetric metri
  2. [§5.3 and §3.5] The equivalent-width validation of the |EW|=5 Å boundary is not fully independent of the model selection. The classification into PL+Galaxy (BL Lac candidate) versus PL+QSO/PL+Lines (FSRQ candidate) is made by AICc, which explicitly rewards line-template fits when emission lines improve the fit. It is therefore not surprising that FSRQ candidates show stronger emission lines. The paper acknowledges this circularity only implicitly. The population-level conclusion (BL Lac mean EW 4.47 Å vs FSRQ 22.86 Å) would be more convincing if the EW analysis were performed on an independently defined sample, e.g., sources classified by WISE colours or by 4FGL class, or if the authors demonstrated that the AICc selection does not simply track EW. As written, the EW statistics partly reflect the construction of the classifier.
  3. [Table A1, Appendix A] The redshift uncertainties reported as ±0.0000 (e.g., z_fit=0.6739±0.0002, or ±0.0000 for some sources) are formal fitting uncertainties from local χ² refinement and do not reflect systematic uncertainty in featureless or weakly featured spectra. For sources where the redshift is anchored only by a diluted host-galaxy template (Section 4.4), the true uncertainty can be much larger. The paper should provide a more robust uncertainty estimate, for example bootstrap or template-variation errors, at least for the PL+Galaxy and PL+Lines families. The current presentation understates the uncertainty and could mislead users of the catalogue.
minor comments (6)
  1. [§4.2, §6, Table A2] There is an inconsistency in the number of sources failing the χ²_r quality cut: §4.2 says 'the remaining 13 sources' and 'these 14 sources' in consecutive sentences, and Table A2 lists 14. The text should be corrected to 14 consistently.
  2. [§3.2, Eq. (1)] Typo: 'we adopt a a flexible dual-form power-law' — extra 'a'. Also, the sign convention in Eq. (1) is not clearly explained; please add a sentence defining what positive/negative δ does to the spectral shape in the observer frame.
  3. [Figure 12 caption] The caption says 'BL Lac candidates (PLonly and PL+Galaxy)' but the text and Figure 7 indicate that only PL+Galaxy and the five pure-PL sources are classified as BL Lac candidates. Clarify whether the five pure-PL sources are included in the WISE plot and in the 405 count.
  4. [§5.1] The claim that 'none of the 6 SDSS STAR-classified sources where we retain the original classification fall within any of the canonical blazar selection regions' should be verified against the actual WISE errors; a source can lie near the boundary. Please add quantitative confirmation (e.g., distance from the wedge) or soften the statement.
  5. [§5.2, Fig. 13] The discussion of '12 sources' where z_fit disagrees with z_3FHL but z_SDSS agrees is a bit confusing after the 16 STAR sources are removed. Please clarify the exact counts and provide a table of these 12 sources, especially the 2/12 'genuine catastrophic failures'.
  6. [Abstract, §5.3] The abstract states '49.5% of individual BL Lac candidates exceed this threshold' — this refers to at least one detected emission line with |EW|>5 Å, but the sentence could be misread as the fraction of all measured lines. Rephrase to '49.5% of BL Lac candidates have at least one detected emission line with |EW|>5 Å'.

Circularity Check

1 steps flagged

EW 'validation' partly circular; core pipeline and external WISE/3FHL checks remain independent.

specific steps
  1. self definitional [Section 3.5 vs Section 5.3 / Abstract]
    "When a combined model is selected, the best-fit label directly informs the physical classification: PL+Galaxy indicates a BL Lac candidate with a detectable host galaxy contribution, while PL+QSO or PL+Line-only indicates an FSRQ candidate with thermal disk or BLR emission coexisting with the jet. ... The equivalent width analysis validates the traditional |EW|=5Å classification boundary at the population level: BL Lac candidates show a mean EW of 4.47±0.15Å ... compared to 22.86±0.86Å for FSRQ candidates."

    The classes compared in the EW analysis are defined by AICc selection among model families that differ precisely in whether emission-line templates are included: PL+Galaxy has no emission-line template, while PL+QSO and PL+Line-only include QSO/line templates. A source with strong emission lines will preferentially select PL+Lines or PL+QSO and be labelled an FSRQ candidate; a source without such lines selects PL+Galaxy and is labelled a BL Lac candidate. The claimed EW validation therefore compares two sets constructed using line-strength information, so the population-level mean EW separation (4.47 vs 22.86 Å) is largely a restatement of the template-selection criterion rather than an independent confirmation of the 5 Å boundary. The paper's own overlap statistics (49.5% of BL Lac candid

full rationale

The central derivation is largely self-contained and not circular. The multi-component fitting pipeline is defined externally (SWIRE galaxy templates, QSOGEN line/QSO templates, a dual-form power law, AICc selection) and the resulting classifications are checked against independent data: WISE colours for the 88 SDSS-STAR sources (96.6% fall in the blazar strip) and 3FHL spectroscopic redshifts for 111 sources. These external benchmarks give the redshift and classification claims genuine content. The 10.4% redshift-improvement comparison uses a deliberately asymmetric definition of catastrophic failure (SDSS CLASS=STAR automatically counts as a failure, while the 16 SDSS-STAR sources where z_fit disagrees with 3FHL are argued to have unreliable 3FHL references); this is a statistical fairness concern, not a circular derivation, because the pipeline's redshifts are not fitted to 3FHL values. The one genuinely circular element is the equivalent-width 'validation' of the |EW|=5 Å boundary: the BL Lac/FSRQ candidate labels are assigned by whether an emission-line template wins the AICc selection, so measuring stronger emission lines in the FSRQ candidate set and calling it an independent validation of the EW boundary is partly restating the classification input. The overlap statistics and the presence of PL+Galaxy sources with strong emission lines show this circularity is partial, not complete. No load-bearing self-citation chain or imported uniqueness theorem is present. Overall score 4 reflects one partially circular supporting claim while the core pipeline and external validations remain independent.

Axiom & Free-Parameter Ledger

5 free parameters · 10 axioms · 0 invented entities

The central empirical claims rest mainly on literature-supplied template libraries and reference catalogues rather than new physical postulates. No invented entities are introduced. The free parameters are the per-source continuum/template amplitudes and slopes; the hand-chosen equal-prior W_f=1/6 is the main modelling choice. The weakest external anchors are the SDSS pipeline description (Morrison et al. in prep) and the 3FHL reference redshifts.

free parameters (5)
  • per-source power-law normalisation A = varies per source; not tabulated
    Free amplitude of the non-thermal jet continuum in PL and multi-component models (Section 3.2); affects jet fractions and model selection.
  • per-source power-law slope alpha = e.g. alpha ~ 0.52, -1.30 in Table A1
    Free spectral slope with bounds -2.5..1.2; central to the shape of the jet component and to whether line features stand out.
  • per-source curvature delta = e.g. delta ~ 1.20, -1.20 in Table A1
    Free curvature parameter with bounds -1.2..1.2; affects the continuum shape and the redshift fit for featureless spectra.
  • per-source template scalings s_gal/s_QSO/s_line = varies per source
    Amplitudes of galaxy/QSO/line templates; constrained to [0,100 F_med]; drive the relative jet/thermal decomposition and f_jet.
  • equal inter-family priors W_f=1/6 = 1/6 each
    Chosen by hand as non-informative priors over six model families (Section 3.4); affects the global posterior and z_MAP; no sensitivity test is given.
axioms (10)
  • domain assumption The SDSS idlspec2d pipeline has no non-thermal jet template and therefore misclassifies featureless blazar continua as stars, galaxies, or quasars.
    Central premise of the paper; sourced from Bolton et al. 2012 and Morrison et al. in prep (Section 2.2.2).
  • domain assumption The WISE blazar wedge (Massaro 2011; D'Abrusco 2012) and Salvato 2018 region identify blazar/AGN infrared colours.
    Used as independent validation of reclassifications in Sections 2.4 and 5.1.
  • domain assumption QSOGEN synthetic spectra adequately represent FSRQ/QSO continuum, line, and reddening properties.
    Used to construct the 8 QSO and 3 line-only templates (Section 3.1).
  • domain assumption SWIRE elliptical templates Ell2/Ell5/Ell13 represent host galaxy light in blazars.
    Used as the host-galaxy component in the PL+Galaxy models (Section 3.1).
  • domain assumption 3FHL spectroscopic redshifts are a valid external reference for the 111-source validation subset.
    Basis of the redshift validation in Section 5.2; authors themselves qualify its reliability.
  • ad hoc to paper Equal prior over the six model families is appropriate.
    W_f=1/6 in Section 3.4; justified only by 'absence of prior information', with no sensitivity analysis.
  • standard math IGM transmission follows Madau 1995 with the stated optical depth prescription.
    Applied to all templates at high redshift (Section 3.2, Eq. 2).
  • standard math Flat Lambda-CDM cosmology with H0=70 km/s/Mpc and Omega_m=0.3 is used for luminosity distances.
    Used for gamma-ray luminosity calculation in Section 4.5.
  • domain assumption The traditional |EW|=5 Angstrom boundary is the classification criterion to validate.
    Adopted from Stickel et al. 1991 and used as the reference in Sections 3.7 and 5.3.
  • domain assumption In PL+Lines fits the power-law absorbs smooth accretion-disk continuum, making f_jet an upper limit.
    Acknowledged caveat in Section 3.6; limits the physical interpretation of PL+Lines jet fractions.

pith-pipeline@v1.3.0-alltime-deepseek · 55272 in / 15500 out tokens · 151760 ms · 2026-08-01T00:34:55.596237+00:00 · methodology

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read the original abstract

The automated spectroscopic pipeline of the Sloan Digital Sky Survey (SDSS) systematically assigns Galactic star, galaxy, or typical quasar classifications to jet-dominated blazars, owing to the absence of a non-thermal jet continuum component in its template library. In this study, we present a new, physically motivated, multi-component spectral fitting pipeline that we apply to 746 optical counterparts of Fermi/4FGL-DR4 $\gamma$-ray sources in the SDSS-V Data Release 20 spectroscopic database, yielding 707 well-fitted blazar candidates dominated by Power-law+Galaxy (59.4%), Power-law+Lines (22.6%), and Power-law+QSO (15.0%) model families. Independent WISE infrared (IR) photometry confirms that 96.6% of the 88 sources originally misclassified as Galactic stars by SDSS fall within the canonical blazar region, with 90.9% reclassified as BL Lacertae object (BL Lac) candidates, demonstrating the success of our new pipeline. The new classification scheme naturally recovers the known cosmological separation between BL Lac and Flat Spectrum Radio Quasar (FSRQ) candidates and a separation of approximately one order of magnitude in median $\gamma$-ray luminosity. We compare our redshift estimates to a validation sample of 111 sources in the Third Fermi-LAT Catalogue of High-Energy Sources (3FHL), finding a 10.4% reduction in catastrophic failures ($\eta = 0.387$ vs 0.432) over the SDSS pipeline. The equivalent width analysis validates the traditional |EW| = 5 Ang classification boundary at the population level (BL Lac: 4.47 $\pm$ 0.15 Ang; FSRQ: 22.86 $\pm$ 0.86 Ang), although 49.5% of individual BL Lac candidates exceed this threshold and 51.4% show simultaneous emission and absorption features. This hybrid population challenges the traditional binary blazar classification and points towards a more physically continuous description of blazar properties.

Figures

Figures reproduced from arXiv: 2607.26175 by Amy L. Rankine, Andrea Merloni, Anton Koekemoer, Axel Schwope, Benny Trakhtenbrot, Catarina Aydar, Donald P. Schneider, Eli Kasai, James Aird, Joel R. Brownstein, Lorena Hern\'andez-Garc\'ia, Mara Salvato, Mohammed Iddrisu Nlowie, Paloma Guetzoyan, Paola Rodr\'iguez Hidalgo, Pranavi Hiremath, Scott Anderson, Sean Morrison, William N. Brandt, Xinyu Dai.

Figure 1
Figure 1. Figure 1: Distribution of 746 unique optical spectroscopic matches for Fermi￾detected sources among the 3 main classification schemes within the SDSS pipeline and divide according to their redshift quality (ZWARNING) flags, as indicated. classification for our sample. The GALAXY class dominates with 389 sources (52%), followed by 249 QSOs (33%) and 109 (15%) sources classified as STAR (15%). The substantial fraction… view at source ↗
Figure 2
Figure 2. Figure 2: Multi-wavelength colour diagnostics for the Fermi-detected SDSS-V spectroscopic sample. Left panel: WISE colour-colour diagram for the 698 sources with valid WISE magnitudes, colour-coded by SDSS pipeline classification: GALAXY (blue triangles, 𝑁 = 366), QSO (orange squares, 𝑁 = 235), and STAR (grey stars, 𝑁 = 97). The coloured regions show established blazar selection criteria from Massaro et al. (2011) a… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the adaptive integration window selection for the [O II] 𝜆3729 emission line in a BL Lac candidate SDSS ID 79336239 (𝑧fit = 0.1366). Left panel: Observed spectrum (black) with ±1𝜎 uncertainty (grey shading) and local linear continuum fit (red dashed). The five integration windows tested are shown as overlapping shaded regions with corresponding boundary lines, colour-coded from narrow (purp… view at source ↗
Figure 4
Figure 4. Figure 4: Multi-component spectral decomposition of SDSS J084712.93+113350.2, a Fermi-detected BL Lac object (4FGL J0847.2+1134) misclassified as a GALAXY by the SDSS automated pipeline. Top panel: Observed SDSS spectrum (black) with multi-component model fits overlaid: pure galaxy (green dashed), pure QSO (blue dashed) power law (orange dashed), PL + galaxy (thick red), PL + QSO (purple), and PL + lines (cyan). The… view at source ↗
Figure 5
Figure 5. Figure 5: Multi-component spectral decomposition revealing systematic SDSS misclassifications of Fermi-detected blazars. Each panel shows the observed SDSS spectrum (black) with our best-fit multi-component model (coloured line) decomposed into jet continuum (orange shaded region) and thermal emission components (green/blue/cyan shaded regions for galaxy/QSO/line templates, respectively). Panel labels indicate: SDSS… view at source ↗
Figure 6
Figure 6. Figure 6: Left: Comparison of reduced chi-squared (𝜒 2 r ) values between the SDSS automated pipeline and the multi-component spectral models developed in this work. Points are colour-coded by sample category: final blazar candidates (blue circles, 𝑁 = 707), non-Fermi blazar sources that passed the quality threshold (green squares, 𝑁 = 25), Fermi blazar sources that failed the threshold (red circles, 𝑁 = 7), and non… view at source ↗
Figure 7
Figure 7. Figure 7: Best-fit spectral model distribution for 746 Fermi-SDSS sources, colour-coded by SDSS pipeline classification (GALAXY: blue, QSO: orange, STAR: grey). The final 707-source sample is dominated by PL+Galaxy (420; 59.4%), PL+Lines (160; 22.6%), and PL+QSO (106; 15.0%) models. Pure single-component models account for 21 sources (3.0%). 13 sources where the SDSS pipeline outperforms our multi-component approach… view at source ↗
Figure 8
Figure 8. Figure 8: Best-fit spectral model distribution for 732 sources that pass the quality threshold, stratified by Fermi blazar classification. Of the 707 confirmed Fermi blazars: BL Lacs (pink; 𝑁 = 326, 46.1%), FSRQs (diagonal hatching; 𝑁 = 180, 25.5%), and BCUs (dots; 𝑁 = 201, 28.4%). The 25 non-blazar sources are shown separately to the right, split into non-blazar AGN (brown; 𝑁 = 12) and Stellar/Other (crimson; 𝑁 = 1… view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of the 25 non-blazar Fermi sources. Bars are coloured by source category: brown for non-blazar AGN types (rdg: 4, css: 3, agn: 2, sbg: 1, NLSY1: 1, sey: 1; total 𝑁 = 12) and crimson for stellar/other types (MSP: 5, PSR: 2, LMB: 2, bin: 1, ssrq: 1, snr: 1, unk: 1; total 𝑁 = 13). redshifts, where spectroscopic redshift determination requires de￾tection of faint host galaxy absorption features or… view at source ↗
Figure 10
Figure 10. Figure 10: Redshift (𝑧fit; left) and jet fraction ( 𝑓jet; right) distributions for BL Lac candidates (PL+Galaxy; 𝑁 = 420; red) and FSRQ candidates (PL+QSO, 𝑁 = 106, purple; PL+Lines, 𝑁 = 159, blue). Solid histograms show sources with spectral S/N ≥ 3; dashed histograms show sources with S/N < 3, which are excluded from all median calculations. Dashed vertical lines demarcate the median estimate of the source populat… view at source ↗
Figure 11
Figure 11. Figure 11: 𝛾-ray luminosity versus spectroscopic redshift for BL Lac candidates (PL+Galaxy, 𝑁 = 420, left panel) and FSRQ candidates (PL+QSO/Lines, 𝑁 = 265, right panel), colour-coded by optical jet fraction from the multi-component spectral decomposition. Sources with spectral S/N < 3 are shown with reduced opacity and smaller markers. Left: The shaded grey region (𝑧 ≳ 1.8) indicates the low-confidence redshift reg… view at source ↗
Figure 12
Figure 12. Figure 12: WISE colour-colour diagram for 671 sources with valid 𝑊1, 𝑊2, 𝑎𝑛𝑑𝑊3 magnitudes, coloured by best-fit model classification: BL Lac candidates (PL only and PL+Galaxy; lightcoral squares, 𝑁 = 405 of 420 with valid WISE photometry), FSRQ candidates (PL+QSO and PL+Lines; mediumpurple circles, 𝑁 = 251 of 266), pure QSO (orange diamonds, 𝑁 = 11), and Galaxy (blue triangles, 𝑁 = 4). Large markers with black edges… view at source ↗
Figure 13
Figure 13. Figure 13: Redshift comparison between the multi-component fit estimates (𝑧fit) and 3FHL spectroscopic redshifts for the 111 sources passing both the quality threshold and the S/N ≥ 3 criterion. Top panels: scatter plots of 𝑧SDSS (left) and 𝑧fit (right) against 𝑧3FHL, colour-coded by the normalised deviation of the opposing pipeline (green: small deviation; red: large deviation). Dotted lines mark the |Δ𝑧 |/(1 + 𝑧) … view at source ↗
Figure 14
Figure 14. Figure 14: Emission line EW distributions for BL Lac (PL+Galaxy; N = 18 to 168 per line) and FSRQ (PL+QSO/Lines; N = 85 − 193 per line) candidates after iterative 3𝜎 clipping. The violin plots show kernel density estimates of the EW distribution, with overlaid box plots, which shows median (thick line), interquartile range (box), and 1.5 × IQR whiskers. Sample sizes (n) are shown below each distribution. The horizon… view at source ↗
Figure 15
Figure 15. Figure 15: Absorption line equivalent width distributions for the same BL Lac candidate (PL+Galaxy) and FSRQ candidate (PL+QSO/Lines) samples after iterative 3𝜎 clipping. Violin and box plot formatting follows [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Emission line EW distributions for the five most common co-detected emission-absorption pairs in BL Lac candidates (left panel) and FSRQ candidates (right panel), using 3𝜎 clipped measurements for consistency with [PITH_FULL_IMAGE:figures/full_fig_p024_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: A representative Powerlaw+Galaxy source at 𝑧 = 0.1366 with simultaneous emission and absorption line detections. Strong [O II] (|EW| = 16.76 ± 0.53 Å, S/N = 31.5) and a strong H𝛼 emission line (|EW| = 8.93 ± 0.18 Å, S/N = 48.3) within the H𝛼-[NII] complex doublet, both well above the 5 Å classification boundary, coexist with host galaxy Ca II K/H absorption, highlighting the complexity of applying the tra… view at source ↗

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