{"id":"4d1ee0dc-3189-45ec-8695-eee22b1724d3","arxiv_id":"2507.03260","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A neural network predicts quasar redshifts from DESI, WISE and GALEX photometry with correlation 0.92 and NMAD 0.197, beating a k-nearest-neighbor baseline.","lead":"This paper trains a neural network to estimate distances (redshifts) of quasars from survey colors, then adds ultraviolet data from the GALEX satellite and reports improved accuracy. The result matters for upcoming sky surveys that will detect millions of quasars but cannot take a spectrum of every one.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The headline GALEX improvement is confounded by sample selection; the same-sample check in Table 9 shows a smaller effect and conflicts with Table 6, so the quantitative claim needs controlled re-running.","rationale":"The reader's weakest assumption correctly identifies that the headline GALEX improvement is measured across different samples (full DESI vs DxS). This is load-bearing because the abstract and conclusions quote metrics from exactly that uncontrolled comparison. However, the appendix Table 9 does include a same-sample baseline without UV, which partially mitigates the concern; the paper is not wholly lacking a controlled test. The deeper problem is that the controlled test and the main table are inconsistent (Table 9b NN DxS+GALEX NMAD=0.270 vs Table 6 NMAD=0.197), and the percentage-change calculations violate the paper's own Eq. (1). This means the quantitative strength of the central claim is not reproducible as stated. The qualitative conclusion that GALEX UV data helps photo-z for quasars is consistent with prior literature and with the direction of Table 9, so rejection is not warranted; conditional acceptance with a required re-analysis and reconciliation is the appropriate outcome, which matches the reader's verdict. Therefore the verdict remains unchanged.","tokens_in":22674,"tokens_out":12622,"duration_ms":135000,"concrete_test":"Rerun the NN on the DxS sample under identical conditions (same 100 randomized train/test splits, same architecture, same early stopping) with features (g,r,z,W1,W2) versus (g,r,z,W1,W2,FUV,NUV), and also recompute the DxS+GALEX metrics to resolve the Table 6 vs Table 9b NMAD discrepancy (0.197 vs 0.270). If the controlled NMAD improvement is ~15% rather than ~29%, revise the abstract and Section 5 to report the smaller effect size; if the Table 6 values do not reproduce, the headline metrics are unreliable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central quantitative claim—GALEX UV photometry reduces NN scatter from NMAD 0.278 (Table 4) to 0.197 (Table 6)—compares two different training samples: the full DESI sample (g,r,z,W1,W2) and the SDSS-matched DxS subset (g,r,z,W1,W2,FUV,NUV). Section 4.2 acknowledges that GALEX-detected quasars are brighter and better constrained, so the improvement conflates added bands with a sample change. The appendix contains a same-sample check (Table 9): on DxS without UV, NN NMAD=0.3171; with UV, 0.2699—only a ~15% reduction, not the ~29% implied by the abstract's comparison. More seriously, Table 9b's UV baseline (NMAD=0.2699) directly contradicts Table 6's NN DxS+GALEX NMAD=0.1971 for what appears to be the same model and data, so the main table and the appendix cannot both be correct. The percentage changes in Tables 4-7 also use the new value as denominator rather than the baseline, contradicting Eq. (1); for example, Table 7 reports r=11.84% while the standard baseline-relative change is 13.4%, and NMAD is reported as -41.1% instead of -29.1%. The qualitative conclusion that UV helps is plausible and supported by the direction of Table 9, but the headline magnitude is not reliably established.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper trains fully connected neural-network and k-nearest-neighbour regressors to predict photometric redshifts for DESI EDR quasars, using DESI g, r, z and WISE W1, W2 fluxes, optionally augmented with GALEX FUV and NUV fluxes obtained through a cross-match with SDSS DR16Q (the DxS subset of 24,616 sources). The authors report that adding GALEX photometry improves the neural network from r=0.8099 and NMAD=0.2781 on the full DESI sample to r=0.9187 and NMAD=0.1971 on DxS, and interpret this as evidence that ultraviolet coverage helps. They also examine redshift-dependent performance, a bimodality in the predictions, feature importances via permutation and drop-column methods, and use of the model to adjudicate between discrepant DESI and SDSS spectroscopic redshifts.","tokens_in":22990,"tokens_out":7534,"duration_ms":81886,"significance":"If the comparison were controlled, the result would be a useful practical demonstration that adding ultraviolet photometry to optical plus infrared quasar photometry improves photometric redshifts, with potential value for future wide surveys. The paper has genuine strengths: 100 randomized train/test runs, multiple standard metrics, held-out evaluation for the main comparisons, and feature-importance analysis with two complementary methods, including the more reliable drop-column approach. However, the headline quantitative claim is not supported as presented because the main GALEX comparison changes both the feature set and the training sample, and the appendix's same-sample check both shows a smaller effect and is internally inconsistent with the main table. The qualitative conclusion that ultraviolet photometry helps is plausible and consistent with the direction of Table 9, but the magnitude of the improvement needs to be re-established with a controlled and internally consistent comparison.","major_comments":[{"comment":"The central claim that adding GALEX ultraviolet photometry improves the neural network from NMAD 0.278 to 0.197 rests on a comparison across different training samples: Table 4 uses the full 87,318-source DESI sample with (g,r,z,W1,W2), while Table 6 uses the 24,616-source DxS subset with (g,r,z,W1,W2,FUV,NUV). Because Section 4.2 acknowledges that GALEX-detected quasars are brighter and better constrained, the improvement conflates added bands with an easier sample. The same-sample check in Table 9b shows a much smaller effect on DxS, NMAD 0.3171 without ultraviolet photometry versus 0.2699 with it, about 15% rather than 29%, so the headline magnitude is not established. Please re-run the DESI-only model on the same DxS subset used for the GALEX model and report both comparisons.","section":"§3.3; Tables 4 and 6 vs Table 9"},{"comment":"Table 9b gives a neural-network NMAD of 0.2699 for the feature set (g,r,z,W1,W2,FUV,NUV) on DxS, whereas Table 6 reports 0.1971 for what appears to be the same feature set and dataset; these two values cannot both describe the same configuration. Table 9a's kNN baseline of 0.2584 matches Table 6's kNN GALEX value, ruling out a simple across-the-board formatting difference. Please clarify whether Table 9b uses a different sample, a single run, a different network, or a different training pipeline, and reconcile the discrepancy.","section":"Table 9b vs Table 6"},{"comment":"The percentage changes in Table 7 contradict Eq. (1), which defines the improvement relative to the NN DESI baseline. For example, the NN DESI/GALEX NMAD is reported as -41.1%, but Eq. (1) gives (0.1971 - 0.2781)/0.2781 = -29.1%; the correlation coefficient is reported as 11.84% instead of 13.4%. The Section 5 text, which states a 13% increase in correlation and a 29% reduction in NMAD, matches Eq. (1), but Table 7 and the surrounding discussion, which says all metrics improve by about 40%, do not. Correct the table and the related sentences.","section":"Table 7; Eq. (1)"},{"comment":"The outlier-adjudication claim is partly circular. The model whose predictions are used in Figures 13 and 14 was trained with zDESI as the target on the DxS sample after removing the 107 outliers, as described in Section 4.4, so a tendency for zphot to sit closer to zDESI than to zSDSS is expected even if the two spectroscopic measurements are equally accurate. To support the conclusion that DESI redshifts are more reliable in the discrepant cases, train a comparison model on zSDSS or validate the outlier assignments against an independent redshift source.","section":"§4.4; Figures 13-14; Conclusion"},{"comment":"Model selection appears to use the test set directly: Section 2.6 states that the neural-network architecture was chosen based on the lowest RMS error on the test set, and Section 2.7 states that the kNN hyperparameters were optimized to minimize RMS error on the test set. If the reported metrics are computed on the same test set used for selection, the absolute performance values are likely optimistic. Please use a separate validation split for hyperparameter selection and report test-set metrics from the final selected configuration.","section":"§2.6-2.7"}],"minor_comments":[{"comment":"The definition of explained variance is malformed as printed: the expression for sigma-squared of the residuals lacks a division by N and appears to use zspec in the denominator; please restate it consistently with the residual definition Delta-z = zspec - zphot.","section":"§2.5"},{"comment":"The text says architectures with 1 to 6 hidden layers and 50 to 300 neurons per layer were explored, but the final architecture is only given in Figure 5; please state the number of layers, neurons per layer, activation function, optimizer, and early-stopping details in the text.","section":"§2.6 / Figure 5"},{"comment":"The symbol sigma-prime-z is introduced without definition in Section 3.1, where the text reports low scatter of sigma-prime-z = 0.387; define it or replace it with a metric defined in Section 2.5.","section":"§3.1"},{"comment":"There is a typo in the introduction, W olf, in the reference to Wolf et al. 2018, and the reference list contains entries for Duncan 2021 and Duncan 2022 with only one clearly cited in the text; please clean up the bibliography.","section":"References"},{"comment":"The abstract and conclusions present r=0.9187 and NMAD=0.197 without stating that these values are for the DxS subset rather than the full DESI sample; please state the sample explicitly to avoid over-generalization.","section":"Abstract / §5"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is a plausible application paper, and the qualitative direction of the GALEX result is likely correct. The blocking issues are the uncontrolled sample comparison behind the headline numbers and the internal inconsistency between Table 6 and Table 9b for the neural network. Both are fixable with a controlled re-run, so I do not recommend rejection, but I would not accept until those are resolved. The percentage-error issue in Table 7 is also easy to correct."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the claim that adding GALEX FUV/NUV improves DESI quasar photo-z is plausible and supported by prior work, but the specific numbers in the abstract (r 0.81→0.92, NMAD 0.28→0.20) come from comparing the full DESI sample with the smaller, brighter DxS subset, so they overstate the effect. The appendix's same-sample check shows a much smaller gain and actually contradicts the main table.\n\nWhat's new: combining DESI EDR photometry with WISE and GALEX in a neural-network photo-z engine, with 100-run averages and drop-column feature importance. That is a useful engineering contribution. The bimodality analysis and the u/i-band comparison (Table 8) are informative.\n\nWhere it falls down: the central comparison is confounded. In Table 9b, the DxS-only baseline (no UV) has NMAD 0.3171, not the 0.2781 in Table 4; with UV it becomes 0.2699, about a 15% improvement, not the 29% in the abstract. Worse, Table 9b's UV baseline (0.2699) does not match Table 6's 0.1971 for what looks like the same model and data. Also, the percentage changes in Table 7 do not follow Eq. (1) – e.g. Table 7 lists NMAD -41.1% while (0.1971-0.2781)/0.2781 is -29.1%. These need fixing. Hyperparameters are selected on the test set, which can inflate results; a proper validation split should be reported. The outlier section in 4.4 is mildly circular but only secondary.\n\nBottom line: this paper is a solid candidate for revision, not desk reject. The direction of the result is probably right, but the quantitative headline needs a controlled re-run with the same training sample and consistent metrics. Send it to a referee, but ask for the same-sample baseline and a resolution of Table 6 vs Table 9.","headline":"The GALEX improvement is real in direction, but the headline numbers compare different training samples, so the magnitude is unproven and there are internal numeric inconsistencies.","tokens_in":23564,"tokens_out":2767,"would_cite":false,"duration_ms":31927,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Adding GALEX ultraviolet photometry to DESI and WISE bands improves neural-network photometric redshifts for quasars, cutting scatter and catastrophic outliers.","keywords":["photometric redshifts","quasars","neural network","GALEX ultraviolet photometry","DESI","WISE","machine learning","active galactic nuclei"],"falsifier":"Train the same neural network on the DxS sample twice, once with g, r, z, W1, W2 only and once with FUV and NUV added, holding all sources and hyperparameters fixed; if the correlation and normalised median absolute deviation do not improve by roughly the amount implied by Table 9b (NMAD from about 0.32 to about 0.27), or if the improvement does not persist when the model is applied to faint, non-SDSS-matched DESI quasars, then the headline gain is largely a sample-selection effect rather than a true benefit of ultraviolet photometry.","tokens_in":22477,"feed_emoji":"🔭","tokens_out":7310,"duration_ms":75556,"temperature":0.7,"pith_summary":"The paper aims to show that a fully connected neural network can estimate photometric redshifts for DESI quasars accurately enough for surveys that cannot obtain spectra for every source, and that accuracy improves markedly when far- and near-ultraviolet fluxes from GALEX are added to the DESI optical and WISE near-infrared bands. On the 24,616 quasars with SDSS and GALEX photometry, the network reaches a correlation coefficient of r = 0.9187 with spectroscopic redshift and a normalised median absolute deviation of 0.197, a 29 per cent reduction in scatter relative to the optical–infrared baseline. The claimed reason is physical: QSO rest-frame UV features, including the Lyman break, shift through the observed bands with redshift, so the FUV-to-W2 wavelength lever arm breaks colour–redshift degeneracies that the narrower DESI/WISE baseline leaves open. Such photometric redshifts would let future wide-area surveys study quasar populations without spectroscopic follow-up of every source.","feed_headline":"Adding GALEX UV bands cuts quasar redshift scatter by 29%","feed_subtitle":"Photometric redshifts reach r=0.92 versus spectra, an anchor for surveys without follow-up spectroscopy.","key_machinery":"The machinery is a three-layer fully connected neural network with 200 neurons per hidden layer, ReLU activations, MSE loss, the Adam optimiser, and early stopping, trained on 80 per cent of the sample over 100 random train/test splits with K-fold cross-validation. The physical mechanism carrying the argument is the wide wavelength baseline from GALEX FUV (λ ≈ 1575 Å) through DESI g, r, z to WISE W1, W2 (λ ≈ 4.6 μm): as redshift increases, rest-frame UV features such as the Lyman break at 1216 Å and the Mg II line at 2800 Å move through these bands, so the added UV bands break colour–redshift degeneracies that a purely optical–infrared set leaves open. The paper deliberately uses raw fluxes and magnitudes rather than colour indices, arguing that this avoids introducing extra feature correlations while retaining the most fundamental observational data.","core_discovery":"The central claim is that adding GALEX FUV and NUV photometry to DESI g, r, z and WISE W1, W2 fluxes substantially improves photometric redshift predictions for quasars: for a fully connected three-layer neural network, the correlation with spectroscopic redshift rises from r = 0.8099 on the DESI-only sample to r = 0.9187 on the DESI+GALEX sample, normalised median absolute deviation falls from 0.278 to 0.197, and the maximum and mean absolute errors both improve. The neural network also outperforms a tuned k-nearest-neighbours model on the same features. The authors attribute the gain to the wavelength lever arm from FUV (rest-frame 1575 Å) to W2, which lets the model span the Lyman break and other rest-frame UV features as they redshift through the observed bands. A secondary claim is that the bimodality seen in the optical-only predictions, driven by separation in the g–r versus z–W1 colour plane, is reduced when UV bands are included, and that when DESI and SDSS spectroscopic redshifts disagree, the photometric prediction agrees with DESI for about 73 per cent of the 107 outliers.","pith_inferences":["The comparison that carries the headline gain mixes two changes at once: the added ultraviolet bands and a switch from the full 87,318-source DESI sample to the smaller, brighter, better-constrained 24,616-source DxS subset; a clean test of the GALEX contribution would retrain the same architecture on the DxS sample with and without FUV/NUV, and the paper's own drop-column results on that fixed sa","Permutation importance places FUV and NUV near the bottom even though add/remove comparisons show they matter, a known failure mode for correlated features; future redshift-model papers should report drop-column or Shapley-value importances alongside permutation scores.","For radio-selected quasar samples expected from next-generation surveys, the same architecture could be retrained, but the selection-bias issue will be more severe because radio-selected samples have different redshift and luminosity distributions."],"forward_implications":["Photometric redshifts of quasars from a neural network are accurate enough that large surveys without full spectroscopic coverage can statistically use them for quasar samples.","Deep ultraviolet photometry is more valuable than additional optical bands for quasar redshift estimation, since the network on DESI+GALEX outperforms the comparable model using SDSS ugriz alone.","The bimodal structure in redshift predictions, tied to g–r versus z–W1 colours, means that explicitly modelling two quasar populations could further improve accuracy.","When DESI and SDSS spectroscopic redshifts disagree, the nine-band photometric prediction sides with DESI for about 73 per cent of the 107 outliers, suggesting DESI's line associations are generally the more reliable ones."],"supporting_citations":[{"why":"Prior evidence that adding GALEX narrowband fluxes improves machine-learning redshift predictions, which the paper extends to DESI quasars.","marker":"Ball et al. 2008"},{"why":"Earlier finding that UV data is especially valuable for blue galaxies and quasars, cited as motivation for including GALEX bands.","marker":"Niemack et al. 2009"},{"why":"Prior study by the same group reporting that inclusion of GALEX fluxes improves photometric redshift performance.","marker":"Curran 2020"},{"why":"Source of the SDSS DR16Q catalogue from which the DxS sample obtains its GALEX UV fluxes and SDSS magnitudes.","marker":"Lyke et al. 2020"},{"why":"Defines the GALEX mission and its FUV/NUV bands, the additional photometry whose value is the paper's central claim.","marker":"Martin et al. 2005"},{"why":"Provides the DESI Early Data Release QSO spectroscopic catalogue that constitutes the base dataset and the spectroscopic redshift targets.","marker":"Adame et al. 2023"},{"why":"Supplies the neural-network architecture and training procedure (K-fold cross-validation, early stopping, Adam) used in this study.","marker":"Curran et al. 2021"}],"fun_headline_variants":["UV bands cut quasar photometric redshift scatter by 29%","Adding GALEX UV improves quasar redshift predictions to r=0.92","GALEX UV data reduce quasar redshift errors by 29%","Neural network + UV photometry sharpen quasar redshifts"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The conclusion that GALEX ultraviolet bands cause the improvement assumes that the gain measured on the smaller, brighter SDSS-matched subset would also appear on the full DESI sample; the paper itself notes in Section 4.2 that GALEX-detected quasars are already among the brighter, better-constrained objects.","fun_headline_variants_meta":{"raw":{"variants":["UV bands cut quasar photometric redshift scatter by 29%","Adding GALEX UV improves quasar redshift predictions to r=0.92","GALEX UV data reduce quasar redshift errors by 29%","Neural network + UV photometry sharpen quasar redshifts"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000353,"raw_usage":{"total_tokens":1980,"prompt_tokens":1059,"completion_tokens":921,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":675,"completion_tokens_details":{"reasoning_tokens":845}},"tokens_in":675,"tokens_out":921,"duration_ms":10668,"temperature":1.0,"reasoning_tokens":845,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T20:14:16.506845+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Train the same neural network on the DxS sample twice, once with g, r, z, W1, W2 only and once with FUV and NUV added, holding all sources and hyperparameters fixed; if the correlation and normalised median absolute deviation do not improve by roughly the amount implied by Table 9b (NMAD from about 0.32 to about 0.27), or if the improvement does not persist when the model is applied to faint, non-SDSS-matched DESI quasars, then the headline gain is largely a sample-selection effect rather than a true benefit of ultraviolet photometry.","supporting_citations":[{"cited_title":"M., Brunner, R","cited_arxiv_id":null,"evidence_quote":"Prior evidence that adding GALEX narrowband fluxes improves machine-learning redshift predictions, which the paper extends to DESI quasars."},{"cited_title":"D., Jimenez, R., Verde, L., et al","cited_arxiv_id":null,"evidence_quote":"Earlier finding that UV data is especially valuable for blue galaxies and quasars, cited as motivation for including GALEX bands."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Prior study by the same group reporting that inclusion of GALEX fluxes improves photometric redshift performance."},{"cited_title":"W., Higley, A","cited_arxiv_id":null,"evidence_quote":"Source of the SDSS DR16Q catalogue from which the DxS sample obtains its GALEX UV fluxes and SDSS magnitudes."},{"cited_title":"C., Fanson, J., Schiminovich, D., et al","cited_arxiv_id":null,"evidence_quote":"Defines the GALEX mission and its FUV/NUV bands, the additional photometry whose value is the paper's central claim."},{"cited_title":"J., Moss, J","cited_arxiv_id":null,"evidence_quote":"Supplies the neural-network architecture and training procedure (K-fold cross-validation, early stopping, Adam) used in this study."}],"review_version":1}