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REVIEW 3 major objections 4 minor 282 references

A neural network trained on known, mostly emission-line CVs and applied to every DESI science spectrum finds 1,029 cataclysmic variables — 221 new, ten of them AM CVn.

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 04:22 UTC pith:ZTRFWGXL

load-bearing objection Big, useful CV catalogue from DESI DR2; the catalog itself is solid, but the ~99% completeness claim doesn't survive the paper's own missed-AM-CVn example. the 3 major comments →

arxiv 2607.22836 v1 pith:ZTRFWGXL submitted 2026-07-24 astro-ph.SR

1000 cataclysmic variables identified from DESI spectroscopy

K. Inight , B. T. G\"ansicke , A. Aungwerojwit , P. Izquierdo , C. J. Manser , A. D. Myers , A. Swan , J. R. Thorstensen
show 36 more authors
J. Aguilar S. Ahlen D. Bianchi D. Brooks T. Claybaugh A. de la Macorra Biprateep Dey P. Doel A. Font-Ribera J. E. Forero-Romero Satya Gontcho A Gontcho G. Gutierrez J. Guy R. Joyce S. Juneau S. E. Koposov A. Kremin M. Landriau L. Le Guillou A. Meisner R. Miquel J. Moustakas J. Najita I. P\'erez-R\`afols W. J. Percival F. Prada G. Rossi E. Sanchez D. Schlegel M. Schubnell H. Seo J. Silber G. Tarl\'e B. A. Weaver R. Zhou H. Zou
This is my paper
classification astro-ph.SR
keywords cataclysmic variableswhite dwarf binariesmachine-learning classificationDESI spectroscopyAM CVn systemsorbital period distributionspace densityspectroscopic completeness
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.

The paper aims to measure the cataclysmic-variable (CV) population — close binaries in which a white dwarf accretes from a companion star — without the bias that has shaped the known census, where systems are mostly found because they erupted in outburst. It runs a convolutional neural network, trained on previously known CV spectra, over roughly 81 million usable spectra from the Dark Energy Spectroscopic Survey and manually inspects the shortlist, recovering 1,029 CVs: 808 already known and 221 new, including ten AM CVn systems (ultra-short-period binaries with hydrogen-poor donors). The authors argue the sample is about 99 per cent complete, roughly two magnitudes deeper than earlier spectroscopic surveys, and skewed toward intrinsically faint, short-period, low-accretion-rate systems — indeed, of the 171 new CVs with light-curve coverage, only 43 show outbursts. If the claims hold, this is the largest spectroscopically selected CV sample to date, the first to test CV population models against a largely variability-free census, and the basis for revised space densities of CV subtypes as well as a dozen new members of a rare class showing erratic state changes.

Core claim

A CNN trained on known, mostly emission-line CVs and applied to every DESI science spectrum finds 1,029 cataclysmic variables — 221 new, ten of them AM CVn. The CNN flagged 293,670 candidates; cross-matching, a redshift cut, and visual inspection of 41,500 spectra yielded 1,014 CVs, with six more from validation. The CNN's per-spectrum reliability is 97.2 per cent and the sample completeness is claimed at ~99 per cent. Of the 171 new CVs with ZTF light curves, only 43 show outbursts — evidence that spectroscopic selection offsets the bias toward outbursting systems. Twelve new members of the peculiar state-change class and five evolved-donor CVs complete the catalogue.

What carries the argument

The load-bearing tool is a convolutional neural network (CNN) trained on 5,484 DESI spectra matched from SDSS samples: 623 known CVs plus galaxies, quasars, stars, white dwarfs, and detached white-dwarf binaries. Trained 200 times on random 80/20 splits, the best model is error-free on the held-out test set. Its job is to shrink 81 million spectra to 293,670 candidates; a catalogue cross-match and redshift cut (Z < 0.01185) then trim the list to ~41,500 spectra for human inspection. Completeness rests on inspecting random and targeted samples of discarded spectra, including the 1.6 < Z < 1.7 spike where DESI's redshift pipeline is fooled — yielding the ~99 per cent estimate.

Load-bearing premise

The completeness argument stands or falls on the premise that the CNN's training spectra — previously known CVs, nearly all with emission lines — cover every spectral shape a CV can present in DESI; the paper itself shows this premise fails for at least one absorption-dominated AM CVn system that was absent from the training set.

What would settle it

Inspect a random sample of the roughly 238,000 spectra the CNN flagged but the paper never manually examined (those with Z > 0.01 and no catalogue counterpart). If CVs appear there at a rate well above the assumed ~1 per cent — or if retraining the CNN with absorption-dominated AM CVn spectra added to the training set causes it to flag many additional systems — the claimed ~99 per cent completeness and the space densities built on it would be overestimates.

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

If this is right

  • The DESI sample is the deepest CV census to date, about two magnitudes fainter than previous spectroscopic surveys, and it contains the largest fraction of short-period systems; the sharp edges of the 2–3 hour period gap fade in such untargeted samples.
  • Revised space densities for SU UMa and WZ Sge subtypes agree with earlier estimates, but the values for every subtype are lower bounds because 156 unclassified CVs — many of them non-outbursting, hence likely short-period — are not counted.
  • With twelve new members added to the eight previously known examples, roughly one per cent of all CVs belong to the peculiar state-change class, and their scatter across orbital period and HR-diagram position argues against a single evolutionary stage as the cause.
  • Almost all 1,029 CVs — 709 of them — were observed serendipitously by DESI's galaxy and quasar programs rather than by CV-specific targeting, showing that deep multi-object surveys harvest CVs as a by-product.
  • Five CVs whose spectra mimic F-type stars but whose absolute magnitudes are too faint for F-type donors are likely systems with highly evolved helium-core donors, a population previously found mainly by targeted variability searches.

Where Pith is reading between the lines

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

  • If the ~99 per cent completeness holds, the implied conclusion is that the known CV census is far from finished at faint magnitudes: the serendipitous detection rate inside DESI's deep but non-CV-focused targeting is high enough that future wide-field spectroscopic surveys will keep uncovering substantial new populations.
  • The CNN's demonstrated blind spot — an absorption-dominated AM CVn prototype missed because the training set contained no such spectrum — suggests that other spectral classes absent from the training sample are silently underrepresented; true completeness for rare or unusual CVs may be below 99 per cent even if the overall claim survives.
  • The near-total reliance on serendipity implies a cheap, testable strategy: deliberately reserving survey fibers for white-dwarf-binary candidates would multiply CV yields, and a future far-UV space survey of the kind the paper highlights would make such targeting efficient.
  • The blurring of the period-gap edges in an unbiased sample sharpens a specific prediction-check for binary evolution models: models that reproduce a sharp gap in outburst-selected samples but not in deep spectroscopic samples would be consistent with these data, whereas models predicting a sharp gap here would not.

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 / 4 minor

Summary. The paper reports a systematic search for cataclysmic variables in DESI DR2, analysing 80,767,382 science spectra. Three shortlisting routes are used: coordinate cross-match with 16,277 known/candidate CVs, a CNN trained on SDSS CV spectra, and a redshift cut; the resulting candidates are manually inspected with the aid of Gaia, GALEX, ZTF, and archival databases. The authors identify 1,029 CVs, of which 221 are new, including ten AM CVn systems and twelve members of a peculiar state-change class; they add 84 new or improved orbital periods and derive revised CV subtype space densities. The catalogue is supported by an independent validation set of 580 DESI CV spectra from a white-dwarf-targeted programme and by a detailed comparison with the Hou et al. (2026) DESI DR1 CV sample.

Significance. If the completeness and space-density claims hold, this is the largest spectroscopically selected CV sample to date, roughly two magnitudes deeper than SDSS, and would provide a largely outburst-unbiased sample for population studies. The paper is commendably transparent: it releases the full catalogue, per-CV spectra, and training/test lists; it documents an independent validation set with only two non-detections (with stated causes); and it explicitly re-examines and reclassifies 13 objects from Hou et al. (2026). The identification of twelve new peculiar state-change CVs and five 'highly evolved donor' candidates are interesting scientific results in their own right. These strengths are partly offset by an overstated completeness statement and by an apparent inconsistency in the completeness corrections used for the space-density estimates.

major comments (3)
  1. [§4.5, §6] The claim 'the sample is ≃99 per cent complete' is not supported by the tests described. The random sample of 9,954 spectra is drawn from the 293,670 CNN-selected spectra that were not otherwise inspected, so it bounds false positives among the selected set, not false negatives among the 80,767,382 unselected science spectra. The 234-spectrum TARGETID check measures the CNN's flagging rate for spectra of CVs that were already identified by another route, not its sensitivity to unfamiliar CV spectral types. Section 6 supplies a concrete false negative: the AM CVn prototype J1234+3737 was missed because its helium-absorption spectrum was not represented in the training set. The validation sample of §4.3.4 is drawn from a white-dwarf-targeted programme and did not include such systems. Therefore the 99% completeness statement is unsubstantiated, and the §10 space densities—which depend on c
  2. [§4.3.3, Fig. 2] The 'perfect' confusion matrix in Fig. 2 is an optimistic selection artifact. The model was chosen among 200 retrainings on the basis of the test-set confusion matrix, so the quoted zero false-positive/zero false-negative result is not an honest out-of-sample evaluation. The independent validation set of 580 spectra in §4.3.4 is better evidence and should be reported as the primary sensitivity estimate. Please report the distribution of test metrics across the 200 splits, or use a validation split during model selection, and state the final CNN sensitivity using the held-out validation sample.
  3. [§10, Eq. (2)] There is an internal inconsistency between the claimed completeness and the completeness corrections used in the space-density calculation. Section 4.5 states the sample is ≃99 per cent complete, but Section 10 adopts completeness values of 0.6 for short-period subtypes and 0.2 for long-period subtypes, and Eq. (2) applies the inverse of these as a correction factor to N_obs. These two statements cannot both describe the same selection pipeline. Clarify what the 99% statement refers to (e.g., contamination among the selected spectra) and justify the 0.6/0.2 values independently of that claim, or recalculate the space densities consistently.
minor comments (4)
  1. [§5.1] 'We found 1029 CVs among the DESI DR1 spectra' should read DR2; the analysis covers DESI DR2 (Section 4.1).
  2. [Fig. 33 caption] The caption says the 150-pc 'All CVs' bar suffers from only seven CVs, whereas Table 7 lists N=8 for that row; please reconcile.
  3. [§4.5] The term 'sample' is ambiguous in the completeness discussion: it is not clear whether '≃99 per cent complete' refers to the final CV list, the CNN-selected set, or the manually inspected set. Please define it explicitly.
  4. [References] Swan et al. (submitted) is cited without a year or arXiv number; please give a fuller reference or state its status.

Circularity Check

0 steps flagged

No circular reduction: the DESI CV catalogue is produced by an externally anchored pipeline; the 99% completeness claim is a validation gap, not a by-construction circularity.

full rationale

The paper's derivation chain is: train a CNN on SDSS CV spectra (Section 4.3), apply it to all 80,767,382 DESI science spectra, supplement with coordinate cross-matches and a redshift filter, visually inspect the shortlist, then use the resulting sample to estimate completeness and space densities. No step in this chain is equivalent to its inputs by construction. The CNN is a fitted classifier, but its outputs are not relabelled training values; they are tested against an external catalogue (Hou et al. 2026) in Section 6, which reports a 409/412 recovery rate. The three misses are explicitly disclosed, including the AM CVn prototype J1234+3737 whose absorption-dominated spectrum was absent from the CNN training set. That disclosure is a genuine limitation for the paper's 'sample is ≃99 per cent complete' statement in Section 4.5: the random-sample check of 9,954 already-selected spectra only bounds false positives among selected spectra and cannot constrain false negatives among unselected ones. However, this is an evidential/correctness gap, not a circular argument. Likewise, the space-density completeness correction in Section 10 uses the authors' own SDSS CV catalogue from Inight et al. (2023a, 2025) as a reference sample; this is standard practice for an external reference, and the catalogue is independently grounded in SDSS spectroscopy. Self-citations are frequent, but the CNN architecture, training set, and validation are described in this paper, and the central catalogue claim is anchored to DESI spectra and an external comparison. No fitted parameter is renamed as a prediction, and no uniqueness theorem or ansatz is imported from the authors' prior work to force the result. The circularity score is therefore low; the substantive concerns belong to completeness validation and selection representativeness, not to circular derivation.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

The central catalogue claim rests mainly on the domain assumption that CVs are spectroscopically identifiable via emission lines and that the training set covers DESI's CV diversity. The space-density and completeness sub-claims rest on hand-set free parameters: completeness corrections 0.6/0.2, the m_G<22.5 limit, and the A1 UV cut coefficients. The completeness correction is referenced to the authors' own SDSS catalogues, which adds a circularity burden. No new physical entities are postulated.

free parameters (4)
  • Completeness correction C_corr (short-period subtypes) = 0.6
    Applied to SUUMa, WZSge, and polar subtypes in Eq. 2 (space density). Chosen by hand from a coarse period-dependent completeness curve (Fig. 32), not derived from a fitted model.
  • Completeness correction C_corr (long-period subtypes) = 0.2
    Applied to UGem, novalike, and intermediate polar subtypes in Eq. 2. Hand-set from the same completeness curve; directly scales the space-density estimate.
  • Limiting magnitude m_G for DESI CV detection = 22.5
    Assumed in Section 10 to compute R_lim and hence V_eff for each subtype. Not derived from data or an independent completeness simulation; a different limit changes all space densities.
  • UV selection cut coefficients (Eq. A1) = slope = 1.5, intercept = -0.3
    Linear cut M_FUV > 1.5*(FUV-G) - 0.3 used in the CWDB target selection (Appendix A). Hand-tuned to recover known white dwarf binaries; no uncertainties or validation on a held-out sample are reported. This affects the targeting analysis, not the main catalogue.
axioms (5)
  • domain assumption Practically all CVs exhibit Balmer and/or He emission lines, so spectral selection can find CVs regardless of outburst behaviour.
    Stated in Section 1 and underpins the entire CNN + visual classification strategy. Citations to Szkody et al. and Inight et al. support this, but it is an empirical generalization that may fail for unusual systems (see the missed absorption-dominated AM CVn in Section 6).
  • domain assumption The SDSS CV training sample is representative of the spectral diversity of CVs in DESI, including faint, low-accretion-rate and absorption-dominated systems.
    Required for the CNN to flag all CVs. Contradicted in Section 6 by the missed AM CVn whose helium absorption spectrum was absent from the training set. This directly affects the completeness claim.
  • domain assumption The Galactic CV population is vertically exponential with adopted scale heights, and DESI's effective volume can be approximated by HEALPix exposures ignoring bright/dark time and duplicate passes.
    Eq. 1 and Section 10. The authors state the uncertainty in space densities is dominated by the scale height, and they tabulate results for several scale-height choices rather than fitting one.
  • ad hoc to paper Completeness measured by re-detecting the authors' SDSS CV catalogues (Inight et al. 2023a, 2025) applies to the DESI CV sample.
    Section 10: 'we used the set of SDSS CVs from Inight et al. (2023a, 2025) as our reference.' This makes the completeness correction dependent on the completeness of the authors' own earlier sample, which may carry the same selection biases.
  • domain assumption Subtype classifications from VSX/literature are reliable, and the 156 unclassified CVs can be neglected in subtype space densities (with a ~20% understatement).
    Section 10 and Table 7 note. The unclassified systems are likely short-period; ignoring them biases subtype space densities, as the authors acknowledge.

pith-pipeline@v1.3.0-alltime-deepseek · 44284 in / 14219 out tokens · 140839 ms · 2026-08-01T04:22:20.720066+00:00 · methodology

0 comments
read the original abstract

Most cataclysmic variables (CVs) are discovered when they have an outburst generating an inherent selection bias against CVs that rarely, or never, outburst. CVs discovered by virtue of their spectroscopic characteristics are particularly valuable to offset this bias and we have used an established machine-learning technique to assist in searching 98 966 000 spectra obtained by the Dark Energy Spectroscopic Survey (DESI) to find such CVs. DESI observations are much deeper than previous spectroscopic surveys and we have identified 1029 CVs, 221 of which are new including ten of the AM CVn subtype. We have spectroscopically confirmed 441 CV candidates and obtained 84 new or improved orbital periods. We present revised space density estimates based upon this new data. We have also added ten more to the eight known examples of an intriguing class of CVs which exhibit peculiar changes in accretion.

Figures

Figures reproduced from arXiv: 2607.22836 by A. Aungwerojwit, A. de la Macorra, A. D. Myers, A. Font-Ribera, A. Kremin, A. Meisner, A. Swan, B. A. Weaver, Biprateep Dey, B. T. G\"ansicke, C. J. Manser, D. Bianchi, D. Brooks, D. Schlegel, E. Sanchez, F. Prada, G. Gutierrez, G. Rossi, G. Tarl\'e, H. Seo, H. Zou, I. P\'erez-R\`afols, J. Aguilar, J. E. Forero-Romero, J. Guy, J. Moustakas, J. Najita, J. R. Thorstensen, J. Silber, K. Inight, L. Le Guillou, M. Landriau, M. Schubnell, P. Doel, P. Izquierdo, R. Joyce, R. Miquel, R. Zhou, S. Ahlen, Satya Gontcho A Gontcho, S. E. Koposov, S. Juneau, T. Claybaugh, W. J. Percival.

Figure 1
Figure 1. Figure 1: Histogram of individual DESI DR2 exposures for the 1029 identified cataclysmic variables within this analysis. Whilst the majority of CVs only have a single spectrum some have been observed multiple times. In general the CNN consistently selects all the spectra of a CV although exceptions can occur typically if an individual spectrum has a poor signal￾to-noise ratio (S/N) or if the CV is in outburst. We do… view at source ↗
Figure 2
Figure 2. Figure 2: The confusion matrix resulting from training where the final CNN is applied to the 1083 test spectra (20 per cent) from the train/test dataset. 4.3.3 Training the CNN with DESI DR2 data Following Inight et al. (2025) the CNN is trained by splitting the train/test dataset (see the final sample in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The redshift distribution of the 1803 DESI CV spectra, as mea￾sured by the DESI Redrock pipeline, shows a spike at 1.6 < 𝑍 < 1.7. This spike is not seen in the full sample of 293 670 candidate CV spectra selected by the CNN. remaining 186 spectra did not show any features that would allow them to be classified as CVs, reasons for that failure include poor quality spectra or a technical failure such as miss… view at source ↗
Figure 4
Figure 4. Figure 4: Three typical examples of non-CV spectra selected by the CNN with 1.6 < 𝑍 < 1.7. Top panel: A very common example having a low S/N. The second panel shows a quasar spectrum whilst the bottom panel shows narrow lines with a negligible redshift which could be an active star or a low-redshift galaxy. MNRAS 000, 1–27 (2020) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: HR diagram of the 667 DESI CVs that have Gaia photometry and parallaxes. When we detected multiple cyclotron humps in the spectrum of a polar we estimated the magnetic field strength (𝐵) by matching the wavelengths of the humps to the harmonics of the cyclotron resonant frequency given by 𝑓 = 𝑒𝐵/2𝜋𝑚𝑒 where 𝑒 and 𝑚𝑒 are the charge and mass of an electron respectively. 5.2 Individual DESI CVs Whilst analysin… view at source ↗
Figure 7
Figure 7. Figure 7: Top panel: The ZTF light curve of the WZ Sge J025612.87−103359.4 with the 𝑟-band filter shown as red triangles, 𝑔- band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectra of J025612.87−103359.4. 5.2.3 J093322.31–030114.2 There are four DESI spectra ( [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Top panel: The ZTF light curve of the polar J093322.31−030114.2 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectra of J093322.31−030114.2. 8500 9000 9500 10000 10500 MJD-50000 16 18 20 m 4000 5000 6000 7000 8000 9000 10000 Wavelength (Å) 0 5 10 15 20 Flu x (10 17 erg cm 2 s 1 Å 1 ) HeI H H HeHI eII H HeI HeI H 20230111 202301… view at source ↗
Figure 9
Figure 9. Figure 9: Top panel: The ZTF light curve of the SU UMa J094325.88+520128.8 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectra of J094325.88+520128.8. et al. 2024). We obtained a period of 2.4525(2) h from the ZTF light curve. 5.2.4 J094325.88+520128.8 This is a previously-studied SU UMa (Szkody et al. 2004). We obtained a superhump per… view at source ↗
Figure 12
Figure 12. Figure 12: Top panel: The ZTF light curve of the intermediate polar J154453.62+255348.9 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectra of J154453.62+255348.9. 8500 9000 9500 10000 10500 MJD-50000 18 19 20 m 4000 5000 6000 7000 8000 9000 10000 Wavelength (Å) 0 20 40 60 Flu x (10 17 erg cm 2 s 1 Å 1 ) HeIH H HeI HeII H HeI HeI H [PI… view at source ↗
Figure 13
Figure 13. Figure 13: Top panel: The ZTF light curve of the novalike J163125.82+735411.6 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectrum of J163125.82+735411.6. 5.2.7 J154453.62+255348.9 One of the DESI spectra ( [PITH_FULL_IMAGE:figures/full_fig_p009_13.png] view at source ↗
Figure 18
Figure 18. Figure 18: Top panel: The ZTF light curve of the intermediate polar J223501.45+264507.3 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectrum of J223501.45+264507.3. HR diagram however this is misleading as the cyclotron radiation dominates that of the donor. The ratio of the equivalent width of the He ii line to the H𝛽 line is 0.87 impl… view at source ↗
Figure 17
Figure 17. Figure 17: Top panel: The ZTF light curve of the polar J180546.34+314017.7 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectra of J180546.34+314017.7. of Δ𝑚 ≃ 2 and what appear to be frequent outbursts and which may be ER UMa supercycles. We obtained a period of 28.74(2) d from the ZTF light curve which is likely to be the supercycle pe… view at source ↗
Figure 19
Figure 19. Figure 19 [PITH_FULL_IMAGE:figures/full_fig_p012_19.png] view at source ↗
Figure 22
Figure 22. Figure 22: Top panel: The ZTF light curve of J094606.92−005601.8 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectra of J094606.92−005601.8. 8500 9000 9500 10000 MJD-50000 16 18 20 22 m 4000 5000 6000 7000 8000 9000 10000 Wavelength (Å) 0 1 2 3 4 Flu x (10 17 erg cm 2 s 1 Å 1 ) HeI H H HeI HeII H HeI HeI H [PITH_FULL_IMAGE:figures/full… view at source ↗
Figure 20
Figure 20. Figure 20: CVs with peculiar state changes from Inight et al. (2023a) (8, orange) and from this work (12, blue). Top panel: HR diagram computed from the Gaia photometry and Bailer-Jones distances (Bailer-Jones et al. 2021a). J165803.75+143634.2 is not shown as it has no Gaia counterpart. Bottom panel: Histogram of the orbital periods where known. 8500 9000 9500 10000 10500 MJD-50000 16 18 20 m 4000 5000 6000 7000 80… view at source ↗
Figure 23
Figure 23. Figure 23: Top panel: The ZTF light curve of J100243.11−024636.0 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectrum of J100243.11−024636.0. et al. (2026), and failed to identify three of them. We show the DESI spectra of these three systems in [PITH_FULL_IMAGE:figures/full_fig_p013_23.png] view at source ↗
Figure 21
Figure 21. Figure 21: Top panel: The ZTF light curve of J084108.10+102536.3 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectrum of J084108.10+102536.3. 8500 9000 9500 10000 10500 MJD-50000 16 18 20 m 4000 5000 6000 7000 8000 9000 10000 Wavelength (Å) 0 5 10 Flu x (10 17 erg cm 2 s 1 Å 1 ) HeI H H HeHIeII H HeI HeI H 20211201 20220225 [PITH_FULL_… view at source ↗
Figure 24
Figure 24. Figure 24: Top panel: The ZTF light curve of J115215.79+491441.7 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectrum of J115215.79+491441.7. 8500 9000 9500 10000 10500 MJD-50000 18.5 19.0 19.5 20.0 m 4000 5000 6000 7000 8000 9000 10000 Wavelength (Å) 0 20 40 60 Flu x (10 17 erg cm 2 s 1 Å 1 ) HeHI H HeI H HeI HeI H [PITH_FULL_IMAGE:fi… view at source ↗
Figure 26
Figure 26. Figure 26: Five CVs whose spectra resemble F–type stars with superim￾posed Balmer emission lines but which have absolute magnitudes fainter than those F–type stars. Top panel: HR diagram with periods where known, showing that the objects are relatively bright CVs close to the main sequence. These are very likely CVs with highly nuclear evolved donor stars, see El￾Badry et al. (2021a,b). Bottom panel: Normalised spec… view at source ↗
Figure 25
Figure 25. Figure 25: Top panel: The ZTF light curve of J150441.75+084752.4 with the 𝑟-band filter shown as red triangles, 𝑔-band data as green dots and 𝑖-band data as black squares. Bottom panel: The spectrum of J150441.75+084752.4. tometry (Section 5.1). We compare in [PITH_FULL_IMAGE:figures/full_fig_p015_25.png] view at source ↗
Figure 27
Figure 27. Figure 27: The three CVs from Hou et al. (2026) that were not identified by our CNN include AM CVn which, unlike most members of that subclass, has helium absorption lines (top), a low S/N spectrum (middle) and a spectrum that does not exhibit any CV features (bottom panel). upper edges of the period gap seen in the early samples, dominated by intrinsically bright CVs, is gradually washed out in homogeneous samples … view at source ↗
Figure 28
Figure 28. Figure 28: Left panel: The normalised cumulative orbital period distributions of the 448 DESI CVs (orange), 507 and 291 CVs observed by SDSS I-IV (green, Inight et al. 2023a) and SDSS V (magenta, Inight et al. 2025), 168 CVs from version 5 of the Ritter and Kolb catalogue (brown, Ritter 1990) and 572 CVs from version 7 of the Ritter and Kolb catalogue (blue, Ritter & Kolb 2003). The red vertical line shows the perio… view at source ↗
Figure 30
Figure 30. Figure 30: Each of the 1029 CVs was targeted by one or more target classes. Here we analyse the three classes that specifically targeted CVs using an UpSet diagram (Lex et al. 2014). The UpSet diagram shows the number of CVs targeted by each target class and the size of the intersections of classes. It is noteworthy that the bulk, 709, of CVs were found serendipitously by surveys targeting other objects rather than … view at source ↗
Figure 29
Figure 29. Figure 29: The spectra of a selection of the faintest DESI CVs, having S/N < 1 across the full spectral range of the DESI spectrum, no Gaia counterpart and no ZTF light curve data. For each CV we provide the S/N ratio and the synthetic magnitude (based on a Gaia 𝐺-band filter). The spectra are offset vertically by suitable amounts. The magnitude limit of DESI at which it can spectroscopically identify CVs is 1 − 2 m… view at source ↗
Figure 31
Figure 31. Figure 31: Each of the 1029 CVs was targeted by one or more surveys. Here we analyse the five main surveys using an UpSet diagram (Lex et al. 2014). The UpSet diagram shows the number of CVs targeted by each category and the size of the intersections of categories. It is noteworthy that all but 29 of the CVs were targeted by at least one of the main surveys. 0 2 4 6 8 Period (h) 0 50 100 150 200 Number of CVs 0.0 0.… view at source ↗
Figure 32
Figure 32. Figure 32: Analysis of CVs by orbital period showing the number of CVs observed by DESI (blue) compared with the number reported in Inight et al. (2023a, 2025). The completeness is the ratio of these two numbers and is shown in green together with a weighted (by number of CVs in each bin) quadratic fit. A linear fit was tried but proved to be poor fit whilst higher order polynomials exhibited physically implausible … view at source ↗
Figure 33
Figure 33. Figure 33: Comparison of the space density estimates for CVs from this work (bar chart) with those from eleven previous papers. All estimates use the assumptions for the scale heights from Pretorius et al. (2007a) unless otherwise stated. Left panel: Estimates for individual sub-types. Note that the values from Pala et al. (2020) are understated as they do not take account of completeness. Right panel: Composite est… view at source ↗

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