Could We Be Fooled about Phantom Crossing?

Arman Shafieloo; Ryan E. Keeley; William L. Matthewson

REVIEW 1 major objections 5 minor 1 cited by

Could We Be Fooled about Phantom Crossing?

T0 review · 1 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that the observed dark-energy phantom-crossing preference can be a statistical artifact, arising in 3.2% of mock universes that truly have no crossing.

desk verdict A careful Monte Carlo caveat on phantom crossing: the 3.2% false-positive rate is real but conditional on the best-fit Pade-w fiducial, and the paper's own caveats keep the result honest. read the letter →

arxiv 2506.15091 v1 pith:MMTIB7DP submitted 2025-06-18 astro-ph.CO

Ryan E. Keeley , Arman Shafieloo , William L. Matthewson This is my paper · ORCID

classification astro-ph.CO

keywords darkenergyphantomcrossingequationofstateMonteCarlosimulationsDESIBAOCPLparametrizationPade-wquintessencemodelcomparison

topics Dark Energy

open problems Dark Energy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Recent DESI, Planck, and supernova data show a preference for dark energy whose equation of state crosses the phantom divide at $w=-1$, but the paper asks whether this crossing is real or an artifact of statistical fluctuations and model flexibility. To test this, the authors generate 1,000 mock data sets from a best-fit non-phantom algebraic quintessence model that never crosses $w=-1$, then fit both the flexible CPL parametrization and the non-phantom model to each mock. In 3.2% of mocks, CPL's improvement over the non-phantom model is as large as or larger than the improvement seen in the real data, and in more than 30% of mocks CPL crosses the phantom divide and fits better even though the mock truth has no crossing. The paper concludes that evolving dark energy is preferred, but the specific claim of phantom crossing is not a 5-sigma detection and needs better data, especially higher-redshift BAO measurements, to confirm.

What carries the argument

The machinery is a Monte Carlo false-positive test built on the Pade-w parametrization, a two-parameter algebraic form designed to reproduce the general dynamics of thawing quintessence. With positive parameters, Pade-w keeps $w(z)>-1$ at all redshifts, so it cannot cross the phantom divide. Fitting this model to the real data fixes a fiducial cosmology; mock data sets are generated by perturbing the fiducial predictions with Gaussian noise from the real covariance matrices. Each mock is then fit with both Pade-w and the flexible CPL form, and the distribution of $\Delta\chi^2$ measures how often the flexible model outperforms the true model by chance alone.

What would settle it

Run the mock-generation pipeline again with the Pade-w fiducial parameters sampled from their full posterior instead of fixed at the best fit, and count how often CPL reaches $\Delta\chi^2 \ge 3.3$; if that fraction moves far from 3.2%, the headline false-positive rate is not stable under parameter uncertainty.

Watch

Extended reading notes

Core claim

Using DESI DR2 BAO, compressed Planck CMB, and Union3 supernovae, the best CPL fit ($w_0=-0.7$, $w_a=-1.0$) beats the best non-phantom Pade-w fit ($\eta_0=59$, $\epsilon_0=1.9$) by $\Delta\chi^2=3.3$. Taking the Pade-w best fit as the true cosmology, the authors add Gaussian noise from the real covariance matrices to build 1,000 mock universes and refit both models. In 363 of the 1,000 mocks, the CPL best fit crosses $w=-1$ and fits better than Pade-w; in 32 mocks (3.2%), the CPL advantage equals or exceeds the real-data $\Delta\chi^2$. The central claim is that a phantom-crossing preference at this level can be produced purely by statistical fluctuations and the uneven redshift distribution of the data, so the signal is not yet conclusive evidence of physics beyond $w=-1$.

Load-bearing premise

The load-bearing assumption is that the best-fit Pade-w model, with its parameters fixed and positive so that the equation of state stays above $w=-1$, faithfully represents the true non-phantom dark-energy cosmology used to generate the mock universes.

Editorial extensions

If this is right

The real-data preference for CPL over non-phantom Pade-w corresponds to a level that occurs in 3.2% of no-crossing mocks, so the crossing claim is not a 5-sigma detection.
A phantom-crossing CPL best fit arises in more than 30% of mocks generated from a non-phantom truth, so model flexibility alone is enough to reproduce the crossing pattern often.
The BAO dataset, especially the $D_H(z)$ measurements, is the main driver of the spurious preference in the most extreme mocks; higher-redshift BAO and better constraints on $\Omega_m h^2$ are the decisive future measurements.
Evolving dark energy remains preferred over $\Lambda$CDM; the paper does not question that quintessence-like evolution fits better, only the interpretation of the crossing.
Because the CPL and Pade-w models are not nested, the paper uses the mock-based $\Delta\chi^2$ distribution rather than Bayesian evidence as the way to assign significance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension is to run the same mock pipeline with the Pade-w fiducial parameters drawn from their posterior rather than fixed at best fit; if the $\Delta\chi^2$ tail widens, the 3.2% figure should be read as a best-case false-positive rate.
The same mock-calibration logic could be applied to other dark-energy features in the data, treating mock frequency as the significance currency rather than a fixed $\sigma$ threshold.
One could invert this result into a design rule: a future phantom-crossing claim should require the real-data $\Delta\chi^2$ to sit beyond, say, the 99th percentile of mocks from a non-phantom fiducial, rather than relying on a conventional significance cutoff.
If higher-redshift BAO data leave the mock tail unchanged while the real-data preference grows, that would shift the balance toward a genuine crossing; conversely, a shrinking mock tail would make the current 3.2% look conservative.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper's key number is a 3.2% false-positive rate for CPL phantom crossing under a non-phantom Pade-w null, using DESI DR2 BAO + Union3 + compressed CMB. That is a useful, timely caveat for the DESI dark energy discussions, and it is not a 5-sigma killer. What is actually new is the quantitative application to the current dataset, not the Monte Carlo technique itself, which is standard parametric bootstrap.

What the paper does well: it recognizes that CPL and Pade-w are non-nested, avoids Bayesian evidence priors, generates 1,000 mocks from the best-fit Pade-w model, refits both models to each mock, and reports the tail probability. The diagnosis that BAO data around z~1 drive the spurious preference is a nice piece of detective work, and the DH/DM relative curves in Fig. 5 make that concrete. The paper also states its limitations clearly: it uses a specific quintessence-like parametrization, not all non-phantom models, and it does not address systematics.

The soft spots are real but minor. The headline 3.2% is computed from a point null: the best-fit Pade-w parameters are fixed at eta0=59, epsilon0=1.9, with no propagation of their uncertainties or averaging over the Pade-w posterior. Other parameter values within the posterior could change how often the more flexible CPL model wins on noise. The paper is explicit that it uses the best-fit Pade-w, so this is a stated limitation rather than an error, but it does mean the false-positive rate is conditional on that specific shape. A sensitivity test varying the fiducial parameters within the posterior would be a reasonable request for revision. Also, the abstract says 3.2%, the results section says 3%, and the conclusions say 3.2%; that is a minor rounding inconsistency worth cleaning up.

The citation pattern looks appropriate: relevant DESI papers, Shlivko/Steinhardt family, Wolf et al., and other quintessence work are all cited. The paper is not overclaiming; it explicitly says the preference is at the >2-sigma level but not 5-sigma, and it reminds readers that Delta-chi-squared values need care in non-nested comparisons.

This is a solid, honest paper for cosmologists working on dark energy phenomenology. It deserves serious peer review. I would recommend accepting with minor revisions, mainly asking for a sensitivity test around the fiducial choice and a consistent rounding of the headline percentage.

Referee Report

1 major / 5 minor

Summary. The paper tests whether the recent DESI DR2 + Planck + Union3 preference for the CPL parameterization over a non-phantom phenomenological quintessence model could arise from statistical fluctuations. The authors first fit both CPL and Pade-w to the real data, obtaining Delta chi^2 = 3.3 in favor of CPL, with best-fit Pade-w parameters eta0 = 59 and epsilon0 = 1.9. They then generate 1,000 mock datasets from this best-fit Pade-w model, adding Gaussian noise with the original covariance matrices of the CMB, BAO, and SN data. Refitting both models to each mock, they find that a Delta chi^2 at least as large as the real-data value occurs in 3-3.2% of the mocks. They further examine the distribution of best-fit CPL parameters in the mocks, show that 363 of 1,000 mocks yield a CPL phantom-crossing best fit that beats Pade-w, and use the most extreme mock to argue that BAO measurements at z > 3 would most efficiently distinguish the two classes. The paper concludes that the observed preference for phantom crossing is not at 5-sigma significance under this null.

Significance. Conditional on the stated null, the paper provides a clean and useful calibration: it converts a naive Delta chi^2 = 3.3 into a one-sided false-positive rate of a few percent, and it correctly separates the robust 'evolving dark energy' signal from the more fragile 'phantom crossing' interpretation. The Monte Carlo procedure is transparent and internally consistent; the p-value is an output of the simulation rather than an input, and the non-nested nature of the models is handled by explicitly constructing the null distribution. The paper also gives a falsifiable recommendation for future data (high-z BAO and better Omega_m h^2 constraints). The main weakness is that the headline 3.2% is conditional on a single point estimate of the null model; the manuscript would be stronger if this limitation were stated in the abstract and accompanied by a sensitivity check over the Pade-w posterior.

major comments (1)

[II.D / III (abstract and conclusions)] The mock null is the point-estimate Pade-w model obtained by fitting the real data (eta0 = 59, epsilon0 = 1.9; Sec. II.D), and the quoted 3.2% tail probability is therefore conditional on that point. The abstract and conclusions present this number as the answer to 'could we be fooled about phantom crossing?', which invites the reader to interpret it as a false-positive rate for non-phantom quintessence as a class. Because CPL's ability to overfit depends on the true w(z) shape, other Pade-w parameter values within the posterior could plausibly yield a different tail probability. I request either (i) a sensitivity test in which Pade-w parameters are drawn from their posterior (or varied over a grid) and the 3.2% is recomputed, or (ii) an explicit caveat in the abstract and conclusions stating that the result applies to the best-fit Pade-w point null only. This is the single most important caveat for the headline result.

minor comments (5)

[III / Figure 2 caption] The text gives the tail probability as 3% in the results and Figure 2 caption, but the abstract and conclusions quote 3.2%. Please make the numbers consistent.
[IV] The typo 'redsfhit' should be 'redshift'.
[III / Figure 5] The statement that BAO is 'the most sensitive' dataset is based on a single extreme mock realization (Table I). The text already says 'for this mock', so this is not wrong, but the later sentence in Sec. IV ('More precise and higher redshift BAO measurements will be useful...') would be better supported by a summary statistic over the ensemble of mocks with large Delta chi^2.
[II.B] The description of the compressed CMB likelihood says Gaussian priors are adopted but does not specify the covariance matrix values or whether the same covariance is used in the mock generation; a brief reference to the compressed likelihood implementation would improve reproducibility.
[II.C / IV] The phrase 'in over 30% of the simulations' in the conclusions (and the 363/1000 count in Sec. III) is not a significance statement: it counts all mocks where CPL's best fit is better, regardless of the size of Delta chi^2. Consider reporting the fraction of mocks with Delta chi^2 > 0 separately from the tail probability for Delta chi^2 >= 3.3.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 3.2% false-positive rate is a Monte Carlo output, not an input or fitted quantity.

full rationale

The paper's central claim is the result of a parametric bootstrap: a Pade-w fiducial model is fit to the real DESI DR2 + Union3 + compressed CMB data, 1,000 mock datasets are generated from that best fit, both CPL and Pade-w are refit to each mock, and the fraction of mocks in which CPL improves on Pade-w by more than the real-data Delta-chi^2 of 3.3 is counted. This fraction (3% or 3.2%) is an output of the simulation, not an input; it depends on the data, the two parameterizations, and the Monte Carlo procedure in a way that is not determined by construction. The use of the best-fit Pade-w model as the null is a standard and explicitly stated parametric-bootstrap choice, and the paper does not disguise the conditioning: it repeatedly states that the mocks are generated using the best-fit Pade-w as the true model. The only caveat is that the false-positive rate is conditional on the point-estimate fiducial rather than averaged over the Pade-w posterior, but this is a limitation of the null specification, not a circular step that equates the prediction with its inputs. There are no load-bearing self-citations: the Pade-w parameterization is adopted from external work by Shlivko, Steinhardt, and collaborators, and no uniqueness theorem or prior result by the present authors is invoked to force the conclusion. The derivation is self-contained against external benchmarks, so the paper receives a circularity score of 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central result is a Monte Carlo false-positive rate. Its main input is the best-fit Pade-w model, which is obtained by fitting the real data; the p-value is therefore conditional on this fiducial. The remaining assumptions concern the realism of the mock generation and the representativeness of Pade-w. No new entities or forces are introduced.

free parameters (3)

Fiducial Pade-w parameters = eta0 = 59, epsilon0 = 1.9
The mock datasets are generated from the best-fit Pade-w model to the real data; the false-positive rate is conditional on these values.
CPL parameters = w0 = -0.7, wa = -1.0
Best-fit parameters of the alternative model; not directly an input to the null, but define the comparison statistic.
Nuisance cosmological parameters
Parameters such as Omega_m and h are fit to data but their best-fit values are not reported; the p-value depends on them through the fiducial model.

assumptions (4)

domain assumption Pade-w with positive epsilon0 and eta0 represents non-phantom quintessence models
The null model is chosen to mimic thawing quintessence; the claim that the false-positive rate applies to 'non-phantom' physics assumes this parametrization covers that class (Sec. II A).
domain assumption Gaussian noise drawn from the original covariance matrices models the data fluctuations
Mock generation perturbs the fiducial predictions with Gaussian noise; non-Gaussian or correlated systematics are not modeled (Sec. II D).
domain assumption Compressed CMB summary statistics capture the relevant CMB constraints
The analysis uses Gaussian priors on theta*, omega_bc, omega_b instead of the full Planck likelihood, relying on refs. [20-22].
ad hoc to paper The fiducial parameters are fixed at their best-fit values rather than drawn from the posterior
The mock ensemble does not propagate uncertainty in the best-fit Pade-w parameters, a simplification not discussed in the paper.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Could We Be Fooled about Phantom Crossing?." pith.science (2026). https://pith.science/paper/MMTIB7DP

@misc{pith2026250615091,
  author       = {Pith},
  title        = {Pith review of: Could We Be Fooled about Phantom Crossing?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MMTIB7DP}},
  note         = {Machine review of arXiv:2506.15091}
}

abstract

Recent data from DESI Year 2 BAO, Planck CMB, and various supernova compilations suggest a preference for evolving dark energy, with hints that the equation of state may cross the phantom divide line ($w = -1$). While this behavior is seen in both parametric and non-parametric reconstructions, comparing reconstructions that support such behavior (such as the best fit of CPL) with those that maintain $w>-1$ (like the best fit algebraic quintessence) is not straightforward, as they differ in flexibility and structure, and are not necessarily nested within one another. Thus, the question remains as to whether the crossing behavior that we observe, suggested by the data, truly represents a dark energy model that crosses the phantom divide line, or if it could instead be a result of data fluctuations and the way the data are distributed. We investigate the likelihood of this possibility. For this analysis we perform 1,000 Monte Carlo simulations based on a fiducial algebraic quintessence model. We find that in $3.2 \% $ of cases, CPL with phantom crossing not only fits better, but exceeds the real-data $\chi^2$ improvement. This Monte Carlo approach quantifies to what extent statistical fluctuations and the specific distribution of the data could fool us into thinking the phantom divide line is crossed, when it is not. Although evolving dark energy remains a robust signal, and crossing $w=-1$ a viable phenomenological solution that seems to be preferred by the data, its precise behavior requires deeper investigation with more precise data.

Figures

Figures reproduced from arXiv: 2506.15091 by the authors.

**Figure 2.** FIG. 2. Distribution (blue) of the difference in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** FIG. 3. Distribution of best-fit CPL parameters fit to mock [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: FIG. 4. Distribution of best-fit CPL parameters fit to mock [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Cosmological constraints on Galileon dark energy with broken shift symmetry
astro-ph.CO 2025-09 conditional novelty 5.0 of 10

A broken shift symmetric cubic Galileon with a quadratic potential, fitted to DESI DR2, supernova and CMB data, is strongly favored over ΛCDM for DESY5/Union3 supernovae and reproduces the phantom-crossing equation of...

Reference graph

Works this paper leans on

23 extracted references · 3 canonical work pages · cited by 1 Pith paper

[1]

Chevallier and D

M. Chevallier and D. Polarski, Accelerating universes with scaling dark matter, International Journal of Mod- ern Physics D10, 213–223 (2001)

work page 2001
[2]

E. V. Linder, Exploring the expansion history of the universe, Physical Review Letters90, 10.1103/phys- revlett.90.091301 (2003)

doi:10.1103/phys- 2003
[3]

A. G. Adameet al.(DESI), DESI 2024 VI: cosmologi- cal constraints from the measurements of baryon acous- tic oscillations, JCAP02, 021, arXiv:2404.03002 [astro- ph.CO]

arXiv 2024
[4]

Abdul Karimet al.(DESI), DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cos- mological Constraints, (2025), arXiv:2503.14738 [astro- ph.CO]

M. Abdul Karimet al.(DESI), DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cos- mological Constraints, (2025), arXiv:2503.14738 [astro- ph.CO]

arXiv 2025
[5]

Lodhaet al.(DESI), DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data, Phys

K. Lodhaet al.(DESI), DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data, Phys. Rev. D111, 023532 (2025), arXiv:2405.13588 [astro-ph.CO]

arXiv 2025
[6]

Lodhaet al.(DESI), Extended Dark Energy anal- ysis using DESI DR2 BAO measurements, (2025), arXiv:2503.14743 [astro-ph.CO]

K. Lodhaet al.(DESI), Extended Dark Energy anal- ysis using DESI DR2 BAO measurements, (2025), arXiv:2503.14743 [astro-ph.CO]

arXiv 2025
[7]

Guet al.(DESI), Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measure- ments, (2025), arXiv:2504.06118 [astro-ph.CO]

G. Guet al.(DESI), Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measure- ments, (2025), arXiv:2504.06118 [astro-ph.CO]

arXiv 2025
[8]

R. R. Caldwell, A Phantom menace?, Phys. Lett. B545, 23 (2002), arXiv:astro-ph/9908168

arXiv 2002

Show all 23 references

[9]

Valiviita, E

J. Valiviita, E. Majerotto, and R. Maartens, Instability in interacting dark energy and dark matter fluids, JCAP 07, 020, arXiv:0804.0232 [astro-ph]

arXiv
[10]

Clifton, P

T. Clifton, P. G. Ferreira, A. Padilla, and C. Skordis, Modified Gravity and Cosmology, Phys. Rept.513, 1 (2012), arXiv:1106.2476 [astro-ph.CO]

2012 arXiv
[11]

Tsujikawa, Quintessence: A Review, Class

S. Tsujikawa, Quintessence: A Review, Class. Quant. Grav.30, 214003 (2013), arXiv:1304.1961 [gr-qc]

2013 arXiv
[12]

S. W. Hawking and G. F. R. Ellis,The Large Scale Structure of Space-Time: 50th Anniversary Edition, Cambridge Monographs on Mathematical Physics (Cam- bridge University Press, 2023)

2023
[13]

Shlivko and P

D. Shlivko and P. J. Steinhardt, Assessing observational constraints on dark energy, Phys. Lett. B855, 138826 (2024), arXiv:2405.03933 [astro-ph.CO]

2024 arXiv
[14]

Payeur, E

G. Payeur, E. McDonough, and R. Brandenberger, Do Observations Prefer Thawing Quintessence?, (2024), arXiv:2411.13637 [astro-ph.CO]

2024 arXiv
[15]

Shlivko, P

D. Shlivko, P. J. Steinhardt, and C. L. Steinhardt, Op- timal parameterizations for observational constraints on thawing dark energy, (2025), arXiv:2504.02028 [astro- ph.CO]

2025 arXiv
[16]

W. J. Wolf, C. Garc´ ıa-Garc´ ıa, and P. G. Ferreira, Robust- ness of dark energy phenomenology across different pa- rameterizations, JCAP05, 034, arXiv:2502.04929 [astro- ph.CO]

arXiv
[17]

Akrami, G

Y. Akrami, G. Alestas, and S. Nesseris, Has DESI detected exponential quintessence?, (2025), arXiv:2504.04226 [astro-ph.CO]

2025 arXiv
[18]

de Putter and E

R. de Putter and E. V. Linder, Calibrating dark energy, Journal of Cosmology and Astroparticle Physics2008 (10), 042
[19]

Alho and C

A. Alho and C. Uggla, New simple and accurate approx- imations for quintessence, (2024), arXiv:2407.14378 [gr- qc]

2024 arXiv
[20]

Aghanimet al.(Planck), Planck 2018 results

N. Aghanimet al.(Planck), Planck 2018 results. VI. Cosmological parameters, Astron. Astrophys.641, A6 (2020), [Erratum: Astron.Astrophys. 652, C4 (2021)], arXiv:1807.06209 [astro-ph.CO]

2020 arXiv
[21]

Wang and P

Y. Wang and P. Mukherjee, Observational constraints on dark energy and cosmic curvature, Physical Review D76, 10.1103/physrevd.76.103533 (2007)

2007 doi
[22]

Lemos and A

P. Lemos and A. Lewis, Cmb constraints on the early uni- verse independent of late-time cosmology, Physical Re- view D107, 10.1103/physrevd.107.103505 (2023)

2023 doi
[23]

Rubinet al., Union Through UNITY: Cosmology with 2,000 SNe Using a Unified Bayesian Framework, (2023), arXiv:2311.12098 [astro-ph.CO]

D. Rubinet al., Union Through UNITY: Cosmology with 2,000 SNe Using a Unified Bayesian Framework, (2023), arXiv:2311.12098 [astro-ph.CO]

2023 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Chevallier and D

M. Chevallier and D. Polarski, Accelerating universes with scaling dark matter, International Journal of Mod- ern Physics D10, 213–223 (2001)

work page 2001

[2] [2]

E. V. Linder, Exploring the expansion history of the universe, Physical Review Letters90, 10.1103/phys- revlett.90.091301 (2003)

doi:10.1103/phys- 2003

[3] [3]

A. G. Adameet al.(DESI), DESI 2024 VI: cosmologi- cal constraints from the measurements of baryon acous- tic oscillations, JCAP02, 021, arXiv:2404.03002 [astro- ph.CO]

arXiv 2024

[4] [4]

Abdul Karimet al.(DESI), DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cos- mological Constraints, (2025), arXiv:2503.14738 [astro- ph.CO]

M. Abdul Karimet al.(DESI), DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cos- mological Constraints, (2025), arXiv:2503.14738 [astro- ph.CO]

arXiv 2025

[5] [5]

Lodhaet al.(DESI), DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data, Phys

K. Lodhaet al.(DESI), DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data, Phys. Rev. D111, 023532 (2025), arXiv:2405.13588 [astro-ph.CO]

arXiv 2025

[6] [6]

Lodhaet al.(DESI), Extended Dark Energy anal- ysis using DESI DR2 BAO measurements, (2025), arXiv:2503.14743 [astro-ph.CO]

K. Lodhaet al.(DESI), Extended Dark Energy anal- ysis using DESI DR2 BAO measurements, (2025), arXiv:2503.14743 [astro-ph.CO]

arXiv 2025

[7] [7]

Guet al.(DESI), Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measure- ments, (2025), arXiv:2504.06118 [astro-ph.CO]

G. Guet al.(DESI), Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measure- ments, (2025), arXiv:2504.06118 [astro-ph.CO]

arXiv 2025

[8] [8]

R. R. Caldwell, A Phantom menace?, Phys. Lett. B545, 23 (2002), arXiv:astro-ph/9908168

arXiv 2002