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arxiv: 2606.17142 · v1 · pith:PHAMLVALnew · submitted 2026-06-15 · 🌌 astro-ph.CO

Hawai`i Supernova Flows: Bulk Flow Measurements using SNe Ia in the Optical and NIR

Aaron Do , Kaisey S. Mandel , Benjamin J. Shappee , R. Brent Tully , John L. Tonry , David Rubin , David O. Jones , Mitchell Dixon
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Thomas de Jaeger Dan Scolnic Erik R. Peterson Christopher R. Burns
This is my paper

Pith reviewed 2026-06-27 03:04 UTC · model grok-4.3

classification 🌌 astro-ph.CO
keywords bulk flowType Ia supernovaepeculiar velocitiesLambda-CDMHawaii Supernova Flowsoptical photometrynear-infrared photometrycosmology
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The pith

Bulk flow speeds from Type Ia supernovae fall between 100 and 400 km/s and match Lambda-CDM predictions in both optical and near-infrared bands.

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

The paper measures the average peculiar velocity within a sphere out to redshift 0.1 by combining redshifts with distance estimates to Type Ia supernovae drawn from the Hawaii Supernova Flows sample. Speeds derived this way range from roughly 100 to 400 km/s, yet every combination of estimator and wavelength band produces a value inside the scatter that Lambda-CDM assigns to such a volume. The work also tests how much the result changes when the analyst switches between two bulk-flow estimators, two supernova distance methods, and optical versus near-infrared photometry. The differences stay small enough that none of the measurements leave the model’s expected band. A sympathetic reader therefore sees the local velocity field as consistent with the standard picture in which primordial density fluctuations have grown into today’s structure under Lambda-CDM.

Core claim

Using redshifts and optical and near-infrared distance estimates to Type Ia supernovae within subsets of the Hawaii Supernova Flows dataset, the bulk flow within z ≲ 0.1 is inferred to have speeds between approximately 100 and 400 km/s. These values are all consistent with the predictions of Lambda-CDM, which expects a mean bulk flow of zero but a variance set by the Hubble constant, the growth rate of structure, and the matter power spectrum. The analysis employs two bulk flow estimators, two types of SN Ia distance estimators, and data in two wavelength regimes to quantify the systematic uncertainty introduced by discrete methodological choices.

What carries the argument

Bulk flow, the volume-averaged peculiar velocity estimated from the difference between observed redshifts and Hubble-flow distances to Type Ia supernovae.

If this is right

  • The local peculiar-velocity field out to z ~ 0.1 aligns with the variance expected under Lambda-CDM.
  • Switching among the two bulk-flow estimators, two distance estimators, and optical versus near-infrared data does not produce offsets large enough to exit the model band.
  • Type Ia supernovae supply usable peculiar-velocity tracers in both optical and near-infrared regimes for bulk-flow studies.
  • No evidence appears for anomalous bulk flows that would require extensions beyond Lambda-CDM at these scales.

Where Pith is reading between the lines

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

  • If the consistency holds in larger samples, local bulk flows are unlikely to explain tensions seen in other cosmological probes.
  • Near-infrared distances may reduce dust-related scatter without changing the bulk-flow conclusion, an effect worth testing with future all-sky NIR surveys.
  • The same dataset and estimators could be applied to modified-gravity models to check whether they predict a different variance band at low redshift.

Load-bearing premise

The discrete choices of bulk flow estimator, SN Ia distance estimator, and wavelength regime do not introduce systematic offsets large enough to move the measurements outside the Lambda-CDM variance band.

What would settle it

A measured bulk-flow speed lying well above the upper edge of the Lambda-CDM variance band for a z ≲ 0.1 volume, or a statistically significant systematic offset between otherwise identical optical and near-infrared results.

Figures

Figures reproduced from arXiv: 2606.17142 by Aaron Do, Benjamin J. Shappee, Christopher R. Burns, Dan Scolnic, David O. Jones, David Rubin, Erik R. Peterson, John L. Tonry, Kaisey S. Mandel, Mitchell Dixon, R. Brent Tully, Thomas de Jaeger.

Figure 1
Figure 1. Figure 1: We use the Peery MVE (top row) and Nusser MLE (bottom row) methods to analyse 1500 mock catalogues with simulated bulk flows. The differences between the recovered (B⃗ ) and simulated (B⃗ sim) values are plotted against simulated values, with the left three columns representing Cartesian projections of the bulk flow and the rightmost column representing the total speed of the bulk flow. The differences in … view at source ↗
Figure 2
Figure 2. Figure 2: The same format as [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The same format as [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The same format as [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: We present the diagonal components of the tensor angle￾averaged window-function W˜ 2 (k) for the SNPY_EBV sample, calculated using Equation 8 and weights inferred for the Peery MVE. The matter power spectrum generated with CAMB using H0 ≈ 70.7 km s−1 Mpc−1 and q0 = −0.51 is provided for compar￾ison since Σij,cos ∝ R ∞ 0 P(k)W˜ 2 ij (k)dk. The solid lines show the nominal window functions calculated with µo… view at source ↗
read the original abstract

The present day peculiar velocity-field was sourced by primordial density fluctuations and sculpted over the lifespan of the Universe. Cosmological models such as $\Lambda$CDM make predictions for various statistical properties of peculiar velocities. Bulk flow, the average velocity within a given volume, has an expectation value of $\vec{0}$ due to isotropy, and a variance directly tied to the Hubble constant, the growth-rate of structure, and the matter power spectrum. In this paper, we use the redshifts and optical and near-infrared distance estimates to Type Ia Supernovae (SNe Ia) within subsets of the Hawai`i Supernova Flows dataset to infer the bulk flow within $z \lesssim 0.1$. The inferred speeds vary between ~100 to 400 km/s but are all consistent with the predictions of $\Lambda$CDM. As a secondary focus, we discuss the systematic uncertainty introduced by the discrete choice of methodology using two bulk flow estimators, two types of SN Ia distance estimators, and data covering two distinct regimes in wavelength space.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript measures the local bulk flow (z ≲ 0.1) using SNe Ia from the Hawai`i Supernova Flows sample in both optical and NIR bands. Two bulk-flow estimators and two SN Ia distance estimators are applied; the resulting speeds lie between ~100 and 400 km/s and are stated to be consistent with the variance expected in ΛCDM. A secondary goal is to quantify the systematic uncertainty introduced by the discrete methodological choices.

Significance. If the consistency claim survives a full quantitative comparison across all eight methodological combinations, the work supplies a useful local-universe test of the predicted bulk-flow variance, leveraging the reduced dust systematics of NIR data. The multi-method approach is a positive feature for assessing robustness.

major comments (2)
  1. [§4 and Table 2] §4 (Results) and Table 2: the central consistency claim requires every one of the 2×2×2 = 8 combinations to lie inside the ΛCDM variance band appropriate to its volume and window function. No table or figure supplies the measured speed, its uncertainty, and the corresponding ΛCDM σ_v for each of the eight cases, so it is impossible to verify that methodological systematics remain smaller than the distance to the boundary.
  2. [§3.1–3.2] §3.1–3.2 (Estimators and variance calculation): the window function, adopted H_0, fσ_8, and P(k) used to compute the ΛCDM variance must be stated explicitly and shown to be identical for all eight data subsets; otherwise the comparison is not uniform.
minor comments (2)
  1. [Figure 1] Figure 1: the caption should list the exact number of SNe retained after each quality cut for the optical and NIR subsamples.
  2. Notation: the symbol for the bulk-flow vector is used interchangeably with its magnitude in several places; a consistent convention would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive suggestions. We address each major comment below and will revise the manuscript to enhance the verifiability of our consistency claims with ΛCDM.

read point-by-point responses

  1. Referee: [§4 and Table 2] §4 (Results) and Table 2: the central consistency claim requires every one of the 2×2×2 = 8 combinations to lie inside the ΛCDM variance band appropriate to its volume and window function. No table or figure supplies the measured speed, its uncertainty, and the corresponding ΛCDM σ_v for each of the eight cases, so it is impossible to verify that methodological systematics remain smaller than the distance to the boundary.

    Authors: We agree that explicit tabulation of all eight combinations is required to allow verification of the consistency claim. In the revised manuscript we will expand Table 2 (or add a new table) to list, for each of the eight cases, the measured bulk-flow speed, its uncertainty, and the corresponding ΛCDM σ_v computed for that specific volume and window function. This will directly demonstrate that every combination lies inside the expected variance band and that methodological systematics do not exceed the distance to the boundary. revision: yes

  2. Referee: [§3.1–3.2] §3.1–3.2 (Estimators and variance calculation): the window function, adopted H_0, fσ_8, and P(k) used to compute the ΛCDM variance must be stated explicitly and shown to be identical for all eight data subsets; otherwise the comparison is not uniform.

    Authors: We will revise §§3.1–3.2 to state explicitly the window function, the adopted H_0, fσ_8, and the matter power spectrum P(k) employed for the variance calculation. We will also add a short paragraph confirming that these parameters are identical for all eight data subsets (with any volume-dependent window-function adjustments documented). This ensures the ΛCDM comparison is performed uniformly across all methodological combinations. revision: yes

Circularity Check

0 steps flagged

No significant circularity: measurements compared to external ΛCDM predictions

full rationale

The paper infers bulk flow speeds from SN Ia redshifts and distance estimates within the Hawai`i Supernova Flows dataset, then states that the resulting 100–400 km/s values lie inside the variance band predicted by standard ΛCDM (tied to H0, fσ8, and P(k)). This comparison uses an external theoretical expectation rather than any quantity fitted or defined from the same dataset. No equation reduces a measured speed to a parameter estimated inside the paper, no self-citation supplies a uniqueness theorem that forces the result, and no ansatz is smuggled in. The secondary discussion of methodological systematics (two estimators × two distance methods × two wavelengths) is presented as an uncertainty budget, not as a derivation that collapses to the input data by construction. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, new entities, or ad-hoc axioms are stated. The comparison rests on the standard assumption that ΛCDM correctly predicts the bulk-flow variance from the matter power spectrum and growth factor.

axioms (1)
  • domain assumption ΛCDM model supplies the expected variance of bulk flows within a given volume
    Paper states that measured speeds are consistent with ΛCDM predictions without deriving the variance from first principles.

pith-pipeline@v0.9.1-grok · 5764 in / 1117 out tokens · 39849 ms · 2026-06-27T03:04:37.189150+00:00 · methodology

discussion (0)

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Reference graph

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