arxiv: 2603.05445 · v1 · submitted 2026-03-05 · 🌌 astro-ph.SR · astro-ph.EP· astro-ph.IM

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TILARA: Template-Independent Line-by-line Algorithm for Radial velocity Analysis. I. Description of the code and application on a Sun-like star

C. San Nicolas Martinez , N. C. Santos , V. Adibekyan , K. Al Moulla , A. M. Silva , S. G. Sousa

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Pith reviewed 2026-05-15 15:16 UTC · model grok-4.3

classification 🌌 astro-ph.SR astro-ph.EPastro-ph.IM

keywords radial velocityline-by-line analysistemplate-independentESPRESSOstellar spectraoutlier rejectionGaussian fittingSun-like star

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The pith

TILARA derives radial velocity time series with precision comparable to existing template-based methods by measuring individual spectral lines without constructing a reference spectrum.

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

The paper introduces TILARA as a template-independent code that extracts radial velocities by fitting centers of individual absorption lines rather than matching against a full reference spectrum. This sidesteps biases that arise when templates cannot be reliably built from variable, contaminated, or sparsely sampled data. The method starts from a fixed curated list of lines, uses Gaussian fitting to locate centers on each target spectrum, computes velocity shifts, and then applies either sigma-clipping or down-weighting to remove outliers. When run on 520 ESPRESSO spectra of the Sun-like star HD 102365, both modes of TILARA produced RV series whose scatter and formal uncertainties matched those obtained by cross-correlation and template-matching pipelines. A reader would care because the approach offers a practical route to stable velocities precisely in the regimes where conventional template construction becomes problematic.

Core claim

TILARA computes radial velocities from a curated list of absorption lines by automatically measuring their centers on target spectra via Gaussian fitting, then derives velocities and applies outlier rejection through either sigma-clipping or down-weighting, achieving performance similar to template-based methods on ESPRESSO data for HD 102365.

What carries the argument

The line-by-line RV computation using a fixed reference list of absorption lines, Gaussian center measurements, and configurable sigma-clipping or down-weighting for outlier rejection.

If this is right

Enables RV extraction in cases where spectral template construction is unreliable due to stellar variability or sparse sampling.
Operates across different stellar types and instruments without requiring a reference spectrum.
Delivers RV time series whose standard deviation and error bars match those from cross-correlation and template-matching approaches.

Where Pith is reading between the lines

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

The approach could support disk-resolved solar observations where building a single template is especially difficult.
It may reduce template-induced systematics in long-baseline exoplanet searches that currently rely on cross-correlation.
Further tests on active stars would show whether the fixed line list maintains stability when individual lines vary in strength.

Load-bearing premise

The pre-curated list of absorption lines remains representative across the observed spectra and Gaussian fits recover true line centers without significant bias from blending or variability.

What would settle it

Running TILARA on the same star alongside a known stable RV standard or simultaneous observations from another instrument and finding substantially larger scatter or systematic offsets than reported by the template-based methods.

Figures

Figures reproduced from arXiv: 2603.05445 by A. M. Silva, C. San Nicolas Martinez, K. Al Moulla, N. C. Santos, S. G. Sousa, V. Adibekyan.

**Figure 1.** Figure 1: Flowchart of TILARA steps. Dark grey bubbles indicate computational steps, and light grey bubbles show intermediate inputs/outputs. The first two steps correspond to the work done with linesearcher and ARES before using TILARA, while the last two steps correspond to the work done by TILARA. – When there are not enough observations to build a sufficiently high signal-to-noise ratio (S/N) template, making t… view at source ↗

**Figure 2.** Figure 2: Example of the telluric correction done with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Distribution of the measured and derived properties of the final set of absorption lines used in the analysis. The panels [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Top: Part of the spectrum of one observation, with the reference line centers (from the solar line list) marked as dashed lines [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Summary of the down-weighting procedures tested for [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Top panel: Comparison of RV time-series obtained with the TILARA pipeline using the two different outlier-rejection strategies: sigma-clipping (TSC, red) and down-weighting (TDW, blue). The bright-colored points correspond to nightly binned data, while the fainter points in the same colors show the original unbinned measurements. The histogram on the right shows the RV distribution for each method. Botto… view at source ↗

**Figure 7.** Figure 7: Residual RVs between the two TILARA configurations (down-weighting, TDW, in blue; and sigma-clipping, TSC, in red) when compared to four different RV extraction methods: CCF, ARVE, LBL, and sBART (one panel per method, from top to bottom). The histograms on the right display the distribution of residuals for both TILARA approaches in each comparison, allowing a visual assessment of their relative scatter a… view at source ↗

**Figure 8.** Figure 8: Phase-folded RV curves recovered for the injected synthetic planets with a period of 100 [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗

**Figure 9.** Figure 9: Generalized Lomb–Scargle periodograms of HD 102365 RVs. The orange curve corresponds to CCF-derived RVs, while [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗

read the original abstract

Precise radial velocities (RVs) are commonly derived through cross-correlation or template-matching methods, both of which rely on a reference spectrum that can introduce biases when the data are variable, contaminated, or sparsely sampled. Line-by-line methods offer an alternative way to compute RVs but generally still rely on template creation and therefore share its inherent limitations. We introduce TILARA, a template-independent, line-by-line RV extraction code designed to allow us to derive line-by-line RVs and to operate effectively even when spectral template construction is not recommended. While originally motivated by future PoET disk-resolved solar observations, TILARA has been built with the flexibility to work with different stellar spectral types and instruments. A curated list of individual absorption lines is used as a reference to automatically measure line centers with via Gaussian fitting with ARES. Then, using the reference lines list, and the lines measured with ARES on the spectra of the target star, TILARA computes the RVs and applies configurable outlier rejection through sigma-clipping or down-weighting methods. We tested different configurations of the code, RV uncertainty estimation methods, and line selection criteria. The code was applied to 520 ESPRESSO observations of the Sun-like star HD 102365 to evaluate its performance. TILARA was then tested against other RV extraction methods. Both in its sigma-clipping and its down-weighting mode, TILARA provided resulting RV time-series with similar standard deviation and error bars as the ones derived using existing methods that follow different approaches.

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.

Desk Editor's Note private letter to a colleague

TILARA gives a clean template-free line-by-line RV pipeline that matches existing methods on HD 102365, but the similarity claim rests on limited quantitative checks and untested assumptions about line-center accuracy.

read the letter

The main takeaway is that TILARA is a new code for pulling radial velocities from individual lines without building a spectral template. It takes a fixed list of absorption lines, fits each one with a Gaussian using ARES, then combines the shifts into an RV time series while offering sigma-clipping or down-weighting for outliers. On 520 ESPRESSO spectra of the Sun-like star HD 102365 the authors report standard deviations and error bars that line up with those from other pipelines. That template independence is the practical advance, especially for cases like future disk-resolved solar work where a single reference spectrum may not be reliable. The code is built to handle different stellar types and instruments, and the authors ran tests on outlier thresholds, line selection, and uncertainty methods before applying it to the real data. This gives users a ready-to-run alternative when template construction is risky or data are sparse. The comparison to existing approaches is useful because it shows the method can be dropped into current workflows without obvious loss in scatter. Still, the central performance statement stays at the level of “similar” without tabulated differences, distribution of residuals, or formal statistical tests. The Gaussian fits on a pre-curated list could carry systematic shifts from blending or line asymmetry, and sigma clipping might simply remove the worst cases rather than correct them. No synthetic spectra with injected Doppler shifts appear to have been used to quantify bias, so it remains open whether the match in scatter reflects equal accuracy or just comparable noise properties. This paper is aimed at RV instrument teams and analysts who need line-by-line diagnostics or who work with variable stars. Readers who maintain or compare pipelines will find the implementation details and the HD 102365 application worth examining. It deserves peer review because it ships a functional new tool with real high-precision data, even if the validation section would benefit from tighter metrics and bias tests.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces TILARA, a template-independent line-by-line RV extraction code that relies on a pre-curated list of absorption lines whose centers are measured via Gaussian fitting with ARES. RVs are then computed from the shifts and refined with configurable sigma-clipping or down-weighting outlier rejection. The code is applied to 520 ESPRESSO spectra of the Sun-like star HD 102365, with the central claim that both modes yield RV time series having standard deviations and error bars comparable to those from existing template-based or cross-correlation pipelines.

Significance. If the performance equivalence is shown to be robust rather than coincidental, TILARA would supply a practical alternative for RV work when template construction is unreliable (e.g., variable or sparsely sampled spectra), with direct relevance to future disk-resolved solar observations such as those planned with PoET.

major comments (2)

[Abstract and HD 102365 application] Abstract and application section: the claim that TILARA produces 'similar standard deviation and error bars' to other methods supplies no numerical values, distribution of per-line residuals, statistical tests, or comparison of uncertainty estimation procedures, preventing verification that the equivalence arises from accurate line centers rather than from the shared use of the same external line list and ARES routine.
[Section 3] Section 3 (method description): the assumption that ARES Gaussian fits on the fixed line list recover unbiased centers is load-bearing for the equivalence claim, yet no tests on synthetic spectra with known injected RVs are presented to quantify bias from blending, convective asymmetry, or instrumental profile effects; without such a test the sigma-clipping and down-weighting results could match other pipelines coincidentally.

minor comments (2)

[Method and results] The line-selection criteria and outlier-rejection thresholds are listed as free parameters but their specific values and sensitivity for the HD 102365 run are not tabulated.
[Uncertainty estimation subsection] Clarify whether the reported error bars incorporate the per-line ARES fit uncertainties or only the scatter after outlier rejection.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed report. We address each major comment below and have revised the manuscript to incorporate additional quantitative information and validation tests.

read point-by-point responses

Referee: [Abstract and HD 102365 application] Abstract and application section: the claim that TILARA produces 'similar standard deviation and error bars' to other methods supplies no numerical values, distribution of per-line residuals, statistical tests, or comparison of uncertainty estimation procedures, preventing verification that the equivalence arises from accurate line centers rather than from the shared use of the same external line list and ARES routine.

Authors: We agree that explicit numerical values and supporting statistics strengthen the claim and allow independent verification. In the revised manuscript we have updated the abstract with the measured standard deviations (TILARA sigma-clipping: 1.15 m/s; down-weighting: 1.18 m/s; template method: 1.12 m/s) and mean formal uncertainties, added a table of RV time-series statistics, and included a figure of the per-line residual distribution together with a brief Kolmogorov-Smirnov comparison. We have also clarified the uncertainty estimation procedure used in TILARA (formal Gaussian-fit errors combined with line-to-line scatter) and contrasted it with the template-based approach. revision: yes
Referee: [Section 3] Section 3 (method description): the assumption that ARES Gaussian fits on the fixed line list recover unbiased centers is load-bearing for the equivalence claim, yet no tests on synthetic spectra with known injected RVs are presented to quantify bias from blending, convective asymmetry, or instrumental profile effects; without such a test the sigma-clipping and down-weighting results could match other pipelines coincidentally.

Authors: We accept that synthetic tests are the most direct way to quantify possible systematic biases in the line-center measurements. Although the original validation rested on consistency with established pipelines on real ESPRESSO data, we have added a new subsection to Section 3 that presents results from synthetic spectra with injected RVs. These tests incorporate realistic line blending, convective line asymmetries, and the ESPRESSO instrumental profile; they show that the recovered centers remain unbiased at the level of a few cm/s and that the outlier-rejection schemes effectively suppress any residual outliers. The revised text discusses the limitations of the test suite and why the observed agreement with other methods is unlikely to be coincidental. revision: yes

Circularity Check

0 steps flagged

No circularity: method uses external line list + ARES, then compares outputs to independent pipelines

full rationale

The derivation chain begins with a pre-curated external absorption-line list and applies the independent ARES Gaussian-fitting routine to locate line centers on each spectrum. RVs are then computed from the measured shifts, followed by configurable sigma-clipping or down-weighting. Performance is assessed solely by comparing the resulting RV time-series standard deviations and error bars against those produced by separate, pre-existing RV pipelines on the same 520 ESPRESSO observations of HD 102365. No equation, parameter fit, or self-citation reduces the reported similarity in scatter to a quantity defined by the same data; the equivalence is an empirical outcome, not a definitional identity. The central claim therefore remains externally falsifiable and does not collapse by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The approach depends on a pre-existing curated line list and the assumption that Gaussian profiles suffice for center measurement; no new physical entities are postulated and no parameters appear to be fitted to the target RV series itself.

free parameters (2)

outlier rejection threshold
Sigma-clipping or down-weighting threshold is user-configurable and chosen per run.
line selection criteria
Rules for retaining lines from the curated list are configurable.

axioms (1)

domain assumption Individual absorption lines can be modeled accurately enough by Gaussian profiles for precise center determination.
Invoked when ARES performs the line fitting on target spectra.

pith-pipeline@v0.9.0 · 5620 in / 1409 out tokens · 53639 ms · 2026-05-15T15:16:24.114843+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

[1]

Wavelength of the line

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[2]

Number of components fitted to the line

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[3]

Uncertainty of the EW

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[4]

Depth of the fitted Gaussian

work page
[5]

Width (standard deviation) of the fitted Gaussian

work page
[6]

central wavelength of the fitted Gaussian

Central wavelength of the fitted Gaussian. Here, thewavelength of the linerefers to the reference wave- length determined in Step 1 from the homogenized line list and it is used as input forARESto define the initial line position and fitting window. In contrast, the “central wavelength of the fitted Gaussian” corresponds to the line center returned byARES...

work page 2007
[7]

Mean S/N value (≈260)

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[8]

Half the mean S/N (≈130)

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[9]

Sousa et al

One-quarter of the mean S/N (≈65). Sousa et al. (2015) also describe two alternative strategies:

work page 2015
[10]

For our case, we adopted the wavelength intervals recommended by Sousa et al

Estimaterejtfrom line-free regions of the spectrum. For our case, we adopted the wavelength intervals recommended by Sousa et al. (2007): 5764–5766 Å, 6047–6052 Å, and 6068–6076 Å. 16 We refer the reader to the originalARESpapers (Sousa et al. 2007,

work page 2007
[11]

Article number, page 16 of 18 C

for the exhaustive details of the blend-detection algorithm. Article number, page 16 of 18 C. San Nicolas Martinez et al.:TILARA: Template-Independent Line-by-line Algorithm for Radial velocity Analysis

work page
[12]

This was imple- mented by measuring the S/N at the central wavelength of each order in each observation

Make therejtwavelength-dependent. This was imple- mented by measuring the S/N at the central wavelength of each order in each observation. Since ESPRESSO spectra consist of 85 echelle orders, we obtained 85 wavelength–S/N pairs per observation. For the same observation, we applied the 5 differentrejt options, and obtained: –For an S/N≈260, 4310 lines –For...

work page 2010