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arxiv: 2605.25840 · v1 · pith:6FYKCCXTnew · submitted 2026-05-25 · 🌌 astro-ph.IM · astro-ph.CO

skysurvey: a pure python package to simulate the transient sky

M. Rigault , M. Ginolin , L. Dellazzeri , B. Popovic , J. O. Hjortlund , A. Gilles-Lordet , S. Conseil , M. Coughlin
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F. Ruppin M. Smith A. Townsend A. Trigui C. Barjou-Delayre R. Kebadian J. Nordin
This is my paper

Pith reviewed 2026-06-29 20:36 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.CO
keywords skysurveytransient simulationastrophysical populationsType Ia supernovaesurvey simulationpython packagemodeldagZTF
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The pith

skysurvey structures simulations of astronomical transients around Target, Survey and DataSet objects plus modeldag to enable complex population modeling with minimal code.

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

The paper presents skysurvey, a pure Python package for simulating how surveys observe astrophysical transients. It claims that the package design with three core objects and the modeldag package makes implementing any complex population model fast and easy. Accurate simulations matter for measuring event rates, validating pipelines, accounting for selection effects and correcting biases. The authors demonstrate the approach by modeling Type Ia supernovae with a varying color-brightness beta term and by replicating the rate and redshift distribution of the ZTF SNe Ia DR2 dataset.

Core claim

The skysurvey package relies on three core objects—a Target that models how an astrophysical target exists in nature, a Survey that specifies how the sky has been observed, and a DataSet that combines these two to generate data as they would have been acquired—along with the modeldag package for parameter modeling; this structure allows users to implement complex populations such as Type Ia Supernovae with varying beta term and to replicate the ZTF SNe Ia DR2 dataset.

What carries the argument

Three core objects Target, Survey and DataSet together with the modeldag structure for parameter modeling.

If this is right

  • Complex populations such as Type Ia Supernovae with varying color-brightness beta term can be implemented straightforwardly.
  • The package can replicate the rate and redshift distribution of the ZTF SNe Ia DR2 dataset.
  • Simulations can account for selection effects and correct biases in analyses.
  • The package supports use in simulation-based inference.
  • The package is ready for general usage in scientific publications.

Where Pith is reading between the lines

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

  • The same three-object structure could be applied to transients other than supernovae.
  • Users might combine skysurvey outputs directly with existing machine-learning detection pipelines.
  • The modeldag component could be extended to include additional physical parameters without rewriting core simulation logic.

Load-bearing premise

The three core objects Target, Survey, DataSet together with modeldag suffice to implement arbitrary complex astrophysical population models with minimal additional user code.

What would settle it

A concrete complex population model that requires substantial additional user code beyond the three core objects and modeldag to implement.

Figures

Figures reproduced from arXiv: 2605.25840 by A. Gilles-Lordet, A. Townsend, A. Trigui, B. Popovic, C. Barjou-Delayre, F. Ruppin, J. Nordin, J. O. Hjortlund, L. Dellazzeri, M. Coughlin, M. Ginolin, M. Rigault, M. Smith, R. Kebadian, S. Conseil.

Figure 1
Figure 1. Figure 1: Illustration of modeldag. left: a python code generating a simple model, where two randomly drawn parameters (x1 and c) are linearly combined to form a third (m). The "@" sign enables to pass parameters from a key to another to form a graph as illustrated in the top￾right panel.right: Corner plot presenting the generated data [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Complexifying a modeldag’s model. Top: a python code cre￾ating a model, including a function that returns two variables (ra and dec) generated simultaneously). There a new model is generated by combining model_2 with the original one shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the skysurvey code structure and associated code. Sky￾survey aims at making easy arbitrarily complex realistic simulations of astro￾physical targets (e.g. a Supernovae, or any user defined) as observed by surveys (e.g. ZTF, LSST, or any user defined). 4.1. Simulating targets Once the Target is loaded, one can use the draw() method to generate a sample. This method internally calls the draw(… view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of how to use multi-template based objects such as the Type II Supernovae SNeII. The actual individual target (dataframe row) template is randomly drawn respecting the rate or model parame￾ters that could be template-dependent [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of how to change a target ; here, specify a redshift dependent rate and make SN Ia standardization parameter beta a vari￾able drawn from a normal distribution. 4.6. Adding effects skysurvey provides a flexible mechanism for adding various physical effects to the simulation. This is handled by the Effect class, which can modify both the intrinsic properties of the tran￾sients and/or the way the… view at source ↗
Figure 6
Figure 6. Figure 6: Illustration of how to add an effect to a target ; here, the Milky Way extinction, whose intensity parameter (mwebv) depends on the tar￾get’s coordinate. dict(ra=’@ra’, dec=’@dec’) to pass the target’s coordinate information to the get_mwebv() function. When the mw is added to the target, mw.model is used to update the target.model, which enables to generate the ’mwebv’ parameter upon the .draw() call, and… view at source ↗
Figure 7
Figure 7. Figure 7: Illustration of how to load the ZTF survey and basic plotting functionalities. The grid system of ZTF is clearly visible in the bottom panel that shows the number of visit for the main grid. The grey band illustrates the Milky Way location. The northern ZTF survey does not cover dec < −30. The ZTF GridSurvey has a multi-polygon footprint based on its 64 quadrants identified by an rcid (see log descrip￾tion… view at source ↗
Figure 9
Figure 9. Figure 9: Illustration of an end-to-end skysurvey simulation, from top to bottom: building a survey (ZTF), generating targets (SNeIa) in the time range of the survey and building the DataSet object that simulate the lightcurve. Then, in the next cells, illustrations of methods to access lightcurves statistics and visualize a simulation. This whole sequence takes ∼ 15 s to run on a laptop (see text). 7.1. Altering su… view at source ↗
Figure 10
Figure 10. Figure 10: Illustration of how to simulate the ZTF SN Ia DR2 dataset, changing the logs (top panel) and default target (mid panel) to respect prescriptions from the literature (see text). In any case, the dataset gen￾eration, which create the actual simulated lightcurve remains unaffected (bottom panel). In that example, the number of target is unspecified and consequently derived from the target.rate. 7.2. Loading … view at source ↗
Figure 12
Figure 12. Figure 12: Redshift distribution of the ZTF SN Ia DR2 sample (orange, Rigault et al. 2025) compared to that simulated using the skysurvey package made without assuming the initial number of target, but di￾rectly deriving it from the volumetric rate. Such an analysis illustrates the ability to the skysurvey package to easily reproduce realistic data. easily be reproduced by a sigmoid function applied on the peak magn… view at source ↗
read the original abstract

Accurate simulation of astronomical observations is a critical element for any modern analyses, be it to measure event rates, analyses population properties, validate or train pipelines, account for selection effects, or correct biases. We present a novel pure python package named skysurvey made to enable the user to quickly simulate astrophysical transients as observed by a survey. The package is structured to make the implementation of any complex population modeling fast and easy. The skysurvey package relies on three core objects: a Target, that models how an astrophysical target exists in nature, a Survey, that specifies how the sky has been observed and, a DataSet that combine these two to generate data as they would have been acquired. In addition, we present a side stand-alone package named modeldag that contains the core structure that simplifies the parameter modeling. We present in this paper how skysurvey is structured and we clearly illustrate how the code can straightforwardly be used to simulate complex populations, such as Type Ia Supernovae with varying color-brightness $\beta$ term. We also illustrate how the package can be made to replicate the rate and redshift distribution of the ZTF SNe Ia DR2 dataset. The skysurvey package, already used in recent scientific publications, is now ready for general usage and paves the way for future use of simulations such as simulation based inference.

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

3 major / 2 minor

Summary. The manuscript describes skysurvey, a pure Python package for simulating astronomical transients as observed by surveys. It introduces three core objects (Target for astrophysical populations, Survey for observational specifications, DataSet for combining them) plus the standalone modeldag package for parameter DAGs. The central claim is that this structure enables fast and easy implementation of arbitrary complex population models, illustrated via a Type Ia supernova example with varying color-brightness eta and a replication of the ZTF SNe Ia DR2 rate/redshift distribution. The package is stated to be already used in publications and suitable for simulation-based inference.

Significance. If the core objects and modeldag indeed allow complex models with minimal additional code, the package would be a useful contribution to transient astronomy for rate measurements, bias corrections, and pipeline validation. The pure-Python implementation, open availability, and existing scientific usage are positive factors that could facilitate reproducible simulations.

major comments (3)
  1. [Abstract] Abstract and the section on complex population modeling: the assertion that Target/Survey/DataSet + modeldag suffice to implement 'any complex population modeling' with 'minimal additional user code' is load-bearing for the paper's main claim but is supported only by the two provided illustrative examples (varying-eta Type Ia and ZTF DR2 replication); no test cases involving multi-population correlations, time-varying selection functions, or non-standard DAG extensions are shown, leaving the generality unverified.
  2. [ZTF replication section] ZTF DR2 replication illustration: the claim of replicating the observed rate and redshift distribution is presented without any quantitative validation (e.g., Kolmogorov-Smirnov statistic, χ^{2}, or residual plots comparing simulated vs. DR2 data), which is required to assess whether the simulation is accurate enough for the scientific applications listed in the abstract.
  3. [Core objects description] Section describing the core objects: no code-complexity metrics, line-count comparisons, or user-effort benchmarks are supplied against direct Python implementations or alternative frameworks, which is needed to substantiate the 'fast and easy' claim for arbitrary models.
minor comments (2)
  1. The manuscript would benefit from embedding short, self-contained code snippets for the two main examples directly in the text (rather than only pointing to external documentation) to improve immediate reproducibility.
  2. A brief comparison table listing skysurvey against other transient simulation tools (e.g., SNANA, simsurvey) would help readers place the new package in context.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment below, making revisions where the points identify clear gaps in the presented evidence.

read point-by-point responses

  1. Referee: [Abstract] Abstract and the section on complex population modeling: the assertion that Target/Survey/DataSet + modeldag suffice to implement 'any complex population modeling' with 'minimal additional user code' is load-bearing for the paper's main claim but is supported only by the two provided illustrative examples (varying-β Type Ia and ZTF DR2 replication); no test cases involving multi-population correlations, time-varying selection functions, or non-standard DAG extensions are shown, leaving the generality unverified.

    Authors: We agree that the generality claim would be stronger with explicit discussion of additional use cases. The two examples already cover intra-population parameter variation and replication of an observed distribution, both of which rely on the modular Target/Survey/DataSet structure and modeldag. In the revision we will add a concise paragraph in the discussion section outlining how the same objects extend to multi-population correlations and time-varying selection functions, without introducing new full test cases. revision: partial

  2. Referee: [ZTF replication section] ZTF DR2 replication illustration: the claim of replicating the observed rate and redshift distribution is presented without any quantitative validation (e.g., Kolmogorov-Smirnov statistic, χ², or residual plots comparing simulated vs. DR2 data), which is required to assess whether the simulation is accurate enough for the scientific applications listed in the abstract.

    Authors: We accept that quantitative metrics are needed. The revised manuscript will include a Kolmogorov-Smirnov test and a residual plot comparing the simulated and DR2 redshift distributions in the ZTF replication section. revision: yes

  3. Referee: [Core objects description] Section describing the core objects: no code-complexity metrics, line-count comparisons, or user-effort benchmarks are supplied against direct Python implementations or alternative frameworks, which is needed to substantiate the 'fast and easy' claim for arbitrary models.

    Authors: We will add a short comparison of lines of user code required for the Type Ia example versus an equivalent direct implementation, placed in the core-objects section of the revision. revision: yes

Circularity Check

0 steps flagged

No circularity: software description with no derivation chain

full rationale

This is a software package paper describing skysurvey and modeldag. It defines three core objects (Target, Survey, DataSet) and illustrates their use via code examples for Type Ia supernovae and ZTF replication. No equations, predictions, fitted parameters, or first-principles derivations are present. The central claim that the objects enable complex modeling is a statement about code structure and API design, supported directly by the presented implementation and examples rather than any self-referential reduction or self-citation load-bearing argument. No patterns from the enumerated circularity kinds apply.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software-package paper; no theoretical free parameters, axioms, or invented physical entities are introduced.

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discussion (0)

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