Neutron star-companion interaction in core collapse supernovae. Population synthesis based on detailed binary evolution models

Neutron star-companion interaction in core collapse supernovae investigates how a newly formed neutron star interacts with an inflated companion star following a supernova explosion, providing predictions for observable light curve modulations. The gist: Periodic CCI is expected to occur in more than half of the binary systems that produce hydrogen-poor core collapse supernovae and are not disrupted, while the occurrence rate in systems producing hydrogen-rich supernovae is small.

Context and Motivation

The study addresses the phenomenon where a supernova ejecta hits a binary companion, leading to an interaction that can cause periodic light curve modulations, as evidenced by events like SN2022jli. This interaction involves two stages: the initial ejecta-companion interaction (ECI), which inflates the companion star, and the subsequent compact-object-companion interaction (CCI), where the newly formed neutron star interacts with this inflated envelope. The paper aims to derive predictions for the occurrence rate and observables of these CCIs based on detailed binary evolution models.

Methodology: Population Synthesis

The researchers employed a comprehensive, state-of-the-art grid of detailed binary stellar evolution models, utilizing the population synthesis code SN-ORACLE (E26). This code is based on MESA stellar evolutionary models at galactic metallicity. Key physical prescriptions implemented in the analysis included:

1. Natal kicks for neutron stars, sampled from distributions such as DM25, K18, and V25.

2. The companion’s response to ECI, modeled using analytic formulae (Eqs. 1-5), where the key parameter is the fraction of kinetic energy intercepted by the companion, given by Eheat = p · Ekin,ej · Ω˜eff.

3. Interaction between the newborn neutron star and the ECI-inflated companion immediately after explosion, requiring that the post-explosion periastron distance satisfy R2 < rperi ≤ Rmax (Eq. 6).

Key Findings on Occurrence Rates

The analysis reveals distinct trends based on the type of supernova:

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H-poor supernovae are predominantly expected to exhibit periodic CCI, with 15.6% of all H-poor supernovae exhibiting this periodicity in the reference model. This is because these transients are predominantly found in tight binary models at the time of the first supernova, which are preferentially H-poor due to previous phases of mass-transfer.

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H-rich supernovae show a small occurrence rate for periodic CCI.

Properties of Periodic CCI Events

The study characterized the physical properties associated with these interactions:

1. Post-supernova orbital periods (Ppost−SN) typically peak around 20–50 days, with broad period ranges peaking around 20–50 d, and interaction lasting for 0.5–10 yr.

2. Orbital eccentricities (epost−SN) peak around epost−SN ∼ 0.4, with a dichotomy between systems where eccentricity increases with orbital period and those with low eccentricities (e.g., < 0.20).

3. The ECI-driven inflation increases the brightness of the companions of H-poor supernovae, which can be 2–200 times higher than their pre-ECI luminosity.

Observational Constraints and Model Comparison

The models were tested against observed transients like SN2022jli, SN2015ap, and SN2022esa. For instance, in the reference model, the best-fitting models for SN2022jli predict a J-band magnitude of 21–33 for up to ∼ 10 yr post-explosion. The study also compared different population models by varying natal kick distributions (DM25 vs. K18 vs. V25) and explodability criteria, showing that the fraction of H-poor supernovae with periodic CCI can range from 3% to 27%. Furthermore, the inferred properties of SN2022jli—including its progenitor's ZAMS mass and initial orbital period—strongly constrain the birth parameter space.

Conclusion and Future Outlook

The results suggest that periodic CCI may be much more common than indicated by current observational candidates. The authors conclude that these findings may help find periodic CCI features in future and archival supernova observations, recommending high-cadence and deeper time-domain surveys to expand the sample of candidates. They also note that the inferred companion luminosities enhance the probability of observing companions while they are inflated.

Discussion on Uncertainties

The study discusses uncertainties related to stellar rotation, accretion efficiency during RLOF, and wind mass-loss prescriptions, noting that these factors can influence orbital evolution and the resulting CCI frequency. The contribution of CCI to light curve modulation may be masked by other power sources like interaction with circumstellar material. The paper also suggests that long-term signatures of CCI may affect the companion's spin through angular momentum losses.

As a fastidious and diligent researcher, I have analyzed this paper, Neutron star-companion interaction in core collapse supernovae, which focuses on deriving predictions for supernova light curve modulations caused by Compact-Object-Companion Interaction (CCI).

The primary contribution of this research is providing a robust framework linking binary evolution models (including natal kicks and Roche-Lobe Overflow dynamics) to observable post-supernova properties, specifically the periodicity of light curves.

Here are specific improvements for AI systems, categorized by the type of improvement:

)AI System Improvement Suggestions:

1. Astro-Physical Constraint Integration Module (APCI):

The AI should be upgraded with a dedicated module capable of integrating complex, multi-parameter binary evolution models (like MESA grids mentioned in the paper) with observational constraints derived from transient data (e.g., period, eccentricity, luminosity).

2. Population Synthesis Code Enhancement:

The current code (SN-ORACLE/E26) should be enhanced to incorporate a richer set of physical prescriptions derived from the paper's findings:

• Implement dynamically calculated ECI expansion models using the empirical relations (Equations 4 and 5) with variable efficiency factors based on companion rotation or accretion efficiency.

• Integrate probabilistic sampling for natal kicks (DM25, K18, V25) that are weighted by their predictive power against observed post-supernova orbital parameters.

3. Observational Predictive Engine (OPE):

The AI should be equipped with an OPE capable of simulating future observational outcomes based on the paper's results:

• Predict companion star magnitudes across various filters (JWST NIRCam, HST WFC3) using the derived luminosity/temperature relationships in Figure D.2.

• Calculate the probability of detecting a companion star given a predicted inflation timescale and eccentricity, allowing for targeted follow-up mission planning (e.g., determining if SN2022jli's companion is detectable with HAWK-I).

4. Model Sensitivity Analysis & Uncertainty Quantification:

The AI must perform rigorous sensitivity analysis across the parameter space defined in Section 3.2 and Appendix A/B:

• Quantify how variations in explodability criteria (e1, e2, e3) and merger criteria (m1–m5) shift the predicted fraction of H-poor supernovae exhibiting periodic CCI (Fig. 6).

• Identify which kick distribution (k1, k2, k3) most effectively constrains the birth parameter space for SN2022jli by maximizing agreement with observational constraints in Table D.1.

5. Classification and Feature Discrimination AI:

An advanced classifier should be trained on light curve features to distinguish between CCI-driven modulations and confounding mechanisms:

• Differentiate periodic CCI signatures (peaking around 20–50 d, high epost−SN) from signals caused by circumstellar material interaction or magnetar spindown.

• Evaluate the likelihood of a transient being a bactarian supernova versus one exhibiting true CCI based on inferred companion luminosity and light curve morphology.

)Improved AI System Capabilities:

The improved AI system will be able to perform the following specific tasks:

1. Determine if an observed core-collapse supernova (like SN2022jli or SN2015ap) is a candidate for CCI by calculating the probability that its progenitor binary evolution path—defined by natal kicks, RLOF history, and merger stability—could result in a companion star being inflated long enough to probe the NS.

2. Predict the post-explosion observable properties of the companion star (its magnitude in JWST/HST filters) given a specific set of inferred physical parameters (e.g., mass ratio, inflation timescale, eccentricity). This allows for direct comparison with archival photometric data.

3. Map the parameter space (kick distribution vs. explodability/merger criteria) to predict the resulting population statistics: specifically, predicting the fraction of H-poor supernovae that will exhibit periodic CCI (ranging from 3% to 27%) and characterizing how these events are distributed in terms of orbital period and eccentricity.

4. Identify favorable binary configurations for observing CCI features, such as those that produce high post-supernova eccentricities or specific initial orbital periods (e.g., short periods < 8 d), which are favored by the models used to fit SN2022jli constraints.

5. Provide a rigorous assessment of observational limits: If a companion star is not detected, the AI can use the derived Lmax and τinfl distributions to estimate if it is likely to be detectable in future observations (e.g., HAWK-I or JWST), thereby guiding telescope scheduling for follow-up studies.

Most massive stars live in binary systems. When the first supernova (SN) in a binary occurs, the ejecta hit the companion, which may inflate as a consequence, and then interact with the newly formed compact object. The recent Type Ic SN2022jli shows a periodic modulation in its emission, which is interpreted as evidence for such interaction. We derive predictions for the occurrence rate and observables of SNe exhibiting these companion - compact-object interactions (CCIs). We analyze a comprehensive, state-of-the-art grid of detailed binary stellar evolution models, and implement analytic prescriptions for the expansion of the companion star following its interaction with the SN ejecta. We employ the newly developed population synthesis code SN-ORACLE to derive the distribution functions of the properties of the SNe affected by CCI and their companions, where we use different explodability and neutron star birth kick distributions. We find that periodic CCI is expected to occur in more than half of the binary systems that produce a hydrogen-poor core-collapse SN and are not disrupted, while the occurrence rate in systems producing hydrogen-rich SNe is small. We find broad period ranges, peaking around 20-50 days, with the interaction lasting for 0.5-10 years. We identify specific binary evolution models that reproduce the observed period of the light curve undulations of SN2022jli, SN2015ap, and SN2022esa. The inflation of the companion also increases its luminosity and brightness, increasing its detectability with current instruments. For SN2022jli, our best-fit models predict a J-band magnitude of 21-23 for up to 10 years. We find that up to 27% of H-poor SNe could show periodicity in their light curves, while only a few such events have been identified so far. Our results may help find periodic CCI features in future and archival SN observations.