Confirming candidate self-lensing binary detections with radial velocity measurements

summary

The gist

The gist The discovery of such systems is a potential method of finding quiescent black holes and neutron stars, most of which will be pristine and unchanged from their formation<ref:2610.30203#pg6>

In short

The study investigates confirming self-lensing binaries by combining photometric light curves with radial velocity (RV) measurements. By simulating these systems and performing joint MCMC fits, researchers found that a small number of RV observations can accurately determine the compact object class and lens mass, especially for higher mass objects. This suggests a staged follow-up strategy is effective for high-priority detections.

Key concepts

Self-Lensing: This occurs when a compact object, like a black hole or neutron star, orbits another star in an edge-on binary system. The compact object acts as a lens, periodically amplifying the light from its companion once per orbit. Detecting this periodic amplification is a direct way to find these unseen objects.
MCMC Fit: Markov Chain Monte Carlo (MCMC) fitting is a statistical method used to explore the range of possible physical parameters for an astronomical system. Researchers use it to fit simulated light curves, sampling the most likely values for parameters like mass and orbital period, given the observed photometric data.
Radial Velocity (RV): Radial velocity measures the speed at which a celestial object moves toward or away from Earth. By observing periodic changes in this velocity over time, astronomers can constrain orbital properties and help distinguish between different physical solutions for a self-lensing system.

Terminology used across episodes

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This episode discusses

The paper

Confirming candidate self-lensing binary detections with radial velocity measurements · Read on arXiv

Adam McMaster, Madeline-Mai Ward, Hugh J. Dickinson, Matthew J. Middleton, D. J. Dixon, Adam Ingram, N. Castro Segura, Simon Vaughan

Department of Physics and Astronomy, University of Southampton · School of Physical Sciences, The Open University School of Physical Sciences Department of Physics, Newcastle University School of Mathematics Statistics and Physics Centre for Extragalactic Astronomy Department of Physics Durham University Department of Physics University of Warwick School of Physics and Astronomy University College London Nicolaus Copernicus Astronomical Center Polish Academy of Sciences

Compact objects in approximately edge-on binary orbits with stars are able to produce a self-lensing effect, amplifying light from their companion once per orbit. To date, five white dwarf self-lensing systems are known, while none are known containing black holes or neutron stars. Discovery of such systems is a potential method of finding quiescent black holes and neutron stars, most of which will be pristine and unchanged from their formation. Photometric detections can suffer from degeneracies in the lens mass and orbital parameters when the amplification and crossing time are not well constrained, while photometric searches produce too many false positives to allow radial velocity investigation of every candidate. Therefore we investigate the minimal radial velocity (RV) observational requirements to characterise such systems by simulating detectable self-lensing light curves, using those light curves to generate targeted RV observations, and fitting RV jointly with photometry. We evaluate the median relative error in the recovered lens mass and orbital period, alongside compact-object class recovery. We find that adding RV measurements substantially reduces parameter error, with compact-object class generally easier to determine than the exact lens mass. Parameter accuracy is driven mainly by number of RV observations, with RV precision playing a weaker role except for the shortest-period systems, with even a small number of RV measurements resulting in substantial improvement in accuracy. These results suggest that multi-stage RV follow-up, starting with a small number of observations of many candidates, can efficiently classify self-lensing candidates and identify high-priority systems for further investigation.

Transcript

Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: We're looking at the paper: "Confirming candidate self-lensing binary detections with radial velocity measurements".

Jocelyn: Here's the gist of the paper. The gist The discovery of such systems is a potential method of finding quiescent black holes and neutron stars, most of which will be pristine and unchanged from their formation<ref:2610.30203#pg6> Compact objects in approximately edge-on binary orbits with stars are able to produce a self-lensing effect, amplifying light from their companion once per orbit<ref:2610.30203#pg8> This phenomenon is a special case of microlensing where the source and the lens are in a binary system together, in an orbit which is close to edge-on, producing a periodic microlensing amplification of the source once per orbit<ref:2610.30203#pg9> Self-lensing is only possible when the lens is a compact object<ref:2610.30203#pg10> A detection of self-lensing (i.e.

Vera: periodic microlensing) is de facto a detection of a compact object To date, five self-lensing binaries have been confirmed, each containing a white dwarf lens, all of which have relatively close, short-period orbits However, it is expected that stellar-compact object binaries should span the full range of compact object masses as well as the full range of orbital configurations The investigation involves simulating detectable self-lensing light curves and using those light curves to generate targeted radial velocity observations, fitting RV jointly with photometry The methods involve several key steps: 1.

Jocelyn: Generating a library of selflensing photometric simulations covering a range of physical parameters, filtered to include only the simulated light curves which contain visible lensing where the peak amplitude is larger than the photometric noise 2.

Vera: Performing an MCMC fit against the simulated photometry to sample the posterior distribution of self-lensing physical parameters and find distinct physical solutions for each light curve, assuming stellar properties are known 3.

Title and authors: Vera: Now, looking at the paper's summary, it’s quite clear that they’re using a clever simulation approach to generate light curves that look real enough to be detected by telescopes.

Jocelyn: They generate these simulated self-lensing photometric light curves and then use them as targets for radial velocity observations to find the best fit for the physical parameters.

Subrahmanyan: The paper explains that they perform several steps, starting with creating a library of simulations filtered to only include events where the lensing amplification is bigger than the photometric noise, which sets up a sort of detectable baseline.

Vera: Then they use an MCMC fit against that simulated photometry to sample the posterior distribution for all the physical parameters, assuming we already know some things about the stellar properties.

Jocelyn: They then design a specific observing schedule—a variable number of observations between three and ten over a five-year campaign—that is optimized specifically to distinguish between different photometric solutions.

Subrahmanyan: The goal here is to use that targeted RV schedule for a second MCMC fit, constrained jointly by the photometry and the radial velocities, using the final state from the photometric fit as their starting point.

The paper's summary: Vera: So what’s actually new or improved by this approach? They are focusing on how many spectroscopic measurements are needed to accurately retrieve those system parameters based on what we already know about them.

Jocelyn: The results show that adding radial velocity measurements substantially reduces the error in the recovered parameters, especially when it comes to determining the compact object class.

Subrahmanyan: They found that for compact-object class determination, it’s generally easier to do than measuring the exact lens mass, because of how RV precision plays a weaker role unless you're looking at very short-period systems.

Vera: And that means they suggest a staged follow-up strategy—you start with a small number of observations for many candidates just to classify them, then you concentrate the high-precision RV measurements on the ones that look most promising.

Jocelyn: That staging is smart because it allows you to efficiently sort through a large population of self-lensing candidates and identify the highest priority systems for deeper investigation.

Subrahmanyan: The paper also looked at how well they could recover the lens mass and orbital period, and they found that larger lens masses are recovered with smaller errors than smaller ones, even in those longer period systems.

The paper's improvements: Vera: So to wrap up, the paper suggests a staged follow-up strategy is the way forward for self-lensing candidates. The second stage would be to focus on the true binaries to get that lens mass determined with higher precision.

Jocelyn: And they emphasize that a targeted radial velocity observing campaign, timed carefully to separate possible solutions, is effective for recovering object properties with a minimal number of observations.

Subrahmanyan: They found that the highest mass lenses tend to have the lowest relative errors, even when you consider large orbital periods. This suggests that focusing on those higher mass systems first might yield the most reliable measurements.

Vera: It sounds like they are optimistic about what we can confirm in surveys like TESS, ZTF, LSST, and PLATO if we follow this kind of approach with their self-lensing candidates.

Jocelyn: They are indeed suggesting that combining these methods allows us to potentially confirm a substantial number of self-lensing systems in those surveys.

Conclusion: Vera: So, looking at the whole thing, this paper on "Confirming candidate self-lensing binary detections with radial velocity measurements" shows how combining photometry and spectroscopy can really help us move past just seeing a signal to actually characterizing the physics of those systems.

Jocelyn: By simulating these events and then designing smart RV schedules, they’re showing that we don't need a huge amount of data upfront to get meaningful constraints on things like lens mass.

Subrahmanyan: From a cosmic perspective, if we can accurately measure the mass of these compact objects, it helps us constrain the population synthesis models for stellar-compact object binaries, which feeds into how we understand structure formation in the universe.

Vera: It's about taking those initial detections and using targeted follow-up to build up a picture that’s much more detailed than what a single photometric event gives you.

Jocelyn: So, the implication is that for future surveys, it’s not just about finding the signal, but about designing an intelligent way to confirm its nature with minimal effort.

Subrahmanyan: And this work on "Confirming candidate self-lensing binary detections with radial velocity measurements" gives us a roadmap for how to get from candidate to confirmed system properties.

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