Optomechanical Detection of Individual Gas Collisions

arXiv:2604.18371 · quant-ph, hep-ex, physics.ins-det · Submitted 2026-04-20 · Read on arXiv

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Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Kai: I'm Kai.

Mira: And I'm Mira, here with Lev.

Kai: We're looking at the paper: "Optomechanical Detection of Individual Gas Collisions".

Mira: The gist The observed event rates accurately measure the gas partial pressures, while the spectral shape provides a sensitive probe of the surface properties of the nanoparticle, including its temperature.

Kai: First, who's behind it and why it matters.

Title and authors: Kai: Moving on to the next part of this discussion, we’re looking at the paper’s title and authors for Optomechanical Detection of Individual Gas Collisions.

Mira: This paper is by Yu-Han Tseng and his collaborators, Clarke A. Hardy, T. W. Penny, Cecily Lowe, Jacqueline Baeza-Rubio, Daniel Carney, and David C. Moore #pg1

Lev: The title itself is very direct: Optomechanical Detection of Individual Gas Collisions; it tells you exactly what they did without any fluff > Kai It’s just describing the experiment. It’s not trying to be clever or poetic about the physics behind it > Mira It frames the work as a demonstration of detecting collisions, which is key because it sets up a specific experimental goal for the entire study #pg1

Lev: So, what does that title mean in terms of how they framed their research?

Kai: It means they are focusing on the physical phenomenon itself rather than just some abstract quantum mechanism > Mira Exactly. It tells you this is about the collision dynamics, which is what’s being measured with those impulses #pg1

Lev: And from an engineering standpoint, does that focus help when you're trying to build something?

Kai: It helps because if you know they are focusing on individual collisions, then your hardware design has to be sensitive enough to see a single tiny interaction > Mira Right. It means the sensitivity of the sensor needs to be tuned for discrete events rather than just broad energy distributions #pg1

The paper's summary: Kai: So, let’s look at the actual summary of Optomechanical Detection of Individual Gas Collisions.

Mira: The authors summarize that they experimentally demonstrate the detection of momentum transfers from individual collisions from Kr, Xe, and SF6 with an optically levitated nanoparticle #pg2

Lev: They are clearly stating that they used a silica nanoparticle as their sensor and they measured the z-position continuously > Kai That’s the hardware setup you described earlier. It confirms that the optical trap is working as intended #Mira]

Mira: They also detail how they handled the gas handling system, mentioning a residual gas analyzer and a cold cathode gauge to monitor composition and pressure > Lev That shows they took care of both the input and output side of the experiment > Kai Right.

Lev: They talk about how an incoming gas particle interacts with the surface, imparting a momentum q over a residence time taures before leaving the surface #Mira] That’s the fundamental physical process they are looking at #pg2

Kai: So, what’s that process like on a listener's level?

Mira: It means when you have gas hitting that sphere, it gives it an instantaneous little push, or momentum q over a short time before the particle leaves the surface > Lev That’s the microscopic view of the collision #pg2

The paper's improvements: Kai: Now, let's talk about what improvements they suggest for this paper.

Mira: They suggest using a binned likelihood model to perform a joint fit over datasets for each gas to show that the measured event rate spectra agree quantitatively with simple models based on the kinetic theory of gases > Lev That’s how you move from just seeing agreement to actually proving it mathematically > Kai It adds that they use a negative binomial likelihood with five percent overdispersion to model the counts, which accounts for fluctuations in the detector response #pg3 <ref:2604.18371#pg3>

Mira: And they use a signal model calculated following Ref. twenty-four by integrating the velocity distributions of incoming and outgoing gas particles over the surface area of the nanoparticle, including both specular and diffuse scattering > Lev That’s where they take it from just a qualitative observation to a more detailed calculation #pg1 <ref:2604.18371#pg3>

Kai: And they incorporate measurement resolution by convolving those event rates calculated from Eq. (one) with a Gaussian kernel of width sigma-q > Mira So, that’s how you account for the resolution, which is important because the measurement resolution can smear the results out #pg3 <ref:2604.18371#pg1>

Conclusion: Kai: To wrap up on Optomechanical Detection of Individual Gas Collisions.

Mira: They conclude that they found best-fit partial pressures agree with those reported by the CCG in the ten-eight to ten-seven mbar range, which is consistent with theoretical expectations > Lev It also shows that fitting the same signal models to background-only datasets indicates a minimum resolvable partial pressure of about two times ten-nine mbar #pg3 <ref:2604.18371#pg1>

Kai: So the main implication is that this technique proves that impulsive forces from individual gas particles scattering from a silica nanosphere can be directly measured using techniques developed in levitated optomechanics > Mira And they also demonstrate the potential of macroscopic mechanical systems in precision force sensing and the measurement of fundamental particle interactions #pg1

Lev: It's about showing that you can measure these recoils from individual gas particles which are about ten-seven times lighter than this sensor, which demonstrates the potential for precision force sensing > Kai And it also helps inform models of collisional decoherence in experiments targeting quantum superposition in levitated systems #pg2 <ref:2604.18371#pg1>

Mira: So, we see that they can extract physical properties like surface temperature and thermal accommodation coefficient from the fits as parameters that affect the spectral shape in distinguishable ways > Lev This is a useful way to understand how environmental factors change the measured spectra #pg1

Wright Laboratory, Department of Physics, Yale University · Physics Division, Lawrence Berkeley National Laboratory · Yale Quantum Institute

quant-ph, hep-ex, physics.ins-det

Submitted: 2026-04-20

Updated: 2026-10-19

Comments: 5 pages, 3 figures; v2: match journal version

Journal ref: Phys. Rev. Lett. 137 (2026), 173601

DOI: 10.1103/xtzh-6t1f

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 79/100

The gist: The gist The observed event rates accurately measure the gas partial pressures, while the spectral shape provides a sensitive probe of the surface properties of the nanoparticle, including its

Key concepts

Impulsive Momentum Transfer: When a gas particle hits the nanosphere's surface, it imparts a sudden change in momentum ($\vec{q}$) over a very short time ($\tau_{res}$). The experiment relies on measuring this discrete 'kick' to infer the properties of the incoming gas and the surface interaction.
Binned Likelihood Model: This statistical method is used to combine data from different gas species. It fits observed collision spectra to a theoretical model based on kinetic theory of gases, allowing researchers to accurately determine partial pressures while accounting for noise and detector fluctuations.
Thermal Accommodation Coefficient ($\alpha$) and Surface Temperature ($T_s$): These parameters describe how the nanosphere's surface interacts with the gas molecules. By fitting the spectral shape, scientists can extract $T_s$ (the surface temperature) and $\alpha$, which indicate how effectively the surface absorbs or reflects energy from incoming particles.
Negative Binomial Likelihood: This statistical distribution is used to model the number of counts observed in each impulse bin. It accounts for 'overdispersion'—variance beyond what a simple Poisson process predicts—which arises from real-world data quality issues and detector response fluctuations.

Terminology

Momentum transfer — 1× in this paper · explained in 2 episodes

Summary

The gist The observed event rates accurately measure the gas partial pressures, while the spectral shape provides a sensitive probe of the surface properties of the nanoparticle, including its temperature.

Experimental Setup and Detection Method

A silica nanosphere (nominal radius R = 50 nm with mass m ≈ 1 fg) is optically trapped in ultra-high vacuum using a 1064 nm laser focused by an aspheric lens (numerical aperture 0.77) The optical trap forms a three-dimensional harmonic potential for the center-ofmass motion of the nanoparticle, which is cooled through feedback damping by parametric modulation of the laser power Throughout the measurement described in this work, the nanoparticle is trapped at a background pressure of (3 ± 1) × 10−8 mbar where H2O and H2 are the dominant residual gases measured with a residual gas analyzer (RGA) The net electric charge of the particle is monitored using an off-resonance electric field oscillating at 137 kHz and is maintained at +6 e throughout data collection.

Gas Interaction and Momentum Transfer

On the microscopic level, an incoming gas particle interacting with the surface of the trapped nanosphere imparts a momentum, ⃗q, over the residence time, τres, before leaving the surface For all of the gas species considered in this work, τres ≪ 2π/omegaz ≈ 20 µs and the collision events are well approximated by impulsive, instantaneous momentum transfers The response of the nanoparticle is calibrated by applying electric impulses in the z-direction with amplitudes spanning 100–1000 keV/c via electrodes surrounding the optical trap, following the procedure established for similar systems.

Impulse Measurement and Data Selection

For each dataset, the impulse amplitudes are reconstructed from the position measurement following the same procedure applied to the calibration data Matched filters are constructed separately in 100 ms-long analysis windows to account for slow drift in the oscillator’s frequency, and the maximum reconstructed amplitude in each 50 µs-long search window is recorded. Data selection cuts are applied to reject data that show evidence of unmodeled transient noise or significant drift in the detector response A noise-level cut rejects noisy time windows for which the RMS reconstruction amplitude in the surrounding 250 µs (excluding the detected impulse) exceeds the mean level by > 1σ An event-level selection is applied based on the goodnessof-fit of each reconstructed waveform to the template derived from calibration data, further removing 2% of impulse candidates.

Data Analysis and Modeling

We use a binned likelihood model to perform a joint fit over datasets for each gas to show that the measured event rate spectra agree quantitatively with a simple model based on the kinetic theory of gases The number of counts in each impulse bin is described by a Poisson process, though in our data we observe ≈ 5% excess variance attributable to data quality cuts and fluctuations in the detector response To model this, we use a negative binomial likelihood [34, 35] with 5% overdispersion The signal model is calculated following Ref. [24] by integrating the velocity distributions of incoming and outgoing gas particles over the surface area of the nanoparticle, including both specular (elastic) and diffuse scattering. The effect of the measurement resolution is incorporated by convolving the event rates calculated from Eq. (1) with a Gaussian kernel of width σq.

Results and Interpretation

We find the best-fit partial pressures from the observed collision spectra agree with those reported by the CCG in the 10−8–10−7 mbar range, to within uncertainties (Fig. 3 insets) Fitting the same signal models to the background-only datasets indicates a minimum resolvable partial pressure of ≈ 2×10−9 mbar We also extract the sphere surface temperature and the thermal accommodation coefficient from the fits, as these parameters affect the spectral shape in distinguishable ways The best-fit sphere surface temperature is found to lie at the lower bound, indicating negligible absorption of laser power.

Conclusions

We have shown that impulsive forces from individual gas particles scattering from a silica nanosphere can be directly measured using techniques developed in levitated optomechanics The detection of recoils from individual gas particles—which are approximately 10−7 times lighter in mass than the levitated sensor used here—demonstrates the potential of macroscopic mechanical systems in precision force sensing and the measurement of fundamental particle interactions The direct observation of gas collisions can also inform models of collisional decoherence in experiments that target the creation of macroscopic quantum superposition in levitated systems Finally, the techniques demonstrated here find applications in metrology, including the possibility of establishing a new pressure standard in the extreme-high vacuum (XHV) regime.

Table I Summary

TABLE I. Thermal accommodation coefficients (α) and sphere surface temperatures (Ts) measured from joint fits to the impulse spectra for each gas Kr 0.55+0.11 −0.14 < 351 K Xe 0.61+0.11 −0.12 < 353 K SF6 0.82+0.07 −0.08 < 334 K

Acknowledgments

We thank Lorenzo Magrini, Daniel Barker, and the QuIPS team at Lawrence Berkeley National Lab for helpful discussions This work was supported by the DOE Office of Science, High Energy Physics, through the QuantISED 2.0 program, award DE-SC0026367, and in part by ONR Grant N00014-23-1-2600 and NSF Grant PHY-2512192 This work has been supported in part by the “Table-top experiments for fundamental physics” program, sponsored by the Gordon and Betty Moore Foundation (Grant GBMF12328, DOI 10.37807/GBMF12328), Simons Foundation, Alfred P. Sloan Foundation (Grant G-2023-21130), and the John Templeton Foundation Y.-H. T. is supported by the Graduate Instrumentation Research Award (GIRA) from the Coordinating Panel for Advanced Detectors (CPAD).

References

[1] R. Brown, Philosophical Magazine 4, 161 (1828)

[2] A. Einstein, Annalen der Physik 322, 549 (1905)

[3] M. Smoluchowski, Annalen der Physik 326, 756 (1906)

[4] T. Li, S. Kheifets, D. Medellin, and M. G. Raizen, Science 328, 1673 (2010)

[5] M. Aspelmeyer, T. J. Kippenberg, and F. Marquardt, Rev. Mod. Phys. 86, 1391 (2014)

[6] C. Gonzalez-Ballestero, M. Aspelmeyer, L. Novotny, R. Quidant, and O. Romero-Isart, Science 374, eabg3027 (2021)

[7] E. Joos and H. D. Zeh, Z. Phys.

Improvements for AI systems

textbf Quantitative Gas Composition Determination for Sensor Calibration; The improved AI system can accurately determine the partial pressures of various gas species from scattering rates, as demonstrated by fitting the measured event rate spectra to models based on the kinetic theory of gases.
textbf Sensitivity Enhancement for Fundamental Interaction Searches; The AI system can achieve a minimum resolvable partial pressure of ≈ 2×10−9 mbar, enabling the detection of events that are well above the noise level, which is crucial for searches like those for dark matter and sterile neutrinos.
textbf Real-time Surface Property Monitoring; The system can extract physical properties of the sensor, specifically the sphere surface temperature and the thermal accommodation coefficient, allowing for continuous monitoring of how environmental factors affect quantum mechanical experiments.
textbf Robust Noise Mitigation in Impulse Reconstruction; The AI system can employ advanced filtering techniques, such as using a matched filter to reconstruct the impulse amplitudes from the z-position measurement, which is essential for accurately capturing impulsive forces in the 200–600 keV/c range.
textbf Optimized Parameter Estimation via Bayesian Inference; The AI system can perform a binned likelihood model to perform a joint fit over datasets for each gas, treating parameters like σq, Ts, and α as nuisance parameters and are marginalized over in the fit to obtain conservative estimates despite systematic uncertainties.

Abstract

We experimentally demonstrate the detection of momentum transfers from individual collisions of Kr, Xe, and SF 6 with an optically levitated nanoparticle, finding good agreement with theoretical expectations. The observed event rates accurately measure the gas partial pressures, while the spectral shape provides a sensitive probe of the surface properties of the nanoparticle, including its temperature. The reconstruction of impulse signals as small as 200 keV/ c further establishes that levitated optomechanical sensors can reach the sensitivity required for precision measurements of fundamental particle interactions, and demonstrates a proof-of-principle for a primary pressure sensor based on the detection of individual gas particle collisions.