Cherenkov Signal Research Articles

Performance of a coarsely pixelated LAPPD photosensor for the SoLID gas Cherenkov detectors

The SoLID spectrometer's gas Cherenkov counters require photosensors that operate in a high luminosity and high background environment. The reference design features arrays of 9 or 16 tiled multi-anode photomultipliers (MaPMTs), distributed across 32 sectors, to serve the light-gas and heavy-gas Cherenkov counters, respectively. To assess the viability of a pixelated INCOM Large Area Picosecond Photodetector (LAPPDTM) as an alternative photosensor to replace MaPMT arrays in either detector, we evaluated its performance under realistic SoLID running conditions in Hall C at the Thomas Jefferson National Accelerator Facility (Jefferson Lab). The results of this test confirmed that the coarse-pixelated (2.5 × 2.5 cm2 pixel size) LAPPD is capable of handling the total projected signal and background rates of the three pillar SoLID experiments. The tested photosensor detected Cherenkov signals with the capability of separating single-electron events from pair production events while rejecting background. Although the design was not aimed at ring-imaging Cherenkov detectors, Cherenkov disk images were captured in two different gas radiators. Through a direct comparison with a GEANT4 simulation, we confirmed the experimental performance of the LAPPD.

Using machine learning to separate Cherenkov and scintillation light in hybrid neutrino detector

This research investigates the separation of Cherenkov and Scintillation light signals within a simulated Water-based Liquid Scintillator (WbLS) detector, utilizing the XGBoost machine learning algorithm. The simulation data were gathered using the Rat-Pac software, which was built on the Geant4 architecture. The use of the WbLS medium has the capability to generate both Scintillation and Cherenkov light inside a single detector. To show the separation power of these two physics events, we will use the supervised learning approach. The assessment utilized a confusion matrix, classification report, and ROC curve, with the ROC curve indicating a performance result of 0.96 ± 1.2× 10-4. The research also aimed to identify essential parameters for effectively distinguishing these physics events through machine learning. For this, the study also introduced the SHAP methodology, utilizing game theory to assess feature contributions. The findings demonstrated that the number of hits has a significant effect on the trained model, while the mean hit time has a somewhat smaller impact. This research advances the utilization of AI and simulation data for accurate Cherenkov and Scintillation light separation in neutrino detectors.

Development of a Cherenkov probe with high time resolution for runaway electron measurements in the HL-2M tokamak

An energetic particle diagnostic system based on the Cherenkov effect has been developed to detect the characteristics of runaway electrons (REs) in the HL-2M tokamak. The Cherenkov radiator is a cylinder made of sapphire with a diameter of 5 mm and a height of 10 mm. Its surface is coated with molybdenum film with thickness of 5 µm. The Cherenkov light spectrum which is emitted by REs when they pass through radiator with different energy is simulated by Geant4. Simulation results show that REs with energy above 120 keV can be effectively measured. The Al2O3 radiator is mounted at the end of an adjustable long shaft. The shaft can enter or withdraw from the chamber to optimize the radial position of the radiator. The Cherenkov signal can be transmitted through an optical path to the Silicon Photomultiplier (SiPM) and the sampling frequency of the signal acquisition system is 100 kHz. This probe was installed on the HL-2M device in 2022 and successfully obtained valid data during the autumn experiments. Measurements of the RE losses by the Cherenkov probe have been performed during HL-2M Ohmic discharges. Clear experimental evidences of the enhancement of RE losses during sawtooth instabilities have been obtained by the Cherenkov probe system. A detailed description of the probe system and the first experimental results are reported.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises. A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths. The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively. Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Photon-limited Cherenkov imaging of radiation therapy dose.

Cherenkov imaging is a unique verification tool that could provide both dosimetric and tissue functional information during radiation therapy. However, the number of interrogated Cherenkov photons in tissue is always limited and tangled with stray radiation photons, severely frustrating the measurement the signal-to-noise ratio (SNR). As such, here, a noise-robust photon-limited imaging technique is proposed by comprehensively exploiting the physical rationale of low-flux Cherenkov measurements together with the spatial correlations of the objects. Validation experiments confirmed that the Cherenkov signal could be promisingly recovered with high SNR by irradiating at as few as one x ray pulse from a linear accelerator (10 mGy dose), and the Cherenkov excited luminescence imaging depth can be extended by >100% on average, for most concentrations of phosphorescent probe. This approach demonstrates that improved applications in radiation oncology could be seen when signal amplitude, noise robustness, and temporal resolution are comprehensively considered in the image recovery process.

Emission characteristics of gadolinium ions in a water Cherenkov detector

Abstract To observe supernova relic neutrino events, 13 tons of gadolinium sulfate octahydrate (Gd2(SO4)3·8H2O), corresponding to 0.01% Gd solution, was dissolved in the Super-Kamiokande water Cherenkov detector in 2020. The aim is to improve the detection efficiency of neutrons from inverse β decay involving electron antineutrinos. However, Gd3+ ions can be excited by the Cherenkov light from cosmic muons, and the subsequent emission at 312 nm is a possible background (BG) source for Cherenkov signal detection. In this work, we constructed an experimental setup based on time-resolved laser-induced luminescence spectroscopy to investigate the emission characteristics of Gd3+ ions in water. The excitation laser wavelength was tuned in the range of 245–255 nm, and large resonant peaks were observed at 246.2 nm and 252.3 nm with measured emission lifetimes of around 3 ms. Good linearity was observed between Gd concentration and emission intensity for these two wavelengths, indicating that our setup is useful for remote monitoring of Gd concentration. According to the simulation results using our spectroscopic data and reference values, the Gd3+ emission BG rate from cosmic muons is expected to be 10−1 counts/μs or less, which seems small but not negligible.

Deep Learning-Based Fluorescence Light Discrimination for High Spatial Resolution Radiotherapy Dose Verification

<h3>Purpose/Objective(s)</h3> High spatial resolution quality assurance techniques are crucial to assess complex dose distributions of modern radiotherapy. Radiation-excited fluorescence imaging has great potential for real-time absolute dosimetry with high spatial resolution. However, the imaging data are generally contaminated by noise due to Cherenkov and scattered lights. This project aims to establish a novel deep learning-based model to correct the fluorescence images for accurate dosimetric applications. <h3>Materials/Methods</h3> An acrylic box phantom containing 1 g/L quinine hemisulfate water solution was constructed. 200 MU single-aperture static beams were delivered to the phantom with varying aperture shapes, gantry angle and collimator angle, and emitted fluorescent signals were detected by a digital camera in 2.0 frames per second cine mode. Temporal filtering and down-sampling were applied to obtain fluorescence images with 1 × 1 mm spatial resolution at the isocenter plane. 2D projected dose distributions were obtained by performing forward projection calculation of the 3D dose distributions calculated by a clinical treatment planning system. The acquired noisy fluorescence images and projected dose distributions were set to inputs and labels of the deep learning modeling, respectively. A 22-layer 2D residual convolutional neural network (CNN) was generated, and 200-epoch supervised learning was performed using 73 training data. For evaluation, accuracy of the calculated projected dose distributions was compared with that of a conventional scatter-kernel deconvolution method. <h3>Results</h3> The deep learning model yielded accurate projected 3D dose distributions with high spatial resolution. In contrast to the conventional method based purely on the analysis of florescence imaging signal, our deep learning-based approach was able to mitigate the adverse effects of Cherenkov and other optical noises. Mean squared error of 7 normalized projected dose distributions in test data were improved from 0.053 for uncorrected to 0.029 for CNN-corrected images. Computation time requiring to calculate a projected dose distribution was 0.2 second with a GPU workstation. <h3>Conclusion</h3> The proposed deep learning-based method improves the fluorescence image quality and the accuracy of dose distribution measurements in nearly real time. This technique will also be applied to Cherenkov radiation removal or signal denoising in other fluorescence imaging modalities. This method will provide an accurate absolute dose verification tool with high spatial resolution.

Picosecond timing planes for future collider detectors

We report experimental test-beam results on dielectric-loaded waveguide detectors that utilize microwave Cherenkov signals to time and characterize high energy particle showers. These results are used to validate models and produce high-fidelity simulations of timing plane systems which yield picosecond time tags and millimeter spatial coordinates for the shower centroid. These timing planes, based on the Askaryan effect in solid dielectrics, are most effective at the high center-of-momentum energies planned for the Future Circular Collider (FCC-hh), and are of particular interest in the forward region due to their high radiation immunity. We use our beam test results and GEANT4 simulations to validate a hybrid microwave detector model, which is used to simulate a reference timing plane design for the FCC forward calorimeters. Our results indicate that 0.5-3 ps particle timing is possible for a wide range of collision products in the reference FCC hadron collider detector, even with current technology.

SiPMs for Dual-Readout Calorimetry

A new fibre-sampling dual-readout calorimeter prototype has been qualified on beam at two facilities (DESY and CERN) using electrons from 1 to 100 GeV. The prototype was designed to almost fully contain electromagnetic showers and a central module (highly granular readout) was equipped with 320 Silicon Photomultipliers (SiPMs) spaced by 2 mm and individually read out. The test beams performed in 2021, allowed to qualify the readout boards used to operate the SiPMs, to define the calibration procedure and to measure the light yield for scintillating and Cherenkov signals produced by the shower development. This paper reports the first results obtained with the highly granular readout and discusses the ongoing R&amp;D to address some open questions concerning the mechanical integration and the scalable readout scheme that will allow to build and operate the next prototype designed for hadronic showers containment.

The (Un)reasonable Effectiveness of Neural Network in Cherenkov Calorimetry

We report a greater than factor of two improvement in the hadronic energy resolution of a simulated Cherenkov calorimeter by estimating the energy with machine learning over traditional techniques. The prompt signal formation and energy threshold properties of Cherenkov radiation provide identifiable features that machine learning techniques can exploit to produce a superior model for energy reconstruction. We simulated a quartz-fiber calorimeter via the GEANT4 framework to study the reconstruction techniques in single events. We compared the machine learning-based reconstruction performance to the traditional simple sum of signal and dual-readout techniques that use both Cherenkov and scintillation signals. We describe why this game-changing approach to Cherenkov hadron calorimetry excels and our plans for a dedicated beam test to validate these findings with a fast, radiation-hard hadron calorimeter prototype.

On the use of polychromatic cameras for high spatial resolution spectral dose measurements

Objective. Despite the demonstrated benefits of hyperspectral formalism for stem effect corrections in the context of fiber dose measurements, this approach has not been yet translated into volumetric measurements where cameras are typically used for their distinguishing spatial resolution. This work investigates demosaicing algorithms for polychromatic cameras based spectral imaging. Approach. The scintillation and Cherenkov signals produced in a radioluminescent phantom are imaged by a polychromatic camera and isolated using the spectral formalism. To do so, five demosaicing algorithms are investigated from calibration to measurements: a clustering method and four interpolation algorithms. The resulting accuracy of scintillation and Cherenkov images is evaluated with measurements of the differences (mean ± standard deviation) between the obtained and expected signals from profiles drawn across a scintillation spot. Signal-to-noise ratio and signal-to-background ratio are further measured and compared in the resulting scintillation images. Finally, the resulting differences on the scintillation signal from a 0.2 × 0.2 cm2 region-of-interest (ROI) were reported. Main results. Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively yielded differences of 3 ± 5%, 1 ± 3%, 1 ± 3%, 1 ± 2% and 2 ± 4% in the resulting scintillation images. For the Cherenkov images, all algorithms provided differences below 1%. All methods enabled measurements over the detectability (SBR > 2) and sensitivity (SNR > 5) thresholds with the bilinear algorithm providing the best SNR value. Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively provided differences on the ROI analysis of 7 ± 5%, 3 ± 2%, 3 ± 2%, 4 ± 2%, 7 ± 3%. Significance. Radioluminescent signals can accurately be isolated using a single polychromatic camera. Moreover, demosaicing using a bilinear kernel provided the best results and enabled Cherenkov signal subtraction while preserving the full spatial resolution of the camera.

Optimization of in vivo Cherenkov imaging dosimetry via spectral choices for ambient background lights and filtering.

.Significance: The Cherenkov emission spectrum overlaps with that of ambient room light sources. Choice of room lighting devices dramatically affects the efficient detection of Cherenkov emission during patient treatment.Aim: To determine optimal room light sources allowing Cherenkov emission imaging in normally lit radiotherapy treatment delivery rooms.Approach: A variety of commercial light sources and long-pass (LP) filters were surveyed for spectral band separation from the red to near-infrared Cherenkov light emitted by tissue. Their effects on signal-to-noise ratio (SNR), Cherenkov to background signal ratio, and image artifacts were quantified by imaging irradiated tissue equivalent phantoms with an intensified time-gated CMOS camera.Results: Because Cherenkov emission from tissue lies largely in the near-infrared spectrum, a controlled choice of ambient light that avoids this spectral band is ideal, along with a camera that is maximally sensitive to it. An RGB LED light source produced the best SNR out of all sources that mimic room light temperature. A 675-nm LP filter on the camera input further reduced ambient light detected (optical density > 3), achieving maximal SNR for Cherenkov emission near 40. Reduction of the room light signal reduced artifacts from specular reflection on the tissue surface and also minimized spurious Cherenkov signals from non-tissue features such as bolus.Conclusions: LP filtering during image acquisition for near-infrared light in tandem with narrow band LED illuminated rooms improves image quality, trading off the loss of red wavelengths for better removal of room light in the image. This spectral filtering is also critically important to remove specular reflection in the images and allow for imaging of Cherenkov emission through clear bolus. Beyond time-gated external beam therapy systems, the spectral separation methods can be utilized for background removal for continuous treatment delivery methods including proton pencil beam scanning systems and brachytherapy.

Estimation of diffuse Cherenkov optical emission from external beam radiation build-up in tissue.

.Significance: Optical imaging of Cherenkov emission during radiation therapy could be used to verify dose delivery in real-time if a more comprehensive quantitative understanding of the factors affecting emission intensity could be developed.Aim: This study aims to explore the change in diffuse Cherenkov emission intensity with x-ray beam energy from irradiated tissue, both theoretically and experimentally.Approach: Derivation of the emitted Cherenkov signal was achieved using diffusion theory, and experimental studies with 6 to 18 MV energy x-rays were performed in tissue phantoms to confirm the model predictions as related to the radiation build-up factor with depth into tissue.Results: Irradiation at lower x-ray energies results in a greater surface dose and higher build-up slope, which results in a greater diffusely emitted Cherenkov signal per unit dose at 6 MV relative to 18 MV x-rays. However, this phenomenon competes with a decrease in signal from less Cherenkov photons being generated at lower energies, a reduction at 6 versus 18 MV. The result is an emitted Cherenkov signal that is nearly constant with beam energy.Conclusions: This study explains why the observed Cherenkov emission from tissue is not a strong function of beam energy, despite the known strong correlation between Cherenkov intensity and particle energy in the absence of build-up and scattering effects.

Modeling the optical Cherenkov signals by cosmic ray extensive air showers directly observed from suborbital and orbital altitudes

Future experiments based on the observation of Earth's atmosphere from sub-orbital and orbital altitudes plan to include optical Cherenkov cameras to observe extensive air showers produced by high-energy cosmic radiation via its interaction with both the Earth and its atmosphere. As discussed elsewhere, particularly relevant is the case of upward-moving showers initiated by astrophysical neutrinos skimming and interacting in the Earth. The Cherenkov cameras, by looking above Earth's limb, can also detect cosmic rays with energies starting from less than a PeV up to the highest energies (tens of EeV). Using a customized computation scheme to determine the expected optical Cherenkov signal from these high-energy cosmic rays, we estimate the sensitivity and event rate for balloon-borne and satellite-based instruments, focusing our analysis on the Extreme Universe Space Observatory aboard a Super Pressure Balloon 2 (EUSO-SPB2) and the Probe of Extreme Multi-Messenger Astrophysics (POEMMA) experiments. We find the expected event rates to be larger than hundreds of events per hour of experimental live time, enabling a promising overall test of the Cherenkov detection technique from sub-orbital and orbital altitudes as well as a guaranteed signal that can be used for understanding the response of the instrument.

The POEMMA (Probe of Extreme Multi-Messenger Astrophysics) observatory

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) is designed to accurately observe ultra-high-energy cosmic rays (UHECRs) and cosmic neutrinos from space with sensitivity over the full celestial sky. POEMMA will observe the air fluorescence produced by extensive air showers (EASs) from UHECRs and potentially UHE neutrinos above 20 EeV. Additionally, POEMMA has the ability to observe the Cherenkov signal from upward-moving EASs induced by Earth-interacting tau neutrinos above 20 PeV. The POEMMA spacecraft are designed to quickly re-orientate to follow up transient neutrino sources and obtain currently unparalleled neutrino flux sensitivity. Developed as a NASA Astrophysics Probe-class mission, POEMMA consists of two identical satellites flying in loose formation in 525 km altitude orbits. Each POEMMA instrument incorporates a wide field-of-view (45∘) Schmidt telescope with an optical collecting area of over 6 m2. The hybrid focal surface of each telescope includes a fast (1 μs) near-ultraviolet camera for EAS fluorescence observations and an ultrafast (10 ns) optical camera for Cherenkov EAS observations. In a 5-year mission, POEMMA will provide measurements that open new multi-messenger windows onto the most energetic events in the universe, enabling the study of new astrophysics and particle physics at these extreme energies.

Reversal of laser wake phase velocity generates high-power broadband Cherenkov signal

As a femtosecond terawatt laser pulse propagates along a positive density gradient within a stratified plasma column, it drives a low-frequency electromagnetic wake wave, the period of electron fluid oscillations in the wake gradually shrinking. Phase velocity of the wake promptly exceeds the vacuum speed of light, setting in near-forward emission of terahertz Cherenkov radiation. As the wake accelerates further, the Cherenkov emission ray rotates by 180∘. Emission from a given plasma locality is sustained for a finite interval of time, in the middle of which the wake experiences a ‘reversal,’ its phase velocity becoming singular and changing sign (Zhang C J et al 2017 Phys. Rev. Lett. 119 064801) At this instant, the electromagnetic energy flows at 90∘, the emission power reaching its peak. After the reversal, the wake keeps radiating into the rear hemisphere until its phase velocity becomes subluminal. Experimentally capturing evolution of the Cherenkov signal may thus shed light onto the plasma wake dynamics. Far away from the plasma, the radiation fills an expanding, almost spherical shell, the shell thickness increasing with an increase in the observation angle. The length of the terahertz signal sampled in the wave zone thus ranges from zero (forward emission) to a few tens of picoseconds (backward emission). The signal is positively chirped, its frequency increasing from the Langmuir frequency at the foot of the column to the Langmuir frequency at the top. Theoretical estimates for the regimes involving 10 TW-class drive pulses promise a few-kW emission power; the energy conversion efficiency, from optical to terahertz, of order 10−7; and an MV m−1-scale electric field strength centimeters away from the plasma.

Modeling of the tau and muon neutrino-induced optical Cherenkov signals from upward-moving extensive air showers

We present a detailed modeling and computation methodology to determine the optical Cherenkov signals produced by upward-moving extensive air showers (EASs) induced by $\ensuremath{\tau}$-leptons and muons, sourced from the interaction of high-energy astrophysical neutrinos interacting in the Earth. Following and extending the physics modeling and Cherenkov signal simulations performed in M. H. Reno et al. [Phys. Rev. D 100, 063010 (2019)], this scheme encompasses a new, state-of-the-art computation of the muon neutrino propagation inside the Earth and the contribution to the $\ensuremath{\tau}$-lepton muon decay channel. The modeling takes into account all possible $\ensuremath{\tau}$-lepton decay and muon energy loss channels that feed the optical Cherenkov emission, produced by both tau and muon initiated EASs. The EAS modeling uses the electron energy, angular, and lateral distributions in the EAS and their evolution as well as the wavelength dependence of the Cherenkov emission and its atmospheric attenuation. The results presented here are focused on the detection capabilities of suborbital (balloon-borne) and orbital (satellite) based instruments. The latter case was calculated for POEMMA [The Probe Of Extreme MultiMessenger Astrophysics] to compare to that presented in M. H. Reno et al. [Phys. Rev. D 100, 063010 (2019)], specifically including the muon-decay channel of $\ensuremath{\tau}$-leptons and the muonic EAS Cherenkov signal from muon neutrino interactions in the Earth. By detailing all these individual contributions to the optical Cherenkov emission and detection, we show how the ensemble that includes muonic channels provides a large detection capability for space-based, high-energy cosmic neutrino detection. Specifically, we show that for neutrino energies $\ensuremath{\lesssim}10\text{ }\text{ }\mathrm{PeV}$, the upward-EAS sensitivity due to muon neutrino interactions in the Earth begin to dominate over that for tau neutrino interactions, effectively extending the neutrino sensitivity to lower energies.

Малоразмерный черенковский телескоп с полупроводниковыми фотодетекторами: полночастичное моделирование порога регистрации космических гамма-источников

Full-particle modeling of gamma-ray and proton induced extensive air showers (EASs) in the Earth's atmosphere as well as Monte Carlo modeling of photon transport in a small size Cherenkov telescope and signal registration with its camera based on the OnSemi MicroFJ SiPM detectors have been performed. Calculations have been carried out for primaries within the 0.3 - 30 TeV range and a telescope with a 10 m(2) mirror similar to the unit employed at the TAIGA observatory. It has been shown that even with strict selection criteria aimed at high quality EAS images, the threshold detection energy of the SiPM-based camera would not exceed 0.8 TeV - about twice as low as the current threshold of the TAIGA-IACT camera based on vacuum photomultipliers.

Design and performance study of a gas-Cherenkov detector with an off-axis parabolic reflector for inertial confinement fusion experiments

In this work, the gas-Cherenkov detector with an off-axis parabolic reflector (OprGCD) is designed using the Geant4 Monte Carlo simulation toolkit, which is helpful to improve the collection efficiency of Cherenkov photons. The method to study the performance of OprGCD based on femtosecond laser-wakefield-accelerated electron beams is presented. Cherenkov signals with high signal-to-noise ratio were obtained, and the measured Cherenkov signals changing with the CO2 pressure were consistent well with the simulation results. The design and study of this OprGCD system lay the foundation for the application of fusion gamma diagnostics system in large laser facilities of China.

Radiation emission at Langmuir frequency from laser wake in longitudinally stratified plasma column

Theoretical analysis shows that a small-amplitude laser wake (an electrostatic Langmuir wave), driven in a periodically stratified, cylindrical plasma column, generates a superluminal, azimuthally polarized rotational current at a Langmuir frequency. The current radiates a radially polarized (i.e. transverse magnetic) Cherenkov signal into the plasma-free space. The opening angle of the Cherenkov emission cone is defined by the spatial period of stratification. Lifetime of the laser wake, limited by wave breaking in the inhomogeneous plasma, defines the terahertz (THz) signal length. This length ranges from a few picoseconds to hundreds of picoseconds, depending on the wake amplitude, amplitude of background density modulation, and stratification period. Monochromaticity and coherence distinguishes this THz signal from the ultrashort, uncollimated, broadband signals generated by photoionization currents in plasma filaments. The efficiency of electromagnetic energy conversion, from optical to THz, reaches its peak when the drive pulse waist size is close to the column radius. The efficiency increases with an increase in the drive pulse wavelength, and reaches the maximum when the drive pulse becomes near-critical for relativistic self-focusing. Numerical examples with sub-Joule, near-infrared terawatt drive pulses demonstrate the highest conversion efficiency of the order 10−5, with the total emitted energy of several µJ, a few-hundred MV m−1 electric field at the column surface, and an MV m−1 field 10 cm away from the source.