Fiber Spectroscopy Research Articles

The Velocity Dispersion Function for Quiescent Galaxies in Massive Clusters from IllustrisTNG

We derive the central stellar velocity dispersion function (VDF) for quiescent galaxies in 280 massive clusters with log(M200/M⊙)>14 in IllustrisTNG300. The VDF is an independent tracer of the dark matter mass distribution of subhalos in galaxy clusters. Based on the IllustrisTNG cluster catalog, we select quiescent member subhalos with a specific star formation rate <2 × 10−11 yr−1 and stellar mass log(M*/M⊙)>9 . We then simulate fiber spectroscopy to measure the stellar velocity dispersion of the simulated galaxies; we compute the line-of-sight velocity dispersions of star particles within a cylindrical volume that penetrates the core of each subhalo. We construct the VDFs for quiescent subhalos within R 200. The simulated cluster VDF exceeds the simulated field VDF for logσ*>2.2 , indicating the preferential formation of large velocity dispersion galaxies in dense environments. The excess is similar in simulations and in the observations. We also compare the simulated VDF for the three most massive clusters with log(M200/M⊙)>15 with the observed VDF for the two most massive clusters in the local Universe, Coma and A2029. Intriguingly, the simulated VDFs are significantly lower for logσ*>2.0 . This discrepancy results from (1) a smaller number of subhalos with log(M*/M⊙)>10 in TNG300 compared to the observed clusters, and (2) a significant offset between the observed and simulated M *–σ * relations. The consistency in the overall shape of the observed and simulated VDFs offers a unique window into galaxy and structure formation in simulations.

Highly sensitive microfluidic sensor using integrated optical fiber and real-time single-cell Raman spectroscopy for diagnosis of pancreatic cancer

Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.

Motion Planning for the Robotic Fiber Positioners of the Large Sky Area Multiobject Fiber Spectrocopy Telescope

The Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) is one of the most effective multiobject spectroscopic instruments. Its survey efficiency is guaranteed by simultaneously positioning multiple fibers via 4000 robotic fiber positioners (RFPs). With the further updates to LAMOST, the new-generation RFPs will be smaller, and the number of RFPs will increase to 5000. The RFPs are densely packed with shared working space. Thus, they may collide with each other, leading to them damaged and reducing the survey speed. In this study, we propose a new motion planning algorithm that prevents the collision of RFPs. To simplify the collision avoidance problem, we transform the motion planning process from a dynamic one into a static one by selecting one of the RFPs in each collision pair as the waiting robot. Accordingly, we design a method for choosing the waiting robot, and use a rapidly exploring random tree to plan a collision-free path for the waiting robot. However, there may be blocks between the waiting robots and their neighbors. Therefore, we also design methods to resolve these blocks. Simulations suggest that the proposed algorithm can prevent 98.4% of the collisions. About 99.9% of the positioners can reach their targets without collisions. Although developed for LAMOST, we believe that our algorithm can also be used for other instruments with equal-arm theta-phi positioners, such as the Dark Energy Spectroscopic Instrument.

Synthesis and thermally-induced gelation of interpenetrating nanogels

Thermally-induced in-situ gelation of polymers and nanogels is of significant importance for injectable non-invasive tissue engineering and delivery systems of drug delivery system. In this study, we for the first time demonstrated that the interpenetrating (IPN) nanogel with two networks of poly (N-isopropylacrylamide) (PNIPAM) and poly (N-Acryloyl-l-phenylalanine) (PAphe) underwent a reversible temperature-triggered sol-gel transition and formed a structural color gel above the phase transition temperature (Tp). Dynamic light scattering (DLS) studies confirmed that the Tp of IPN nanogels are the same as that of PNIPAM, independent of Aphe content of the IPN nanogels at pH of 6.5 ∼ 7.4. The rheological and optical properties of IPN nanogels during sol-gel transition were studied by rheometer and optical fiber spectroscopy. The results showed that the gelation time of the hydrogel photonic crystals assembled by IPN nanogel was affected by temperature, PAphe composition, concentration, and sequence of interpenetration. As the temperature rose above the Tp, the Bragg reflection peak of IPN nanogels exhibited blue shift due to the shrinkage of IPN nanogels. In addition, these colored IPN nanogels demonstrated good injectability and had no obvious cytotoxicity. These IPN nanogels will open an avenue to the preparation and thermally-induced in-situ gelation of novel NIPAM-based nanogel system.

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with R 2 P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

Total and dark mass from observations of galaxy centers with machine learning

The galaxy total mass inside the effective radius is a proxy of the galaxy dark matter content and the star formation efficiency. As such, it encodes important information on the dark matter and baryonic physics. Total central masses can be inferred via galaxy dynamics or gravitational lensing, but these methods have limitations. We propose a novel approach based on machine learning to make predictions on total and dark matter content using simple observables from imaging and spectroscopic surveys. We used catalogs of multiband photometry, sizes, stellar mass, kinematic measurements (features), and dark matter (targets) of simulated galaxies from the Illustris-TNG100 hydrodynamical simulation to train a Mass Estimate machine Learning Algorithm ( Mela ) based on random forests. We separated the simulated sample into passive early-type galaxies (ETGs), both normal and dwarf, and active late-type galaxies (LTGs) and showed that the mass estimator can accurately predict the galaxy dark masses inside the effective radius in all samples. We finally tested the mass estimator against the central mass estimates of a series of low-redshift (zlsim 0.1) datasets, including SPIDER, MaNGA/DynPop, and SAMI dwarf galaxies, derived with standard dynamical methods based on the Jeans equations. We find that Mela predictions are fully consistent with the total dynamical mass of the real samples of ETGs, LTGs, and dwarf galaxies. learns from hydro-simulations how to predict the dark and total mass content of galaxies, provided that the real galaxy samples overlap with the training sample or show similar scaling relations in the feature and target parameter space. In this case, dynamical masses are reproduced within 0.30 dex ($ with a limited fraction of outliers and almost no bias. This is independent of the sophistication of the kinematical data collected (fiber vs. 3D spectroscopy) and the dynamical analysis adopted (radial vs. axisymmetric Jeans equations, virial theorem). This makes a powerful alternative to predict the mass of galaxies of massive stage IV survey datasets using basic data, such as aperture photometry, stellar masses, fiber spectroscopy, and sizes. We finally discuss how to generalize these results to account for the variance of cosmological parameters and baryon physics using a more extensive variety of simulations and the further option of reverse engineering this approach and using model-free dark matter measurements (e.g., via strong lensing), plus visual observables, to predict the cosmology and the galaxy formation model.

Identification of Carbon Stars from LAMOST DR7

Carbon stars are excellent kinematic tracers of galaxies and play important roles in understanding the evolution of the Galaxy. Therefore, it is worthwhile to search for them in a large number of spectra. In this work, we build a new carbon star catalog based on the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) DR7 spectra. The catalog contains 4542 spectra of 3546 carbon stars, identified through line index and near-infrared color–color diagrams. Through visual inspection of the spectra, we further subclassify them into 925 C–H, 384 C–R, 608 C–N, and 1292 Ba stars. However, 437 stars could not be subclassified due to their low signal-to-noise. Moreover, by comparing with the LAMOST DR7 pipeline we find 567 more carbon stars and visually subclassify them. We find that on the J − H versus H − K s two-color diagram, C–N stars can be reliably distinguished from the other three subtypes. Additionally, by utilizing the Gaia distance, we study the distribution of carbon stars in the H-R diagram and identify 258 dwarf carbon stars by the criterion M G > 5.0 mag. Finally, we present the spatial distribution in Galactic coordinates of the 3546 carbon stars. The majority of C–N, C–R, and Ba stars are distributed at low Galactic latitudes, while most C–H and dwarf carbon stars are distributed at high Galactic latitudes.

The Calibration of theta-phi Fiber Positioners Based on the Differential Evolution Algorithm

Robotic fiber positioner (RFP) arrays are commonly adopted in multiobject spectroscopic instruments. The positioning accuracy is a common but vital issue for RFP as inaccurate fiber placement may heavily affect the observation performance. The calibration of RFP can effectively improve the positioning accuracy. Least-square is a widely used calibration method. However, it has disadvantages, such as sensitivity to the initial values and calculation complexity. To improve the positioning accuracy and reduce the iteration moves, we propose a new calibration method based on the differential evolution algorithm and verify it by calibrating the RFP of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope. We first build the kinematic models of the RFP based on the Denavit–Hartenberg matrix and geometry relationship. Then, we analyze the error components and present the proposed calibration algorithms. The experiments are done with the digital universal tool microscope 19JC and the errors are calculated using the distance between the positions of achieved and target. Results show that the proposed algorithm can achieve higher accuracy than the least-square method and the average positioning accuracy is improved by 78.94% after calibration. Combined with the “pulse reduction” strategy and close-loop compensation, after two moves, the positioners can place the fiber ends within 40 μm of the intended location. The proposed calibration method is also suitable for other similar theta-phi positioners.

Antiresonant fiber-enhanced Raman spectroscopy gas sensing with 1 ppm sensitivity.

Antiresonant hollow-core fiber (AR-HCF) exhibits unprecedented optical performance in low transmission attenuation, broad transmission bandwidth, and single spatial mode quality. However, due to its lower numerical aperture, when utilizing the Fiber-Enhanced Raman Spectroscopy (FERS) principle for gas detection, the efficiency of AR-HCF in collecting Raman signals per unit length is significantly lower than that of hollow-core photonic crystal fiber. Nonetheless, AR-HCF effectively suppresses higher-order modes and offers bandwidth in hundreds of nanometers. By increasing the length of AR-HCF, its advantages can be effectively harnessed, leading to a considerable enhancement in the system's ability for low-concentration gas detection. We combine the nodeless antiresonant hollow-core fiber and Raman spectroscopy for enhanced Raman gas sensing in a forward scattering measurement configuration to investigate the attenuation behavior of the silica background signals. The silica background attenuation behavior enables the low baseline of the gas Raman spectroscopy and extends the integration time of the system. In addition, a convenient spatial filtering method is investigated. A multimode fiber with a suitable core diameter was employed to transmit the signal so that the fiber end face plays the role of pinhole, thus filtering the silica signal and reducing the baseline. The natural isotopes 12C16O2, 13C16O2, and 12C18O16O in ambient air can be observed using a 5-meter-long AR-HCF at 1 bar with a laser output power of 1.8 W and an integration time of 300 seconds. Limits of detection have been determined to be 0.5 ppm for 13C16O2 and 1.2 ppm for 12C16O2, which shows that the FERS with AR-HCF has remarkable potential for isotopes and multigas sensing.

C- and L-Bands Wavelength-Tunable Mode-Locked Fiber Laser

We report a single-wavelength tunable mode-locked fiber laser. The single wavelength can be tuned from 1537.49 nm to 1608.06 nm by introducing a Sagnac loop filter. As far as we know, this is the widest single-wavelength tuning range achieved in an erbium-doped mode-locked all-fiber laser based on nonlinear amplifying loop mirror (NALM). The laser’s pulse width changes from 549 fs to 808 fs throughout the tuning process, the maximum average output power is 5.72 mW, and the single-pulse energy is 0.34 nJ at a central wavelength of 1556.53 nm. This laser source can serve as an efficient tool for applications that require a broad tunability range. The combination of femtosecond pulses and extensive wavelength tuning capabilities makes this laser system highly valuable in fields such as fiber optic communications, spectroscopy, sensing, and other applications that benefit from ultrafast and tunable laser sources.

An efficient astronomical seeing forecasting method by random convolutional Kernel transformation

Astronomical seeing is the primary indicator of the observation quality of large-scale optical telescopes. Forecasting astronomical seeing is an essential and challenging task in the operation of large optical telescopes, as it can help observers more efficiently arrange observation tasks as well as providing hints for mechanism optimization of optical telescopes. In this paper, we propose an efficient data-driven astronomical seeing forecasting method by leveraging the environment monitoring data from multiple sensors deployed with optical telescopes. The basic idea is to use a large number of random yet fixed convolution kernels to extract features from the original data, and then train a simple tree-based predictor based on these features. In particular, the raw monitoring data is transformed by differential operations, enabling a more explicit representation of spatial and temporal features; then an elaborate convolution mechanism, including the convolution kernel and the pooling operator, is designed to extract a variety set of features of the input time series, which serve as the foundation for constructing the time series forecasting model. In contrast to existing deep learning models, there is no need to calculate and back-propagate the gradients during the training procedure, making the proposed method much more efficient. We collect real-world monitoring data from Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST), one of the world’s largest optical reflecting telescopes, and conduct extensive empirical evaluations. The experimental results demonstrate that the proposed method can achieve state-of-the-art prediction accuracy, i.e., 0.1125 in MAE, 0.0252 in MSE, and 0.0322 in MAPE, outperforming most deep-learning-based forecasting models. Moreover, it surpasses both machine learning and deep learning approaches in terms of training efficiency with a significant margin.

Determination of Lead in Herbal Medicine by a Lab-on-a-Tip (LOT) Optical Fiber Sensor

A lab-on-a-tip (LOT) has been constructed as a sensing platform for the detection of lead using a disposable pipette tip. The LOT has been constructed with optical fiber spectroscopy where the inside wall of a pipette tip is modified with a polymeric inclusion membrane (PIM) immobilized with dithizone as a chemical sensor for Pb(II) in herbal samples. Under optimal conditions, the LOT determined Pb(II) across a linear concentration range from 1.9 to 20 mg/L with a detection limit of 0.621 mg/L. The analysis of samples shows good agreement with the flame atomic absorption spectroscopy (FAAS).

Intelligent monitoring and diagnosis of telescope image quality

ABSTRACT The imaging quality of a telescope directly affects the reliability of astronomical research. Through the monitoring and diagnosis of imaging quality, the cause of the deterioration of imaging quality can be found in time, which is essential for ensuring the peaking performance of the telescope and high-quality imaging. Moreover, these operations are complex and crucial for achieving high-quality imaging of future giant telescope systems involving active optics, adaptive optics, and other advanced techniques. We propose a three-component method based on cutting-edge artificial intelligence technology to real-time monitor and efficiently diagnose the telescope image quality. The first component, an image quality monitoring system, monitors and outputs the telescope’s image quality. The second component is a query system with a knowledge graph, which outputs the node chains as the possible cause of poor image quality based on the input. The third component, a final estimator, uses the node parameter, which contains historical fault data and real-time updated data from sensors, to give the probability of each node chain. We construct and test the system in the Large Sky Area Multi-Object Fiber Spectroscopy Telescope.

Diffuse Large B cell Lymphoma of the Broca Area presented as a Rare Cause of Progressive Cognitive Impairment in a Middle Aged Woman uncovered with DTI Fiber tracking and Spectroscopy

We present the case of a female patient in her 50s who presented with progressive language and cognitive impairments. The patient had a history of ocular vitreoretinal lymphoma, diagnosed three years prior to admission. Radiological investigations, including diffusion tensor imaging, fibertracking, dynamic contrast-enhanced MRI and spectroscopy were performed to establish a differential diagnosis for a relapsing lymphoma

Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector.

Interstitial fiber-based spectroscopy is gaining interest for real-time in vivo optical biopsies, endoscopic interventions, and local monitoring of therapy. Different from other photonics approaches, time-domain diffuse optical spectroscopy (TD-DOS) can probe the tissue at a few cm distance from the fiber tip and disentangle absorption from the scattering properties. Nevertheless, the signal detected at a short distance from the source is strongly dominated by the photons arriving early at the detector, thus hampering the possibility of resolving late photons, which are rich in information about depth and absorption. To fully benefit from the null-distance approach, a detector with an extremely high dynamic range is required to effectively collect the late photons; the goal of our paper is to test its feasibility to perform TD-DOS measurements at null source-detector separations (NSDS). In particular, we demonstrate the use of a superconducting nanowire single photon detector (SNSPD) to perform TD-DOS at almost NSDS by exploiting the high dynamic range and temporal resolution of the SNSPD to extract late arriving, deep-traveling photons from the burst of early photons. This approach was demonstrated both on Monte Carlo simulations and on phantom measurements, achieving an accuracy in the retrieval of the water spectrum of better than 15%, spanning almost two decades of absorption change in the 700- to 1100-nm range. Additionally, we show that, for interstitial measurements at null source-detector distance, the scattering coefficient has a negligible effect on late photons, easing the retrieval of the absorption coefficient. Utilizing the SNSPD, broadband TD-DOS measurements were performed to successfully retrieve the absorption spectra of the liquid phantoms. Although the SNSPD has certain drawbacks for use in a clinical system, it is an emerging field with research progressing rapidly, and this makes the SNSPD a viable option and a good solution for future research in needle guided time-domain interstitial fiber spectroscopy.

Monitoring stimulus-evoked hemodynamic response during deep brain stimulation with single fiber spectroscopy.

Deep brain stimulation (DBS) is a revolutionary treatment for movement disorders. Measuring DBS-induced hemodynamic responses may be useful for surgical guidance of DBS electrode implantation as well as to study the mechanism and assess therapeutic effects of DBS. In this study, we evaluated the performance of a single fiber spectroscopic (SFS) system for measuring hemodynamic response in different cortical layers in a DBS animal model. We showed that SFS is capable of measuring minute relative changes in oxygen saturation and blood volume fraction in-vivo at a sampling rate of 22-33 Hz. During stimulation, blood volume fraction increased, while oxygen saturation showed both increases and decreases at different cortical depths across animals. In addition, we showed the potential of using SFS for measuring other physiological parameters, for example, heart rate, and respiratory rate.

Infrared Excess of a Large OB Star Sample

The infrared (IR) excess from OB stars is commonly considered to be a contribution from ionized stellar wind or circumstellar dust. With the newly published Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST)-OB catalog and Galactic O-Star Spectroscopic Survey data, this work steps further on understanding the IR excess of OB stars. Based on a forward-modeling approach comparing the spectral slope of observational spectral energy distributions and photospheric models, 1147 stars are found to have IR excess out of 7818 stars with good-quality photometric data. After removing the objects in the sightline of dark clouds, 532 (∼7%) B-type stars and 118 (∼23%) O-type stars are identified to be true OB stars with circumstellar IR excess emission. The ionized stellar wind model and the circumstellar dust model are adopted to explain the IR excess, and Bayes factors are computed to quantitatively compare the two. It is shown that the IR excess can be accounted for by the stellar wind for about 65% cases, of which 33% by free–free emission and 32% by synchrotron radiation. Other 30% sources could have and 4% should have a dust component or other mechanisms to explain the sharp increase in flux at λ > 10 μm. The parameters of the dust model indicate a large-scale circumstellar halo structure, which implies the origin of the dust from the birthplace of the OB stars. A statistical study suggests that the proportion with IR excess in OB stars increases with the stellar effective temperature and luminosity, and that there is no systematic change in the mechanism for IR emission with stellar parameters.

Aperture-corrected spectroscopic type Ia supernova host galaxy properties

We use type Ia supernova (SN Ia) data obtained by the Sloan Digital Sky Survey-II Supernova Survey (SDSS-II SNS) in combination with the publicly available SDSS DR16 fiber spectroscopy of supernova (SN) host galaxies to correlate SN Ia light-curve parameters and Hubble residuals with several host galaxy properties. Fixed-aperture fiber spectroscopy suffers from aperture effects: the fraction of the galaxy covered by the fiber varies depending on its projected size on the sky, and thus measured properties are not representative of the whole galaxy. The advent of integral field spectroscopy has provided a way to correct the missing light, by studying how these galaxy parameters change with the aperture size. Here we study how the standard SN host galaxy relations change once global host galaxy parameters are corrected for aperture effects. We recover previous trends on SN Hubble residuals with host galaxy properties, but we find that discarding objects with poor fiber coverage instead of correcting for aperture loss introduces biases into the sample that affect SN host galaxy relations. The net effect of applying the commonly used g-band fraction criterion is that intrinsically faint SNe Ia in high-mass galaxies are discarded, thus artificially increasing the height of the mass step by 0.02 mag and its significance. Current and next-generation fixed-aperture fiber-spectroscopy surveys, such as OzDES, DESI, or TiDES with 4MOST, that aim to study SN and galaxy correlations must consider, and correct for, these effects.

Compact object candidates with K/M-dwarf companions from LAMOST low-resolution survey

Searching for compact objects (black holes, neutron stars, or white dwarfs) in the Milky Way is essential for understanding the stellar evolution history, the physics of compact objects, and the structure of our Galaxy. Compact objects in binaries with a luminous stellar companion are perfect targets for optical observations. Candidate compact objects can be achieved by monitoring the radial velocities of the companion star. However, most of the spectroscopic telescopes usually obtain stellar spectra at a relatively low efficiency, which makes a sky survey for millions of stars practically impossible. The efficiency of a large-scale spectroscopic survey, the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST), presents a specific opportunity to search for compact object candidates, i.e., simply from the spectroscopic observations. Late-type K/M stars are the most abundant populations in our Galaxy. Owing to the relatively large Keplerian velocities in the close binaries with a K/M-dwarf companion, a hidden compact object could be discovered and followed-up more easily. In this study, compact object candidates with K/M-dwarf companions are investigated with the LAMOST low-resolution stellar spectra. Based on the LAMOST Data Release 5, we obtained a sample of $56$ binaries, each containing a K/M-dwarf with a large radial velocity variation $\Delta V_{\rm R} > 150~{\rm km~s}^{-1}$. Complemented with the photometric information from the Transiting Exoplanet Survey Satellite, we derived a sample of $35$ compact object candidates, among which, the orbital periods of $16$ sources were revealed by the light curves. Considering two sources as examples, we confirmed that a compact object existed in the two systems by fitting the radial velocity curve. This study demonstrates the principle and the power of searching for compact objects through LAMOST.

Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7

The search for special and rare celestial objects has always played an important role in astronomy. Cataclysmic Variables (CVs) are special and rare binary systems with accretion disks. Most CVs are in the quiescent period, and their spectra have the emission lines of Balmer series, HeI, and HeII. A few CVs in the outburst period have the absorption lines of Balmer series. Owing to the scarcity of numbers, expanding the spectral data of CVs is of positive significance for studying the formation of accretion disks and the evolution of binary star system models. At present, the research for astronomical spectra has entered the era of Big Data. The Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) has produced more than tens of millions of spectral data. the latest released LAMOST-DR7 includes 10.6 million low-resolution spectral data in 4926 sky regions, providing ideal data support for searching CV candidates. To process and analyze the massive amounts of spectral data, this study employed the Light Gradient Boosting Machine (LightGBM) algorithm, which is based on the ensemble tree model to automatically conduct the search in LAMOST-DR7. Finally, 225 CV candidates were found and four new CV candidates were verified by SIMBAD and published catalogs. This study also built the Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) models and used Accuracy, Precision, Recall, the F1-score, and the ROC curve to compare the four models comprehensively. Experimental results showed that LightGBM is more efficient. The search for CVs based on LightGBM not only enriches the existing CV spectral library, but also provides a reference for the data mining of other rare celestial objects in massive spectral data.