National Security Applications Research Articles

High-precision inversion of dynamic radiography using hydrodynamic features.

While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science to shock physics to inertial confinement fusion and other national security applications, complications resulting from noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing density from being accurate enough to identify the underlying physics with sufficient confidence. In this work, we show that using only features that are robustly identifiable in radiographs and combining them with the underlying hydrodynamic equations of motion using a machine learning approach of a conditional generative adversarial network (cGAN) provides a new and effective approach to determine density fields from a dynamic sequence of radiographs. In particular, we demonstrate the ability of this method to outperform a traditional, direct radiograph to density reconstruction in the presence of scatter, even when relatively small amounts of scatter are present. Our experiments on synthetic data show that the approach can produce high quality, robust reconstructions. We also show that the distance (in feature space) between a testing radiograph and the training set can serve as a diagnostic of the accuracy of the reconstruction.

Ab initio Cu alloy design for high-gradient accelerating structures

Operation of normal conducting accelerator structures at high accelerating gradients is beneficial for many accelerator applications in basic science, industry, medicine, and National Security. RF breakdown is the major factor that limits the achievable accelerating gradients. Previous experiments on copper (Cu) have demonstrated that RF breakdown probability can be significantly decreased by hardening the material and alloying Cu with solutes such as silver (Ag). In this paper, we propose a figure-of-merit (FOM) that characterizes the ability of Cu alloys to withstand high-gradients. The FOM represents a trade-off between hardening through solid solution strengthening and the additional thermal stress induced by incremental RF pulse heating resulting from changes in electronic properties induced by alloying. We performed high-throughput ab initio calculations and computed the FOM for a large number of binary Cu alloys. Several promising candidate alloys for high-gradient accelerating structures were identified, such as CuAg, CuCd, CuHg, CuAu, CuIn, and CuMg. CuAg alloys have previously exhibited low RF breakdown rates in experiments. The results provide guidance for selecting alloys for the future high-gradient normal conducting accelerating structures operating at very high gradients.

Assessment of image reconstruction algorithm coupled with fine-resolution array of Cherenkov detectors

The ability to reconstruct fine-resolution images in a high-count-rate environment is an ongoing challenge to the fields of nuclear security, medicine, and high energy physics. This study presents the characterization and performance of an image reconstruction algorithm and detector array in such an environment. The detector array is composed of quartz Cherenkov radiators and lutetium–yttrium oxyorthosilicate inorganic scintillators detector elements with light collection via silicon photomultipliers (SiPM). The reconstruction algorithm was evaluated using ANSI testing standard N42.46-2008 for imaging performance of active interrogation systems for national security applications; this included spatial resolution, wire detection, and penetration studies. The array was tested using a 6-MVp pulsed photon beam where test objects were translated through the detector field of view demonstrating a capability to resolve a 2.05-mm wire at a source standoff of 2.2 m, a horizontal spatial resolution of 3 mm, and a contrast sensitivity of 1.5%.

Compact cyclotron resonance high-power accelerator for electrons

Exact numerical solutions for the single particle equations of motion have revealed conditions for highly nonlinear rapid acceleration near cyclotron resonance for electrons injected into a ${\mathrm{TE}}_{111}$-rotating-mode cylindrical microwave cavity immersed in a uniform axial magnetic field. We dub this the eCRA interaction. Magnitudes of acceleration energy in eCRA are shown to exceed to a large degree the limits for the related cyclotron autoresonance acceleration (CARA) interaction, wherein autoresonance acceleration is sustained for traveling rotating ${\mathrm{TE}}_{11}$-mode waves in a cylindrical waveguide. As with CARA, all injected electrons in an idealized eCRA enjoy equal energy gain without bunching. Injection of high currents that involve heavy beam loading allow acceleration in eCRA to multi-MeV levels for beams with average powers of hundreds of kW and rf-to-beam power efficiencies that exceed 80%. It is shown, to cite one example, that an effective acceleration gradient of over $90\text{ }\text{ }\mathrm{MV}/\mathrm{m}$ can be sustained with a maximum cavity surface field of only $40\text{ }\text{ }\mathrm{MV}/\mathrm{m}$, when producing a 4.5 MeV, 300 kW average power electron beam, with an rf-to-beam efficiency of about 86%. In that example, the cavity operates at 2.856 GHz, and the cavity's average surface heating rate is $100\text{ }\text{ }\mathrm{W}/{\mathrm{cm}}^{2}$. Other examples are given for beams with over one MW levels of average power and energies up to about 20 MeV. This paper's goal is only to elucidate and give examples of the basic mechanism for the strongly nonlinear acceleration that is predicted to occur in eCRA, rather than to present a particular engineered design. Still, the predicted parameters for an idealized eCRA suggest that practical realizations could emerge to satisfy a range of needs for efficient, compact accelerators for industrial, commercial, and national security applications.

Efficient Prompt Scintillation and Fast Neutron-Gamma Ray Discrimination Using Amorphous Blends of Difluorenylsilane Organic Glass and In Situ Polymerized Vinyltoluene

High-performance radiation detection materials are an integral part of national security, medical imaging, and nuclear physics applications. Those that offer compositional and manufacturing versatility are of particular interest. Here, we report a new family of radiological particle-discriminating scintillators containing bis(9,9-dimethyl-9H-fluoren-2-yl)diphe-nylsilane (compound “P2”) and in situ polymerized vinyltoluene (PVT) that is phase stable and mechanically robust at any blend ratio. The gamma-ray light yield increases nearly linearly across the composition range, to ~16 400 photons/MeV at 75 wt.% P2. These materials are also capable of performing γ/n pulse shape discrimination (PSD), and between 20% and 50% P2 loading is competitive with the PSD quality of commercially available plastic scintillators. The <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">137</sup> Cs scintillation rise and decay times are sensitive to P2 loading and approach the values for “pure” P2. Additionally, the radiation detection performance of P2-PVT blends can be made stable in 60 °C air for at least 1.5 months with the application of a thin film of poly(vinylalcohol) to the scintillator surfaces.

Characterization of 304L laser welds using digital image correlation and x-ray computed tomography

304L stainless steel partial-penetration laser welds with varying penetration depths are needed for a variety of national security applications. Their mechanical performance depends on many parameters including weld schedule, penetration depth, and weld porosity. Unfortunately, these parameters are not easily divorced from one another as each has varying impacts upon the others. Therefore, an understanding of the interplay between these parameters as well as their impact on weld performance is rather useful. In this work, partial penetration laser welds are produced with four varying penetration depths at two different weld schedules. Mechanical testing is conducted to characterize the mechanical performance while full-field digital image correlation (DIC) is applied during testing to obtain the displacement field down to the micron-scale to better understand both global and local deformation behavior. Porosity of these welds is also investigated using x-ray computed tomography (XCT) prior to mechanical testing. The integration of DIC and XCT techniques with mechanical testing provides an expanded experimental dataset to evaluate the variations in mechanical response as a function of penetration depth, weld schedule and porosity. These experimental techniques also provide a promising approach for future study of laser performance.

(Invited) Fieldable Optical Biosensors with Integrated Sample Processing for Universal Surveillance and Diagnostics – Application to COVID-19

There is an urgent need for broad-based surveillance and diagnostic strategies that can be applied quickly and effectively to address emerging pathogens, and preferably at the point of need, as is evident with the current covid-19 crisis. Our innate immune system is an example of such a sensor – a recognition system that is capable of identifying all pathogens quickly and sensitively. Conserved pathogen associated molecular patterns (PAMPs) produced by the infectious agents are recognized by immune receptors (e.g. Toll like Receptors, TLRs) in an exquisite scheme of pattern recognition, culminating in universal early identification pipeline. Mimicking innate immune recognition in the laboratory can therefore provide a strategy for universal diagnosis of infection, and this has been the focus of our research for over a decade.There are three objectives to be addressed in order to develop a universal diagnostic platform based on innate immune recognition. They are: 1) understanding host-pathogen biology, and the development of tailored assays to address biochemically disparate pathogen associated molecular patterns in complex clinical samples, 2) development of multiplex detection assays and ultra-sensitive sensor platforms for their detection, 3) field adaptation of sensors and sample processing and 4) validation in blinded clinical samples. Herein, we present the strategy and progress in each of these areas of development, as outlined below.In bacterial pathogens, pathogen signatures recognized by our innate immune receptors are typically amphiphilic in biochemistry – lipoproteins or glycolipids. These lipidic molecules are intrinsically unstable in the aqueous milieu of blood, making them difficult to detect. Work from our team lead to the understanding that all bacterial amphiphilic pathogen associated molecular patterns associate with host lipoprotein carriers, which function as a biological taxi service for their transport in blood. Understanding this, we developed tailored sample processing pipeline and assays (membrane insertion and lipoprotein capture) for the detection of amphiphiles in serum samples.In viral pathogens, the pathogen signatures recognized by our innate immune receptors are nucleic acids – DNA and RNA. We developed a bioinformatic pipeline – FEVER- to capture the conserved aspects of these signatures and develop our recognition probes to target these pathogens, and then developed amplification-free assays for their detection using molecular beacon technology.Assays developed for both bacterial and viral signatures were applied to a novel, fieldable waveguide-based optical biosensor platform developed by our team. This platform uses single mode planar optical waveguides for bio-detection within an evanescent field, offering much greater sensitivity (10X or higher) than conventional immunoassay platforms. This fieldable sensor (<8Lbs) with integrated phone based controls allows for bio-detection in the field, as is required for surveillance technologies. We have developed a multiplex detection pipeline on this platform using photostable and tunable quantum dots as the fluorescence reporters – allowing for the simultaneous discriminatory detection of said signatures. This novel patented approach allows for the use of conventional platforms, in addition to our biosensor for such interrogations. For adaptation of the method to field detection, we developed the first ever microfluidic lipid and nucleic acid separation system – which allows for 5 min disposable separation of signatures from blood in the field. The performance of this platform has been validated for the detection of biomarkers not only in laboratory samples, but also in complex clinical samples from high-disease burden clinical cohorts for a variety of diseases such as COVID-19, tuberculosis, bacterial sepsis, food-borne diseases as well as biosecurity and national security applications. Figure: Schematic representing the fieldable universal diagnostic platform and approach. Blood is a universal sample – where all pathogens are recognized by innate immune receptors. Blood is collected and processed on site using a microfluidics sample processing cassette for biomarker extraction. The presence of the biomarker is then measured using novel tailored assays on a fieldable portable waveguide biosensor offering an answer within 15 min. Figure 1

New approach to precisely measure [formula omitted]-ray intensities for long-lived fission products, with results for the decay of 95Zr

For many fission products, the γ rays emitted following β decay provide an easily-detectable signature that can be used to identify their quantities and distributions in a sample. As a result, γ-ray spectroscopy is often exploited to study fission-product yields, provided sufficiently accurate information on the γ-ray intensity is available. However, in many cases, the uncertainties in the existing nuclear data are large enough that they compromise the precision achievable for modern experiments and applications. To address this need, we have developed a new experimental method that is well suited to precisely measure absolute γ-ray intensities in the β decay of long-lived fission products. The approach involves the production of a radiopure sample by implantation of a mass-separated ion beam from the CAlifornium Rare Isotope Breeder Upgrade (CARIBU) facility on a thin carbon foil. The emitted β-decay radiation is detected with a 4π gas proportional counter and a meticulously efficiency-calibrated high-purity germanium (HPGe) detector. As a first measurement to demonstrate the approach, we studied the absolute γ-ray intensities of the strongest transitions following the β decay of 95Zr and its decay-daughter 95Nb, and determined them to fractional precisions of better than 1–2%. In addition, with a larger sample of activity produced through neutron irradiation of an isotopically-enriched Zr foil, we performed a high-precision measurement of the relative γ-ray intensities following the decay of 95Zr with just the HPGe detector. The sample-production method at CARIBU and the coincidence detection approach demonstrated here can be applied to study fission products with half-lives longer than a day, which includes isotopes important not only for nuclear-energy and national-security applications, but also for medical-isotope research and environmental monitoring.

Proton-induced reactions on Fe, Cu, and Ti from threshold to 55 MeV

Theoretical models often differ significantly from measured data in their predictions of the magnitude of nuclear reactions that produce radionuclides for medical, research, and national security applications. In this paper, we compare a priori predictions from several state-of-the-art reaction modeling packages (CoH, EMPIRE, TALYS, and ALICE) to cross sections measured using the stacked-target activation method. The experiment was performed using the Lawrence Berkeley National Laboratory 88-Inch Cyclotron with beams of 25 and 55 MeV protons on a stack of iron, copper, and titanium foils. Thirty-four excitation functions were measured from 4–55 MeV, including the first measurement of the independent cross sections for ^{mathrm{nat}}hbox {Fe}(p,x)^{49,51}hbox {Cr}, ^{51,{mathrm{52m}},{mathrm{52g}},56}hbox {Mn}, and ^{{mathrm{58m,58g}}}hbox {Co}. All of the models, using default input parameters to assess their predictive capabilities, failed to reproduce the isomer-to-ground state ratio for reaction channels at compound and pre-compound energies, suggesting issues in modeling the deposition or distribution of angular momentum in these residual nuclei.

A Review on Comparative Analysis on Different Sort of Physiological and Behavioral Biometric Framework

Biometrics as the investigation of seeing an individual ward on their physical or conduct characteristics, biometric have now been conveyed in diverse business, ordinary resident and national security applications. Customarily the usage of biometrics devices has improved our capacity to give approved entry to material foundations. Biometric is the usage of a person's novel physiological, lead, and morphological trademark to give valuable person distinguishing proof. Biometric structures that are starting at now available today break down fingerprints, engravings, iris and retina models, and face. Mechanisms that are similar to biometrics anyway are not named such are lead systems, for instance, voice, imprint and keystroke mechanisms. These days biometrics is in effect effectively executed in numerous fields like measurable, security, recognizable proof and approval frameworks.

ShipRSImageNet: A Large-Scale Fine-Grained Dataset for Ship Detection in High-Resolution Optical Remote Sensing Images

Ship detection in optical remote sensing images has potential applications in national maritime security, fishing, and defense. Many detectors, including computer vision and geoscience-based methods, have been proposed in the past decade. Recently, deep-learning-based algorithms have also achieved great success in the field of ship detection. However, most of the existing detectors face difficulties in complex environments, small ship detection, and fine-grained ship classification. One reason is that existing datasets have shortcomings in terms of the inadequate number of images, few ship categories, image diversity, and insufficient variations. This article publishes a public ship detection dataset, namely ShipRSImageNet, which contributes an accurately labeled dataset in different scenes with variant categories and image sources. The proposed ShipRSImageNet contains over 3435 images with 17 573 ship instances in 50 categories, elaborately annotated with both horizontal and orientated bounding boxes by experts. From our knowledge, up to now, the proposed ShipRSImageNet is the largest remote sensing dataset for ship detection. Moreover, several state-of-the-art detection algorithms are evaluated on our proposed ShipRSImageNet dataset to give a benchmark for deep-learning-based ship detection methods, which is valuable for assessing algorithm improvement. 1

High-Performance Vertical Organic Phototransistors Enhanced by Ferroelectrics

Organic phototransistors with high sensitivity and responsivity to light irradiance have great potential applications in national defense, meteorology, industrial manufacturing, and medical security. However, undesired dark current and photoresponsivity limit their practical applications. Here, a novel vertical organic phototransistor combined with ferroelectric materials is developed. The device structure has nanometer channel length, which can effectively separate photogenerated carriers and reduce the probability of carrier recombination and defect scattering, thus improving the device performance of phototransistors. Moreover, by inserting the poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) ferroelectric layer, the Schottky barrier at the interface between the semiconductor and source can be adjusted by the polarization of the external electric field, which can effectively reduce the dark current of the phototransistor to further improve the device performance. Therefore, our phototransistors exhibit a high photoresponsivity of more than 5.7 × 105A/W, an outstanding detectivity of 1.15 × 1018 Jones, and an excellent photosensitivity of 5 × 107 under 760 nm light illumination, which are better than those of conventional lateral organic phototransistors. This work provides a new approach for the development of high-performance phototransistors, which opens a new pathway for organic phototransistors in practical application.

Dense Seismic Array Study of a Legacy Underground Nuclear Test at the Nevada National Security Site

ABSTRACTThe complex postdetonation geologic structures that form after an underground nuclear explosion are hard to constrain because increased heterogeneity around the damage zone affects seismic waves that propagate through the explosion site. Generally, a vertical rubble-filled structure known as a chimney is formed after an underground nuclear explosion that is composed of debris that falls into the subsurface cavity generated by the explosion. Compared with chimneys that collapse fully, leaving a surface crater, partially collapsed chimneys can have remnant subsurface cavities left in place above collapsed rubble. The 1964 nuclear test HADDOCK, conducted at the Nevada test site (now the Nevada National Security Site), formed a partially collapsed chimney with no surface crater. Understanding the subsurface structure of these features has significant national security applications, such as aiding the study of suspected underground nuclear explosions under a treaty verification. In this study, we investigated the subsurface architecture of the HADDOCK legacy nuclear test using hybrid 2D–3D active source seismic reflection and refraction data. The seismic data were acquired using 275 survey shots from the Seismic Hammer (a 13,000 kg weight drop) and 65 survey shots from a smaller accelerated weight drop, both recorded by ∼1000 three-component 5 Hz geophones. First-arrival, P-wave tomographic modeling shows a low-velocity anomaly at ∼200 m depth, likely an air-filled cavity caused by partial collapse of the rock column into the temporary postdetonation cavity. A high-velocity anomaly between 20 and 60 m depth represents spall-related compaction of the shallow alluvium. Hints of low velocities are also present near the burial depth (∼364 m). The reflection seismic data show a prominent subhorizontal reflector at ∼300 m depth, a short-curved reflector at ∼200 m, and a high-amplitude reflector at ∼50 m depth. Comparisons of the reflection sections to synthetic data and borehole stratigraphy suggest that these features correspond to the alluvium–tuff contact, the partial collapse cavity, and the spalled layer, respectively.

Mitigation of beam losses in LANSCE linear accelerator

The LANSCE accelerator has been in operation for nearly five decades. During this period, it was transformed from a 0.8 MW average proton beam power machine used primarily for the study of meson physics, to a multi-user high-power facility for fundamental research and national security applications, concurrently delivering beams to five distinct experimental areas. Minimization of beam losses in multi-beam application is a key issue for effective operation of accelerator facility. This paper summarizes recent experimental results in understanding and lowering of beam losses in LANSCE linear accelerator and high-energy beam transport lines.

Thermal Characterization of Tl₂LiYCl₆:Ce (TLYC)

Tl$_2$LiYCl$_6$:Ce (TLYC) is a new dual-detection elpasolite scintillator that can detect and distinguish between gamma rays and neutrons using pulse-shape discrimination (PSD). It has a higher density and Z-number than the more mature and well-known elpasolite Cs$_2$LiYCl$_6$:Ce (CLYC), causing it to have a significantly better gamma-ray stopping power. These properties make TLYC an attractive alternative to CLYC for resource-constrained applications where size and weight are important, such as space or national security applications. Such applications may be subjected to a wide range of temperatures, and therefore TLYC's performance was characterized for the first time over a temperature range of -20$^{\circ}$C to +50$^{\circ}$C in 10$^{\circ}$C increments. TLYC's thermal response effects on light-output linearity with energy, gamma-ray photopeak energy resolution, detected neutron energy, pulse shapes, and figure of merit is analyzed and reported. The light output of TLYC was found to be linear with energy over the tested temperature range and was observed to decrease with increasing temperature. The decay time of the scintillation light output was observed to decrease with decreasing temperature at short times, leading to a decreasing PSD figure of merit. The gamma-ray photopeak energy resolution was also observed to degrade with decreasing temperature, due to an asymmetric broadening of the photopeak at low temperatures.

Toward short-lived and energy-dependent fission product yields from neutron-induced fission

Fission product yields (FPYs) are an important source of information that are used for basic and applied physics. They are essential observables to address questions relevant to nucleosynthesis in the cosmos that created the elements from iron to uranium, for example, in energy generating processes from fission recycling in binary neutron star mergers; resolving the reactor neutrino anomaly; decay heat release in nuclear reactors; and many national security applications. While new applications will require accurate energy-dependent FPY data over a broad set of incident neutron energies, the current evaluated FPY data files contain only three energy points: thermal, fast, and 14-MeV incident energies.Recent measurements using mono-energetic and pulsed neutron beams at the Triangle Universities Nuclear Laboratory (TUNL) tandem accelerator and employing a dual fission ionization chambers setup have produced self-consistent, high-precision data critical for testing fission models for the neutron-induced fission of the major actinide nuclei. This paper will present new campaign just beginning utilizing a RApid Belt-driven Irradiated Target Transfer System (RABITTS) to measure shorter-lived fission products and the time dependence of fission yields, expanding the measurements from cumulative towards independent fission yields.

A Guide for Private Outlier Analysis.

The increasing societal demand for data privacy has led researchers to develop methods to preserve privacy in data analysis. However, outlier analysis, a fundamental data analytics task with critical applications in medicine, finance, and national security, has only been analyzed for a few specialized cases of data privacy. This work is the first to provide a general framework for private outlier analysis, which is a two-step process. First, we show how to identify the relevant problem-specifications and then provide a practical solution that formally meets these specifications.

Proton Light Yield of Fast Plastic Scintillators for Neutron Imaging

Plastic organic scintillators have been tailored in composition to achieve ultra-fast temporal response, thereby enabling the design and development of fast neutron detection systems with high timing resolution. Eljen Technology's plastic organic scintillators -- EJ-230, EJ-232, and EJ-232Q -- are prospective candidates for use in emerging neutron imaging systems, where fast timing is paramount. To support the neutron response characterization of these materials, the relative proton light yields of EJ-230, EJ-232, and EJ-232Q were measured at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. Using a broad-spectrum neutron source and a double time-of-flight technique, the proton light yield relations were obtained over a proton recoil energy range of approximately 300 keV to 4 MeV. The EJ-230, EJ-232, and EJ-232Q scintillators exhibited similar proton light yield relations to each other as well as to other plastic scintillators with the same polymer base material. A comparison of the relative proton light yield of different sized cylindrical EJ-232 and EJ-232Q scintillators also revealed consistent results. This work provides key input data for the realistic computational modeling of neutron detection technologies employing these materials, thereby supporting new capabilities in near-field radionuclide detection for national security applications.

One‐Step Nanoextraction and Ultrafast Microanalysis Based on Nanodroplet Formation in an Evaporating Ternary Liquid Microfilm

AbstractPreconcentration is key for detection from an extremely low concentration solution, but requires separation steps from a large volume of samples using extracting solvents. Here, a simple approach is presented for ultrafast and sensitive microanalysis from a tiny volume of aqueous solutions. In this approach, liquid–liquid nanoextraction in an evaporating thin liquid film on a spinning substrate is coupled with quantitative analysis in one step. The approach is exemplified using a liquid mixture comprising a target compound to be analyzed in water, mixed with extractant oil and co‐solvent ethanol. With rapid evaporation of ethanol, nanodroplets of oil form spontaneously in the film. The compounds are highly concentrated by liquid evaporation and meanwhile extracted to nanodroplets. A detection limit of nanomolar to picomolar is demonstrated for fluorescent model compounds in only ≈5 µL of solution with the entire process taking ≈10 s. The combination of nanoextraction and infrared microscopy also enables simultaneous chemical identification. The dynamics of thin film evaporation are revealed using fast imaging. The principle behind this approach is general, providing a powerful technique for fast and sensitive chemical analysis of a vast library of compounds for environment monitoring, national security, early diagnosis, and many other applications.

Optimal Accuracy Zone Identification in Object Detection Technique – A Learning Rate Methodology

In the recent past, Deep Learning models [1] are predominantly being used in Object Detection algorithms due to their accurate Image Recognition capability. These models extract features from the input images and videos [2] for identification of objects present in them. Various applications of these models include Image Processing, Video analysis, Speech Recognition, Biomedical Image Analysis, Biometric Recognition, Iris Recognition, National Security applications, Cyber Security, Natural Language Processing [3], Weather Forecasting applications, Renewable Energy Generation Scheduling etc. These models utilize the concept of Convolutional Neural Network (CNN) [3], which constitutes several layers of artificial neurons. The accuracy of Deep Learning models [1] depends on various parameters such as ‘Learning-rate’, ‘Training batch size’, ‘Validation batch size’, ‘Activation Function’, ‘Drop-out rate’ etc. These parameters are known as Hyper-Parameters. Object detection accuracy depends on selection of Hyper-parameters and these in-turn decides the optimum accuracy. Hence, finding the best values for these parameters is a challenging task. Fine-Tuning is a process used for selection of a suitable Hyper-Parameter value for improvement of object detection accuracy. Selection of an inappropriate Hyper-Parameter value, leads to Over-Fitting or Under-Fitting of data. Over-Fitting is a case, when training data is larger than the required, which results in learning noise and inaccurate object detection. Under-fitting is a case, when the model is unable to capture the trend of the data and which leads to more erroneous results in testing or training data. In this paper, a balance between Over-fitting and Under-fitting is achieved by varying the ‘Learning rate’ of various Deep Learning models. Four Deep Learning Models such as VGG16, VGG19, InceptionV3 and Xception are considered in this paper for analysis purpose. The best zone of Learning-rate for each model, in respect of maximum Object Detection accuracy, is analyzed. In this paper a dataset of 70 object classes is taken and the prediction accuracy is analyzed by changing the ‘Learning-rate’ and keeping the rest of the Hyper-Parameters constant. This paper mainly concentrates on the impact of ‘Learning-rate’ on accuracy and identifies an optimum accuracy zone in Object Detection.