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DOE OSTI2026

Optimal filtering and generation of entangled photons for quantum applications in the presence of noise

Filtering is commonly used in quantum optics to reject noise photons, and also to enable interference between independent photons. However, filtering the joint spectrum of photon pairs can reduce the inherent coincidence probability or loss-independent heralding efficiency. Here we investigate filtering for multiphoton applications based on entanglement and interference (e.g., quantum teleportation). We multiplex C-band entangled photons and C-band classical communications into the same long-distance fibers, which enables scalable low-loss quantum networking but requires filtering of spontaneous Raman scattering noise from classical light. Using tunable-bandwidth filters, low-jitter detectors, and polarization filters, we distribute wavelength-degenerate time-bin entangled photons in the C band (1536.5 nm) over 25 km/25 km standard fibers (50 km) each carrying 10 Gbit/s C-band classical data signals. Narrow filtering enables milliwatt-level C-band power, which exceeds the power achieved by comparable studies by roughly an order of magnitude and could feasibly support terabit per second classical rates. We evaluate how performance depends on pump and filter bandwidths, multipair emission, filter shapes, loss, phase matching, and how quantum information is measured. We find a trade-off between reducing noise impact and increasing single-mode purity, and discuss mitigation methods toward optimal multiphoton applications. Importantly, these results apply to noise in free space and in quantum devices (sources, frequency converters, switches, detectors, etc.) and provide insight into filter-induced degradation of single-photon purity and rates even in noise-free environments.

Thomas, Jordan M. [Fermilab; Northwestern U.]↗

DOE OSTI2023

A Deep Learning Filter for the Intraseasonal Variability of the Tropics

Abstract This paper presents a novel application of convolutional neural network (CNN) models for filtering the intraseasonal variability of the tropical atmosphere. In this deep learning filter, two convolutional layers are applied sequentially in a supervised machine learning framework to extract the intraseasonal signal from the total daily anomalies. The CNN-based filter can be tailored for each field similarly to fast Fourier transform filtering methods. When applied to two different fields (zonal wind stress and outgoing longwave radiation), the index of agreement between the filtered signal obtained using the CNN-based filter and a conventional weight-based filter is between 95% and 99%. The advantage of the CNN-based filter over the conventional filters is its applicability to time series with the length comparable to the period of the signal being extracted. Significance Statement This study proposes a new method for discovering hidden connections in data representative of tropical atmosphere variability. The method makes use of an artificial intelligence (AI) algorithm that combines a mathematical operation known as convolution with a mathematical model built to reflect the behavior of the human brain known as artificial neural network. Our results show that the filtered data produced by the AI-based method are consistent with the results obtained using conventional mathematical algorithms. The advantage of the AI-based method is that it can be applied to cases for which the conventional methods have limitations, such as forecast (hindcast) data or real-time monitoring of tropical variability in the 20–100-day range.

Stan, Cristiana↗

DOE OSTI2022

Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

Abstract. Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts that are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability of our proposed RFR algorithm, we trained and tested it on a dataset of laboratory measurements of combustion aerosols. Input variables to the algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based Tricolor Absorption Photometer, and scattering coefficients from a multiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning approaches offer a promising path to correct for biases in long-term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A Sub-Threshold Low-Power Integrated Bandpass Filter for Highly-Integrated Spectrum Analyzers

Low-power analog filter banks provide frequency analysis with minimal power and space requirements, making them viable solutions for integrated remote audio- and vibration-sensing applications. Compared with their digital counterparts, analog filter banks are better suited to achieve the lower power consumption necessary for IoT applications, while maintaining high dynamic range and a wide operational frequency range. In this work, the design and implementation of a sub-threshold complementary metal-oxide semiconductor (CMOS) integrated low-power tunable analog filter channel for signal spectrum analysis and signal discrimination is presented. Project specifications required a programmable, high-order, monolithic bandpass filter channel with small chip area, low power consumption, high dynamic range, and wide operational frequency range. The 8th-order filter channel presented in this work provides an effective Q-factor of 4.5 and a minimum dynamic range (DR) of approximately 85 dB, while allowing for tuning across a range of center frequencies from 2 kHz to 100 kHz, with power consumption of a single 8th-order filter channel measured at 256 µW nominally at the highest center frequency. An integrated analog Gm-C filter topology was selected for this application. Functionally, the high-Q bandpass filter transfer function is implemented via four cascaded 2nd-order filter cells, resulting in a single 8th-order filter channel, fabricated in 130-nm 1.2-V CMOS technology, suitable for use in monolithic integrated spectral analysis (MISA1) applications.

Long, Gavin B.↗

DOE OSTI2024

Epoxy-based infrared filter assembly and associated fabrication devices and method

Epoxy-based inline infrared (IR) filter assembly, and manufacture and use of the same. Co-axial infrared filter assemblies comprise a substantially cylindrical filter body forming a central cavity characterized by opposing holes at each end. The filter body forms an outer conductor, and SMA connectors coupled to the opposing holes at each end of the body are electrically coupled to form an inner conductor positioned along a long axis of the filter body. An infrared absorbing material (such as castable epoxy resin) fills the central cavity of the filter body. Methods for producing the co-axial infrared filter include pressing SMA connectors into the respective ends of the filter body, electrically coupling the SMA connectors, and filling the filter body with epoxy. Electronic systems for operating a dark matter detector include a feedline comprising a coaxial filter configured to advantageously block infrared noise.

Spahn, Gabriel↗

DOE OSTI2022

Radiological HEPA Filter 10-year Lifetime Evaluation in Research Facilities

High-efficiency particulate air (HEPA) filters are widely employed by nuclear facilities to remove radiological particulate matter from their effluent exhaust streams. The purpose of this study is to evaluate the relationships between the 10-year HEPA filter lifetime deployment and its other performance indicators. This 10-year-long endeavor to collect and analyze data regarding the service life of HEPA filters at the Pacific Northwest National Laboratory began in 2010. A set of HEPA filters were selected and have been surveyed and analyzed at least annually to verify compliance with permit conditions. The study suggests the frequency of filter replacement should be based on the actual operational requirements, such as fume hood face velocity and/or efficiency test results, instead of on the prescribed filter “age limit” of 10 years from the date of manufacture (e.g., birth date) when operating under dry conditions. The study has now been completed, and over the past decade all the HEPA filters have been replaced, due to either technical issues as listed in this report or the previously recommended filter “age limit” of 10 years as prescribed by the oversight bodies. Experimentally determined failure rates are also determined from the data set and can be used to estimate the chances of HEPA filters surviving 15, 20, or even 30 years.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2025

In-orbit operation of Resolve Filter Wheel and modulated X-ray source

The Resolve soft X-ray spectrometer is a high spectral resolution microcalorimeter spectrometer for the X-ray Imaging and Spectroscopy Mission. In the beam of Resolve, there is a filter wheel containing X-ray filters. In the beam, there is also an active calibration source, the modulated X-ray source (MXS), which can provide pulsed X-rays to facilitate gain calibration. The filter wheel consists of six filter positions. Two open positions, one 55 Fe source to aid in spectrometer characterization during the commissioning phase, and three transmission filters: a neutral density filter, an optical blocking filter, and a beryllium filter. The X-ray intensity, pulse period, and pulse separation of an MXS are highly configurable. Furthermore, the switch-on time is synchronized with the spacecraft’s internal clock to give accurate start and end times of the pulses. One of the issues raised during ground testing was the susceptibility of an MXS at high voltage to ambient light. Although measures were taken to mitigate the light leak, the efficacy of those measures must be verified in orbit. Along with an overview of issues raised during ground testing, we will discuss the calibration source and the filter performance in-flight and compare with the transmission curves present in the Resolve calibration database.

X-ray Imaging and Spectroscopy Mission/Resolve↗

DOE OSTI2024

The Role of Data Filtering in Open Source Software Ranking and Selection

Faced with more than 100M open source projects, a more manageable small subset is needed for most empirical investigations. More than half of the research papers in leading venues investigated filtering projects by some measure of popularity with explicit or implicit arguments that unpopular projects are not of interest, may not even represent "real" software projects, or that less popular projects are not worthy of study. However, such filtering may have enormous effects on the results of the studies if and precisely because the sought-out response or prediction is in any way related to the filtering criteria.This paper exemplifies the impact of this common practice on research outcomes, specifically how filtering of software projects on GitHub based on inherent characteristics affects the assessment of their popularity. Using a dataset of over 100,000 repositories, we used multiple regression to model the number of stars -a commonly used proxy for popularity- based on factors such as the number of commits, the duration of the project, the number of authors and the number of core developers. Our control model included the entire dataset, while a second filtered model considered only projects with ten or more authors. The results indicated that while certain characteristics of the repository consistently predict popularity, the filtering process significantly alters the relationships between these characteristics and the response. We found that the number of commits exhibited a positive correlation with popularity in the control sample but showed a negative correlation in the filtered sample. These findings highlight the potential biases introduced by data filtering and emphasize the need for careful sample selection in empirical research of mining software repositories. We recommend that empirical work should either analyze complete datasets such as World of Code, or employ stratified random sampling from a complete dataset to ensure that filtering is not biasing the results.

Malviya Thakur, Addi↗

DOE OSTI2025

A novel methodology for assessing the hygroscopicity of aerosol filter samples

Abstract. Due to US regulations, concentrations of hygroscopic inorganic sulfate and nitrate have declined in recent years, leading to an increased importance of the hygroscopic nature of organic matter (OM). The hygroscopicity of OM is poorly characterized because only a fraction of the multitude of organic compounds in the atmosphere is readily measured, and there is limited information on their hygroscopic behaviors. Hygroscopicity of aerosol is traditionally measured using a humidified tandem differential mobility analyzer (HTDMA) or electrodynamic balance (EDB). EDB measures water uptake by a single particle. For ambient and chamber studies, HTDMA measurements provide water uptake and particle size information but not chemical composition. To fill this information gap, we developed a novel methodology to assess the water uptake by particles collected on Teflon filters. This method uses the same filter sample for both hygroscopicity measurements and chemical characterization, thereby providing an opportunity to link the measured hygroscopicity with ambient particle composition. To test the method, hygroscopic measurements were conducted in the laboratory for ammonium sulfate, sodium chloride, glucose, and malonic acid, which were collected on 25 mm Teflon filters using an aerosol generator and sampler. Constant-humidity solutions (CHSs), including potassium chloride, barium chloride dihydrate, and potassium sulfate, were employed in a saturated form to maintain the relative humidity (RH) at approximately 84 %, 90 %, and 97 % in small chambers. Our preliminary experiments revealed that, without the pouch, water uptake measurements were not feasible due to rapid water loss during weighing. Additionally, we observed some absorption by the aluminum pouch itself. To account for this, concurrent measurements were conducted for both the loaded and the blank filters at each RH level. Thus, the dry loaded and blank Teflon filters were placed in aluminum pouches with one side open and in RH-controlled chambers for more than 24 h. The wet loaded samples and wet blanks were then weighed using an ultramicrobalance to determine the water uptake by the respective compound and the blank Teflon filter. The net amount of water absorbed by each compound was calculated by subtracting the water uptake of the blank filter from that of the wet loaded filter. Hygroscopic parameters, including the water-to-solute (W / S) ratio, molality, mass fraction solute (mfs), and growth factors (GFs), were calculated from the measurements. The results obtained are consistent with those reported by the Extended Aerosol Inorganics Model (E-AIM) and previous studies utilizing HTDMA and EDB for these compounds, highlighting the accuracy of this new methodology. This new approach enables the hygroscopicity and chemical composition of individual filter samples to be assessed so that in complex mixtures, such as chamber and ambient samples, the total water uptake can be parsed between the inorganic and organic components of the aerosol.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A micromorphic filter for determining stress and deformation measures from direct numerical simulations of lower length scale behavior

A micromorphic filter is presented for the extraction of stress and deformation measures from underlying direct numerical simulation (DNS) of microstructural mechanical response. The filter is consistent with the micromorphic continuum theory of Eringen and Suhubi (1964), and allows the interrogation of a DNS to generate higher order constitutive models. The filter adopts aspects of the overlap coupling techniques for atomistic-continuum scale-bridging in order to update the nodal degrees of freedom of the finite-element-based filter. Here, two methods for the extraction of quantities of interest from the underlying DNS are presented. The first is a least squares fitting technique which is appropriate for finite-element-style macro-scale domains, and the second is a variationally based approach which generalizes the method to any macro-scale domain which has a variational form. No restrictions on the form of the micro-scale simulation are inherent to the approach, provided that the required quantities (stress, position, etc.) can be provided. The filter is applied to a simple homogeneous medium undergoing homogeneous deformation to demonstrate the recovery of a classical continuum response, what we call the null case. We then apply the framework to the same homogeneous medium with a central and edge penny-shaped crack to show micromorphic effects. We demonstrate that varying the size of the filter causes the micromorphic behaviors to arise and then dissipate as the filter becomes smaller than the crack's length scale. Additionally we show the effect of filter size on a chiral element network along with a plate perforated with evenly spaced and perturbed holes through the thickness. The micromorphic effects are shown to be present for the chiral elements under any loading but become most pronounced when a single element is located within each averaging domain. Micromorphic effects become pronounced for the plate upon the onset of localization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC

In this work, we present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn–Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗

DOE OSTI2025

Ceramic High Efficiency Particulate Air (HEPA) Filter Research and Development at Lawrence Livermore National Laboratory

Ceramic high efficiency particulate air (HEPA) filter development program began at Lawrence Livermore National Laboratory (LLNL) over 20 years ago. Historical incidents motivated current safety systems at nuclear facilities. A ceramic filter that can survive a fire can provide robust, passive safety protection. Research has been conducted to improve the safety of nuclear facilities and to reduce operational and lifecycle costs, through ceramic filter technology that can survive fire conditions. Here, the research focused on applications in both new facilities as well as meeting operational requirements necessary to retrofit filters into existing DOE facilities. The research has developed multiple filter technologies spanning traditional HEPA filter materials to advanced manufacturing technologies (e.g., electrospinning, additive manufacturing, etc.). This communication will present highlights of selected development efforts for ceramic HEPA filter research, current state-of-the-art for ceramic filters, and future needs including technical, regulatory, and commercial efforts.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Final Status of HEPA Filter 10-Year Lifetime Evaluation

High-efficiency particulate air (HEPA) filters are widely employed by nuclear facilities to remove radiological particulate matter from their effluent exhaust streams. The purpose of this study is to evaluate the relationships between the 10-year HEPA filter lifespan and its other performance indicators. The 10-year-long endeavor to collect and analyze data regarding the lifetime of HEPA filters at the Pacific Northwest National Laboratory began in 2010. Forty-nine HEPA filters were selected and have been surveyed and analyzed at least annually to verify compliance with permit conditions. The study suggests the frequency of filter replacement should be based on the actual operational requirements, such as fume hood face velocity and/or efficiency test results, instead of on the prescribed filter “age limit” of 10 years from the date of manufacture (e.g., birth date) when operating under dry conditions. Over the past decade, the study has now been completed, and all forty-nine HEPA filters have been replaced, due to either technical issues listed in this report, or the previously recommended filter “age limit” of 10 years.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Applying Information Theory to Design Optimal Filters for Photometric Redshifts

In this paper we apply ideas from information theory to create a method for the design of optimal filters for photometric redshift estimation. We show the method applied to a series of simple example filters in order to motivate an intuition for how photometric redshift estimators respond to the properties of photometric passbands. We then design a realistic set of six filters covering optical wavelengths that optimize photometric redshifts for z <= 2.3 and i < 25.3. We create a simulated catalog for these optimal filters and use our filters with a photometric redshift estimation code to show that we can improve the standard deviation of the photometric redshift error by 7.1% overall and improve outliers 9.9% over the standard filters proposed for the Large Synoptic Survey Telescope (LSST). We compare features of our optimal filters to the LSST and find that the LSST filters incorporate key features for optimal photometric redshift estimation. Finally, we describe how information theory can be applied to a range of optimization problems in astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Filter replacement lifetime prediction

Systems for predicting a filter lifetime. A filter monitoring system includes a filter effectiveness history based on contaminant sensor information associated with a filter received from one or more contaminant sensors. A lifetime module is configured to determine a rate of filter consumption based on the filter effectiveness history and to determine a remaining filter lifetime based on the determined rate of filter consumption.

42 ENGINEERING↗

DOE OSTI2020

A Sub-Threshold Low-Power Integrated Bandpass Filter for Highly-Integrated Spectrum Analyzers

Low-power analog filter banks provide frequency analysis with minimal power and space requirements, making them viable solutions for integrated remote audio- and vibration-sensing applications. Compared with their digital counterparts, analog filter banks are better suited to achieve the lower power consumption necessary for IoT applications. In this work, the design and implementation of a sub-threshold complementary metal-oxide semiconductor (CMOS) integrated low-power tunable bandpass filter channel for signal spectrum analysis and signal discrimination is presented, including performance improvements to stability and precise matching between filter stages. The 8 th -order filter channel achieves an effective Q-factor of 4.5 and dynamic range of 60 dB, has an operational frequency range from 2 kHz to 100 kHz, and consumes 256 µW nominally at the highest center frequency. An integrated analog Gm-C filter topology is selected for this application. Functionally, the high-Q bandpass filter transfer function is implemented via four cascaded 2 nd -order filter cells and is fabricated in 130-nm 1.2-V CMOS technology, making it suitable for use in monolithic integrated spectral analysis (MISA) applications.

Long, Gavin↗

DOE OSTI2021

Fractional delay filter for a digital signal processing system

A processing element for implementation in a digital signal processing system is provided. The processing element is configured to receive a first data stream comprising a plurality of digital values where each value represents a sample of an analog signal. The processing element is further configured to receive a second data stream comprising a series of digital values where each value represents a sample of the analog signal. The processing element is configured to filter the first data stream via a first Farrow-structured fractional delay (FD) filter and output a filtered first data stream; filter the second data stream via a second Farrow-structured FD filter and output a filtered second data stream; and temporarily store values from the second data stream and output the stored values to the first Farrow-structured FD filter so that the stored values can be used to filter the first data stream.

Stanley, Dennis L.↗