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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

The White Dwarf Mass and the Accretion Rate of Recurrent Novae: An X-ray Perspective

We present recent results of quiescent X-ray observations of recurrent novae (RNe) and related objects. Several RNe are luminous hard X-ray sources in quiescence, consistent with accretion onto a near Chandrasekhar mass white dwarf. Detection of similar hard X-ray emissions in old novae and other cataclysmic variables may lead to identification of additional RN candidates. On the other hand, other RNe are found to be comparatively hard X-ray faint. We present several scenarios that may explain this dichotomy, which should be explored further.

Mukai, Koji↗
NASA NTRS2011

The Spectroscopic Evolution of the Symbiotic-like Recurrent Nova V407 Cygni During Its 2010 Outburst. 2. The Circumstellar Environment and the Aftermath

The nova outburst of V407 Cyg in 2010 Mar. 10 was the first observed for this star but its close resemblance to the well known symbiotic-like recurrent nova RS Oph suggests that it is also a member of this rare type of Galactic novae. The nova was the first detected at gamma-ray energies and is the first known nova explosion for this system. The extensive multiwavelength coverage of this outburst makes it an ideal comparison with the few other outbursts known for similar systems. We extend our previous analysis of the Mira and the expanding shock from the explosion to detail the time development of the photoionized Mira wind, circumstellar medium, and shocked circumstellar environment to derive their physical parameters and how they relate to large scale structure of the environment, extending the previous coverage to more than 500 days after outburst. We use optical spectra obtained at high resolution with the Nordic Optical Telescope (NOT) (R approx. =.45000 to 65000) and medium resolution Ondrejov Observatory (R approx. = 12000) data and compare the line variations with publicly available archival measurements at 30 GHz OVNR and at X-rays with Swift during the first four months of the outburst, through the end of the epoch of strong XR emission. We use nebular diagnostics and high resolution profile variations to derive the densities and locations of the extended emission. We find that the higher the ionization and/or the higher the excitation energy, the more closely the profiles resemble the He II/Ca V-type high velocity shock profile discussed in Paper I. This also accounts for the comparative development of the [N II] and [O III] isoelectronic transitions: the [O III] 4363A profile does not show the low velocity peaks while the excited [N II] 5754A does. If nitrogen is mainly N(+3) or higher in the shock, the upper state of the [N II] nebular lines will contribute but if the oxygen is O(+2) then this line is formed by recombination, masking the nebular contributor, and the lower states are collisionally quenched but emit from the low density surroundings. Absorption lines of Fe-peak ions formed in the Mira wind were visible as P Cyg profiles at low velocity before Day 69, around the time of the X-ray peak and we identified many absorption transitions without accompanying emission for metal lines. The H Balmer lines showed strong P Cyg absorption troughs that weakened during the 2010 observing period, through Day 128. The Fe-peak line profiles and flux variations were different for permitted and forbidden transitions: the E1 transitions were not visible after Day 128 but had shown a narrow peak superimposed on an extended (200 km/s) blue wing, while the M1 and E2 transitions persisted to Day 529, the last observation, and showed extended redshifted wings up of the same velocity. We distinguish the components from the shock, the photoionized environment, and the chromosphere and inner Mira wind using spectra taken more than one year after outburst. The multiple shells and radiative excitation phenomenology are similar to those recently cited for GRBs and SNIa .

Shore, S. N.↗
NASA NTRS2014

The 2011 Outburst of Recurrent Nova T Pyx: X-Ray Observations Expose the White Dwarf Mass and Ejection Dynamics

The recurrent nova T Pyx underwent its sixth historical outburst in 2011, and became the subject of an intensive multi-wavelength observational campaign.We analyze data from the Swift and Suzaku satellites to produce a detailed X-ray light curve augmented by epochs of spectral information. X-ray observations yield mostly non-detections in the first four months of outburst, but both a super-soft and hard X-ray component rise rapidly after Day 115. The super-soft X-ray component, attributable to the photosphere of the nuclear-burning white dwarf, is relatively cool (approximately 45 electron volts) and implies that the white dwarf in T Pyx is significantly below the Chandrasekhar mass (approximately 1 M). The late turn-on time of the super-soft component yields a large nova ejecta mass (approximately greater than 10(exp −5) solar mass), consistent with estimates at other wavelengths. The hard X-ray component is well fit by a approximately 1 kiloelectron volt thermal plasma, and is attributed to shocks internal to the 2011 nova ejecta. The presence of a strong oxygen line in this thermal plasma on Day 194 requires a significantly super-solar abundance of oxygen and implies that the ejecta are polluted by white dwarf material. The X-ray light curve can be explained by a dual-phase ejection, with a significant delay between the first and second ejection phases, and the second ejection finally released two months after outburst. A delayed ejection is consistent with optical and radio observations of T Pyx, but the physical mechanism producing such a delay remains a mystery.

nova T Pyx↗
NASA NTRS2016

Assessing Spontaneous Combustion Instability with Recurrence Quantification Analysis

Spontaneous instabilities can pose a significant challenge to verification of combustion stability, and characterizing its onset is an important avenue of improvement for stability assessments of liquid propellant rocket engines. Recurrence Quantification Analysis (RQA) is used here to explore nonlinear combustion dynamics that might give insight into instability. Multiple types of patterns representative of different dynamical states are identified within fluctuating chamber pressure data, and markers for impending instability are found. A class of metrics which describe these patterns is also calculated. RQA metrics are compared with and interpreted against another metric from nonlinear time series analysis, the Hurst exponent, to help better distinguish between stable and unstable operation.

Eberhart, Chad J.↗
NASA NTRS2018

Automatic Speech Recognition for Launch Control Center Communication Using Recurrent Neural Networks with Data Augmentation and Custom Language Model

Transcribing voice communications in NASA’s launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch control center will help the machine analyze information and accelerate knowledge generation.

Chow, Edward↗
DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗
DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗
DOE OSTI2025

Recurrent features of amplitudes in planar $\mathcal{N}$ = 4 super Yang-Mills theory

The planar three-gluon form factor for the chiral stress tensor operator in planar maximally supersymmetric Yang-Mills theory is an analog of the Higgs-to-three-gluon scattering amplitude in QCD. The amplitude (symbol) bootstrap program has provided a wealth of high-loop perturbative data about this form factor, with results up to eight loops available. The symbol of the form factor at L loops is given by words of length 2L in six letters with associated integer coefficients. In this paper, we analyze this data, describing patterns of zero coefficients and relations between coefficients. We find many sequences of words whose coefficients are given by closed-form expressions which we expect to be valid at any loop order. Moreover, motivated by our previous machine-learning analysis, we identify simple recursion relations that relate the coefficient of a word to the coefficients of particular lower-loop words. These results open an exciting door for understanding scattering amplitudes at all loop orders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Recurrent acquisition of nuclease-protease pairs in antiviral immunity

Antiviral immune systems diversify by integrating new genes into existing pathways, creating new mechanisms of viral resistance. We identified genes encoding a predicted nuclease paired with a trypsin-like protease repeatedly acquired by multiple, otherwise unrelated antiviral immune systems in bacteria. Cell-based and biochemical assays revealed that the nuclease is a proenzyme that cleaves DNA only after activation by its partner protease. Two distinct immune systems, Hachiman and AVAST (antiviral adenosine triphosphatase/nucleoside triphosphatase of the STAND superfamily, Avs), use the same mechanism of proteolytic activation despite their independent evolutionary origins. Examination of nuclease-protease inheritance patterns identified caspase-nuclease (canu) genomic loci that confer antiviral defense in a pathway reminiscent of eukaryotic caspase activation. These results uncover the coordinated activities of pronucleases and their activating proteases within different immune systems and show how coevolution enables defense system innovation.

Tuck, Owen T↗