We present a maximum likelihood method for determining the spatial properties of tidal debris and of the Galactic spheroid. With this method we characterize Sagittarius debris using stars with the colors of blue F turnoff stars in SDSS stripe 82. The debris is located at (; ; R) ¼ (31:37 AE 0:26 ; 0:0 ; 29:22 AE 0:20 kpc), with a (spatial) direction given by the unit vector (À0:991 AE 0:007 kpc; 0:042 AE 0:033 kpc; 0:127 AE 0:046 kpc), in galactocentric Cartesian coordinates, and with FWHM ¼ 6:74 AE 0:06 kpc. This 2.5 wide stripe contains 0.9% as many F turnoff stars as the current Sagittarius dwarf galaxy. Over small spatial extent, the debris is modeled as a cylinder with a density that falls off as a Gaussian with distance from the axis, while the smooth component of the spheroid is modeled with a Hernquist profile. We assume that the absolute magnitude of F turnoff stars is distributed as a Gaussian, which is an improvement over previous methods which fixed the absolute magnitude atM g 0 ¼ 4:2. The effectiveness and correctness of the algorithm is demonstrated on a simulated set of F turnoff stars created to mimic SDSS stripe 82 data, which shows that we have a much greater accuracy than previous studies. Our algorithm can be applied to divide the stellar data into two catalogs: one which fits the stream density profile and one with the characteristics of the spheroid. This allows us to effectively separate tidal debris from the spheroid population, both facilitating the study of the tidal stream dynamics and providing a test of whether a smooth spheroidal population exists.

This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.

No abstract

We measure the spatial density of F turnoff stars in the Sagittarius dwarf tidal stream, from Sloan Digital Sky Survey (SDSS) data, using statistical photometric parallax. We find a set of continuous, consistent parameters that describe the leading Sgr stream's position, direction, and width for 15 stripes in the North Galactic Cap, and 3 stripes in the South Galactic Cap. We produce a catalog of stars that has the density characteristics of the dominant leading Sgr tidal stream that can be compared with simulations. We find that the width of the leading (North) tidal tail is consistent with recent triaxial and axisymmetric halo model simulations. The density along the stream is roughly consistent common disruption models in the North, but possibly not in the South. We explore the possibility that one or more of the dominant Sgr streams has been mis-identified, and that one or more of the 'bifurcated' pieces is the real Sgr tidal tail, but we do not reach definite conclusions. If two dwarf progenitors are assumed, fits to the planes of the dominant and 'bifurcated' tidal tails favor an association of the Sgr dwarf spheroidal galaxy with the dominant Southern stream and the 'bifurcated' stream in the North. In the North Galactic Cap, the best fit Hernquist density profile for the smooth component of the stellar halo is oblate, with a flattening parameter q = 0.53, and a scale length of r 0 = 6.73. The Southern data for both the tidal debris and the smooth component of the stellar halo do not match the model fits to the North, although the stellar halo is still overwhelmingly oblate. Finally, we verify that we can reproduce the parameter fits on the asynchronous Milkyway@home volunteer computing platform.

No abstract