Photometric redshift calibration with self-organising maps

Accurate photometric redshift calibration is central to the robustness of all cosmology constraints from cosmic shear surveys. Analyses of the Kilo-Degree Survey (KiDS) re-weighted training samples from all overlapping spectroscopic surveys to provide a direct redshift calibration. Using self-organising maps we demonstrate that this spectroscopic compilation is sufficiently complete for KiDS, representing 99% of the effective 2D cosmic shear sample. We used the SOM to define a 100% represented “gold” cosmic shear sample, per tomographic bin. Using mock simulations of KiDS and the spectroscopic training set, we estimated the uncertainty on the SOM redshift calibration, and we find that photometric noise, sample variance, and spectroscopic selection effects (including redshift and magnitude incompleteness) induce a combined maximal scatter on the bias of the redshift distribution reconstruction (Δ⟨z⟩ = ⟨z⟩est − ⟨z⟩true) of σΔ⟨z⟩ ≤ 0.006 in all tomographic bins. Photometric noise and spectroscopic selection effects contribute equally to the observed scatter. We show that the SOM calibration is unbiased in the cases of noiseless photometry and perfectly representative spectroscopic datasets, as expected from theory. The inclusion of both photometric noise and spectroscopic selection effects in our mock data introduces a maximal bias of Δ⟨z⟩ = 0.013 ± 0.006, or Δ⟨z⟩ ≤ 0.025 at 97.% confidence, once quality flags have been applied to the SOM. The method presented here represents a significant improvement over the previously adopted direct redshift calibration implementation for KiDS, owing to its diagnostic and quality assurance capabilities. The implementation of this method in future cosmic shear studies will allow better diagnosis, examination, and mitigation of systematic biases in photometric redshift calibration.

Streaming of repeated noise in primary and secondary fields of auditory cortex

Statistical regularities in natural sounds facilitate the perceptual segregation of auditory sources, or streams. Repetition is one cue that drives stream segregation in humans, but the neural basis of this perceptual phenomenon remains unknown. We demonstrated a similar perceptual ability in animals by training ferrets to detect a stream of repeating noise samples (foreground) embedded in a stream of random samples (background). During passive listening, we recorded neural activity in primary (A1) and secondary (PEG) fields of auditory cortex. We used two context-dependent encoding models to test for evidence of streaming of the repeating stimulus. The first was based on average evoked activity per noise sample and the second on the spectro-temporal receptive field (STRF). Both approaches tested whether changes in the neural response to repeating versus random stimuli were better modeled by scaling the response to both streams equally (global gain) or by separately scaling the response to the foreground versus background stream (stream-specific gain). Consistent with previous observations of adaptation, we found an overall reduction in global gain when the stimulus began to repeat. However, when we measured stream-specific changes in gain, responses to the foreground were enhanced relative to the background. This enhancement was stronger in PEG than A1. In A1, enhancement was strongest in units with low sparseness (i.e., broad sensory tuning) and with tuning selective for the repeated sample. Enhancement of responses to the foreground relative to the background provides evidence for stream segregation that emerges in A1 and is refined in PEG.

Objective Evaluation of FCV Interior Sound Quality During Acceleration

While driving a FCV during acceleration, many sorts of sounds could be heard, which influence the interior sound quality. A typical FCV is taken as a sample, four interior noises generated under the acceleration operation are collected in the whole vehicle semi-anechoic chamber, and the noise sample database of diesel engine radiation noise is established after preprocessing. Based on sound quality theory (physical and psychoacoustic features), the Kernel Principal Component Analysis (KPCA) is used to extract the key objective features mainly influencing the sound quality, which realize the dimension reduction target; the variations of objective features are analyzed to qualitatively analyze the law of the sound quality varying during acceleration. According to the objective evaluation of FCV interior sound quality, combining with FCV operating parameters, the influencing law of the FCV sound quality could be obtained.

Comparison of the capabilities of histograms and a method of ranged amplitudes in noise analysis of singlephoton detectors

A comparative analysis of the method of histograms and the sequence of the ranged amplitudes (SRA) for statistical parametrization of the operation regime of a single-photon avalanche photodetector has been performed. It was shown that in addition to providing all the information that can be obtained using the histogram method, the SRA method also provides a quick and robust description of the dark counts of the detector for a shorter (compared to histograms) noise sample of ~103 points. The revealed advantages open prospects for introducing the SRA method in the software of high-sensitivity photodetectors.

Membership Calculation Based on Dimension Hierarchical Division

Since dataset usually contain noises, it is very helpful to find out and remove the noise in a preprocessing step. Fuzzy membership can measure a samples weight. The weight should be smaller for noise sample but bigger for important sample. Therefore, appropriate sample memberships are vital. The article proposed a novel approach, Membership Calculate based on Hierarchical Division (MCHD), to calculate the membership of training samples. MCHD uses the conception of dimension similarity, which develop a bottom-up clustering technique to calculate the sample membership iteratively. The experiment indicates that MCHD can effectively detect noise and removes them from the dataset. Fuzzy support vector machine based on MCHD outperforms most of approaches published recently and hold the better generalization ability to handle the noise.

The Exploration of Evaluation Method about the Driving Electromotor Acoustic Comfort of the Pure Electric Vehicles

The driving electromotor noise of a pure electric bus was taken as the evaluation object in this paper. The noise signals were gathered by dual channels and to simulating human auditory by synthetic stereo, and were processed into a series of noise samples for human subjective testing generated according to the 3dB differential progressive attenuation of noise sound pressure level. Then the author investigated the human body comfort/discomfort subjective feelings under various noise samples through the high fidelity audio playback, described the subjective feelings with ‘descriptor’, and quantified the subjective feelings with scores at the same time. On this basis, the correlation of subjective feelings between acoustic comfort and discomfort were revealed, and the noise sample sets corresponding with comfort feeling were found out. Based on these, an evaluation method of electromotor acoustic comfort was established.