The Impact of Bias Row Noise to Photometric Accuracy:Case Study Based on a Scientific CMOS Detector

We tested a new model of CMOS detector manufactured by the Gpixel Inc,for potential space astronomical application.In laboratory,we obtain some bias images under the typical application environment.In these bias images,clear random row noise pattern is observed.The row noise also contains some characteristic spatial frequencies.We quantitatively estimated the impact of this feature to photometric measurements,by making simulated images.We compared different bias noise types under strict parameter control.The result shows the row noise will significantly deteriorate the photometric accuracy.It effectively increases the readout noise by a factor of2-10.However,if it is properly removed,the image quality and photometric accuracy will be significantly improved.

The Stellar Spectra Factory(SSF)Based on SLAM

An empirical stellar spectral library with large coverage of stellar parameters is essential for stellar population synthesis and studies of stellar evolution.In this work,we present Stellar Spectra Factory(SSF),a tool to generate empirical-based stellar spectra from arbitrary stellar atmospheric parameters.The relative flux-calibrated empirical spectra can be predicted by SSF given arbitrary effective temperature,surface gravity,and metallicity.SSF constructs the interpolation approach based on the Stellar LAbel Machine,using ATLAS-A library,which contains spectra covering from O type to M type,as the training data set.SSF is composed of four data-driven sub-models to predict empirical stellar spectra.Sub-model SSF-N can generate spectra from A to K type and some M giant stars,covering 3700<T_(eff)<8700 K,0<logg<dex,and-1.5<[M/H]<0.5 dex.Sub-model SSF-gM is mainly used to predict M giant spectra with 3520<T_(eff)<4000 K and-1.5<[M/H]<0.4 dex.Sub-model SSF-dM is for generating M dwarf spectra with 3295<T_(eff)<4040 K,-1.0<[M/H]<0.1 dex.Sub-model SSF-B can predict B-type spectra with 9000<T_(eff)<24,000 K and-5.2<M_(G)<1.5 mag.The accuracy of the predicted spectra is validated by comparing the flux of predicted spectra to those with same stellar parameters selected from the known spectral libraries,MILES and MaStar.The averaged difference of flux over optical wavelength between the predicted spectra and the corresponding ones in MILES and MaStar is less than 5%.More verification is conducted between the magnitudes calculated from the integration of the predicted spectra and the observations in PS1 and APASS bands with the same stellar parameters.No significant systematic difference is found between the predicted spectra and the photometric observations.The uncertainty is 0.08 mag in the r band for SSF-gM when comparing with the stars with the same stellar parameters selected from PS1.The uncertainty becomes 0.31 mag in the i band for SSF-dM when comparing with the stars with the same stellar parameters selected from APASS.

DRC-Net Method for Two-dimensional Spectral Classification

Spectral classification plays a crucial role in the analysis of astronomical data.Currently,stellar spectral classification primarily relies on one-dimensional(1D)spectra and necessitates a sufficient signal-to-noise ratio(S/N).However,in cases where the S/N is low,obtaining valuable information becomes impractical.In this paper,we propose a novel model called DRC-Net(Double-branch celestial spectral classification network based on residual mechanisms)for stellar classification,which operates solely on two-dimensional(2D)spectra.The model consists of two branches that use 1D convolutions to reduce the dimensionality of the 2D spectral composed of both blue and red arms.In the following,the features extracted from both branches are fused,and the fused result undergoes further feature extraction before being fed into the classifier for final output generation.The data set is from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope,comprising 15,680 spectra of F,G,and K types.The preprocessing process includes normalization and the early stopping mechanism.The experimental results demonstrate that the proposed DRC-Net achieved remarkable classification precision of 93.0%,83.5%,and86.9%for F,G,and K types,respectively,surpassing the performance of 1D spectral classification methods.Furthermore,different S/N intervals are tested to judge the classification ability of DRC-Net.The results reveal that DRC-Net,as a 2D spectral classification model,can deliver superior classification outcomes for the spectra with low S/Ns.These experimental findings not only validate the efficiency of DRC-Net but also confirm the enhanced noise resistance ability exhibited by 2D spectra.

Panel positioning error and support mechanism for a 30-m THz radio telescope

A 30-m TeraHertz（THz） radio telescope is proposed to operate at 200 μm with an active primary surface.This paper presents sensitivity analysis of active surface panel positioning errors with optical performance in terms of the Strehl ratio.Based on Ruze＇s surface error theory and using a Monte Carlo simulation,the effects of six rigid panel positioning errors,such as piston,tip,tilt,radial,azimuthal and twist displacements,were directly derived.The optical performance of the telescope was then evaluated using the standard Strehl ratio.We graphically illustrated the various panel error effects by presenting simulations of complete ensembles of full reflector surface errors for the six different rigid panel positioning errors.Study of the panel error sensitivity analysis revealed that the piston error and tilt/tip errors are dominant while the other rigid errors are much less important.Furthermore,as indicated by the results,we conceived of an alternative Master-Slave Concept-based（MSC-based） active surface by implementating a special Series-Parallel Concept-based（SPC-based） hexapod as the active panel support mechanism.A new 30-m active reflector based on the two concepts was demonstrated to achieve correction for all the six rigid panel positioning errors in an economically feasible way.

Photometric redshifts of galaxies from SDSS and 2MASS

In order to find the physical parameters which determine the accuracy of pho- tometric redshifts, we compare the spectroscopic and photometric redshifts （photo-z＇s） for a large sample of -80000 SDSS-2MASS galaxies. Photo-z＇s in this paper are estimated by using the artificial neural network photometric redshift method （ANNz）. For a subset of -40000 randomly selected galaxies, we find that the photometric redshift recovers the spectroscopic redshift distribution very well with rms of 0.016. Our main results are as follows： （1） Using magnitudes directly as input parameters produces more accurate photo-z＇s than using colors; （2） The inclusion of 2MASS （J, H, Ks） bands does not improve photo-z＇s significantly, which indicates that near infrared data might not be important for the low-redshift sample; （3） Adding the concentration index （essentially the steepness of the galaxy brightness profile） as an extra input can improve the photo-z＇s estimation up to -10 percent; （4） Dividing the sample into early- and late-type galaxies by using the concentration index, normal and abnormal galaxies by using the emission line flux ratios, and red and blue galaxies by using color index （g - r）, we can improve the accuracy of photo-z＇s significantly; （5） Our analysis shows that the outliers （where there is a big difference between the spectroscopic and photometric redshifts） are mainly correlated with galaxy types, e.g., most outliers are late-type （blue） galaxies.