اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA Critical Assessment of Photometric Redshift Methods: A CANDELS Investigation
106
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present results from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) photometric redshift methods investigation. In this investigation, the results from eleven participants, each using a different combination of photometric redshift code, template spectral energy distributions (SEDs) and priors, are used to examine the properties of photometric redshifts applied to deep fields with broad-band multi-wavelength coverage. The photometry used includes U-band through mid-infrared filters and was derived using the TFIT method. Comparing the results, we find that there is no particular code or set of template SEDs that results in significantly better photometric redshifts compared to others. However, we find codes producing the lowest scatter and outlier fraction utilize a training sample to optimize photometric redshifts by adding zero-point offsets, template adjusting or adding extra smoothing errors. These results therefore stress the importance of the training procedure. We find a strong dependence of the photometric redshift accuracy on the signal-to-noise ratio of the photometry. On the other hand, we find a weak dependence of the photometric redshift scatter with redshift and galaxy color. We find that most photometric redshift codes quote redshift errors (e.g., 68% confidence intervals) that are too small compared to that expected from the spectroscopic control sample. We find that all codes show a statistically significant bias in the photometric redshifts. However, the bias is in all cases smaller than the scatter, the latter therefore dominates the errors. Finally, we find that combining results from multiple codes significantly decreases the photometric redshift scatter and outlier fraction. We discuss different ways of combining data to produce accurate photometric redshifts and error estimates.
قيم البحث
اقرأ أيضاً
In this paper we perform a comprehensive study of the main sources of random and systematic errors in stellar mass measurement for galaxies using their Spectral Energy Distributions (SEDs). We use mock galaxy catalogs with simulated multi-waveband ph
otometry (from U-band to mid-infrared) and known redshift, stellar mass, age and extinction for individual galaxies. Given different parameters affecting stellar mass measurement (photometric S/N ratios, SED fitting errors, systematic effects, the inherent degeneracies and correlated errors), we formulated different simulated galaxy catalogs to quantify these effects individually. We studied the sensitivity of stellar mass estimates to the codes/methods used, population synthesis models, star formation histories, nebular emission line contributions, photometric uncertainties, extinction and age. For each simulated galaxy, the difference between the input stellar masses and those estimated using different simulation catalogs, $Deltalog(M)$, was calculated and used to identify the most fundamental parameters affecting stellar masses. We measured different components of the error budget, with the results listed as follows: (1). no significant bias was found among different codes/methods, with all having comparable scatter; (2). A source of error is found to be due to photometric uncertainties and low resolution in age and extinction grids; (3). The median of stellar masses among different methods provides a stable measure of the mass associated with any given galaxy; (4). The deviations in stellar mass strongly correlate with those in age, with a weaker correlation with extinction; (5). the scatter in the stellar masses due to free parameters are quantified, with the sensitivity of the stellar mass to both the population synthesis codes and inclusion of nebular emission lines studied.
Using spectroscopy from the Large Binocular Telescope and imaging from the Hubble Space Telescope we discovered the first strong galaxy lens at z(lens)>1. The lens has a secure photometric redshift of z=1.53+/-0.09 and the source is spectroscopically
confirmed at z=3.417. The Einstein radius (0.35; 3.0 kpc) encloses 7.6 x 10^10 Msol, with an upper limit on the dark matter fraction of 60%. The highly magnified (40x) source galaxy has a very small stellar mass (~10^8 Msol) and shows an extremely strong [OIII]_5007A emission line (EW_0 ~ 1000A) bolstering the evidence that intense starbursts among very low-mass galaxies are common at high redshift.
We present and describe a catalog of galaxy photometric redshifts (photo-zs) for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial Neural Network (ANN) technique to calculate photo-zs and the Nearest Neighbor Error (NNE) method to
estimate photo-z errors for $sim$ 13 million objects classified as galaxies in the coadd with $r < 24.5$. The photo-z and photo-z error estimators are trained and validated on a sample of $sim 83,000$ galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Deep Extragalactic Evolutionary Probe Data Release 3(DEEP2 DR3), the VIsible imaging Multi-Object Spectrograph - Very Large Telescope Deep Survey (VVDS) and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than $sigma_{68} =0.031$. After presenting our results and quality tests, we provide a short guide for users accessing the public data.
In order for Wide-Field Infrared Survey Telescope (WFIRST) and other Stage IV dark energy experiments (e.g., Large Synoptic Survey Telescope; LSST, and Euclid) to infer cosmological parameters not limited by systematic errors, accurate redshift measu
rements are needed. This accuracy can be met by using spectroscopic subsamples to calibrate the photometric redshifts for the full sample. In this work we employ the Self Organizing Map (SOM) spectroscopic sampling technique, to find the minimal number of spectra required for the WFIRST weak lensing calibration. We use galaxies from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) to build the LSST+WFIRST lensing analog sample of ~36 k objects and train the LSST+WFIRST SOM. We find that 26% of the WFIRST lensing sample consists of sources fainter than the Euclid depth in the optical, 91% of which live in color cells already occupied by brighter galaxies. We demonstrate the similarity between faint and bright galaxies as well as the feasibility of redshift measurements at different brightness levels. Our results suggest that the spectroscopic sample acquired for calibration to the Euclid depth is sufficient for calibrating the majority of the WFIRST color-space. For the spectroscopic sample to fully represent the synthetic color-space of WFIRST, we recommend obtaining additional spectroscopy of ~0.2-1.2 k new sources in cells occupied by mostly faint galaxies. We argue that either the small area of the CANDELS fields and the small overall sample size or the large photometric errors might be the reason for no/less bright galaxies mapped to these cells. Acquiring the spectra of these sources will confirm the above findings and will enable the comprehensive calibration of the WFIRST color-redshift relation.
Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We combine the redshift information from the galaxy photometry with constraints
from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least $0.002(1+z)$, consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
