اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدThe impact of JWST broad-band filter choice on photometric redshift estimation
225
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The determination of galaxy redshifts in James Webb Space Telescope (JWST)s blank-field surveys will mostly rely on photometric estimates, based on the data provided by JWSTs Near-Infrared Camera (NIRCam) at 0.6-5.0 {mu}m and Mid Infrared Instrument (MIRI) at {lambda}>5.0 {mu}m. In this work we analyse the impact of choosing different combinations of NIRCam and MIRI broad-band filters (F070W to F770W), as well as having ancillary data at {lambda}<0.6 {mu}m, on the derived photometric redshifts (zphot) of a total of 5921 real and simulated galaxies, with known input redshifts z=0-10. We found that observations at {lambda}<0.6 {mu}m are necessary to control the contamination of high-z samples by low-z interlopers. Adding MIRI (F560W and F770W) photometry to the NIRCam data mitigates the absence of ancillary observations at {lambda}<0.6 {mu}m and improves the redshift estimation. At z=7-10, accurate zphot can be obtained with the NIRCam broad bands alone when S/N>=10, but the zphot quality significantly degrades at S/N<=5. Adding MIRI photometry with one magnitude brighter depth than the NIRCam depth allows for a redshift recovery of 83-99%, depending on SED type, and its effect is particularly noteworthy for galaxies with nebular emission. The vast majority of NIRCam galaxies with [F150W]=29 AB mag at z=7-10 will be detected with MIRI at [F560W, F770W]<28 mag if these sources are at least mildly evolved or have spectra with emission lines boosting the mid-infrared fluxes.
قيم البحث
اقرأ أيضاً
In the next years, several cosmological surveys will rely on imaging data to estimate the redshift of galaxies, using traditional filter systems with 4-5 optical broad bands; narrower filters improve the spectral resolution, but strongly reduce the t
otal system throughput. We explore how photometric redshift performance depends on the number of filters n_f, characterizing the survey depth through the fraction of galaxies with unambiguous redshift estimates. For a combination of total exposure time and telescope imaging area of 270 hrs m^2, 4-5 filter systems perform significantly worse, both in completeness depth and precision, than systems with n_f >= 8 filters. Our results suggest that for low n_f, the color-redshift degeneracies overwhelm the improvements in photometric depth, and that even at higher n_f, the effective photometric redshift depth decreases much more slowly with filter width than naively expected from the reduction in S/N. Adding near-IR observations improves the performance of low n_f systems, but still the system which maximizes the photometric redshift completeness is formed by 9 filters with logarithmically increasing bandwidth (constant resolution) and half-band overlap, reaching ~0.7 mag deeper, with 10% better redshift precision, than 4-5 filter systems. A system with 20 constant-width, non-overlapping filters reaches only ~0.1 mag shallower than 4-5 filter systems, but has a precision almost 3 times better, dz = 0.014(1+z) vs. dz = 0.042(1+z). We briefly discuss a practical implementation of such a photometric system: the ALHAMBRA survey.
In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of about 25,000 galaxies from the second data release of
the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.
We present results of broad band photometric reverberation mapping (RM) to measure the radius of the broad line region, and subsequently the black hole mass (M$_{rm BH}$), in the nearby, low luminosity active galactic nuclei (AGN) NGC 4395. Using the
Wise Observatorys 1m telescope equipped with the SDSS g$$, r$$ and i$$ broad band filters, we monitored NGC 4395 for 9 consecutive nights and obtained 3 light curves each with over 250 data points. The g$$ and r$$ bands include time variable contributions from H$beta$ and H$alpha$ (respectively) plus continuum. The i$$ band is free of broad lines and covers exclusively continuum. We show that by looking for a peak in the difference between the cross-correlation and the auto-correlation functions for all combinations of filters, we can get a reliable estimate of the time lag necessary to compute M$_{rm BH}$. We measure the time lag for H$alpha$ to be $3.6 pm 0.8 $ hours, comparable to previous studies using the line resolved spectroscopic RM method. We argue that this lag implies a black hole mass of M$_{rm BH} = (4.9 pm 2.6) times 10^{4}$ Msun .
We present results of using individual galaxies redshift probability information derived from a photometric redshift (photo-z) algorithm, SPIDERz, to identify potential catastrophic outliers in photometric redshift determinations. By using two test d
ata sets comprised of COSMOS multi-band photometry spanning a wide redshift range (0<z<4) matched with reliable spectroscopic or other redshift determinations we explore the efficacy of a novel method to flag potential catastrophic outliers in an analysis which relies on accurate photometric redshifts. SPIDERz is a custom support vector machine classification algorithm for photo-z analysis that naturally outputs a distribution of redshift probability information for each galaxy in addition to a discrete most probable photo-z value. By applying an analytic technique with flagging criteria to identify the presence of probability distribution features characteristic of catastrophic outlier photo-z estimates, such as multiple redshift probability peaks separated by substantial redshift distances, we can flag potential catastrophic outliers in photo-z determinations. We find that our proposed method can correctly flag large fractions (>50%) of the catastrophic outlier galaxies, while only flagging a small fraction (<5%) of the total non-outlier galaxies, depending on parameter choices. The fraction of non-outlier galaxies flagged varies significantly with redshift and magnitude, however. We examine the performance of this strategy in photo-z determinations using a range of flagging parameter values. These results could potentially be useful for utilization of photometric redshifts in future large scale surveys where catastrophic outliers are particularly detrimental to the science goals.
Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopical
ly following up all the SNe. We investigate the performance of a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an AUC of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99$%$ SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.012 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0294$ without using a host-galaxy photo-z prior, and a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.0017 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0116$ using a host-galaxy photo-z prior. Assuming a flat $Lambda CDM$ model with $Omega_m=0.3$, we obtain $Omega_m$ of $0.305pm0.008$ (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter $sigma_mathrm{int}=0.11$) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
