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.
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 photom
etric 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.
We use a sample of 45 core collapse supernovae detected with the Advanced Camera for Surveys on-board the Hubble Space Telescope to derive the core collapse supernova rate in the redshift range 0.1<z<1.3. In redshift bins centered on <z>=0.39, <z>=0.
73, and <z>=1.11, we find rates 3.00 {+1.28}{-0.94}{+1.04}{-0.57}, 7.39 {+1.86}{-1.52}{+3.20}{-1.60}, and 9.57 {+3.76}{-2.80}{+4.96}{-2.80}, respectively, given in units yr^{-1} Mpc^{-3} 10^{-4} h_{70}^3. The rates have been corrected for host galaxy extinction, including supernovae missed in highly dust enshrouded environments in infrared bright galaxies. The first errors represent statistical while the second are the estimated systematic errors. We perform a detailed discussion of possible sources of systematic errors and note that these start to dominate over statistical errors at z>0.5, emphasizing the need to better control the systematic effects. For example, a better understanding of the amount of dust extinction in the host galaxies and knowledge of the supernova luminosity function, in particular the fraction of faint M > -15 supernovae, is needed to better constrain the rates. When comparing our results with the core collapse supernova rate based on the star formation rate, we find a good agreement, consistent with the supernova rate following the star formation rate, as expected.
We report the discovery of a Type Ia supernova (SNIa) at redshift z=1.55 with the infrared detector of the Wide Field Camera 3 (WFC3-IR) on the Hubble Space Telescope (HST). This object was discovered in CANDELS imaging data of the Hubble Ultra Deep
Field, and followed as part of the CANDELS+CLASH Supernova project, comprising the SN search components from those two HST multi-cycle treasury programs. This is the highest redshift SNIa with direct spectroscopic evidence for classification. It is also the first SN Ia at z>1 found and followed in the infrared, providing a full light curve in rest-frame optical bands. The classification and redshift are securely defined from a combination of multi-band and multi-epoch photometry of the SN, ground-based spectroscopy of the host galaxy, and WFC3-IR grism spectroscopy of both the SN and host. This object is the first of a projected sample at z>1.5 that will be discovered by the CANDELS and CLASH programs. The full CANDELS+CLASH SN Ia sample will enable unique tests for evolutionary effects that could arise due to differences in SN Ia progenitor systems as a function of redshift. This high-z sample will also allow measurement of the SN Ia rate out to z~2, providing a complementary constraint on SN Ia progenitor models.
We use the deepest and the most comprehensive photometric data currently available for GOODS-South galaxies to measure their photometric redshifts. The photometry includes VLT/VIMOS (U-band), HST/ACS (F435W, F606W, F775W, and F850LP bands), VLT/ISAAC
(J-, H-, and Ks-bands), and four Spitzer/IRAC channels (3.6, 4.5, 5.8, and 8.0 micron). The catalog is selected in the z-band (F850LP) and photometry in each band is carried out using the recently completed TFIT algorithm, which performs PSF matched photometry uniformly across different instruments and filters, despite large variations in PSFs and pixel scales. Photometric redshifts are derived using the GOODZ code, which is based on the template fitting method using priors. The code also implements training of the template SED set, using available spectroscopic redshifts in order to minimize systematic differences between the templates and the SEDs of the observed galaxies. Our final catalog covers an area of 153 sq. arcmin and includes photometric redshifts for a total of 32,505 objects. The scatter between our estimated photometric and spectroscopic redshifts is sigma=0.040 with 3.7% outliers to the full z-band depth of our catalog, decreasing to sigma=0.039 and 2.1% outliers at a magnitude limit m(z)<24.5. This is consistent with the best results previously published for GOODS-S galaxies, however, the present catalog is the deepest yet available and provides photometric redshifts for significantly more objects to deeper flux limits and higher redshifts than earlier works. Furthermore, we show that the photometric redshifts estimated here for galaxies selected as dropouts are consistent with those expected based on the Lyman break technique.
We use the HST ACS imaging of the two GOODS fields during Cycles 11, 12, and 13 to derive the Type Ia supernova rate in four redshift intervals in the range 0.2<z<1.8. Compared to our previous results based on Cycle 11 observations only, we have incr
eased the coverage of the search by a factor 2.7, making the total area searched equivalent to one square degree. The sample now consists of 56 Type Ia supernovae. The rates we derive are consistent with our results based on the Cycle 11 observations. In particular, the few detected supernovae at z>1.4 supports our previous result that there is a drop in the Type Ia supernova rate at high redshift, suggesting a long time delay between the formation of the progenitor star and the explosion of the supernova. If described by a simple one-parameter model, we find a characteristic delay time of 2-3 Gyr. However, a number of recent results based on e.g., low redshift supernova samples and supernova host galaxy properties suggest that the supernova delay time distribution is bimodal. In this model, a major fraction of the Type Ia supernova rate is prompt and follows the star formation rate, while a smaller fraction of the rate has a long delay time, making this channel proportional to mass. While our results are fully consistent with the bimodal model at low redshifts, the low rate we find at z>1.4 appears to contradict these results. Models that corrects for star formation hidden by dust may explain at least part of the differences. Here we discuss this possibility together with other ways to reconcile data and models.
The aim of this paper is to investigate ways to optimize the accuracy of photometric redshifts for a SNAP like mission. We focus on how the accuracy of the photometric redshifts depends on the magnitude limit and signal-to-noise ratio, wave-length co
verage, number of filters and their shapes and observed galaxy type. We use simulated galaxy catalogs constructed to reproduce observed galaxy luminosity functions from GOODS, and derive photometric redshifts using a template fitting method. By using a catalog that resembles real data, we can estimate the expected number density of galaxies for which photometric redshifts can be derived. We find that the accuracy of photometric redshifts is strongly dependent on the signal-to-noise (S/N) (i.e., S/N>10 is needed for accurate photometric redshifts). The accuracy of the photometric redshifts is also dependent on galaxy type, with smaller scatter for earlier type galaxies. Comparing results using different filter sets, we find that including the U-band is important for decreasing the fraction of outliers, i.e., ``catastrophic failures. Using broad overlapping filters with resolution ~4gives better photometric redshifts compared to narrower filters (resolution >~5) with the same integration time. We find that filters with square response curves result in a slightly higher scatter, mainly due to a higher fraction of outliers at faint magnitudes. We also compare a 9-filter set to a 17-filter set, where we assume that the available exposure time per filter in the latter set is half that of the first set. We find that the 9-filter set gives more accurate redshifts for a larger number of objects and reaches higher redshift, while the 17-filter set is gives better results at bright magnitudes.
