Template Fitting Method Research Articles

Euclid preparation

Euclid will collect an enormous amount of data during the mission’s lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning (ML) algorithms have been presented for computing their photometric redshifts and physical parameters (PPs), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information entering the model (the features), to a level where the recovery of some well-established physical relationships between parameters might not be guaranteed – for example, the star-forming main sequence (SFMS). To forecast the reliability of Euclid photo-zs and PPs calculations, we produced two mock catalogs simulating the photometry with the UNIONS ugriz and Euclid filters. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF), alongside two auxiliary fields. We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-zs, PPs (stellar masses and star formation rates), and the SFMS on the simulated Euclid fields. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels and tested on the simulated ground truth. For the EWS, we found that the best results are achieved with a mixed labels approach, training the models with wide survey features and labels from the Phosphoros results on deeper photometry, that is, with the best possible set of labels for a given photometry. This imposes a prior to the input features, helping the models to better discern cases in degenerate regions of feature space, that is, when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than 3% performance degradation using a COSMOS-like reference sample or removing u band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-z, PPs, and the SFMS.

Photometric redshift estimation for CSST survey with LSTM neural networks

ABSTRACT Accurate estimation of photometric redshifts (photo-zs) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based on recurrent neural networks with long short-term memory (LSTM) to predict photo-z. Unlike many existing machine learning models, our method requires only flux measurements from different observed filters as input. The model can automatically learn the complex relationships between the flux data across different wavelengths, eliminating the need for manually extracted or derived input features, thereby providing precise photo-z estimates. The effectiveness of our proposed model is evaluated using simulated data from the Chinese Space Station Telescope (CSST) sourced from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and the COSMOS catalogue, considering anticipated instrument effects of the future CSST. Results from experiments demonstrate that our LSTM model, compared to commonly used template fitting and machine learning approaches, requires minimal input parameters and achieves high precision in photo-z estimation. For instance, when trained on the same data set and provided only with photometric fluxes as input features, the proposed LSTM model yields one-third of the outliers $f_{\mathrm{out}}$ observed with a multi-layer perceptron neural network (MLP) model, while the normalized median absolute deviation $\rm \sigma _{NMAD}$ is only two-thirds that of the MLP model. This study presents a novel approach to accurately estimate photo-zs of galaxies using photometric data from large-scale survey projects.

GAINN: The Galaxy Assembly and Interaction Neural Networks for High-redshift JWST Observations

We present the Galaxy Assembly and Interaction Neural Networks (Gainn), a series of artificial neural networks for predicting the redshift, stellar mass, halo mass, and mass-weighted age of simulated galaxies based on James Webb Space Telescope (JWST) photometry. Our goal is to determine the best neural network for predicting these variables at 11 < z < 15. The parameters of the optimal neural network can then be used to estimate these variables for real, observed galaxies. The inputs of the neural networks are JWST filter magnitudes of a subset of five broadband filters (F150W, F200W, F277W, F356W, and F444W) and two medium-band filters (F162M and F182M). We compare the performance of the neural networks using different combinations of these filters, as well as different activation functions and numbers of layers. The best neural network predicted redshift with a normalized rms error of 0.010−0.001+0.003 , stellar mass with rms = 0.089−0.022+0.044 , halo mass with a mean-squared error of 0.022−0.008+0.014 , and mass-weighted age with rms = 12.466−2.408+5.065 . We also test the performance of Gainn on real data from MACS0647JD, an object observed by JWST. Predictions from Gainn for the first projection of the object (JD1) have normalized bias 〈Δz〉 < 0.00228, which is significantly smaller than found with template-fitting methods. We find that the optimal filter combination is F277W, F356W, F162M, and F200W when considering both theoretical accuracy and observational resources from JWST.

Hayate: photometric redshift estimation by hybridizing machine learning with template fitting

ABSTRACT Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template-fitting methods but may not generalize well on new data that deviates from that in the training set. In this work, we present a Hybrid Algorithm for WI(Y)de-range photo-z estimation with Artificial neural networks and TEmplate fitting (hayate), a novel photo-z method that combines template fitting and data-driven approaches and whose training loss is optimized in terms of both redshift point estimates and probability distributions. We produce artificial training data from low-redshift galaxy spectral energy distributions (SEDs) at z &amp;lt; 1.3, artificially redshifted up to z = 5. We test the model on data from the ZFOURGE surveys, demonstrating that hayate can function as a reliable emulator of eazy for the broad redshift range beyond the region of sufficient spectroscopic completeness. The network achieves precise photo-z estimations with smaller errors (σNMAD) than eazy in the initial low-z region (z &amp;lt; 1.3), while being comparable even in the high-z extrapolated regime (1.3 &amp;lt; z &amp;lt; 5). Meanwhile, it provides more robust photo-z estimations than eazy with the lower outlier rate ($\eta _{0.2}\lesssim 1~{{\ \rm per\ cent}}$) but runs ∼100 times faster than the original template-fitting method. We also demonstrate hayate offers more reliable redshift probability density functions, showing a flatter distribution of Probability Integral Transform scores than eazy. The performance is further improved using transfer learning with spec-z samples. We expect that future large surveys will benefit from our novel methodology applicable to observations over a wide redshift range.

Estimating photometric redshift from mock flux for CSST survey by using weighted Random Forest

ABSTRACT Accurate estimation of photometric redshifts (photo-z) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to estimate photo-z and investigate the systematic uncertainties affecting the results. Using galaxy flux and colour as input features, we construct a mapping between input features and redshift by using a training set of simulated data, generated from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and COSMOS catalogue, with the expected instrumental effects of the planned China Space Station Telescope (CSST). To improve the accuracy and confidence of predictions, we incorporate inverse variance weighting and perturb the catalogue using input feature errors. Our results show that weighted RF can achieve a photo-z accuracy of $\rm \sigma _{NMAD}=0.025$ and an outlier fraction of $\rm \eta =2.045\ \hbox{per cent}$, significantly better than the values of $\rm \sigma _{NMAD}=0.043$ and $\rm \eta =6.45\ \hbox{per cent}$ obtained by the widely used Easy and Accurate Zphot from Yale (EAZY) software, which uses template-fitting method. Furthermore, we have calculated the importance of each input feature for different redshift ranges and found that the most important input features reflect the approximate position of the break features in galaxy spectra, demonstrating the algorithm’s ability to extract physical information from data. Additionally, we have established confidence indices and error bars for each prediction value based on the shape of the redshift probability distribution function, suggesting that screening sources with high confidence can further reduce the outlier fraction.

Position-Specific Isotope Analysis of Propane by Mid-IR Laser Absorption Spectroscopy.

Intramolecular or position-specific carbon isotope analysis of propane (13CH3-12CH2-12CH3 and 12CH3-13CH2-12CH3) provides unique insights into its formation mechanism and temperature history. The unambiguous detection of such carbon isotopic distributions with currently established methods is challenging due to the complexity of the technique and the tedious sample preparation. We present a direct and nondestructive analytical technique to quantify the two singly substituted, terminal (13Ct) and central (13Cc), propane isotopomers, based on quantum cascade laser absorption spectroscopy. The required spectral information on the propane isotopomers was first obtained using a high-resolution Fourier-transform infrared (FTIR) spectrometer and then used to select suitable mid-infrared regions with minimal spectral interference to obtain the optimum sensitivity and selectivity. We then measured high-resolution spectra around 1384 cm-1 of both singly substituted isotopomers by mid-IR quantum cascade laser absorption spectroscopy using a Stirling-cooled segmented circular multipass cell (SC-MPC). The spectra of the pure propane isotopomers were acquired at both 300 and 155 K and served as spectral templates to quantify samples with different levels of 13C at the central (c) and terminal (t) positions. A prerequisite for the precision using this reference template fitting method is a good match of amount fraction and pressure between the sample and templates. For samples at natural abundance, we achieved a precision of 0.33 ‰ for δ13Ct and 0.73 ‰ for δ13Cc values within 100 s integration time. This is the first demonstration of site-specific high-precision measurements of isotopically substituted non-methane hydrocarbons using laser absorption spectroscopy. The versatility of this analytical approach may open up new opportunities for the study of isotopic distribution of other organic compounds.

Combining the CLAUDS and HSC-SSP surveys

We present the combination of the Canada-France-Hawaii Telescope (CHFT) Large AreaU-bands Deep Survey (CLAUDS) and the Hyper-Suprime-Cam (HSC) Subaru Strategic Program (HSC-SSP) data over their four deep fields. We provide photometric catalogs foru, u*(CFHT-MegaCam),g,r, i, z, andy(Subaru-HSC) bands over ~20 deg2, complemented in two fields by data from the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey and the UltraVISTA survey, thus extending the wavelength coverage toward near-infrared with VIRCAMY, J, H, andKsobservations over 5.5 deg2. The extraction of the photometry was performed with two different softwares: the HSC pipelinehscPipeand the standard and robustSExtractorsoftware. Photometric redshifts were computed with template-fitting methods using the newPhosphoroscode for thehscPipephotometry and the well-knownLe Pharecode for theSExtractorphotometry. The products of these methods were compared with each other in detail. We assessed their quality using the large spectroscopic sample available in those regions, together with photometry and photometric redshifts from COSMOS2020, the latest version of the Cosmic Evolution Survey catalogs. We find that both photometric data sets are in good agreement inUgrizydown to magnitude ~26, and to magnitude ~24.5 in theYJHKsbands. We achieve good performance for the photometric redshifts, reaching precisions of σNMAD≲ 0.04 down tomi~25, even using only the CLAUDS and HSC bands. At the same magnitude limit, we measured an outlier fraction ofη≲ 10% when using theUgrizybands, and down toη≲ 6% when considering near-infrared data. ThehscPipeplusPhosphorospipeline performs slightly worse in terms of photometric-redshifts precision and outlier fraction than itsSExtractorplusLe Pharecounterpart, which has essentially been tracked down to differences in the photometry. Thus, this work is also a validation of thePhosphoroscode. The photometric catalogs with the data and photometric redshifts from the two pipelines are presented and made publicly available.

Measuring photometric redshifts for high-redshift radio source surveys

Abstract With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to $z = 3$ before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify $\approx$ 76% of the highest redshift sources—sources at redshift $z &gt; 2.51$ —as being in either the highest bin ( $z &gt; 2.51$ ) or second highest ( $z = 2.25$ ).

Spectroscopic redshift determination with Bayesian convolutional networks

Astronomy is facing large amounts of data, so astronomers have to rely on automated methods to analyse them. However, automated methods might produce incorrect values. Therefore, we need to develop different automated methods and perform a consistency check to identify them. If there is a lot of labelled data, convolutional neural networks are a powerful method for any task. We illustrate the consistency check on spectroscopic redshift determination with a method based on a Bayesian convolutional neural network inspired by VGG networks. The method provides predictive uncertainties that enable us to (1.) determine unusual or problematic spectra for visual inspection; (2.) do thresholding that allows us to balance between the error of redshift predictions and coverage. We used the 12th Sloan Digital Sky Survey quasar superset as the training set for the method. We evaluated its generalisation capability on about three-quarters of a million spectra from the 16th quasar superset of the same survey. On the 16th quasar superset, the method performs better in terms of the root-mean-squared error than the most used template fitting method. Using redshift predictions of the proposed method, we identified spectra with incorrectly determined redshifts that are unrecognised quasars or were misclassified as them.

Hybrid photometric redshifts for sources in the COSMOS and XMM-LSS fields

ABSTRACT In this paper, we present photometric redshifts for 2.7 million galaxies in the XMM-LSS and COSMOS fields, both with rich optical and near-infrared data from VISTA and HyperSuprimeCam. Both template fitting (using galaxy and active galactic nuclei templates within LePhare) and machine learning (using gpz) methods are run on the aperture photometry of sources selected in the Ks-band. The resulting predictions are then combined using a Hierarchical Bayesian model, to produce consensus photometric redshift point estimates and probability distribution functions that outperform each method individually. Our point estimates have a root mean square error of ∼0.08–0.09, and an outlier fraction of ∼3–4 percent when compared to spectroscopic redshifts. We also compare our results to the COSMOS2020 photometric redshifts, which contain fewer sources, but had access to a larger number of bands and greater wavelength coverage, finding that comparable photo-z quality can be achieved (for bright and intermediate luminosity sources where a direct comparison can be made). Our resulting redshifts represent the most accurate set of photometric redshifts (for a catalogue this large) for these deep multisquare degree multiwavelength fields to date.

Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. It achieves single-point photometric redshifts with scatter ($\sigma_\text{NMAD}$) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r-auto magnitudes between 16 and 21.

Dissecting the inner Galaxy with gamma-ray pixel count statistics

The nature of the GeV gamma-ray Galactic center excess (GCE) in the data of Fermi-LAT is still under investigation. Different techniques, such as template fitting and photon-count statistical methods, have been applied in the past few years in order to disentangle between a GCE coming from sub-threshold point sources or rather from diffuse emissions, such as the dark matter annihilation in the Galactic halo. A major limit to all these studies is the modeling of the Galactic diffuse foreground, and the impact of residual mis-modeled emission on the results’ robustness. We combine for the first time adaptive template fitting and pixel count statistical methods in order to assess the role of sub-threshold point sources to the GCE, while minimizing the mis-modelling of diffuse emission components. We reconstruct the flux distribution of point sources in the inner Galaxy well below the Fermi-LAT detection threshold, and measure their radial and longitudinal profiles. We find that point sources and diffuse emission from the Galactic bulge each contributes about 10% of the total emission therein, disclosing a potential sub-threshold point-source contribution to the GCE.

Limits on Polarized Dust Spectral Index Variations for CMB Foreground Analysis

Abstract Using Planck polarization data, we search for and constrain spatial variations of the polarized dust foreground for cosmic microwave background (CMB) observations, specifically in its spectral index, β d . Failure to account for such variations will cause errors in the foreground cleaning that propagate into errors on cosmological parameter recovery from the cleaned CMB map. It is unclear how robust prior studies of the Planck data that constrained β d variations are due to challenges with noise modeling, residual systematics, and priors. To clarify constraints on β d and its variation, we employ two pixel space analyses of the polarized dust foreground at &gt;3.°7 scales on ≈60% of the sky at high Galactic latitudes. A template fitting method, which measures β d over three regions of ≈20% of the sky, does not find significant deviations from a uniform β d = 1.55, consistent with prior Planck determinations. An additional analysis in these regions, based on multifrequency fits to a dust and CMB model per pixel, puts limits on σ β d , the Gaussian spatial variation in β d . The data support σ β d up to 0.45 at the highest latitudes, 0.30 at midlatitudes, and 0.15 at low latitudes. We also demonstrate that care must be taken when interpreting the current Planck constraints, β d maps, and noise simulations. Due to residual systematics and low dust signal-to-noise ratios at high latitudes, forecasts for ongoing and future missions should include the possibility of large values of σ β d as estimated in this paper, based on current polarization data.

Two-particle azimuthal correlations in photonuclear ultraperipheral Pb+Pb collisions at 5.02 TeV with ATLAS

Two-particle long-range azimuthal correlations are measured in photonuclear collisions using 1.7 nb$^{-1}$ of 5.02 TeV Pb+Pb collision data collected by the ATLAS experiment at the LHC. Candidate events are selected using a dedicated high-multiplicity photonuclear event trigger, a combination of information from the zero-degree calorimeters and forward calorimeters, and from pseudorapidity gaps constructed using calorimeter energy clusters and charged-particle tracks. Distributions of event properties are compared between data and Monte Carlo simulations of photonuclear processes. Two-particle correlation functions are formed using charged-particle tracks in the selected events, and a template-fitting method is employed to subtract the non-flow contribution to the correlation. Significant nonzero values of the second- and third-order flow coefficients are observed and presented as a function of charged-particle multiplicity and transverse momentum. The results are compared with flow coefficients obtained in proton-proton and proton-lead collisions in similar multiplicity ranges, and with theoretical expectations. The unique initial conditions present in this measurement provide a new way to probe the origin of the collective signatures previously observed only in hadronic collisions.

Spectroscopic and Photometric Redshift Estimation by Neural Networks for the China Space Station Optical Survey (CSS-OS)

Abstract The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of the universe, such as weak lensing and galaxy clustering. In this work, we explore the accuracies of spec-z and photo-z that can be obtained by the China Space Station Optical Surveys, which is a next-generation space survey, using a neural network. The one-dimensional Convolutional Neural Networks and Multi-Layer Perceptron (MLP, the simplest form of an artificial neural network) are employed to derive spec-z and photo-z, respectively. The mock spectral and photometric data used for training and testing the networks are generated based on the COSMOS catalog. The networks have been trained with noisy data by creating Gaussian random realizations to reduce the statistical effects, resulting in a similar redshift accuracy for data with both high and low signal-to-noise ratios. The probability distribution functions of the predicted redshifts are also derived via Gaussian random realizations of the testing data, and then the best-fit redshifts and 1σ errors also can be obtained. We find that our networks can provide excellent redshift estimates with accuracies of ∼0.001 and 0.01 on spec-z and photo-z, respectively. Compared to existing photo-z codes, our MLP has a similar accuracy but is more efficient in the training process. The fractions of catastrophic redshifts or outliers can be dramatically suppressed compared to the ordinary template-fitting method. This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys.

Comparison of Observed Galaxy Properties with Semianalytic Model Predictions Using Machine Learning

Abstract With current and upcoming experiments such as the Wide Field Infrared Survey Telescope, Euclid, and Large Synoptic Survey Telescope, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data set behaves like a high-dimensional nonlinear surface, an excellent target for machine learning. In this work, we use a lightcone of semianalytic galaxies tuned to match Cosmic Assembly Near-infrared Deep Legacy Survey (CANDELS) observations from Lu et al. to train a set of neural networks on a set of galaxy physical properties. We add realistic photometric noise and use trained neural networks to predict stellar masses and average star formation rates (SFRs) on real CANDELS galaxies, comparing our predictions to SED-fitting results. On semianalytic galaxies, we are nearly competitive with template-fitting methods, with biases of 0.01 dex for stellar mass, 0.09 dex for SFR, and 0.04 dex for metallicity. For the observed CANDELS data, our results are consistent with template fits on the same data at 0.15 dex bias in and 0.61 dex bias in the SFR. Some of the bias is driven by SED-fitting limitations, rather than limitations on the training set, and some is intrinsic to the neural network method. Further errors are likely caused by differences in noise properties between the semianalytic catalogs and data. Our results show that galaxy physical properties can in principle be measured with neural networks at a competitive degree of accuracy and precision to template-fitting methods.

The effect of emission lines on the performance of photometric redshift estimation algorithms

ABSTRACT We investigate the effect of strong emission line galaxies on the performance of empirical photometric redshift estimation methods. In order to artificially control the contribution of photometric error and emission lines to total flux, we develop a PCA-based stochastic mock catalogue generation technique that allows for generating infinite signal-to-noise ratio model spectra with realistic emission lines on top of theoretical stellar continua. Instead of running the computationally expensive stellar population synthesis and nebular emission codes, our algorithm generates realistic spectra with a statistical approach, and – as an alternative to attempting to constrain the priors on input model parameters – works by matching output observational parameters. Hence, it can be used to match the luminosity, colour, emission line and photometric error distribution of any photometric sample with sufficient flux-calibrated spectroscopic follow-up. We test three simple empirical photometric estimation methods and compare the results with and without photometric noise and strong emission lines. While photometric noise clearly dominates the uncertainty of photometric redshift estimates, the key findings are that emission lines play a significant role in resolving colour space degeneracies and good spectroscopic coverage of the entire colour space is necessary to achieve good results with empirical photo-z methods. Template-fitting methods, on the other hand, must use a template set with sufficient variation in emission line strengths and ratios, or even better, first estimate the redshift empirically and fit the colours with templates at the best-fit redshift to calculate the K-correction and various physical parameters.

Photometric redshifts in the North Ecliptic Pole Wide field based on a deep optical survey with Hyper Suprime-Cam

ABSTRACT The AKARI space infrared telescope has performed near-infrared to mid-infrared (MIR) observations on the North Ecliptic Pole Wide (NEPW) field (5.4 deg2) for about 1 yr. AKARI took advantage of its continuous nine photometric bands, compared with NASA's Spitzer and Wide-field Infrared Survey Explorer(WISE) space telescopes, which had only four filters with a wide gap in the MIR. The AKARI NEPW field lacked deep and homogeneous optical data, limiting the use of nearly half of the IR sources for extragalactic studies, because of the absence of photometric redshift (photo-z). To remedy this, we have recently obtained deep optical imaging over the NEPW field with five bands (g, r, i, z and Y) of the Hyper Suprime-Camera (HSC) on the Subaru 8-m telescope. We optically identify AKARI-IR sources along with supplementary Spitzer and WISE data as well as pre-existing optical data. In this work, we derive new photo-z using a χ2 template-fitting method code, PHotometric Analysis for Redshift Estimate (Le Phare) and reliable photometry from 26 selected filters including HSC, AKARI, Canada–France–Hawaii Telescope, Maidanak, Kitt Peak National Observatory, Spitzer and WISE data. We take 2026 spectroscopic redshifts (spec-z) from all available spectroscopic surveys over the NEPW field to calibrate and assess the accuracy of the photo-z. At z &amp;lt; 1.5, we achieve a weighted photo-z dispersion of σΔz/(1+z) = 0.053 with η = 11.3 per cent catastrophic errors.

Euclidpreparation

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2 − 2.6 redshift range that theEuclidmission will probe. We designed a challenge using emulatedEucliddata drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-zdeviates by more than 0.15(1 + z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for examplez &gt; 1. However they generally perform better than template-fitting methods at low redshift (z &lt; 0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-zprecision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear fromEuclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.

Revival of the Dark Matter Hypothesis for the Galactic Center Gamma-Ray Excess.

Statistical evidence has previously suggested that the galactic center GeV excess (GCE) originates largely from point sources, and not from annihilating dark matter. We examine the impact of unmodeled source populations on identifying the true origin of the GCE using non-Poissonian template fitting (NPTF) methods. In a proof-of-principle example with simulated data, we discover that unmodeled sources in the Fermi bubbles can lead to a dark matter signal being misattributed to point sources by the NPTF. We discover striking behavior consistent with a mismodeling effect in the real Fermi data, finding that large artificial injected dark matter signals are completely misattributed to point sources. Consequently, we conclude that dark matter may provide a dominant contribution to the GCE after all.