Sloan Digital Sky Survey Data Research Articles

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Star Formation and Dust in the Cosmic Web

The large-scale environment of the cosmic web is believed to impact galaxy evolution, but there is still no consensus regarding the mechanisms. We use a semi-analytic model (SAM) galaxy catalog to study the star formation and dust content of local galaxies in different cosmic environments of the cosmic web—namely voids, filaments, walls, and nodes. We find a strong impact of the environment only for galaxies with M stars ≲ 1010.8 M ⊙: the less dense the environment, the larger the star formation rate and dust content at fixed stellar mass. This is attributed to the fact that galaxies in less dense environments typically feature younger stellar populations, a slower evolution of their stellar mass, and delayed star formation compared to galaxies in denser environments. As for galaxies with M stars ≳ 1010.8 M ⊙, the differences among environments are milder, due to the disk-instability-driven supermassive black hole (SMBH) growth implemented in the SAM, which makes SMBH growth, and thus galaxy quenching, environment-insensitive. We qualitatively test our predictions against observations by identifying environments in Sloan Digital Sky Survey Data Release 16 using dust masses derived from the GAMA survey. The agreement is encouraging, particularly at logMstars/M⊙≳10.5–11 , where the specific star formation rates and dust masses appear quite environment-insensitive. This result confirms the importance of in situ growth channels of SMBHs.

Optical Photometric Indicators of Galaxy Cluster Relaxation

The most dynamically relaxed clusters of galaxies play a special role in cosmological studies as well as astrophysical studies of the intracluster medium (ICM) and active galactic nucleus feedback. While high-spatial-resolution imaging of the morphology of the ICM has long been the gold standard for establishing a cluster’s dynamical state, such data are not available from current or planned surveys, and thus require separate, pointed follow-up observations. With optical and/or near-IR photometric imaging, and red-sequence cluster finding results from those data, expected to be ubiquitously available for clusters discovered in upcoming optical and millimeter-wavelength surveys, it is worth asking how effectively photometric data alone can identify relaxed cluster candidates, before investing in, e.g., high-resolution X-ray observations. Here we assess the ability of several simple photometric measurements, based on the redMaPPer cluster finder run on Sloan Digital Sky Survey data, to reproduce X-ray classifications of dynamical state for an X-ray selected sample of massive clusters. We find that two simple metrics contrasting the bright central galaxy (BCG) to other cluster members can identify a complete sample of relaxed clusters with a purity of ∼40% in our data set. Including minimal ICM information in the form of a center position increases the purity to ∼60%. However, all three metrics depend critically on correctly identifying the BCG, which is presently a challenge for optical red-sequence cluster finders.

Changing-look Active Galactic Nuclei. I. Tracking the Transition on the Main Sequence of Quasars

This paper is the first in a series of papers that prepare and analyze spectral and other properties for a database of already-discovered changing-look active galactic nuclei (CL AGNs). Here we focus on the spectral fitting and analysis of broad emission lines in a sample of 93 CL AGNs collected from the literature with existing Sloan Digital Sky Survey (SDSS)/Baryon Oscillation Spectroscopic Survey (BOSS)/extended-BOSS spectroscopy where the Hβ emission-line profile does not completely disappear in any epochs. Additionally, we have gathered older/newer spectral epochs from all the available SDSS data releases to make the database more complete. We use PyQSOFit and perform a homogeneous spectral decomposition of all of our SDSS spectra and tabulate the AGN continuum and emission-line properties per epoch per source, chronologically. This further allows us to categorize the sources in our sample as Turn-On or Turn-Off and subsequently check for repeated occurrences of such phases. We then estimate the black hole mass (M BH) and the Eddington ratio (λ Edd) per epoch per source where the required parameters are available and well estimated. We demonstrate the movement of the source in the M BH–λ Edd plane, allowing us to check for systematic changes in the source’s fundamental properties. We then track their transition along the optical plane of the Eigenvector 1 schema and categorize sources that either stay within the same population (A or B) or make an interpopulation movement as a function of spectral epoch. We also test the Balmer decrement (Hα/Hβ) of a subset of our sample of CL AGNs as a function of time and AGN luminosity.

StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys

Extracting precise stellar labels is crucial for large spectroscopic surveys like the Sloan Digital Sky Survey (SDSS) and APOGEE. In this paper, we report the newest implementation of StellarGAN, a data-driven method based on generative adversarial networks (GANs). Using 1D operators like convolution, the 2D GAN is modified into StellarGAN. This allows it to learn the relevant features of 1D stellar spectra without needing labels for specific stellar types. We test the performance of StellarGAN on different stellar spectra trained on SDSS and APOGEE data sets. Our result reveals that StellarGAN attains the highest overall F1-score on SDSS data sets (F1-score = 0.82, 0.77, 0.74, 0.53, 0.51, 0.61, and 0.55, for O-type, B-type, A-type, F-type, G-type, K-type, and M-type stars) when the signal-to-noise ratio (S/N) is low (90% of the spectra have an S/N < 50), with 1% of labeled spectra used for training. Using 50% of the labeled spectral data for training, StellarGAN consistently demonstrates performance that surpasses or is comparable to that of other data-driven models, as evidenced by the F1-scores of 0.92, 0.77, 0.77, 0.84, 0.84, 0.80, and 0.67. In the case of APOGEE (90% of the spectra have an S/N < 500), our method is also superior regarding its comprehensive performance (F1-score = 0.53, 0.60, 0.56, 0.56, and 0.78 for A-type, F-type, G-type, K-type, and M-type stars) with 1% of labeled spectra for training, manifesting its learning ability out of a limited number of labeled spectra. Our proposed method is also applicable to other types of data that need to be classified (such as gravitational-wave signals, light curves, etc.).

Living with Neighbors. V. Better-aligned Spiral+Spiral Galaxy Pairs Show Stronger Star Formation

Enhanced star formation (SF) with star-forming neighboring galaxies bolsters the hydrodynamical contributions during paired interactions. Although the relative spin orientation between interacting galaxies can influence this effect, it has not been comprehensively explored. In this study, utilizing a meticulously curated sample of nearby (0.02 < z < 0.06) spiral+spiral pairs and an isolated control sample from Sloan Digital Sky Survey Data Release 7, coupled with Galaxy Zoo 2, and an approach to estimate spin–spin alignment (SSA), we systematically compare the impact of relative orientation, cosξ , on interaction-induced SF. We reveal that SSA plays a pivotal role along with the conventionally recognized parameters of projected separation and SF of neighboring galaxies. Our results show an increase in SF augmentation as configurations transition from perpendicular ( cosξ∼0 ) to well aligned ( cosξ>0.7 ). This enhancement is especially pronounced when neighboring galaxies have higher SF, suggesting that hydrodynamical processes drive the effects of relative orientation. The heightened hydrodynamical friction, attributed to increased ram pressure and the propensity for prograde orbits in well-aligned pairs, is consistent with our findings.

The Mass Assembly History for Galaxies with MaNGA

How galaxies assemble masses through their own star formation or interaction with the external environment is still an important topic in the field of galaxy formation and evolution. We use Value Added Catalogs with galaxy features that are spatially and temporally resolved from Sloan Digital Sky Survey Data Release 17 to investigate the mass growth histories of early-type galaxies (ETGs) and late-type galaxies (LTGs). We find that the mass growth of ETGs is earlier than that of LTGs for massive galaxies (M * > 1010 M ⊙), while low-mass (M * ≤ 1010 M ⊙) ETGs have statistically similar mass assembly histories as low-mass LTGs. The stellar metallicity of all massive galaxies shows a negative gradient and basically does not change with time. However, in low-mass galaxies, the stellar metallicity gradient of elliptical galaxies is negative, and the stellar metallicity gradient of lenticular and spiral galaxies evolves from positive to negative. ETGs are not all in a high-density environment, but exhibit mass dependence. As the tidal strength increases, the star formation rate of low-mass ETGs rapidly decreases. These results support a picture where massive galaxies exhibit inside-out quenching mode, while low-mass galaxies show outside-in quenching mode. Environmental effects play an important role in regulating the mass assembly histories of low-mass ETGs.

Black Hole Mass and Eddington-ratio Distributions of Less-luminous Quasars at z ∼ 4 in the Subaru Hyper Suprime-Cam Wide Field

We investigate the black hole mass function (BHMF) and Eddington-ratio distribution function (ERDF) of broad-line active galactic nuclei (AGNs) at z = 4, based on a sample of 52 quasars with i < 23.2 at 3.50 ≤ z ≤ 4.25 from the Hyper Suprime-Cam Subaru Strategic Program S16A-Wide2 data set, and 1462 quasars with i < 20.2 in the same redshift range from the Sloan Digital Sky Survey data release 7 quasar catalog. Virial black hole (BH) masses of quasars are estimated using the width of the CIV 1549 Å line and the continuum luminosity at 1350 Å. To obtain the intrinsic broad-line AGN BHMF and ERDF, we correct for the incompleteness in the low-mass and/or low-Eddington-ratio ranges caused by the flux-limited selection. The resulting BHMF is constrained down to logMBH/M⊙∼7.5 . In comparison with broad-line AGN BHMFs at z ∼ 2 in the literature, we find that the number density of massive SMBHs peaks at higher redshifts, consistent with the downsizing evolutionary scenario. Additionally, the resulting ERDF shows a negative dependence on BH mass, suggesting more massive SMBHs tend to accrete at lower-Eddington ratios at z = 4. With the derived intrinsic broad-line AGN BHMF, we also evaluate the active fraction of broad-line AGNs among the entire SMBH population at z = 4. The resulting active fraction may suggest a positive dependence on BH mass. Finally, we examine the time evolution of broad-line AGN BHMF between z = 4 and 6 through solving the continuity equation. The results suggest that the broad-line AGN BHMFs at z = 4–6 only show evolution in their normalization, but with no significant changes in their shape.

Outflow-related radio emission in radio-quiet quasars

ABSTRACT In this work, we revisit the relationship between [O iii] line width w90 (as the indicator of active galactic nucleus outflow velocity) and the radio emission in radio-quiet quasars (RQQs) by employing a large sample of Type I quasars (∼37 000) selected from the Sloan Digital Sky Survey (SDSS) Data Release 16. By median stacking the radio images (to include the dominant fraction of individually radio non-detected RQQs) of Karl G. Jansky Very Large Array Sky Survey for subsamples of RQQs with different w90, our study demonstrates that the correlation between w90 and radio emission in our SDSS RQQs is significant, and remains solid after controlling the effects of black hole mass, quasar luminosity, Eddington ratio, and redshift. This intrinsic link supports that the [O iii] outflows in quasars, most likely resulting from wide-angled sub-relativistic quasar winds launched from the accretion disc, could make a dominant contribution to radio emission in the general RQQs. Alternatively, the correlation may be attributed to low-power jets in RQQs if they are ubiquitous and could efficiently enhance the [O iii] width through interacting with the interstellar medium. Meanwhile, the star formation rates traced by the flux ratio of [Ne v]/[O ii] emission lines display no dependence on w90 after controlling the effects of black hole mass, quasar luminosity, Eddington ratio, and redshift. This suggests that the stronger radio emission in RQQs with larger w90 could not be attributed to outflow-enhanced (positive feedback) star formation in the hosts. However, this also indicates that the outflows, though exhibiting robust correlation with radio power, produce neither positive nor negative feedback to the star formation in their hosts.

A Markedly Expanded Sample of Candidate X-Ray-emitting Isolated Neutron Stars

The current sample of 12 radio-quiet isolated neutron stars that emit strongly in X-rays (XINSs) is both small and heterogeneous, limiting its usefulness for understanding the physics of neutron star atmospheres and cooling rates and for constraining the equation of state of neutron degenerate matter. Utilizing the ROSAT 1RXS and 2RXS data sets, in conjunction with the Sloan Digital Sky Survey Data Release 17 and other companion multiwavelength surveys, we have extended previous searches for blank-field X-ray source candidate XINSs, ultimately recovering two known XINSs while identifying 46 new, unstudied candidate fields devoid of likely multiwavelength counterparts. In this publication, we describe our selection approach and provide detailed information regarding our sample of new candidate XINSs. Future opportunities to verify or to refute these X-ray sources as isolated neutron stars by obtaining more accurate X-ray source positions, quality X-ray spectra, or deeper optical imaging are also discussed.

Study of the Blazhko Type RRc stars in the Stripe 82 Region Using SDSS and ZTF

RR Lyrae stars (RRLs) are pulsating stars, many of which also show a long-term variation called the Blazhko effect which is a modulation of amplitude and phase of the lightcurve. In this work, we searched for the incidence rate of the Blazhko effect in the first-overtone pulsating RR Lyrae (RRc) stars of the Galactic halo. The focus was on the Stripe 82 region in the Galactic halo which was studied by Sesar et al. using the Sloan Digital Sky Survey (SDSS) data. In their work, 104 RRLs were classified as RRc type. We combined their SDSS light curves with Zwicky Transient Facility (ZTF) data, and used them to document the Blazhko properties of these RRc stars. Our analysis showed that among the 104 RRc stars, eight were rather RRd stars, and were excluded from the study. Out of the remaining 96, 34 were Blazhko type, 62 were non-Blazhko type, giving the incidence rate of 35.42% for Blazhko RRc stars. The shortest Blazhko period found was 12.808 ± 0.001 days for SDSS 747380, while the longest was 3088 ± 126 days for SDSS 3585856. Combining SDSS and ZTF data sets increased the probability of detecting the small variations due to the Blazhko effect, and thus provided a unique opportunity to find longer Blazhko periods. We found that 85% of RRc stars had the Blazhko period longer than 200 days.

Finding Multiply Lensed and Binary Quasars in the DESI Legacy Imaging Surveys

The time delay between multiple images of strongly lensed quasars is a powerful tool for measuring the Hubble constant (H 0). To achieve H 0 measurements with higher precision and accuracy using the time delay, it is crucial to expand the sample of lensed quasars. We conduct a search for strongly lensed quasars in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. The DESI Legacy Surveys comprise 19,000 deg2 of the extragalactic sky observed in three optical bands (g, r, and z), making it well suited for the discovery of new strongly lensed quasars. We apply an autocorrelation algorithm to ∼5 million objects classified as quasars in the DESI Quasar Sample. These systems are visually inspected and ranked. Here, we present 436 new multiply lensed and binary quasar candidates, 65 of which have redshifts from Sloan Digital Sky Survey Data Release 16. We provide redshifts for an additional 18 candidates from the SuperNova Integral Field Spectrograph.

Edge-on Low-surface-brightness Galaxy Candidates Detected from SDSS Images Using YOLO

Low-surface-brightness galaxies (LSBGs), fainter members of the galaxy population, are thought to be numerous. However, due to their low surface brightness, the search for a wide-area sample of LSBGs is difficult, which in turn limits our ability to fully understand the formation and evolution of galaxies as well as galaxy relationships. Edge-on LSBGs, due to their unique orientation, offer an excellent opportunity to study galaxy structure and galaxy components. In this work, we utilize the You Only Look Once object detection algorithm to construct an edge-on LSBG detection model by training on 281 edge-on LSBGs in Sloan Digital Sky Survey (SDSS) gri-band composite images. This model achieved a recall of 94.64% and a purity of 95.38% on the test set. We searched across 938,046 gri-band images from SDSS Data Release 16 and found 52,293 candidate LSBGs. To enhance the purity of the candidate LSBGs and reduce contamination, we employed the Deep Support Vector Data Description algorithm to identify anomalies within the candidate samples. Ultimately, we compiled a catalog containing 40,759 edge-on LSBG candidates. This sample has similar characteristics to the training data set, mainly composed of blue edge-on LSBG candidates. The catalog is available online at https://github.com/worldoutside/Edge-on_LSBG.

Narrow-line Seyfert 1 galaxies in Sloan Digital Sky Survey: a new optical spectroscopic catalogue

ABSTRACT Narrow-line Seyfert 1 (NLSy1) galaxies are an enigmatic class of active galactic nuclei (AGN) that exhibit peculiar multiwavelength properties across the electromagnetic spectrum. For example, these sources have allowed us to explore the innermost regions of the central engine of AGN using X-ray observations and have also provided clues about the origin of relativistic jets considering radio and gamma-ray bands. Keeping in mind the ongoing and upcoming wide-field, multifrequency sky surveys, we present a new catalogue of NLSy1 galaxies. This was done by carrying out a detailed decomposition of &amp;gt;2 million optical spectra of quasars and galaxies from the Sloan Digital Sky Survey Data Release 17 (SDSS-DR17) using the publicly available software ‘Bayesian AGN Decomposition Analysis for SDSS Spectra’. The catalogue contains 22656 NLSy1 galaxies which is more than twice the size of the previously identified NLSy1s based on SDSS-DR12. As a corollary, we also release a new catalogue of 52273 broad-line Seyfert 1 (BLSy1) galaxies. The estimated optical spectral parameters and derived quantities confirm the previously known finding of NLSy1 galaxies being AGN powered by highly accreting, low-mass black holes. We conclude that this enlarged sample of NLSy1 and BLSy1 galaxies will enable us to explore the low-luminosity end of the AGN population by effectively utilizing the sensitive, high-quality observations delivered by ongoing/upcoming wide-field sky surveys. The catalogue has been made public at https://www.ucm.es/blazars/seyfert.

Cosmological Parameter Constraints from the SDSS Density and Momentum Power Spectra

We extract the galaxy density and momentum power spectra from a subset of early-type galaxies in the Sloan Digital Sky Survey (SDSS) DR7 main galaxy catalog. Using galaxy distance information inferred from the improved fundamental plane described in Yoon and Park, we reconstruct the peculiar velocities of the galaxies and generate number density and density-weighted velocity fields, from which we extract the galaxy density and momentum power spectra. We compare the measured values to the theoretical expectation of the same statistics, assuming an input Λ cold dark matter (CDM) model and using a third-order perturbative expansion. After validating our analysis pipeline with a series of mock data sets, we apply our methodology to the SDSS data and arrive at constraints and at a mean redshift . Our result is consistent with the Planck cosmological best-fit parameters for the ΛCDM model. The momentum power spectrum is found to be strongly contaminated by small-scale velocity dispersion, which suppresses power by on intermediate scales k ∼ 0.05 h Mpc−1.

Machine learning-based photometric classification of galaxies, quasars, emission-line galaxies, and stars

ABSTRACT This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad-line), quasars, and stars. We utilized samples from Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17) and the ALLWISE catalogue, which contain spectroscopically labelled sources from SDSS. Our methodology comprises two parts. First, we conducted experiments, including three-class, four-class, and seven-class classifications, employing the Random Forest (RF) algorithm. This phase aimed to achieve optimal performance with balanced data sets. In the second part, we trained various machine learning methods, such as k-nearest neighbours (KNN), RF, XGBoost (XGB), voting, and artificial neural network (ANN), using all available data based on promising results from the first phase. Our results highlight the effectiveness of combining optical and infrared features, yielding the best performance across all classifiers. Specifically, in the three-class experiment, RF and XGB algorithms achieved identical average F1 scores of 98.93 per cent on both balanced and unbalanced data sets. In the seven-class experiment, our average F1 score was 73.57 per cent. Using the XGB method in the four-class experiment, we achieved F1 scores of 87.9 per cent for normal galaxies (NGs), 81.5 per cent for ELGs, 99.1 per cent for stars, and 98.5 per cent for quasars (QSOs). Unlike classical methods based on time-consuming spectroscopy, our experiments demonstrate the feasibility of using automated algorithms on carefully classified photometric data. With more data and ample training samples, detailed photometric classification becomes possible, aiding in the selection of follow-up observation candidates.

Critical assessment of the recent report on the gigaparsec-scale correlation of the orientations of large quasar groups

ABSTRACT Recently, it was reported that large quasar groups (LQGs) identified from the Sloan Digital Sky Survey (SDSS) data release seven catalogue are not randomly oriented but preferentially aligned or orthogonal over scales 1–2 Gpc. To confirm this claim, I reproduced the same LQG sample and performed Sobolev tests of uniformity on the LQG orientation axes in the redshift space. Contrary to the original report based on the bimodal distribution of the LQG position angles in the sky, I found no departure from uniformity in the distribution of the LQG orientation axes. I also examined whether the LQGs are physical structures using a statistically more reliable data set constructed from the SDSS data release 16 (DR16) large-scale structure (LSS) quasar catalogue. Considering the Gaussian primordial density fluctuations and shot noise, I estimated the mass density contrasts of the LQGs from the number counts of the DR16 LSS quasars and found that most of the LQGs do not trace statistically significant high-density regions. I conclude that the LQG sample is a collection of unphysical chance associations and should not be used for any cosmological studies.

Properties of isolated galaxies in the Digital Survey Isolated Galaxies (DSIG) catalogue within a redshift range (0.005 &amp;lt; z &amp;lt; 0.080)

ABSTRACT Evolution of galaxies is known to be influenced by a number of factors such as the environment that hosts the galaxy as well as the galaxies intrinsic properties. The environmental effects on galaxy properties have not been fully quantified. In our study, we analysed a sample of isolated galaxies within 0.005 &amp;lt; z &amp;lt; 0.080 from Sloan Digital Sky Survey data release 16, as part of a Digital Survey Isolated Galaxies catalogue. The aim was to investigate intrinsic physical properties of singly isolated galaxies in low-density environment. We investigated the galaxies morphology, colour, luminosity, stellar masses, and star formation rates (SFRs). A concentration index, Cr, of 2.65 separates our isolated sample into early and late types, with the late-types (spiral galaxies) dominating the isolated sample at 68 per cent as confirmed from the visually classified sample obtained from Galaxy Zoo. Our isolated ellipticals are redder, massive, and more luminous, while the isolated spirals are blue, less massive, and less luminous. Both the isolated spirals and ellipticals have steeper colour relations indicating a fast transition to the red sequence. In the colour–colour analysis, most ellipticals were quiescent with the majority of spirals being star forming. 5 per cent of the isolated ellipticals have recently quenched their star formation and are transiting to the red sequence. The isolated spirals experiences higher star-forming activities, with a small fraction of passively evolving high-mass isolated spirals. Similarly, isolated ellipticals exhibit low SFRs indicating passive evolution, with a fraction being actively star forming.

Quasar Factor Analysis—An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis

Since their first discovery, quasars have been essential probes of the distant Universe. However, due to our limited knowledge of its nature, predicting the intrinsic quasar continua has bottlenecked their usage. Existing methods of quasar continuum recovery often rely on a limited number of high-quality quasar spectra, which might not capture the full diversity of the quasar population. In this study, we propose an unsupervised probabilistic model, quasar factor analysis (QFA), which combines factor analysis with physical priors of the intergalactic medium to overcome these limitations. QFA captures the posterior distribution of quasar continua through generatively modeling quasar spectra. We demonstrate that QFA can achieve the state-of-the-art performance, ∼2% relative error, for continuum prediction in the Lyα forest region compared to previous methods. We further fit 90,678 2 < z < 3.5, signal-to-noise ratio >2 quasar spectra from Sloan Digital Sky Survey Data Release 16 and found that for ∼30% quasar spectra where the continua were ill-determined with previous methods, QFA yields visually more plausible continua. QFA also attains ≲1% error in the 1D Lyα power spectrum measurements at z ∼ 3 and ∼4% in z ∼ 2.4. In addition, QFA determines latent factors representing more physical motivation than principal component analysis. We investigate the evolution of the latent factors and report no significant redshift or luminosity dependency except for the Baldwin effect. The generative nature of QFA also enables outlier detection robustly; we showed that QFA is effective in selecting outlying quasar spectra, including damped Lyα systems and potential Type II quasar spectra.

Stratified learning: A general‐purpose statistical method for improved learning under covariate shift

AbstractWe propose a simple, statistically principled, and theoretically justified method to improve supervised learning when the training set is not representative, a situation known as covariate shift. We build upon a well‐established methodology in causal inference and show that the effects of covariate shift can be reduced or eliminated by conditioning on propensity scores. In practice, this is achieved by fitting learners within strata constructed by partitioning the data based on the estimated propensity scores, leading to approximately balanced covariates and much‐improved target prediction. We refer to the overall method as Stratified Learning, or StratLearn. We demonstrate the effectiveness of this general‐purpose method on two contemporary research questions in cosmology, outperforming state‐of‐the‐art importance weighting methods. We obtain the best‐reported AUC (0.958) on the updated “Supernovae photometric classification challenge,” and we improve upon existing conditional density estimation of galaxy redshift from Sloan Digital Sky Survey (SDSS) data.