Photometric Survey Research Articles

Strong Balmer break objects at z~7-10 uncovered with JWST

We report the discovery of robust spectroscopically confirmed Balmer break (BB) galaxies and candidates, with secure spectroscopic redshifts $7.1 z 9.6$, from publicly available James Webb Space Telescope (JWST) extra-galactic photometric and spectroscopic surveys. To achieve this, we used dedicated filters probing the BB and inspected the objects with NIRSpec spectroscopy. We have recovered the previously known objects with strong BBs and here reveal 10-11 new objects with clear BBs, thus tripling the number of spectroscopically confirmed galaxies with a BB at $z >7$. Approximately half of them show a pure BB and no signs of recent star formation, whereas the other half show BB and emission lines, most likely indicating galaxies whose star formation ceased earlier and has restarted recently. Overall, we find that $ 10-20$<!PCT!> of all galaxies from our sample show signatures of an evolved stellar population. Furthermore, we find that the strength of the BB does not significantly depend on the rest-UV and rest-optical brightness of these sources. In short, our work confirms that photometry alone has the potential to measure BB strengths and to identify evolved stellar populations at high redshift, and that such objects may be more frequent than previously thought. The presence of galaxies with a range of break strengths and the joint presence of BB and emission lines indicate a bursty nature of the star formation in the early Universe.

The mass profiles of dwarf galaxies from Dark Energy Survey lensing

ABSTRACT We present a novel approach to extracting dwarf galaxies from photometric data to measure their average halo mass profile with weak lensing. We characterize their stellar mass and redshift distributions with a spectroscopic calibration sample. By combining the ${\sim} 5000\,\mathrm{deg}^2$ multiband photometry from the Dark Energy Survey and redshifts from the Satellites Around Galactic Analogs Survey with an unsupervised machine learning method, we select a low-mass galaxy sample spanning redshifts $z\lt 0.3$ and divide it into three mass bins. From low to high median mass, the bins contain [146 420, 330 146, 275 028] galaxies and have median stellar masses of $\log _{10}(M_*/\text{M}_\odot)=\left[8.52\substack{+0.57 -0.76},\, 9.02\substack{+0.50 -0.64},\, 9.49\substack{+0.50 -0.58}\right]$ . We measure the stacked excess surface mass density profiles, $\Delta \Sigma (R)$, of these galaxies using galaxy–galaxy lensing with a signal-to-noise ratio of [14, 23, 28]. Through a simulation-based forward-modelling approach, we fit the measurements to constrain the stellar-to-halo mass relation and find the median halo mass of these samples to be $\log _{10}(M_{\rm halo}/\text{M}_\odot)$ = [$10.67\substack{+0.2 -0.4}$, $11.01\substack{+0.14 -0.27}$, $11.40\substack{+0.08 -0.15}$]. The cold dark matter profiles are consistent with NFW (Navarro, Frenk, and White) profiles over scales ${\lesssim} 0.15 \, {h}^{-1}$ Mpc. We find that ${\sim} 20$ per cent of the dwarf galaxy sample are satellites. This is the first measurement of the halo profiles and masses of such a comprehensive, low-mass galaxy sample. The techniques presented here pave the way for extracting and analysing even lower mass dwarf galaxies and for more finely splitting galaxies by their properties with future photometric and spectroscopic survey data.

The Power of High-precision Broadband Photometry: Tracing the Milky Way Density Profile with Blue Horizontal Branch Stars in the Dark Energy Survey

Blue horizontal branch stars (BHBs), excellent distant tracers for probing the Milky Way’s halo density profile, are distinguished in the g−r0 versus (i − z)0 color space from another class of stars, blue straggler stars. We develop a Bayesian mixture model to classify BHBs using high-precision photometry data from the Dark Energy Survey Data Release 2 (DES DR2). We select ∼2100 highly probable BHBs based on their griz photometry and the associated uncertainties, and we use these stars to map the stellar halo over the Galactocentric radial range 20 kpc ≲ R ≲ 70 kpc. After excluding known stellar overdensities, we find that the number density n ⋆ of BHBs can be represented by a power-law density profile n ⋆ ∝ R −α with an index of α=4.34−0.12+0.13±0.52 , consistent with existing literature values. In addition, we examine the impact of systematic errors and the spatial inhomogeneity on the fitted density profile. Our work demonstrates the effectiveness of high-precision griz photometry in selecting BHBs. The upcoming photometric survey from the Rubin Observatory, expected to reach depths 2–3 mag greater than DES during its 10 yr mission, will enable us to investigate the density profile of the Milky Way’s halo out to the virial radius, unraveling the complex processes of formation and evolution in our Galaxy.

Mesiri: Mephisto Early Supernovae Ia Rapid Identifier

The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (u, g, i or v, r, z), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (Mesiri), which, for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae. Mesiri is based on the deep learning approach and can achieve an accuracy of 96.75% ± 0.79%, and AUC of 98.87% ± 0.53% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The BiLSTM architecture shows the best performance compared to others that have been tested in this work.

Variability of young stellar objects in the Orion nebula

The Orion Nebula is one of the best-studied star-forming regions in our Galaxy and provides an ideal laboratory to study the early stages of star formation. Understanding the variability of young stellar objects (YSOs) can provide valuable insights into the underlying physical processes that occur during this crucial phase of stellar evolution. In this context, we have conducted a photometric survey with high temporal resolution of a six-square-degree region centered on NGC\,1980. We used two filters ($R$ and $I$) between 2012 and 2015 with the 15\,cm twin telescope RoBoTT at the Universit\"atssternwarte Bochum in Chile. Our goal is to identify and characterize the variable objects in this sample using a unique observational setup that allows us to obtain high-quality light curves with time sampling on the order of minutes. This is the first time such an intensive ground-based photometric campaign has been conducted in the Orion Nebula. After careful quality control, more than 13000 light curves were produced in either $R$ or $I$; the total number of matched light curves is 11889. The corresponding stars were subsequently matched with the 2MASS point source catalog, resulting in 8197 stars with $I < 16.5$ mag and AAA quality flags. We employed three methods -- amplitude, standard deviation, and the Stetson J index -- to identify variable objects in our sample, successfully classifying 561 stars as variables (irregular, periodic, and quasi-periodic). These stars serve as the foundation for further analysis. We confirmed 456 of these as known pre-main-sequence stars through cross-referencing with existing YSO catalogs, supported by spectroscopic and photometric data. The youth of the remaining stars was established based on their optical and infrared color signatures. The period, $P$, could be determined for 147 sources and ranges from 0.2 days to several weeks. The frequency of very short periods ($ < 1$ day) is two to four times higher than reported in previous studies. A significant population (10<!PCT!>) comes primarily from weak-line T Tauri stars, which are characterized by rotation periods of less than 1 day. These results emphasize the advantages of observations with an exceptional time sampling strategy that allows the identification of variability on scales from hours to years.

Photometry and Models of Seven Main-Belt Asteroids

The China Near-Earth Object Survey Telescope (CNEOST) conducted four photometric surveys from 2015 to 2018 using image processing and aperture photometry techniques to obtain extensive light curve data on asteroids. The second-order Fourier series method was selected for its efficiency in determining the rotation periods of the observed asteroids. Our study successfully derived rotation periods for 892 asteroids, with 648 of those matching values recorded in the LCDB (for asteroids with U > 2). To enhance the reliability of the derived spin parameters and shape models, we also amassed a comprehensive collection of published light curve data supplemented by additional photometric observations on a targeted subset of asteroids conducted using multiple telescopes between 2021 and 2022. Through the application of convex inversion techniques, we successfully derived spin parameters and shape models for seven main-belt asteroids (MBAs): (2233) Kuznetsov, (2294) Andronikov, (2253) Espinette, (4796) Lewis, (1563) Noel, (2912) Lapalma, and (5150) Fellini. Our thorough analysis identified two credible orientations for the rotational poles of these MBAs, shedding light on the prevalent issue of “ambiguity in pole direction” that often accompanies photometric inversion processes. CNEOST continues its observational endeavors, and future collected data combined with other independent photometric measurements will facilitate further inversion to better constrain the spin parameters and yield more refined shape models.

Detection of High-frequency Pulsation in WR 135: Investigation of Stellar Wind Dynamics

We report the detection of high-frequency pulsations in WR 135 from short-cadence (10 minute) optical photometric and spectroscopic time series surveys. The harmonics up to the sixth order are detected from the integrated photometric flux variations, while the comparatively weaker eighth harmonic is detected from the strengths of the emission lines. We investigate the driving source of the stratified winds of WR 135 using the radiative transfer modeling code, CMFGEN, and find the physical conditions that can explain the propagation of such pulsations. From our study, we find that the optically thick subsonic layers of the atmosphere are close to the Eddington limit and are launched by the Fe opacity. The outer optically thin supersonic winds (τ ross = 0.1–0.01) are launched by the He II and C iv opacities. The stratified winds above the sonic point undergo velocity perturbation that can lead to clumps. In the optically thin supersonic winds, dense clumps of smaller size (f VFF = 0.27–0.3, where f VFF is the volume filling factor) pulsate with higher-order harmonics. The larger clumps ( f VFF = 0.2) oscillate with lower-order harmonics of the pulsation and affect the overall wind variability.

Electromagnetic signatures from accreting massive black hole binaries in time domain photometric surveys

We study spectral and time variability of accreting massive black hole binaries (MBHBs) at milli-parsec separations surrounded by a geometrically thin circumbinary disc. To this end, we present the first computation of the expected spectral energy distribution (SED) and light curves (LCs) from 3D hyper-Lagrangian resolution hydrodynamic simulations of these systems. We modelled binaries with a total mass of $10^6$ M$_ eccentricities of $e=0,\, 0.9$, and a mass ratio of $q=0.1,\, 1$. The circumbinary disc has an initial aspect ratio of 0.1, features an adiabatic equation of state, and evolves under the effect of viscous heating, black-body cooling, and self gravity. To construct the SED, we considered black-body emission from each element of the disc and we added a posteriori an X-ray corona with a luminosity proportional to that of the mini-discs that form around each individual black hole. We find significant variability of the SED, especially at high energies, which translates into LCs displaying distinctive modulations of a factor of $ 2$ in the optical and of $ 10$ in UV and X-rays. We analysed in detail the flux variability in the optical band that will be probed by the Vera Rubin Observatory (VRO). We find clear modulations on the orbital period and half of the orbital period in all systems. Only in equal-mass binaries, we find an additional, longer-timescale modulation, associated with an over-density forming at the inner edge of the circumbinary disc (commonly referred to as a lump). When considering the VRO flux limit and nominal survey duration, we find that equal-mass, circular binaries are unlikely to be identified, due to the lack of prominent peaks in their Fourier spectra. Conversely, unequal-mass and/or eccentric binaries can be singled out up to $z 0.5$ (for systems with bol $ erg s$^ $) and $z 2$ (for systems with bol $ erg s$^ $). Identifying electromagnetic signatures of MBHBs at separations of $ $ pc is of paramount importance to understand the physics of the gravitational wave (GW) sources of the future Laser Interferometer Space Antenna, and to pin down the origin of the GW background (GWB) observed in pulsar timing arrays.

Reconstructing redshift distributions with photometric galaxy clustering

The accurate determination of the true redshift distributions in tomographic bins is critical for cosmological constraints from photometric surveys. The proposed redshift self-calibration method, which utilizes the photometric galaxy clustering alone, is highly convenient and avoids the challenges from incomplete or unrepresentative spectroscopic samples in external calibration. However, the imperfection of the theoretical approximation on broad bins as well as the flaw of the algorithm in previous work [1] risk the accuracy and application of the method. In this paper, we propose the improved self-calibration algorithm that incorporates novel update rules, which effectively accounts for heteroskedastic weights and noisy data with negative values. The improved algorithm greatly expands the application range of self-calibration method and accurately reconstructs the redshift distributions for various mock data. Using the luminous red galaxy (LRG) sample of the Dark Energy Spectroscopic Instrument (DESI) survey, we find that the reconstructed results are comparable to the state-of-the-art external calibration. This suggests the exciting prospect of using photometric galaxy clustering to reconstruct redshift distributions in the cosmological analysis of survey data.

Planetary nebulae seen with TESS: New and revisited short-period binary central star candidates from Cycles 1 to 4

Context. High-precision and high-cadence photometric surveys such as Kepler or TESS are making huge progress not only in the detection of new extrasolar planets but also in the study of a great number of variable stars. This is the case for central stars of planetary nebulae (PNe), which have similarly benefited from the capabilities of these missions, increasing the number of known binary central stars and helping us to constrain the relationship between binarity and the complex morphologies of their host PNe. Aims. In this paper, we analyse the TESS light curves of a large sample of central stars of PNe with the aim of detecting signs of variability that may hint at the presence of short-period binary nuclei. This will have important implications in understanding PN formation evolution as well as the common envelope phase. Methods. We analysed 62 central stars of true, likely, or possible PNe and modelled the detected variability through an MCMC approach accounting for three effects: reflection, ellipsoidal modulations due to tidal forces, and the so-called Doppler beaming. Among the 62 central stars, only 38 are amenable for this study. The remaining 24 show large contamination from nearby sources preventing an optimal analysis. Also, eight targets are already known binary central stars, which we revisit here with the new high precision of the TESS data. Results. In addition to recovering the eight already known binaries in our sample, we find that 18 further central stars show clear signs of periodic variability in the TESS data, probably resulting from different physical effects compatible with the binary scenario. We propose them as new candidate binary central stars. We also discuss the origin of the detected variability in each particular case by using the TESS_localize algorithm. Finally, 12 targets show no or only weak evidence of variability at the sensitivity of TESS. Conclusions. Our study demonstrates the power of space-based photometric surveys in searching for close binary companions of central stars of PNe. Although our detections can only be catalogued as candidate binaries, we find a large percentage of possible stellar pairs associated with PNe, supporting the hypothesis that binarity plays a key role in shaping these celestial structures.

Cosmological forecasts from the combination of Stage-IV photometric galaxy surveys and the magnification from forthcoming GW observatories

In this work we have investigated the synergy between Stage-IV galaxy surveys and future GW observatories for constraining the underlying cosmological model of the Universe, focussing on photometric galaxy clustering, cosmic shear and GW magnification as cosmological probes. We have implemented a Fisher matrix approach for the evaluation of the full 6×2pt statistics composed by the angular power spectra of the single probes together with their combination. For our analysis, we have in particular considered dynamical dark energy and massive neutrino scenarios. We have found that the improvement to galaxy survey performance is below 1%, in the case of ℓ GW max=100 and a luminosity distance error of σ dL /dL =10%. However, when extending the analysis to ℓ GW max=1000, we find that the GW magnification improves the galaxy survey performance on all the cosmological parameters, reducing their errors by 3%-5%, when σ dL /dL =10%, and by 10%-18% when σ dL /dL =1%, especially for Mν , w 0 and wa . However, here our analysis is unavoidably optimistic: a much more detailed and realistic approach will be needed, especially by including systematic effects. But we can conclude that, in the case of future gravitational wave observatories, the inclusion of the gravitational wave magnification can improve Stage-IV galaxy surveys performance on constraining the underlying cosmological model of the Universe.

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5σ point-source depth of g= 24.3, r=23.9, i= 23.5, and z= 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of −0.0013, a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

Superphot+: Real-time Fitting and Classification of Supernova Light Curves

Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F 1-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future.

A discussion on estimating small bodies taxonomies using phase curves results

Upcoming large multiwavelength photometric surveys will provide a leap in our understanding of small body populations, among other fields of modern astrophysics. Serendipitous observations of small bodies in different orbital locations allow us to study diverse phenomena related to how their surfaces scatter solar light.In particular, multiple observations of the same object in different epochs permit us to construct their phase curves to obtain absolute magnitudes and phase coefficients. In this work, we tackle a series of long-used relationships associating these phase coefficients with the taxa of small bodies and suggest that some may need to be revised in the light of large-number statistics.

Prospect of Precision Cosmology and Testing General Relativity using Binary Black Holes- Galaxies Cross-correlation

Abstract Modified theories of gravity predict deviations from General Relativity (GR) in the propagation of gravitational waves (GW) across cosmological distances. A key prediction is that the GW luminosity distance will vary with redshift, differing from the electromagnetic (EM) luminosity distance due to varying effective Planck mass. We introduce a model-independent, data-driven approach to explore these deviations using multi-messenger observations of dark standard sirens (Binary Black Holes, BBH). By combining GW luminosity distance measurements from dark sirens with Baryon Acoustic Oscillation (BAO) measurements, BBH redshifts inferred from cross-correlation with spectroscopic or photometric galaxy surveys, and sound horizon measurements from the Cosmic Microwave Background (CMB), we can make a data-driven test of GR (jointly with the Hubble constant) as a function of redshift. Using the multi-messenger technique with the spectroscopic DESI galaxy survey, we achieve precise measurements of deviations in the effective Planck mass variation with redshift. For the Cosmic Explorer and Einstein Telescope (CEET), the best precision is approximately 3.6%, and for LIGO-Virgo-KAGRA (LVK), it is 7.4% at a redshift of $\rm {z = 0.425}$. Additionally, we can measure the Hubble constant with a precision of about 1.1% from CEET and 7% from LVK over five years of observation with a 75% duty cycle. We also explore the potential of cross-correlation with photometric galaxy surveys from the Rubin Observatory, extending measurements up to a redshift of $\rm {z \sim 2.5}$. This approach can reveal potential deviations from models affecting GW propagation using numerous dark standard sirens in synergy with DESI and the Rubin Observatory.

Application of Convolutional Neural Networks to time domain astrophysics. 2D image analysis of OGLE light curves

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large-scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies commonly employed in the study of variable objects. As a response, deep learning techniques are increasingly being explored to effectively classify, analyze, and interpret these large datasets. In this paper we use two-dimensional histograms to represent Optical Gravitational Lensing Experiment (OGLE) phasefolded light curves as images. We use a Convolutional Neural Network (CNN) to classify variable objects within eight different categories (from now on labels): Classical Cepheid (CEP), RR Lyrae (RR), Long Period Variable (LPV), Miras (M), Ellipsoidal Binary (ELL), Delta Scuti (DST), Eclipsing Binary (E), and spurious class with Incorrect Periods (Rndm). We set up different training sets to train the same CNN architecture in order to characterize the impact of the training. The training sets were built from the same source of labels but different filters and balancing techniques were applied. Namely: Undersampling (U), Data Augmentation (DA), and Batch Balancing (BB). The best performance was achieved with the BB approach and a training sample size of sim 370000 stars. Regarding computational performance, the image representation production rate is of sim 76 images per core per second, and the time to predict is sim 60$\ s $ per star. The accuracy of the classification improves from sim 92<!PCT!>, when based only on the CNN, to sim 98<!PCT!> when the results of the CNN are combined with the period and amplitude features in a two step approach. This methodology achieves comparable results with previous studies but with two main advantages: the identification of miscalculated periods and the improvement in computational time cost.

Exploring mass measurements of supermassive black holes in AGN using GAMA photometry and spectroscopy

Abstract In the era of massive photometric surveys, we explore several approaches to estimate the masses of supermassive black holes (SMBHs) in active galactic nuclei (AGN) from optical ground-based imaging, in each case comparing to the independent SMBH mass measurement obtained from spectroscopic data. We select a case-study sample of 28 type 1 AGN hosted by nearby galaxies from the Galaxy And Mass Assembly (GAMA) survey. We perform multi-component spectral decomposition, extract the AGN component and calculate the SMBH mass from the broad Hα emission line width and luminosity. The photometric g and i band data is decomposed into AGN+spheroid(+disc)(+bar) components with careful surface brightness fitting. From these, the SMBH mass is estimated using its relation with the spheroid Sérsic index or effective radius (both used for the first time on ground-based optical imaging of AGN); and the more widely used scaling relations based on bulge or galaxy stellar mass. We find no correlation between the Hα-derived SMBH masses and those based on the spheroid Sérsic index or effective radius, despite these being the most direct methods involving only one scaling relation. The bulge or galaxy stellar mass based methods both yield significant correlations, although with considerable scatter and, in the latter case, a systematic offset. We provide possible explanations for this and discuss the requirements, advantages and drawbacks of each method. These considerations will be useful to optimise stategies for upcoming high quality ground-based and space-borne sky surveys to estimate SMBH masses in large numbers of AGN.

On the capability of high redshift kSZ measurement with galaxy surveys

The kinematic Sunyaev-Zel'dovich (kSZ) effect has been detected at z < 1 using various techniques and data sets. The ongoing and upcoming spectroscopic galaxy surveys such as DESI (Dark Energy Spectroscopic Instrument) and PFS (Prime Focus Spectrograph) will push the detection beyond z = 1, and therefore map the baryon distribution at high redshifts. Such detection can be achieved by both the kSZ stacking and tomography methods. While the two methods are theoretically equivalent, they differ significantly in the probed physics and scales, and required data sets. Taking the combination of PFS and ACT (Atacama Cosmology Telescope) as an example, we build mocks of kSZ and galaxies, quantify the kSZ detection S/N, and compare between the two methods. We segment the PFS galaxies into three redshift bins: 0.6 < z < 1.0, 1.0 < z < 1.6, and 1.6 < z < 2.4. For tomography method, our analysis reveals that the two higher redshift bins exhibit significantly higher S/N ratios, with values of 32 and 28, respectively, compared to the first redshift bin, which yielded an S/N of 8. This is attributed to not only the increasing of electron density with redshifts, but also the larger survey volume and the reduced non-linearity, facilitating velocity reconstruction at higher redshifts. Therefore, the capability of the PFS survey to measure high redshift kSZ effect stands as a substantial advantage over other spectroscopic surveys at lower redshift. The S/N of kSZ stacking largely depends on the number of galaxy groups available from another photometric survey. But in general, its S/N is lower than that of kSZ tomography, largely due to CMB instrument noise and error in galaxy group redshift. Incorporating next-generation CMB surveys like CMB-S4, characterized by significantly reduced instrument noise and improved angular resolution, is expected to enhance tomographic detection by a factor of ten and stacking detection by fivefold. This future high S/N detection holds the promise of not only providing precise constraints on the overall baryon abundance but also initiating a new insight into baryon distribution.

Periodic variable A-F spectral type stars in the southern TESS continuous viewing zone

Aims. Our primary objective is to accurately identify and classify the variability of A-F stars in the southern continuous viewing zone of the TESS satellite. The brightness limit was set to 10 mag to ensure the utmost reliability of our results and allow for spectroscopic follow-up observations using small telescopes. We aim to compare our findings with existing catalogues of variable stars. Methods. The light curves from TESS and their Fourier transform were used to manually classify stars in our sample. Cross-matching with other catalogues was performed to identify contaminants and false positives. Results. We have identified 1171 variable stars (51% of the sample). Among these variable stars, 67% have clear classifications, which includes δ Sct and γ Dor pulsating stars and their hybrids, rotationally variables, and eclipsing binaries. We have provided examples of the typical representatives of variable stars and discussed the ambiguous cases. We found 20 pairs of stars with the same frequencies and identified the correct source of the variations. Additionally, we found that the variations in 12 other stars are caused by contamination from the light of faint nearby large-amplitude variable stars. To compare our sample with other variable star catalogues, we have defined two parameters reflecting the agreement in identification of variable stars and their classification. This comparison reveals intriguing disagreements in classification ranging from 52 to 100%. However, if we assume that stars without specific types are only marked as variable, then the agreement is relatively good, ranging from 57 to 85% (disagreement 15–43%). We have demonstrated that the TESS classification is superior to the classification based on other photometric surveys. Conclusions. The classification of stellar variability is complex and requires careful consideration. Caution should be exercised when using catalogue classifications.

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of “true” redshift posteriors that can be used for photo-z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo-z estimation. We discuss how PZFlow will be used to validate the photo-z estimation pipeline of the Dark Energy Science Collaboration, and the wider applicability of PZFlow for statistical modeling of any tabular data.