arXiv subjects

Masao Sako

Publications and source records attributed to Masao Sako.

At least 19 recordsLinked to original sources

arXiv2026

Supernovae Unite: Combining Pantheon+ and DES-SN5YR

We present the Hubble diagram and cosmological constraints resulting from the combination of the Pantheon+ and DES-SN5YR supernova (SN) samples, which we refer to as Unite. Unite updates Pantheon+, including methodology improvements that the DES sample enabled, and adds the new SN from the DES survey. This represents the most comprehensive and internally consistent SN Ia dataset available, consisting of 2884 likely type Ia supernovae. This work features consistent SN Ia modelling, sample selection, and bias corrections. We also redetermine host-galaxy stellar masses for over 98% of the sample using a consistent framework. Using Unite alone, for a flat universe with a cosmological constant (Flat-$\Lambda$CDM), we find $\Omega_{\rm m} = 0.310^{+0.012}_{-0.011}$. We further present constraints using the Unite sample combined with Cosmic Microwave Background and both DES and DESI Baryon Acoustic Oscillation data. We find strong evidence of tension between CMB measurements and the combined BAO and supernova datasets when fitting a model with constant dark energy equation of state ($w$), which is alleviated when allowing $w$ to vary with time. For a flat universe with dark energy that has a time-evolving equation of state parametrized by $w_0$ and $w_a$ we find $(\Omega_{\rm m},w_0,w_a) = (0.305\pm0.004,-0.861^{+0.044}_{-0.042},-0.60^{+0.17}_{-0.19})$ with a dark energy Figure of Merit of 315. This corresponds to a $\sim30\%$ reduction in the area of the $w_0-w_a$ confidence region relative to previous constraints from the combination of DES Y6, DESI-DR2, and CMB data (arXiv:2605.27221). The Bayesian evidence indicates only weak preference for time evolving dark energy, whereas frequentist analyses yield a preference over Flat-$\Lambda$CDM at a significance of $3.3\sigma$ ($3.1\sigma$) when using the maximum $\textit{a posteriori}$ probability (maximum likelihood).

astro-ph.CO↗

arXiv2026

Chromatic Effects Across the Roman Focal Plane: Implications for Supernova Photometry and Measurements of Cosmological Parameters

Calibration uncertainties are the leading systematics in cosmological analyses using Type Ia supernovae (SNe Ia). For the \textit{Nancy Grace Roman Space Telescope (Roman)}, we quantify the impact of chromatic effects on SNe Ia photometry and derived cosmological parameters, using simulated light curves from the High-Latitude Time Domain Survey. We investigate two sources of wavelength-dependent bias: focal plane array (FPA)-dependent wavelength shifts arising from spatial variations across \textit{Roman's} 18 detectors, and coherent wavelength shifts corresponding to the measured $0.06\%$ uncertainty in absolute filter wavelength calibration. Using simulated SNe Ia light curves, we find that the FPA-dependent shifts -- which range from +6 to -80 $\rm \AA$ introduce a redshift-dependent distance modulus bias that, if left uncorrected, propagates to $\Delta w_0 \sim -0.06$ and $\Delta w_a = -0.236$, which are larger than the forecast statistical uncertainties of $\sigma_{\rm stat, w_0} = 0.025$ and $\sigma_{\rm stat, w_a} = 0.114$, rendering the survey systematics-limited. We probe the impact of chromatic effects by employing detector-specific filter curves that recover unbiased cosmological constraints; to remain below the statistical noise floor, FPA wavelength shifts must be characterized to within 20\%. In contrast, a coherent 0.06\% offset in filter wavelength calibration -- ranging from -3 to -11 $\rm \AA$ -- produces negligible redshift-dependent bias, with a minimal spread in $w_a$ ($\Delta w_a = -0.0004, \sigma_{w_{a,\rm sys}} = 0.114)$, demonstrating that the achieved pre-launch calibration precision is sufficient for this systematic to remain subdominant. Our results establish that chromatic effects are a required component of SN Ia cosmology with \textit{Roman}.

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arXiv2026

Three-dimensional scene reconstruction using Roman slitless spectra

The Nancy Grace Roman Space Telescope will carry out a wide-field imaging and slitless spectroscopic survey of Type Ia Supernovae to improve our understanding of dark energy. Crucial to this endeavor is obtaining supernova spectra uncontaminated by light from their host galaxies. However, obtaining such spectra is made more difficult by the inherent problem in wide-field slitless spectroscopic surveys: the blending of spectra of close objects. The spectrum of a supernova will blend with the host galaxy, even from regions distant from the supernova on the sky. If not properly removed, this contamination will introduce systematic bias when the supernova spectra are later used to determine intrinsic supernova parameters and to infer the parameters of dark energy. To address this problem we developed an algorithm that makes use of the spectroscopic observations of the host galaxy at all available observatory roll angles to reconstruct a three-dimensional (3d; 2d spatial, 1d spectral) representation of the underlying host galaxy that accurately matches the 2d slitless spectrum of the host galaxy when projected to an arbitrary rotation angle. We call this ``scene reconstruction''. The projection of the reconstructed scene can be subtracted from an observation of a supernova to remove the contamination from the underlying host. Using simulated Roman data, we show that our method has extremely small systematic errors and significantly less random noise than if we subtracted a single perfectly aligned spectrum of the host obtained before or after the supernova was visible.

astro-ph.IM↗

arXiv2025

Cosmology Constraints from Type Ia Supernova Simulations of the Nancy Grace Roman Space Telescope Strategy Recommended by the High Latitude Time Domain Survey Definition Committee

Within the next few years, the upcoming Nancy Grace Roman Space Telescope will be gathering data for the High Latitude Time Domain Survey (HLTDS) that will be used to significantly improve the Type Ia supernova measurement of the dark energy equation of state parameters w0 and wa. Here we generate a catalog-level simulation of the in-guide strategy recommended by the HLTDS definition committee, and determine dark energy parameter constraints using a detailed analysis that includes light curve fitting, photometric redshifts and classification, BEAMS formalism, systematic uncertainties, and cosmology fitting. After analysis and selection requirements, the sample includes 10,000 Roman SNe Ia that we combine with 4,400 events from LSST. The resulting dark energy figure of merit is well above the NASA mission requirement of 326, with the caveat that SN Ia model training systematics have not been included.

astro-ph.CO↗

arXiv2025

phrosty: A difference imaging pipeline for Roman

NASA's Nancy Grace Roman Space Telescope (Roman) will provide an opportunity to study dark energy with unprecedented precision using several techniques, including measurements of Type Ia Supernovae (SNe Ia). Here, we present `phrosty` (PHotometry for ROman with SFFT for tYpe Ia supernovae): a difference imaging pipeline for measuring the brightness of transient point sources in the sky, primarily SNe Ia, using Roman data. `phrosty` is written in Python. We implement a GPU-accelerated version of the Saccadic Fast Fourier Transform (SFFT) method for difference imaging.

astro-ph.IM↗

arXiv2025

Transformer-Based Neural Network for Transient Detection without Image Subtraction

We introduce a transformer-based neural network for the accurate classification of real and bogus transient detections in astronomical images. This network advances beyond the conventional convolutional neural network (CNN) methods, widely used in image processing tasks, by adopting an architecture better suited for detailed pixel-by-pixel comparison. The architecture enables efficient analysis of search and template images only, thus removing the necessity for computationally-expensive difference imaging, while maintaining high performance. Our primary evaluation was conducted using the autoScan dataset from the Dark Energy Survey (DES), where the network achieved a classification accuracy of 97.4% and diminishing performance utility for difference image as the size of the training set grew. Further experiments with DES data confirmed that the network can operate at a similar level even when the input images are not centered on the supernova candidate. These findings highlight the network's effectiveness in enhancing both accuracy and efficiency of supernova detection in large-scale astronomical surveys.

cs.CV↗

arXiv2025

The Dark Energy Bedrock All-Sky Supernova Program: Motivation, Design, Implementation, and Preliminary Data Release

Precise measurements of Type Ia supernovae (SNe Ia) at low redshifts ($z$) serve as one of the most viable keys to unlocking our understanding of cosmic expansion, isotropy, and growth of structure. The Dark Energy Bedrock All-Sky Supernovae (DEBASS) program will deliver the largest uniformly calibrated low-$z$ SN Ia data set in the southern hemisphere to date. DEBASS utilizes the Dark Energy Camera to image supernovae in conjunction with the Wide-Field Spectrograph (WiFeS) to gather comprehensive host galaxy information. By using the same photometric instrument as both the Dark Energy Survey (DES) and the DECam Local Volume Exploration Survey, DEBASS not only benefits from a robust photometric pipeline and well-calibrated images across the southern sky, but can replace the historic and external low-$z$ samples that were used in the final DES supernova analysis. DEBASS has accumulated more than 400 spectroscopically confirmed SNe Ia in the redshift range of $0.01<z<0.08$ from 2021 to mid-2025, and, in this paper along with a companion paper Acevedo et al. submitted, we present an early data release of 77 SNe within the DES footprint to demonstrate the merit and constraining power of the data set. Here, we introduce the DEBASS program, discuss its scientific goals and the advantages it offers for supernova cosmology, and present our initial results. With this early data release, we find a robust median absolute standard deviation of Hubble diagram residuals of $\sim$0.10 mag and an initial measurement of the host-galaxy mass step of $0.06\pm0.04$ mag, both before performing bias corrections. This low scatter shows the promise of a low-$z$ SN Ia program with a well-calibrated telescope and high signal-to-noise ratio across multiple bands.

astro-ph.CO↗

arXiv2025

The FIX Benchmark: Extracting Features Interpretable to eXperts

Feature-based methods are commonly used to explain model predictions, but these methods often implicitly assume that interpretable features are readily available. However, this is often not the case for high-dimensional data, and it can be hard even for domain experts to mathematically specify which features are important. Can we instead automatically extract collections or groups of features that are aligned with expert knowledge? To address this gap, we present FIX (Features Interpretable to eXperts), a benchmark for measuring how well a collection of features aligns with expert knowledge. In collaboration with domain experts, we propose FIXScore, a unified expert alignment measure applicable to diverse real-world settings across cosmology, psychology, and medicine domains in vision, language, and time series data modalities. With FIXScore, we find that popular feature-based explanation methods have poor alignment with expert-specified knowledge, highlighting the need for new methods that can better identify features interpretable to experts.

cs.LG↗

arXiv2025

Fishing for the Optimal Roman High Latitude Time Domain Survey: Cosmological Constraints for 1,000 Possible Surveys

The upcoming Nancy Grace Roman Space Telescope is set to conduct a generation-defining SN Ia cosmology measurement with its High Latitude Time Domain Survey (HLTDS). However, between optical elements, exposure times, cadences, and survey areas, there are many survey parameters to consider. This work was part of a Roman Project Infrastructure Team effort to help the Core Community Survey (CCS) Committee finalize the HLTDS recommendation to the Roman Observations Time Allocation Committee. We simulate 1,000 surveys, with and without a conservative (volume-limited) version of the Vera C. Rubin Observatory Deep Drilling Field SNe Ia, and compute Fisher-matrix-analysis Dark Energy Task Force Figures of Merit (FoM, based on w0-wa constraints) for each. We investigate which survey parameters correlate with FoM, as well as the dependence of the FoM values on calibration uncertainties and the SN dispersion model. The exact optimum depends on the assumed dispersion model and whether Rubin DDF SNe Ia are also considered, but ~20% time in prism, ~30--40% time in Wide imaging and the remainder in Deep imaging seems most promising. We also advocate for "interlaced" cadences where not every filter is used in every cadence step to reduce overheads while maintaining a good cadence and increasing the number of filters compared to the Rose et al. (2021) reference survey (the prism has proportionately lower overheads and can be used for each cadence step). We show simulated light curves and spectra for the baseline HLTDS CCS recommendation and release distance-modulus covariance matrices for all surveys to the community.

astro-ph.CO↗

arXiv2024

Constraints on compact objects from the Dark Energy Survey five-year supernova sample

Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $α$ of the total matter density in compact objects (which comprise stars, stellar remnants, small stellar groupings and primordial black holes) of mass $M > 0.03 M_{\odot}$ over cosmological distances. Using 1,532 SNe Ia from the Dark Energy Survey Year 5 sample (DES-SN5YR) combined with a Bayesian prior for the absolute magnitude $M$, we obtain $α< 0.12$ at the 95\% confidence level after marginalisation over cosmological parameters, lensing due to large-scale structure, and intrinsic non-Gaussianity. Similar results are obtained using priors from the cosmic microwave background, baryon acoustic oscillations and galaxy weak lensing, indicating our results do not depend on the background cosmology. We argue our constraints are likely to be conservative (in the sense of the values we quote being higher than the truth), but discuss scenarios in which they could be weakened by systematics of the order of $Δα\sim 0.04$

astro-ph.CO↗

arXiv2024

Astrometric Redshifts of Supernovae

Differential Chromatic Refraction (DCR) is caused by the wavelength dependence of our atmosphere's refractive index, which shifts the apparent positions of stars and galaxies and distorts their shapes depending on their spectral energy distributions (SEDs). While this effect is typically mitigated and corrected for in imaging observations, we investigate how DCR can instead be used to our advantage to infer the redshifts of supernovae from multi-band, time-series imaging data. We simulate Type Ia supernovae (SNe Ia) in the proposed Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Deep Drilling Field (DDF), and evaluate astrometric redshifts. We find that the redshift accuracy improves dramatically with the statistical quality of the astrometric measurements as well as with the accuracy of the astrometric solution. For a conservative choice of a 5-mas systematic uncertainty floor, we find that our redshift estimation is accurate at $z < 0.6$. We then combine our astrometric redshifts with both host galaxy photometric redshifts and supernovae photometric (light-curve) redshifts and show that this considerably improves the overall redshift estimates. These astrometric redshifts will be valuable especially since Rubin will discover a vast number of supernovae for which we will not be able to obtain spectroscopic redshifts.

astro-ph.CO↗

arXiv2024

Recovered SN Ia rate from simulated LSST images

The Legacy Survey of Space and Time (LSST) will revolutionize Time Domain Astronomy by detecting millions of transients. In particular, it is expected to increment the number of type Ia supernovae (SNIa) of a factor of 100 compared to existing samples up to z~1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of SNIa properties and rates. However, the impact of all other sources of uncertainty on the measurement must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SNIa rate can put constraints on the evolutionary scenarios of different SNIa progenitors. We use simulated data from the DESC Data Challenge 2 (DC2) and LSST Data Preview 0 (DP0) to measure the SNIa rate on a 15 deg2 region of the Wide-Fast-Deep area. We select a sample of SN candidates detected on difference images, associate them to the host galaxy, and retrieve their photometric redshifts (z-phot). Then, we test different light curves classification methods, with and without redshift priors. We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. We measure the SNIa rate analyzing the impact of uncertainties due to z-phot, host galaxy association and classification on the distribution in redshift of the starting sample. We found a 17% average lost fraction of SNIa with respect to the simulated sample. As 10% of the bias is due to the uncertainty on the z-phot alone (which also affects classification when used as a prior), it results to be the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SNIa rate, including synergies with other surveys, which may help using the rate to discriminate different progenitor models.

astro-ph.CO↗

arXiv2023

Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of spectroscopic information, we must accurately extract key parameters, such as SN redshifts, with photometric information alone. We present Photo-zSNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera C. Rubin Legacy Survey of Space and Time (LSST) data as well as observed SDSS SNe. We show major improvements over predictions from existing methods on both simulations and real observations as well as minimal redshift-dependent bias, which is a challenge due to selection effects, e.g. Malmquist bias. Specifically, we show a 61x improvement in prediction bias on PLAsTiCC simulations and 5x improvement on real SDSS data compared to results from a widely used photometric redshift estimator, LCFIT+Z. The PDFs produced by this method are well-constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.

astro-ph.CO↗

arXiv2023

What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction

We present a study of the potential for Convolutional Neural Networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as "real-bogus" classification without requiring a template subtracted (or difference) image which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the "real-bogus" classification, (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses "image triplets" (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input finding that the testing accuracy is reduced from 96% to 91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for "real-bogus" classification that rely exclusively on the imaging data and require no feature engineering task; (2) demonstrates that high-accuracy (> 90%) models can be built without the need to construct difference images, but some accuracy is lost. Since once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the Difference Image Analysis entirely.

cs.CV↗

arXiv2023

Roman CCS White Paper: Measuring Type Ia Supernovae Discovered in the Roman High Latitude Time Domain Survey

We motivate the cosmological science case of measuring Type Ia supernovae with the Nancy Grace Roman Space Telescope as part of the High Latitude Time Domain Survey. We discuss previously stated requirements for the science, and a baseline survey strategy. We discuss the various areas that must still be optimized and point to the other white papers that consider these topics in detail. Overall, the baseline case should enable an exquisite measurement of dark energy using SNe Ia from z=0.1 to z>2, and further optimization should only strengthen this once-in-a-generation experiment.

astro-ph.IM↗

arXiv2023

Roman CCS White Paper: Optimizing the HLTDS Cadence at Fixed Depth

The current proposal for the High Latitude Time Domain Survey (HLTDS) is two tiers (wide and deep) of multi-band imaging and prism spectroscopy with a cadence of five days (Rose et al., 2021). The five-day cadence is motivated by the desire to measure mid-redshift SNe where time dilation is modest as well as to better photometrically characterize the transients detected. This white paper does not provide a conclusion as to the best cadence for the HLTDS. Rather, it collects a set of considerations that should be used for a careful study of cadence by a future committee optimizing the Roman survey. This study should optimize the HLTDS for both SN Ia cosmology and other transient science.

astro-ph.IM↗

arXiv2023

Roman CCS White Paper: Considerations for Selecting Fields for the Roman High-latitude Time Domain Core Community Survey

In this white paper, we review five top considerations for selecting locations of the fields of the Roman High-latitude Time Domain Survey. Based on these considerations, we recommend Akari Deep Field South (ADFS)/Euclid Deep Field South (EDFS) in the Southern Hemisphere has it avoids bright stars, has minimal Milky Way dust, is in Roman Continuous viewing zone, overlaps with multiple past and future surveys, and minimal zodiacal background variation. In the North, Extended Groth Strip (EGS) is good except for its zodiacal variation and Supernova/Acceleration Probe North (SNAP-N) and European Large Area Infrared Space Observatory Survey-North 1 (ELAIS N-1) are good except for their synergistic archival data.

astro-ph.IM↗

arXiv2023

Roman CCS White Paper: Options to Increase the Coverage Area of Prism Time Series in the High-Latitude Time Domain Core Community Survey

The current reference High-latitude time domain survey increases the completeness of transients with prism temporal time series data by adjusting the ratio of prism-to-imaging time. However, there are two other nobs that allow for a more complete prism coverage: prism cadence and exposure time. In this white paper, we discuss how changes to the prism cadence and exposure time -- in order to increase the fraction of observed transients with spectral time series -- affect supernova cosmology, transient typing and template building, and the study of rare transients.

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