Characterizing the Sample Selection for Supernova Cosmology

Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.Comment: 13 pages, 7 figure

Model Independent Expansion History from Supernovae: Cosmology versus Systematics

We examine the Pantheon supernovae distance data compilation in a model independent analysis to test the validity of cosmic history reconstructions beyond the concordance $\Lambda$CDM cosmology. Strong deviations are allowed by the data at $z\gtrsim1$ in the reconstructed Hubble parameter, $Om$ diagnostic, and dark energy equation of state. We explore three interpretations: 1) possibility of the true cosmology being far from $\Lambda$CDM, 2) supernovae property evolution, and 3) survey selection effects. The strong (and theoretically problematic) deviations at $z\gtrsim1$ vanish and good consistency with $\Lambda$CDM is found with a simple Malmquist-like linear correction. The adjusted data is robust against the model independent iterative smoothing reconstruction. However, we caution that while by eye the original deviation from $\Lambda$CDM is striking, $\chi^2$ tests do not show the extra linear correction parameter is statistically significant, and a model-independent Gaussian Process regression does not find significant evidence for the need for correction at high-redshifts.Comment: 9 pages, 6 figures, accepted for publication in MNRA

Measuring the growth rate of structure with Type IA Supernovae from LSST

We investigate measuring the peculiar motions of galaxies up to $z=0.5$ using Type Ia supernovae (SNe Ia) from LSST, and predict the subsequent constraints on the growth rate of structure. We consider two cases. Our first is based on measurements of the volumetric SNe Ia rate and assumes we can obtain spectroscopic redshifts and light curves for varying fractions of objects that are detected pre-peak luminosity by LSST (some of which may be obtained by LSST itself and others which would require additional follow-up). We find that these measurements could produce growth rate constraints at $z<0.5$ that significantly outperform those using Redshift Space Distortions (RSD) with DESI or 4MOST, even though there are $\sim4\times$ fewer objects. For our second case, we use semi-analytic simulations and a prescription for the SNe Ia rate as a function of stellar mass and star formation rate to predict the number of LSST SNe IA whose host redshifts may already have been obtained with the Taipan+WALLABY surveys, or with a future multi-object spectroscopic survey. We find $\sim 18,000$ and $\sim 160,000$ SN Ia with host redshifts for these cases respectively. Whilst this is only a fraction of the total LSST-detected SNe Ia, they could be used to significantly augment and improve the growth rate constraints compared to only RSD. Ultimately, we find that combining LSST SNe Ia with large numbers of galaxy redshifts will provide the most powerful probe of large scale gravity in the $z<0.5$ regime over the coming decades.Comment: 12 pages, 1 table, 5 figures. Accepted for publication in ApJ. The Fisher matrix forecast code used in this paper can be found at: https://github.com/CullanHowlett/PV_fisher. Updated to fix error in Eq. 1 (thanks to Eric Linder for pointing this out

Complementarity of Peculiar Velocity Surveys and Redshift Space Distortions for Testing Gravity

Peculiar-velocity surveys of the low-redshift universe have significant leverage to constrain the growth rate of cosmic structure and test gravity. Wide-field imaging surveys combined with multi-object spectrographs (e.g. ZTF2, LSST, DESI, 4MOST) can use Type Ia supernovae as informative tracers of the velocity field, reaching few percent constraints on the growth rate $f\sigma_8$ at $z\lesssim0.2$ where density tracers cannot do better than $\sim10\%$. Combining the high-redshift DESI survey mapping redshift space distortions with a low-redshift supernova peculiar velocity survey using LSST and DESI can determine the gravitational growth index to $\sigma(\gamma)\approx0.02$, testing general relativity. We study the characteristics needed for the peculiar velocity survey, and how its complementarity with clustering surveys improves when going from a $\Lambda$CDM model assumption to a $w_0$-$w_a$ cosmology.Comment: 6 pages, 4 figure

Fast and efficient identification of anomalous galaxy spectra with neural density estimation

Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up subtle variations in rich data sets and are fast to apply once trained. Here, we study the applicability of an unsupervised and probabilistic deep learning framework, the Probabilistic Autoencoder (PAE), to the detection of peculiar objects in galaxy spectra from the SDSS survey. Different to supervised algorithms, this algorithm is not trained to detect a specific feature or type of anomaly, instead it learns the complex and diverse distribution of galaxy spectra from training data and identifies outliers with respect to the learned distribution. We find that the algorithm assigns consistently lower probabilities (higher anomaly score) to spectra that exhibit unusual features. For example, the majority of outliers among quiescent galaxies are E+A galaxies, whose spectra combine features from old and young stellar population. Other identified outliers include LINERs, supernovae and overlapping objects. Conditional modeling further allows us to incorporate additional information. Namely, we evaluate the probability of an object being anomalous given a certain spectral class, but other information such as metrics of data quality or estimated redshift could be incorporated as well. We make our code publicly available at https://github.com/VMBoehm/Spectra_PAEComment: 16 pages, 14 figures, MNRAS revised manuscript after addressing the report from the referee. Our first paper is available at arXiv:2211.11783 . Our code is publicly available at https://github.com/VMBoehm/Spectra_PA