Reconciling the observed star-forming sequence with the observed stellar mass function

We examine the connection between the observed star-forming sequence (SFR $\propto$ $M^{\alpha}$) and the observed evolution of the stellar mass function between $0.2 < z < 2.5$. We find the star-forming sequence cannot have a slope $\alpha$ $\lesssim$ 0.9 at all masses and redshifts, as this would result in a much higher number density at $10 < \log(\mathrm{M/M_{\odot}}) < 11$ by $z=1$ than is observed. We show that a transition in the slope of the star-forming sequence, such that $\alpha=1$ at $\log(\mathrm{M/M_{\odot}})<10.5$ and $\alpha=0.7-0.13z$ ({Whitaker} {et~al.} 2012) at $\log(\mathrm{M/M_{\odot}})>10.5$, greatly improves agreement with the evolution of the stellar mass function. We then derive a star-forming sequence which reproduces the evolution of the mass function by design. This star-forming sequence is also well-described by a broken-power law, with a shallow slope at high masses and a steep slope at low masses. At $z=2$, it is offset by $\sim$0.3 dex from the observed star-forming sequence, consistent with the mild disagreement between the cosmic SFR and recent observations of the growth of the stellar mass density. It is unclear whether this problem stems from errors in stellar mass estimates, errors in SFRs, or other effects. We show that a mass-dependent slope is also seen in other self-consistent models of galaxy evolution, including semi-analytical, hydrodynamical, and abundance-matching models. As part of the analysis, we demonstrate that neither mergers nor hidden low-mass quiescent galaxies are likely to reconcile the evolution of the mass function and the star-forming sequence. These results are supported by observations from {Whitaker} {et~al.} (2014).Comment: 17 pages, 13 figures, accepted to ApJ Oct 31st 201

The superluminous supernova SN 2017egm in the nearby galaxy NGC 3191: a metal-rich environment can support a typical SLSN evolution

At redshift z=0.03, the recently-discovered SN 2017egm is the nearest Type I superluminous supernova (SLSN) to date, and first near the center of a massive spiral galaxy (NGC 3191). Using SDSS spectra of NGC 3191, we find a metallicity ~2 Z$_\odot$ at the nucleus and ~1.3 Z$_\odot$ for a star forming region at a radial offset similar to SN 2017egm. Archival radio-to-UV photometry reveals a star-formation rate ~15 M$_\odot$ yr$^{-1}$ (with ~70% dust-obscured), which can account for a Swift X-ray detection, and stellar mass ~$10^{10.7}$ M$_\odot$. We model the early UV-optical light curves with a magnetar central-engine model, using the Bayesian light curve fitting tool MOSFiT. The fits indicate ejecta mass 2-4 M$_\odot$, spin period 4-6 ms, magnetic field (0.7-1.7)$\times 10^{14}$G, and kinetic energy 1-2 $\times10^{51}$ erg. These parameters are consistent with the overall distributions for SLSNe, modeled by Nicholl et al (2017), although the derived mass and spin are towards the low end, possibly indicating enhanced loss of mass and angular momentum before explosion. This has two implications: (i) SLSNe can occur at solar metallicity, although with a low fraction ~10%; and (ii) metallicity has at most a modest effect on their properties. Both conclusions are in line with results for long gamma-ray bursts. Assuming a monotonic rise gives an explosion date MJD $57889\pm1$. However, a short-lived excess in the data relative to the best-fitting models may indicate an early-time `bump'. If confirmed, SN 2017egm would be the first SLSN with a spectrum during the bump-phase; this shows the same O II lines seen at maximum light, which may be an important clue for explaining these bumps.Comment: Accepted for publication in ApJ

Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference

Upcoming astronomical surveys will observe billions of galaxies across cosmic time, providing a unique opportunity to map the many pathways of galaxy assembly to an incredibly high resolution. However, the huge amount of data also poses an immediate computational challenge: current tools for inferring parameters from the light of galaxies take $\gtrsim 10$ hours per fit. This is prohibitively expensive. Simulation-based Inference (SBI) is a promising solution. However, it requires simulated data with identical characteristics to the observed data, whereas real astronomical surveys are often highly heterogeneous, with missing observations and variable uncertainties determined by sky and telescope conditions. Here we present a Monte Carlo technique for treating out-of-distribution measurement errors and missing data using standard SBI tools. We show that out-of-distribution measurement errors can be approximated by using standard SBI evaluations, and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. While these techniques slow the inference process from $\sim 1$ sec to $\sim 1.5$ min per object, this is still significantly faster than standard approaches while also dramatically expanding the applicability of SBI. This expanded regime has broad implications for future applications to astronomical surveys.Comment: 8 pages, 2 figures, accepted to the Machine Learning and the Physical Sciences workshop at NeurIPS 202

Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models

We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-consistently inferring all model parameters, from high-level hyper-parameters to SPS parameters of individual galaxies, one can separate sources of bias and uncertainty in the data. We demonstrate the strengths and flexibility of this approach by deriving accurate photometric redshifts for a sample of spectroscopically-confirmed galaxies in the COSMOS field, achieving a performance competitive with publicly-released photometric redshift catalogs based on the same data. Prior to this work, this approach was computationally intractable in practice due to the heavy computational load of SPS model calls; we overcome this challenge using with neural emulators. We find that the largest photometric residuals are associated with poor calibration for emission line luminosities and thus build a framework to mitigate these effects. This combination of physics-based modeling accelerated with machine learning paves the path towards meeting the stringent requirements on the accuracy of photometric redshift estimation imposed by upcoming cosmological surveys. The approach also has the potential to create new links between cosmology and galaxy evolution through the analysis of photometric datasets.Comment: 16 pages, 6 figures. To be submitted to APJ

SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Application

Flagship near-future surveys targeting $10^8-10^9$ galaxies across cosmic time will soon reveal the processes of galaxy assembly in unprecedented resolution. This creates an immediate computational challenge on effective analyses of the full data-set. With simulation-based inference (SBI), it is possible to attain complex posterior distributions with the accuracy of traditional methods but with a $>10^4$ increase in speed. However, it comes with a major limitation. Standard SBI requires the simulated data to have identical characteristics to the observed data, which is often violated in astronomical surveys due to inhomogeneous coverage and/or fluctuating sky and telescope conditions. In this work, we present a complete SBI-based methodology, ``SBI$^{++}$,'' for treating out-of-distribution measurement errors and missing data. We show that out-of-distribution errors can be approximated by using standard SBI evaluations and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. In addition to the validation set, we apply SBI$^{++}$ to galaxies identified in extragalactic images acquired by the James Webb Space Telescope, and show that SBI$^{++}$ can infer photometric redshifts at least as accurately as traditional sampling methods and crucially, better than the original SBI algorithm using training data with a wide range of observational errors. SBI$^{++}$ retains the fast inference speed of $\sim$1 sec for objects in the observational training set distribution, and additionally permits parameter inference outside of the trained noise and data at $\sim$1 min per object. This expanded regime has broad implications for future applications to astronomical surveys.Comment: 12 pages, 5 figures. Code and a Jupyter tutorial are made publicly available at https://github.com/wangbingjie/sbi_p

Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference

We present a forward-modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of the physical characteristics of galaxies, encoding galaxy evolution physics; a stellar population synthesis model connecting the physical properties of galaxies to their photometry; a data model characterizing the observation and calibration processes for a given survey; and explicit treatment of selection cuts, both into the main analysis sample and for the subsequent sorting into tomographic redshift bins. This approach has the appeal that it does not rely on spectroscopic calibration data, provides explicit control over modeling assumptions and builds a direct bridge between photo-z inference and galaxy evolution physics. In addition to redshift distributions, forward modeling provides a framework for drawing robust inferences about the statistical properties of the galaxy population more generally. We demonstrate the utility of forward modeling by estimating the redshift distributions for the Galaxy And Mass Assembly (GAMA) survey and the Vimos VLT Deep Survey (VVDS), validating against their spectroscopic redshifts. Our baseline model is able to predict tomographic redshift distributions for GAMA and VVDS with respective biases of Δz ≲ 0.003 and Δz ≃ 0.01 on the mean redshift—comfortably accurate enough for Stage III cosmological surveys—without any hyperparameter tuning (i.e., prior to doing any fitting to those data). We anticipate that with additional hyperparameter fitting and modeling improvements, forward modeling will provide a path to accurate redshift distribution inference for Stage IV surveys