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Redshift measurements, primarily obtained from host galaxies, are essential for inferring cosmological parameters from type Ia supernovae (SNe Ia). Matching SNe to host galaxies using images is nontrivial, resulting in a subset of SNe with mismatched hosts and thus incorrect redshifts. We evaluate the host galaxy mismatch rate and resulting biases on cosmological parameters from simulations modeled after the Dark Energy Survey 5 Yr (DES-SN5YR) photometric sample. For both DES-SN5YR data and simulations, we employ the directional light radius method for host galaxy matching. In our SN Ia simulations, we find that 1.7% of SNe are matched to the wrong host galaxy, with redshift differences between the true and matched hosts of up to 0.6. Using our analysis pipeline, we determine the shift in the dark energy equation of state parameter (Δw) due to including SNe with incorrect host galaxy matches. For SN Ia–only simulations, we find Δw= 0.0013 ± 0.0026 with constraints from the cosmic microwave background. Including core-collapse SNe and peculiar SNe Ia in the simulation, we find that Δwranges from 0.0009 to 0.0032, depending on the photometric classifier used. This bias is an order of magnitude smaller than the expected total uncertainty onwfrom the DES-SN5YR sample of ∼0.03. We conclude that the bias onwfrom host galaxy mismatch is much smaller than the uncertainties expected from the DES-SN5YR sample, but we encourage further studies to reduce this bias through better host-matching algorithms or selection cuts.

 

Redshift measurements, primarily obtained from host galaxies, are essential for inferring cosmological parameters from type Ia supernovae (SNe Ia). Matching SNe to host galaxies using images is non-trivial, resulting in a subset of SNe with mismatched hosts and thus incorrect redshifts. We evaluate the host galaxy mismatch rate and resulting biases on cosmological parameters from simulations modeled after the Dark Energy Survey 5-Year (DES-SN5YR) photometric sample. For both DES-SN5YR data and simulations, we employ the directional light radius method for host galaxy matching. In our SN Ia simulations, we find that 1.7% of SNe are matched to the wrong host galaxy, with redshift difference between the true and matched host of up to 0.6. Using our analysis pipeline, we determine the shift in the dark energy equation of state parameter (Dw) due to including SNe with incorrect host galaxy matches. For SN Ia-only simulations, we find Dw = 0.0013 +/- 0.0026 with constraints from the cosmic microwave background (CMB). Including core-collapse SNe and peculiar SNe Ia in the simulation, we find that Dw ranges from 0.0009 to 0.0032 depending on the photometric classifier used. This bias is an order of magnitude smaller than the expected total uncertainty on w from the DES-SN5YR sample of around 0.03. We conclude that the bias on w from host galaxy mismatch is much smaller than the uncertainties expected from the DES-SN5YR sample, but we encourage further studies to reduce this bias through better host-matching algorithms or selection cuts. 

Recent analyses have found intriguing correlations between the colour (c) of type Ia supernovae (SNe Ia) and the size of their ‘mass-step’, the relationship between SN Ia host galaxy stellar mass (Mstellar) and SN Ia Hubble residual, and suggest that the cause of this relationship is dust. Using 675 photometrically classified SNe Ia from the Dark Energy Survey 5-yr sample, we study the differences in Hubble residual for a variety of global host galaxy and local environmental properties for SN Ia subsamples split by their colour. We find a 3σ difference in the mass-step when comparing blue (c < 0) and red (c > 0) SNe. We observe the lowest r.m.s. scatter (∼0.14 mag) in the Hubble residual for blue SNe in low mass/blue environments, suggesting that this is the most homogeneous sample for cosmological analyses. By fitting for c-dependent relationships between Hubble residuals and Mstellar, approximating existing dust models, we remove the mass-step from the data and find tentative ∼2σ residual steps in rest-frame galaxy U − R colour. This indicates that dust modelling based on Mstellar may not fully explain the remaining dispersion in SN Ia luminosity. Instead, accounting for a c-dependent relationship between Hubble residuals and global U − R, results in ≤1σ residual steps in Mstellar and local U − R, suggesting that U − R provides different information about the environment of SNe Ia compared to Mstellar, and motivating the inclusion of galaxy U − R colour in SN Ia distance bias correction.

 

Cosmological analyses with type Ia supernovae (SNe Ia) often assume a single empirical relation between colour and luminosity (β) and do not account for varying host-galaxy dust properties. However, from studies of dust in large samples of galaxies, it is known that dust attenuation can vary significantly. Here, we take advantage of state-of-the-art modelling of galaxy properties to characterize dust parameters (dust attenuation AV, and a parameter describing the dust law slope RV) for 1100 Dark Energy Survey (DES) SN host galaxies. Utilizing optical and infrared data of the hosts alone, we find three key aspects of host dust that impact SN cosmology: (1) there exists a large range (∼1–6) of host RV; (2) high-stellar mass hosts have RV on average ∼0.7 lower than that of low-mass hosts; (3) for a subsample of 81 spectroscopically classified SNe there is a significant (>3σ) correlation between the Hubble diagram residuals of red SNe Ia and the host RV that when corrected for reduces scatter by $\sim 13{{\ \rm per\ cent}}$ and the significance of the ‘mass step’ to ∼1σ. These represent independent confirmations of recent predictions based on dust that attempted to explain the puzzling ‘mass step’ and intrinsic scatter (σint) in SN Ia analyses.

 

Current and future cosmological analyses with Type Ia supernovae (SNe Ia) face three critical challenges: (i) measuring the redshifts from the SNe or their host galaxies; (ii) classifying the SNe without spectra; and (iii) accounting for correlations between the properties of SNe Ia and their host galaxies. We present here a novel approach that addresses each of these challenges. In the context of the Dark Energy Survey (DES), we analyze an SN Ia sample with host galaxies in the redMaGiC galaxy catalog, a selection of luminous red galaxies. redMaGiC photo-zestimates are expected to be accurate toσ_Δz/(1+z)∼ 0.02. The DES-5YR photometrically classified SN Ia sample contains approximately 1600 SNe, and 125 of these SNe are in redMaGiC galaxies. We demonstrate that redMaGiC galaxies almost exclusively host SNe Ia, reducing concerns relating to classification uncertainties. With this subsample, we find similar Hubble scatter (to within ∼0.01 mag) using photometric redshifts in place of spectroscopic redshifts. With detailed simulations, we show that the bias due to using redMaGiC photo-zs on the measurement of the dark energy equation of statewis up to Δw∼ 0.01–0.02. With real data, we measure a difference inwwhen using the redMaGiC photo-zs versus the spec-zs of Δw= 0.005. Finally, we discuss how SNe in redMaGiC galaxies appear to comprise a more standardizable population, due to a weaker relation between color and luminosity (β) compared to the DES-3YR population by ∼5σ. These results establish the feasibility of performing redMaGiC SN cosmology with photometric survey data in the absence of spectroscopic data.

 

Sustainability and long‐term prosperity are chronic challenges in the agriculture sector of many countries. To address such challenges, farmer cooperatives are formed as an innovative approach to improve the livelihoods of millions of farmers around the world. Inspired by real‐life practice in the Kenya coffee industry, we study a class of stochastic and dynamic inventory models for storable agricultural products with random exogenous supply and price. For a variety of cost functions relevant in practice, we characterize the optimal selling policies to maximize the farmer cooperatives’ expected profit. We show that for concave inventory holding cost, thesell‐all‐or‐retain‐all(r, R) (orsell‐all‐or‐retain‐all R) policies are optimal with (without) the fixed selling cost; for convex holding cost, thesell‐down‐to(S, s) (orsell‐down‐to s) policies are optimal with (without) the fixed selling cost. For the special case of linear holding cost, the optimal policy is acut‐off pricepolicy and we derive closed‐form expressions for the optimal policy and the optimal total discounted profit. We discuss the model extensions to include general stochastic harvest and price processes, selling/storage capacity limits, price‐dependent random demand with a spot market, and the flexibility of procurement from other producers, and then perform a numerical study to quantify the impact of the optimal solutions. Reconciling the theory with practice, useful insights and guidelines are provided to help farmer cooperatives make strategic selling decisions.