We present a new technique for modelling submillimetre galaxies (SMGs): the 'Count Matching' approach. Using light cones drawn from a semi-analytic model of galaxy formation, we choose physical ...galaxy properties given by the model as proxies for their submillimetre luminosities, assuming a monotonic relationship. As recent interferometric observations of the Extended Chandra Deep Field-South show that the brightest sources detected by single-dish telescopes are comprised by emission from multiple fainter sources, we assign the submillimetre fluxes so that the combined Large APEX BOlometer CAmera (LABOCA) plus bright-end Atacama Large Millimeter/submillimeter Array observed number counts for this field are reproduced. After turning the model catalogues given by the proxies into submillimetre maps, we perform a source extraction to include the effects of the observational process on the recovered counts and galaxy properties. We find that for all proxies, there are lines of sight giving counts consistent with those derived from LABOCA observations, even for input sources with randomized positions in the simulated map. Comparing the recovered redshift, stellar mass and host halo mass distributions for model SMGs with observational data, we find that the best among the proposed proxies is that in which the submillimetre luminosity increases monotonically with the product between dust mass and star formation rate (SFR). This proxy naturally reproduces a positive trend between SFR and bolometric IR luminosity. The majority of components of blended sources are spatially unassociated.
Abstract
We present an ALMA-Herschel joint analysis of sources detected by the ALMA Lensing Cluster Survey (ALCS) at 1.15 mm. Herschel/PACS and SPIRE data at 100–500
μ
m are deblended for 180 ALMA ...sources in 33 lensing cluster fields that are detected either securely (141 sources; in our main sample) or tentatively at S/N ≥ 4 with cross-matched HST/Spitzer counterparts, down to a delensed 1.15 mm flux density of ∼0.02 mJy. We performed far-infrared spectral energy distribution modeling and derived the physical properties of dusty star formation for 125 sources (109 independently) that are detected at >2
σ
in at least one Herschel band. A total of 27 secure ALCS sources are not detected in any Herschel bands, including 17 optical/near-IR-dark sources that likely reside at
z
= 4.2 ± 1.2. The 16th, 50th, and 84th percentiles of the redshift distribution are 1.15, 2.08, and 3.59, respectively, for ALCS sources in the main sample, suggesting an increasing fraction of
z
≃ 1 − 2 galaxies among fainter millimeter sources (
f
1150
∼ 0.1 mJy). With a median lensing magnification factor of
μ
=
2.6
−
0.8
+
2.6
, ALCS sources in the main sample exhibit a median intrinsic star formation rate of
94
−
54
+
84
M
⊙
yr
−1
, lower than that of conventional submillimeter galaxies at similar redshifts by a factor of ∼3. Our study suggests weak or no redshift evolution of dust temperature with
L
IR
< 10
12
L
⊙
galaxies within our sample at
z
≃ 0 − 2. At
L
IR
> 10
12
L
⊙
, the dust temperatures show no evolution across
z
≃ 1–4 while being lower than those in the local universe. For the highest-redshift source in our sample (
z
= 6.07), we can rule out an extreme dust temperature (>80 K) that was reported for MACS0416 Y1 at
z
= 8.31.
Abstract
Astronomical broker systems, such as Automatic Learning for the Rapid Classification of Events (ALeRCE), are currently analyzing hundreds of thousands of alerts per night, opening up an ...opportunity to automatically detect anomalous unknown sources. In this work, we present the ALeRCE anomaly detector, composed of three outlier detection algorithms that aim to find transient, periodic, and stochastic anomalous sources within the Zwicky Transient Facility data stream. Our experimental framework consists of cross-validating six anomaly detection algorithms for each of these three classes using the ALeRCE light-curve features. Following the ALeRCE taxonomy, we consider four transient subclasses, five stochastic subclasses, and six periodic subclasses. We evaluate each algorithm by considering each subclass as the anomaly class. For transient and periodic sources the best performance is obtained by a modified version of the deep support vector data description neural network, while for stochastic sources the best results are obtained by calculating the reconstruction error of an autoencoder neural network. Including a visual inspection step for the 10 most promising candidates for each of the 15 ALeRCE subclasses, we detect 31 bogus candidates (i.e., those with photometry or processing issues) and seven potential astrophysical outliers that require follow-up observations for further analysis.
16
16
The code and the data needed to reproduce our results are publicly available at
https://github.com/mperezcarrasco/AnomalyALeRCE
.
Abstract
In recent years, automatic classifiers of image cutouts (also called “stamps”) have been shown to be key for fast supernova discovery. The Vera C. Rubin Observatory will distribute about ten ...million alerts with their respective stamps each night, enabling the discovery of approximately one million supernovae each year. A growing source of confusion for these classifiers is the presence of satellite glints, sequences of point-like sources produced by rotating satellites or debris. The currently planned Rubin stamps will have a size smaller than the typical separation between these point sources. Thus, a larger field-of-view stamp could enable the automatic identification of these sources. However, the distribution of larger stamps would be limited by network bandwidth restrictions. We evaluate the impact of using image stamps of different angular sizes and resolutions for the fast classification of events (active galactic nuclei, asteroids, bogus, satellites, supernovae, and variable stars), using data from the Zwicky Transient Facility. We compare four scenarios: three with the same number of pixels (small field of view with high resolution, large field of view with low resolution, and a multiscale proposal) and a scenario with the full stamp that has a larger field of view and higher resolution. Compared to small field-of-view stamps, our multiscale strategy reduces misclassifications of satellites as asteroids or supernovae, performing on par with high-resolution stamps that are 15 times heavier. We encourage Rubin and its Science Collaborations to consider the benefits of implementing multiscale stamps as a possible update to the alert specification.
ABSTRACT The Large Synoptic Survey Telescope (LSST) will survey the southern sky from 2022-2032 with unprecedented detail. Since the observing strategy can lead to artifacts in the data, we ...investigate the effects of telescope-pointing offsets (called dithers) on the r-band coadded 5 depth yielded after the 10-year survey. We analyze this survey depth for several geometric patterns of dithers (e.g., random, hexagonal lattice, spiral) with amplitudes as large as the radius of the LSST field of view, implemented on different timescales (per season, per night, per visit). Our results illustrate that per night and per visit dither assignments are more effective than per season assignments. Also, we find that some dither geometries (e.g., hexagonal lattice) are particularly sensitive to the timescale on which the dithers are implemented, while others like random dithers perform well on all timescales. We then model the propagation of depth variations to artificial fluctuations in galaxy counts, which are a systematic for LSS studies. We calculate the bias in galaxy counts caused by the observing strategy accounting for photometric calibration uncertainties, dust extinction, and magnitude cuts; uncertainties in this bias limit our ability to account for structure induced by the observing strategy. We find that after 10 years of the LSST survey, the best dither strategies lead to uncertainties in this bias that are smaller than the minimum statistical floor for a galaxy catalog as deep as r < 27.5. A few of these strategies bring the uncertainties close to the statistical floor for r < 25.7 after the first year of survey.
ABSTRACT We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model ...against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.
Abstract
We present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real time identify the host galaxies of extragalactic ...transients. The proposed algorithm receives as input compact, multiresolution images centered at the position of a transient candidate and outputs two-dimensional offset vectors that connect the transient with the center of its predicted host. The multiresolution input consists of a set of images with the same number of pixels, but with progressively larger pixel sizes and fields of view. A sample of 16,791 galaxies visually identified by the Automatic Learning for the Rapid Classification of Events broker team was used to train a convolutional neural network regression model. We show that this method is able to correctly identify both relatively large (10″ <
r
< 60″) and small (
r
≤ 10″) apparent size host galaxies using much less information (32 kB) than with a large, single-resolution image (920 kB). The proposed method has fewer catastrophic errors in recovering the position and is more complete and has less contamination (<0.86%) recovering the crossmatched redshift than other state-of-the-art methods. The more efficient representation provided by multiresolution input images could allow for the identification of transient host galaxies in real time, if adopted in alert streams from new generation of large -etendue telescopes such as the Vera C. Rubin Observatory.
ABSTRACT
We develop new tools for continuum and spectral stacking of Atacama Large Millimeter/submillimeter Array (ALMA) data, and apply these to the ALMA Lensing Cluster Survey. We derive average ...dust masses, gas masses, and star-formation rates (SFRs) from the stacked observed 260-GHz continuum of 3402 individually undetected star-forming galaxies, of which 1450 are cluster galaxies and 1952 field galaxies, over three redshift and stellar mass bins (over z = 0–1.6 and log$M_{*} \, {\rm M}_{\odot } = 8$–11.7), and derive the average molecular gas content by stacking the emission line spectra in a SFR-selected subsample. The average SFRs and specific SFRs of both cluster and field galaxies are lower than those expected for main-sequence (MS) star-forming galaxies, and only galaxies with stellar mass of log$M_{*} \, {\rm M}_{\odot } = 9.35$–10.6 show dust and gas fractions comparable with those in the MS. The ALMA-traced average ‘highly obscured’ SFRs are typically lower than the SFRs observed from optical to near-infrared spectral analysis. Cluster and field galaxies show similar trends in their contents of dust and gas, even when field galaxies were brighter in the stacked maps. From spectral stacking we find a potential CO (J = 4 → 3) line emission (signal-to-noise ratio being ∼4) when stacking cluster and field galaxies with the highest SFRs.
Time domain astronomy is advancing towards the analysis of multiple massive datasets in real time, prompting the development of multi-stream machine learning models. In this work, we study Domain ...Adaptation (DA) for real/bogus classification of astronomical alerts using four different datasets: HiTS, DES, ATLAS, and ZTF. We study the domain shift between these datasets, and improve a naive deep learning classification model by using a fine tuning approach and semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced accuracy of these models for different source-target scenarios. We find that both the fine tuning and MME models improve significantly the base model with as few as one labeled item per class coming from the target dataset, but that the MME does not compromise its performance on the source dataset.
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