Probabilistic Classification of Infrared-selected targets for SPHEREx mission: In search of YSOs

We apply machine learning algorithms to classify Infrared (IR)-selected targets for NASA's upcoming SPHEREx mission. In particular, we are interested in classifying Young Stellar Objects (YSOs), which are essential for understanding the star formation process. Our approach differs from previous work, which has relied heavily on broadband color criteria to classify IR-bright objects, and are typically implemented in color-color and color-magnitude diagrams. However, these methods do not state the confidence associated with the classification and the results from these methods are quite ambiguous due to the overlap of different source types in these diagrams. Here, we utilize photometric colors and magnitudes from seven near and mid-infrared bands simultaneously and employ machine and deep learning algorithms to carry out probabilistic classification of YSOs, Asymptotic Giant Branch (AGB) stars, Active Galactic Nuclei (AGN) and main-sequence (MS) stars. Our approach also sub-classifies YSOs into Class I, II, III and flat spectrum YSOs, and AGB stars into carbon-rich and oxygen-rich AGB stars. We apply our methods to infrared-selected targets compiled in preparation for SPHEREx which are likely to include YSOs and other classes of objects. Our classification indicates that out of $8,308,384$ sources, $1,966,340$ have class prediction with probability exceeding $90\%$, amongst which $\sim 1.7\%$ are YSOs, $\sim 58.2\%$ are AGB stars, $\sim 40\%$ are (reddened) MS stars, and $\sim 0.1\%$ are AGN whose red broadband colors mimic YSOs. We validate our classification using the spatial distributions of predicted YSOs towards the Cygnus-X star-forming complex, as well as AGB stars across the Galactic plane.Comment: 17 pages, 12 figures, Accepted for publication in MNRA

Validation of the HERA Phase i Epoch of Reionization 21 cm Power Spectrum Software Pipeline

We describe the validation of the HERA Phase I software pipeline by a series of modular tests, building up to an end-to-end simulation. The philosophy of this approach is to validate the software and algorithms used in the Phase I upper-limit analysis on wholly synthetic data satisfying the assumptions of that analysis, not addressing whether the actual data meet these assumptions. We discuss the organization of this validation approach, the specific modular tests performed, and the construction of the end-to-end simulations. We explicitly discuss the limitations in scope of the current simulation effort. With mock visibility data generated from a known analytic power spectrum and a wide range of realistic instrumental effects and foregrounds, we demonstrate that the current pipeline produces power spectrum estimates that are consistent with known analytic inputs to within thermal noise levels (at the 2σ level) for k > 0.2h Mpc-1 for both bands and fields considered. Our input spectrum is intentionally amplified to enable a strong "detection"at k ∼ 0.2 h Mpc-1 - at the level of ∼25σ - with foregrounds dominating on larger scales and thermal noise dominating at smaller scales. Our pipeline is able to detect this amplified input signal after suppressing foregrounds with a dynamic range (foreground to noise ratio) of ⪆107. Our validation test suite uncovered several sources of scale-independent signal loss throughout the pipeline, whose amplitude is well-characterized and accounted for in the final estimates. We conclude with a discussion of the steps required for the next round of data analysis

Methods of Error Estimation for Delay Power Spectra in 21 cm Cosmology

Precise measurements of the 21 cm power spectrum are crucial for understanding the physical processes of hydrogen reionization. Currently, this probe is being pursued by low-frequency radio interferometer arrays. As these experiments come closer to making a first detection of the signal, error estimation will play an increasingly important role in setting robust measurements. Using the delay power spectrum approach, we have produced a critical examination of different ways that one can estimate error bars on the power spectrum. We do this through a synthesis of analytic work, simulations of toy models, and tests on small amounts of real data. We find that, although computed independently, the different error bar methodologies are in good agreement with each other in the noise-dominated regime of the power spectrum. For our preferred methodology, the predicted probability distribution function is consistent with the empirical noise power distributions from both simulated and real data. This diagnosis is mainly in support of the forthcoming HERA upper limit and also is expected to be more generally applicable

Measuring HERA's Primary Beam in Situ: Methodology and First Results

The central challenge in 21 cm cosmology is isolating the cosmological signal from bright foregrounds. Many separation techniques rely on the accurate knowledge of the sky and the instrumental response, including the antenna primary beam. For drift-scan telescopes, such as the Hydrogen Epoch of Reionization Array (HERA), that do not move, primary beam characterization is particularly challenging because standard beam-calibration routines do not apply (Cornwell et al.) and current techniques require accurate source catalogs at the telescope resolution. We present an extension of the method from Pober et al. where they use beam symmetries to create a network of overlapping source tracks that break the degeneracy between source flux density and beam response and allow their simultaneous estimation. We fit the beam response of our instrument using early HERA observations and find that our results agree well with electromagnetic simulations down to a -20 dB level in power relative to peak gain for sources with high signal-to-noise ratio. In addition, we construct a source catalog with 90 sources down to a flux density of 1.4 Jy at 151 MHz.The central challenge in 21 cm cosmology is isolating the cosmological signal from bright foregrounds. Many separation techniques rely on the accurate knowledge of the sky and the instrumental response, including the antenna primary beam. For drift-scan telescopes, such as the Hydrogen Epoch of Reionization Array (HERA), that do not move, primary beam characterization is particularly challenging because standard beam-calibration routines do not apply (Cornwell et al.) and current techniques require accurate source catalogs at the telescope resolution. We present an extension of the method from Pober et al. where they use beam symmetries to create a network of overlapping source tracks that break the degeneracy between source flux density and beam response and allow their simultaneous estimation. We fit the beam response of our instrument using early HERA observations and find that our results agree well with electromagnetic simulations down to a -20 dB level in power relative to peak gain for sources with high signal-to-noise ratio. In addition, we construct a source catalog with 90 sources down to a flux density of 1.4 Jy at 151 MHz

First Results from HERA Phase I: Upper Limits on the Epoch of Reionization 21 cm Power Spectrum

We report upper limits on the Epoch of Reionization 21 cm power spectrum at redshifts 7.9 and 10.4 with 18 nights of data (∼36 hr of integration) from Phase I of the Hydrogen Epoch of Reionization Array (HERA). The Phase I data show evidence for systematics that can be largely suppressed with systematic models down to a dynamic range of ∼109 with respect to the peak foreground power. This yields a 95% confidence upper limit on the 21 cm power spectrum of 212≤(30.76)2mK2 at k = 0.192 h Mpc-1 at z = 7.9, and also 212≤(95.74)2mK2 at k = 0.256 h Mpc-1 at z = 10.4. At z = 7.9, these limits are the most sensitive to date by over an order of magnitude. While we find evidence for residual systematics at low line-of-sight Fourier k π modes, at high k π modes we find our data to be largely consistent with thermal noise, an indicator that the system could benefit from deeper integrations. The observed systematics could be due to radio frequency interference, cable subreflections, or residual instrumental cross-coupling, and warrant further study. This analysis emphasizes algorithms that have minimal inherent signal loss, although we do perform a careful accounting in a companion paper of the small forms of loss or bias associated with the pipeline. Overall, these results are a promising first step in the development of a tuned, instrument-specific analysis pipeline for HERA, particularly as Phase II construction is completed en route to reaching the full sensitivity of the experiment

A Real Time Processing system for big data in astronomy: Applications to HERA

As current- and next-generation astronomical instruments come online, they will generate an unprecedented deluge of data. Analyzing these data in real time presents unique conceptual and computational challenges, and their long-term storage and archiving is scientifically essential for generating reliable, reproducible results. We present here the real-time processing (RTP) system for the Hydrogen Epoch of Reionization Array (HERA), a radio interferometer endeavoring to provide the first detection of the highly redshifted 21 cm signal from Cosmic Dawn and the Epoch of Reionization by an interferometer. The RTP system consists of analysis routines run on raw data shortly after they are acquired, such as calibration and detection of radio-frequency interference (RFI) events. RTP works closely with the Librarian, the HERA data storage and transfer manager which automatically ingests data and transfers copies to other clusters for post-processing analysis. Both the RTP system and the Librarian are public and open source software, which allows for them to be modified for use in other scientific collaborations. When fully constructed, HERA is projected to generate over 50 terabytes (TB) of data each night, and the RTP system enables the successful scientific analysis of these data

Impact of instrument and data characteristics in the interferometric reconstruction of the 21 cm power spectrum

Combining the visibilities measured by an interferometer to form a cosmological power spectrum is a complicated process. In a delay-based analysis, the mapping between instrumental and cosmological space is not a one-to-one relation. Instead, neighbouring modes contribute to the power measured at one point, with their respective contributions encoded in the window functions. To better understand the power measured by an interferometer, we assess the impact of instrument characteristics and analysis choices on these window functions. Focusing on the Hydrogen Epoch of Reionization Array (HERA) as a case study, we find that long-baseline observations correspond to enhanced low-k tails of the window functions, which facilitate foreground leakage, whilst an informed choice of bandwidth and frequency taper can reduce said tails. With simple test cases and realistic simulations, we show that, apart from tracing mode mixing, the window functions help accurately reconstruct the power spectrum estimator of simulated visibilities. The window functions depend strongly on the beam chromaticity and less on its spatial structure – a Gaussian approximation, ignoring side lobes, is sufficient. Finally, we investigate the potential of asymmetric window functions, down-weighting the contribution of low-k power to avoid foreground leakage. The window functions presented here correspond to the latest HERA upper limits for the full Phase I data. They allow an accurate reconstruction of the power spectrum measured by the instrument and will be used in future analyses to confront theoretical models and data directly in cylindrical space

HERA Phase i Limits on the Cosmic 21 cm Signal: Constraints on Astrophysics and Cosmology during the Epoch of Reionization

Recently, the Hydrogen Epoch of Reionization Array (HERA) has produced the experiment's first upper limits on the power spectrum of 21 cm fluctuations at z ∼ 8 and 10. Here, we use several independent theoretical models to infer constraints on the intergalactic medium (IGM) and galaxies during the epoch of reionization from these limits. We find that the IGM must have been heated above the adiabatic-cooling threshold by z ∼ 8, independent of uncertainties about IGM ionization and the radio background. Combining HERA limits with complementary observations constrains the spin temperature of the z ∼ 8 neutral IGM to 27 K 630 K (2.3 K 640 K) at 68% (95%) confidence. They therefore also place a lower bound on X-ray heating, a previously unconstrained aspects of early galaxies. For example, if the cosmic microwave background dominates the z ∼ 8 radio background, the new HERA limits imply that the first galaxies produced X-rays more efficiently than local ones. The z ∼ 10 limits require even earlier heating if dark-matter interactions cool the hydrogen gas. If an extra radio background is produced by galaxies, we rule out (at 95% confidence) the combination of high radio and low X-ray luminosities of L r,ν /SFR > 4 × 1024 W Hz-1 yr and L X /SFR < 7.6 × 1039 erg s-1 yr. The new HERA upper limits neither support nor disfavor a cosmological interpretation of the recent Experiment to Detect the Global EOR Signature (EDGES) measurement. The framework described here provides a foundation for the interpretation of future HERA results

Optimizing sparse RFI prediction using deep learning

Radio frequency interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array (HERA) grow larger in number of receivers. To address this, we present a deep fully convolutional neural network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known \u2018ground truth\u2019 data set for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6 7 105 HERA time-ordered 1024 channelled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time\u2013frequency context which increases discrimination between RFI and non-RFI. The inclusion of phase when predicting achieves a recall of 0.81, precision of 0.58, and F2 score of 0.75 as applied to our HERA-67 observations