2025-10-09 05:28:09
Each time a writing assistant completes a citation, something larger than convenience is taking place. A small transfer of visibility occurs, often invisible to the writer. The tool suggests a name, a title, and a year. The sentence looks finished. You accept it because it reads smoothly and feels professional. That fluency is not neutral. The model behind the suggestion has learned from archives of published texts that already overrepresent some names and underrepresent others.
\ When the interface proposes “as established by Smith (2017),” it is not evaluating relevance. It is reproducing a statistical pattern that privileges what appears most often. Accepting the suggestion takes a second, but over time, those seconds add up to a measurable redistribution of recognition. The process narrows the range of visible authors while creating the illusion of objectivity.
\ The study Citation by Completion: LLM Writing Aids and the Redistribution of Academic Credit examines this process as an economy of legitimacy that operates inside the sentence. Predictive text is not only a technical feature. It is a market of authority that functions through frequency. What appears most often in the model’s corpus becomes what is most often suggested, and what is most often suggested becomes what writers cite.
\ In controlled experiments, participants wrote short abstracts under three conditions: with prediction turned off, with neutral phrasing turned on, and with authority phrasing that included expressions such as “seminal work” or “canonical theory.” When authority phrasing appeared, citation diversity dropped sharply. The same few authors dominated the outputs, while novelty and variation declined. The findings show that predictive phrasing amplifies existing hierarchies by merging fluency with credibility.
\ The pattern is familiar in other fields. Streaming services recommend songs because they are already popular. Social media feeds amplify posts that match earlier engagement. Predictive writing applies the same logic to academic language. The model has seen certain names more often, so it offers them first. New or regional authors appear less because they occupy smaller parts of the corpus. Their visibility does not reflect quality but statistical presence.
\ For a researcher in Nairobi, Bogotá, or Dhaka, this means that their work may be absent from suggestion lists even if it addresses the same topic. Predictive writing, therefore, reproduces global asymmetries that already exist in publishing. The exclusion is not intentional but structural. The machine reflects the imbalance of its own training data, and the writer completes the cycle by accepting what reads as natural.
\ The study proposes a corrective structure called the Fair Citation Prompt. It reframes the predictive interface as a transparent mediator instead of an invisible assistant. Each time a citation is suggested, the system should show basic metadata: the frequency of the source within the corpus, the date of its last appearance, and its disciplinary or regional origin. Alongside the most probable suggestion, the interface should present an alternative drawn from a different field or location.
\ This small design change restores deliberation. The writer remains efficient but becomes aware of the pattern behind the prediction. Accepting a citation becomes an informed decision rather than a default action.
\ This issue also concerns domains beyond academia. Journalists use predictive text to finish common expressions such as “experts agree,” “according to reports,” or “widely accepted.” Corporate writers repeat “industry standard” and “best practice.” Legal professionals accept “established precedent” without checking its origin. These phrases are not neutral. They create an atmosphere of certainty that can replace evidence with familiarity.
\ Predictive systems accelerate this effect by reproducing the same formulations that appear in their training data. The result is language that feels authoritative even when it lacks verification. Form begins to replace truth, and fluency becomes the disguise of bias.
\ The practical lesson is clear. Every suggested citation is a decision about distribution. Before accepting it, ask whether the recommendation reflects relevance or repetition. Add one more source that represents a different perspective or linguistic community.
\ For example, when an English-language author appears as the default reference on digital ethics, look for a related study from Africa, Asia, or South America. The effort is small but significant. It keeps the advantages of predictive efficiency while preventing linguistic probability from becoming a filter that hides alternative viewpoints. Transparency in how suggestions are ranked preserves both speed and fairness.
\ In the long term, the goal is concrete. Writing tools should separate evidential phrasing from name prediction, reveal simple metadata for every recommendation, and always include at least one low-frequency alternative. Fairness then becomes a feature of syntax, not a moral afterthought. When systems adopt this approach, credit follows reasoning instead of inertia. Writers keep ownership of their decisions. Readers encounter arguments that reflect judgment, not only the recurrence of familiar names.
\ Predictive systems will continue to influence how text is produced. Their task is not to disappear but to become transparent. A sentence that reads well is not necessarily a sentence that represents knowledge well. Fluency must not conceal bias. The Fair Citation Prompt is one way to make this awareness operational. It transforms predictive writing from an invisible mechanism of repetition into a visible instrument of reflection. By revealing how linguistic probability shapes recognition, it allows authorship to remain deliberate even in an automated environment.
\ Read Citation by Completion: LLM Writing Aids and the Redistribution of Academic Credit to see how the Fair Citation Prompt can reshape academic writing and improve digital transparency. Write one paragraph with autocomplete on and another with it off. Compare which names appear and how authority syntax alters tone. Share your findings with editors or colleagues who use predictive systems. Each observation adds to a growing understanding of how fairness can begin in the structure of a sentence.
\ SSRN Author Page: https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=7639915 \n Website: https://www.agustinvstartari.com/
Ethos: I do not use artificial intelligence to write what I do not know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is authored. — Agustin V. Startari
2025-10-09 04:31:56
In 2016, the world changed; timelines that were once filled with real conversations have been quietly replaced by recycled opinions, trending hashtags, and algorithmically sculpted outrage. Everywhere you looked, there were automated accounts, engagement pods, and content farms.
\ Political bots disguised as people sparked endless debates, fake news sites spawned overnight, and armies of anonymous accounts shaped what millions came to believe.
\ For the first time, it seemed possible that the internet, this beautiful, chaotic experiment in human connection, had turned into a mirror hall designed to manipulate perception itself.
\ Of course, that was just a conspiracy theory… right?
As a web developer, I spend lots of time on LinkedIn, scrolling through my daily dose of “wisdom” from CEOs and fellow technologists. For years, I got used to the usual copy-paste trend: recycled motivational quotes, “inspiring” hiring stories that never really happened, and the same fanfics about “giving opportunities” for career growth.
\ But recently, things started to feel different; every post started to sound like an “author” that I’m quite familiar with: ChatGPT. As someone who has been using AI to speed up work since the launch of ChatGPT, it’s quite easy to spot when it’s spitting out AI text, and I’m not even talking about the enormous amount of emojis :upsidedownface:. \n
If you prompt ChatGPT to “write a viral, SEO-friendly LinkedIn post about career growth,” the result was nearly identical to what I had been seeing on my feed. That’s when I realized that they weren’t even pretending anymore.
\ And to make matters worse, it wasn’t just the posts. The comments were following the same pattern. When you look at them, it sounds like a constructive contribution to the topic. But if you look closely, they are more spit out AI slop.
\ Some users might be manually prompting an AI to generate their replies. Others probably have automated the whole flow with tools like n8n or custom scripts that scrape posts, feed them into an AI model, and auto-generate responses for engagement.
\ That’s when I remembered something I had read years ago: the Dead Internet Theory.
Around 2019, users on boards like Wizardchan and 4chan’s /x/ (the paranormal board) started posting that the internet no longer felt alive. They noticed the same comments repeating across websites, weirdly similar discussions popping up in unrelated threads, and the sense that fewer real people were actually posting. Some even started to claim that governments, corporations, or AI systems had taken over the web, flooding it with fake profiles to manipulate public opinion.
\ But the theory didn’t truly take off until January 2021, when a user named “IlluminatiPirate” posted a now-infamous essay on a small, retro-themed forum called Agora Road’s Macintosh Café.
\ In his post, he described his belief that the internet had “died” around 2016 or 2017 and has been replaced by a mixture of bots, algorithmic manipulation, and AI-generated content designed to simulate human activity. He argued that the web we use today is an illusion built by algorithms to keep us entertained, distracted, and predictable.
\ The post went viral on tech niche and conspiracy circles. For some, it was nothing more than a sci-fi paranoia. For others, it explained a feeling they couldn’t describe, how the modern web felt hollow and empty.
\ It was easy to think of it as another weird conspiracy theory. But as I scrolled through my LinkedIn feed and starred posts that felt just out of a ChatGPT prompt, the theory suddenly didn’t sound so absurd.
If the Dead Internet Theory was born on Agora Road’s Macintosh Café, then that’s where I needed to go.
\ I went there expecting it to be long gone, one of those places that died on the old web. But there it was, still alive in its own nostalgic way, with a minimalist layout straight from the early 2000s, pixel fonts, retro gradients, and threads that looked like they hadn’t changed since the MySpace era.
\ So, I decided to make my own post. I introduced myself and asked the remaining members what they believed now that we are in a world where AI chatbots, content farms, and algorithmic feeds are a reality.
\ While I was going through the replies on the forum, one comment caught my attention because of how human it sounded.
Humans will adapt. They just don’t want to grow anymore, maybe they’re too young, or maybe they’ve stopped caring. Still, even the bots serve a human purpose. People will get sick of them eventually. They can’t create, they can’t be original. I’m already sick of them now.
Scroll through Facebook or Instagram and you’ll see surreal AI mashups like Shrimp Jesus, an image of Christ reimagined as a shrimp that went viral in late 2023. At first sight, it was absurd and funny, but as the meme spread, it became clear that something had changed.
\ This isn’t the first time automation has quietly manipulated the way we think. Back in 2016, bots played an important role in shaping narratives. A study analyzing over 20 million tweets from that year’s U.S. election showed that automated accounts amplified misinformation on a massive scale, often being the first to push low-credibility links into trending spaces before any real user even saw them.
\ AI systems now generate entire posts, comment threads, and even fake personalities that maintain “consistent engagement,” complete with profile pictures, emojis, and scheduled activity. Some are controlled manually, others entirely automated through workflows like n8n or proprietary API chains. What you see on your feed might not be a person sharing their thoughts.
\ OpenAI’s upcoming Sora app, for example, integrates text-to-video generation directly into a social feed, blurring the line between creator and consumer.
\ Meanwhile, Meta recently revealed Vibes, an AI-centric content network where users explore surreal, algorithmically seeded media, which critics have already called “a showcase of AI slop.”
If there was ever a moment that confirmed the shift, it came in Imperva’s 2025 Bad Bot Report.
\ For the first time in over a decade, automated traffic officially surpassed human activity, making up 51% of all web traffic.
\ The report breaks down that more than 30% of this traffic came from malicious bots, scrapers, fake engagement accounts, and automated systems designed to harvest data or manipulate visibility. The rest were “good” bots: search crawlers, uptime monitors, and API calls. But the distinction doesn’t change the implication.
If bots now rule the web, then what happens when AIs start feeding on the content those bots produce?
\ Modern AI systems, including large language models, are constantly retrained on new data scraped from the web. The problem is that the web itself is increasingly filled with AI-generated articles, fake news, and fabricated citations that look authentic but aren’t.
\ So, pretty much, the machine is now learning from its own output, a closed loop of hallucinations reinforcing themselves.
\ A 2023 study titled The Curse of Recursion demonstrated how training models on their own generations leads to rapid degradation in quality; it means that the model starts producing distorted, repetitive, and meaningless content.
\ The same pattern is beginning to show up all over. AI news sites are publishing factually incorrect articles that end up cited by other AIs as reliable sources.
\ Social bots regurgitate hallucinated “facts” that slowly crawl their way into legitimate search indexes.
\ And as generative models get integrated into browsers, search engines, and personal assistants, the boundary between original data and AI echo grows thinner every day.
Maybe the internet didn’t die.
\ Maybe we did, a little bit, every time we stopped noticing the difference between what’s real and what’s not.
\ I’ve been online long enough to remember when it was all different. When blogs had weird layouts, the forums had blinking, bright signatures, everything was managed by the community, and no algorithm was deciding what to show and what not.
\ When I scroll through LinkedIn and see endless AI-written posts pretending to be human, the first thing that pops into my mind is that old forum thread I stumbled on years ago, that one claiming that the internet was already dead.
\ Probably, the web won’t stop existing; it will just stop being ours.
\
2025-10-09 03:50:02
Tel Aviv, Israel, October 8th, 2025/CyberNewsWire/--Miggo Security, pioneer and innovator in Application Detection & Response (ADR) and AI Runtime Defense, today announced it has been recognized as a Gartner Cool Vendor in AI Security. To us, this recognition underscores Miggo’s mission to close the detection-to-mitigation gap that plagues security teams today by providing comprehensive, fast, and precise analysis and response for what applications actually do at runtime.
Traditional security approaches are failing to match the dynamic, behavioral reality of modern applications. In fact, Gartner writes, “Through 2029, over 50% of successful cybersecurity attacks against AI agents will exploit access control issues, using direct or indirect prompt injection as an attack vector.”
However, Miggo’s runtime behavioral security can handle any application from traditional to AI-incorporated features, to AI apps themselves and AI agents. We believe Miggo Security’s ADR platform is cool because of how it detects and responds to security flaws in applications in a matter of minutes, combining unique runtime context with AI-augmented reasoning, risk analysis and actionable defense.
Miggo’s predictive analysis, preemptive protection, and real-time response is built specifically for the risks of AI-driven environments.
“This recognition by Gartner, in my opinion, validates the vision and innovation that define Miggo Security,” said Daniel Shechter, CEO and Co-Founder of Miggo Security. “We believe Application Detection & Response is the future of runtime security in the AI era to give CISOs and security teams the ability to know, prove, and shield AI-native threats in real time.”
Miggo’s differentiators include:
The GARTNER COOL VENDOR badge is a trademark and service mark of Gartner, Inc., and/or its affiliates, and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation.
Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Cool Vendors is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.
Miggo Security delivers real-time application detection and response (ADR), empowering enterprises to identify and neutralize application threats. With its AI-augmented platform, Miggo helps organizations secure both traditional and AI-driven applications at scale, reducing exposure windows by up to 99% and cutting operational overhead by 30% or more.
For more information, users can visit www.miggo.io.
CEO
Omri Hurwitz
Omri Hurwitz Media
:::tip This story was published as a press release by Cybernewswire under HackerNoon’s Business Blogging Program. Do Your Own Research before making any financial decision.
:::
\n
\n
2025-10-09 03:39:48
Westlake Village, California, USA, October 8th, 2025/Chainwire/--Rome Protocol, backed by a $9M seed round led by Hack VC, Portal Ventures, and Solana Founder Anatoly, has officially announced the mint date for its genesis NFT collection, Imperia: Rome Citizens. The mint goes live on October 14 via Magic Eden, priced at 0.753 SOL.
Forged at the intersection of identity and interoperability, Imperia: Rome Citizens represents the beginning of Rome’s on-chain empire. These NFTs are not merely collectibles, they are digital citizenships, granting holders early access to campaigns, drops, leaderboard, and integrations across the expanding Rome ecosystem.
Early contributors, top Solana NFT holders, and participants in the Megaphone or other Ecosystem Partner Campaigns are advised to check their WL eligibility.
Mint Details:
Supply & Phases:
Total Supply: 10,000 NFTs
Season 1 Mint: 3,677 NFTs
Phases:
556 — Guaranteed Whitelist
1,444 — Overallocated Whitelist
1,677 — Public Sale (FCFS)
Imperia NFTs will serve as the foundation for Rome’s identity and participation layer, offering:
About Rome Protocol
Rome Protocol is an interoperability and settlement layer designed to unify fragmented blockchains into a connected “empire.” It brings Solana’s performance to rollups, appchains, and EVM ecosystems, enabling builders to:
Rome has raised $9M in seed funding led by Hack VC and Portal Ventures, with backing from Anatoly Yakovenko and other industry leaders.
Website — https://www.rome.builders/
Docs — https://docs.rome.builders/
X — @RomeProtocol
Discord — https://discord.com/invite/romeprotocol
Marketing Lead
Chirag Ravishankar
Rome Protocol
:::tip \ :::
\ \n
\
2025-10-09 02:00:46
\ When it comes to modern selling, few voices carry more practical weight, or more personality, than Wes Schaeffer, host of *The BJJ and Biz Podcast *and the man behind The Sales Whisperer® brand.
\
\ A former Air Force officer turned sales leader, Wes has built a reputation for teaching people how to sell smarter, automate ethically, and never lose the human element. Wes has explored every facet of selling in the AI era through The BJJ and Biz Podcast, where he’s interviewed hundreds of founders, marketers, and operators, covering both automation’s upside and its very real dangers.
\ As AI tools promise to replace intuition and personalization, Wes remains clear: “Humanity is the next killer app.” In this conversation, he shares what that means for every salesperson, founder, and leader trying to navigate 2025’s noisy, automated landscape.
\
\
\ Wes: AI has made it easier to gather data and automate outreach, but the fundamentals haven’t changed. People still buy from people they like, trust, and remember. What’s different is that AI can now help youfind and understand those people faster.
\ On The BJJ and Biz Podcast, I tell guests all the time, 'If you don’t have a real conversation as you sell, you’re just automating rejection.' AI gives you speed, but not connection.
\
\
\ Wes: We’ve automated ourselves into numbness…and dumbness. Everyone’s inbox looks the same. Every pitch comes from the same guru who sold an overpriced retreat that became an overpriced masterclass that became an overpriced Black Friday sale that became an overpriced AI prompt that every side-hustle bro bought to resell to their unsuspecting clients who thought they were hiring an agency.
\
The salespeople who are winning today are the ones who sound alive. The ones who make you laugh, who actually listen, who remember your real concerns, be they professional or personal. I talk about this constantly on my podcast; it’s not about better tech, it’s about better timing and tone. The irony is that in a world full of machines, sounding human is now a differentiator.
\ \
\
\ Wes: They expect technology to fix a human problem. If your reps can’t sell without HubSpot, no CRM in the world is going to save you.
\ On The BJJ and Biz Podcast, I’ve had guests from billion-dollar startups admit they used automation to hide from prospects. Automation should make good reps faster, not make average reps lazier.
\
\
\ Wes: Use it to prepare, not to pretend. Let AI help you summarize notes, build cadences, and analyze win-loss patterns. But when you’re on the call, be fully present.
\ If you need ChatGPT to write your discovery questions, you’re not selling, you’re performing. I tell my audience every week: AI can give you words, but not wisdom.
\
\
\ Wes: In Jiu-Jitsu, you don’t panic, you adapt. You learn timing, leverage, and control. Same thing in sales.
\ AI has introduced new “attacks,” but the principles are timeless: stay calm, study your opponent, and use their momentum. The best sellers aren’t the strongest or the fastest; they’re the ones who stay composed when things change.
\ I’ve had black belts, CEOs, and authors on The BJJ and Biz Podcast who all say the same thing: mastery is just controlled curiosity under pressure.
\
\
\ Wes: Don’t chase tools, chase truth. Before you buy the next shiny SaaS product, ask, “What problem am I solving?”
\ If you can’t answer that, you’re not ready for the tool. I’ve talked with tech founders on The BJJ and Biz Podcast who built million-dollar platforms no one needed. If you’re buying software just because everyone else is, congratulations, you’re the demo, not the deal.
\
\
Wes:
\ Curiosity, communication, and courage. \n Curiosity helps you ask better questions. \n Communication builds trust. \n Courage helps you tell the truth, even when it’s uncomfortable.
\ AI can mimic curiosity and communication, but not courage. Until a bot can look someone in the eye and say, “No, this isn’t a fit,” we still have a job.
\
\
\ Wes: Yes. It’s happening now, and it will continue to get better. But what kind of deal? What size? Who will be held responsible if the deal structure is wrong? There will always be hard-chargers, fast-movers, and early-adopters who will assume the risk if they can be first to market, so they’ll demand to NOT speak with a human since “everyone is silly, stupid, and ignorant. I know what I want. Give it to me. I have places to be, things to do, and deals to crush!” But that’s a small percentage of the population. AI can suggest timing, write a great follow-up, suggest options and alternatives, but it doesn’t understand fear, ego, or pride, the real drivers of every deal. That will require a sales professional to land.
\
Selling is about emotion wrapped in logic. Until machines feel rejection, they’ll never truly sell. I ask every tech guest on my show the same thing: “If AI’s so smart, why can’t it negotiate a raise?”
\
\
\ Wes:
\ If it saves timeandmakes you more personal, use it. \n If it saves timebut makes you less human, skip it.
\ Efficiency without empathy is just spam at scale. Use technology to amplify your personality, not replace it. \n That’s the mindset I share with every guest onThe BJJ and Biz Podcast: tools come and go, but trust compounds.
\
\ Whether he’s on the mat practicing Jiu-Jitsu or behind the mic hosting The BJJ and Biz Podcast, his philosophy remains the same: don’t automate the moments that make you human.
As businesses race to plug in AI everywhere, Wes offers a refreshing reset. One that every salesperson, marketer, and leader should hear before their next pitch.
\
\
→ The BJJ and Biz Podcast with Wes Schaeffer, The Sales Whisperer®
Weekly conversations on selling smarter, marketing better, and leading with authenticity, featuring entrepreneurs, authors, and practitioners who understand thatwords still matter.
\ \n
2025-10-09 00:15:07
Dara & Instruments
3.1. CHIME and GMIMS surveys and 3.2. CHIME/GMIMS Low Band North
Features of the Tadpole
The Origin of the Tadpole
5.1. Neutral Hydrogen Structure
5.2. Ionized Hydrogen Structure
\ APPENDIX
A. RESOLVED AND UNRESOLVED FARADAY COMPONENTS IN FARADAY SYNTHESIS
\
\ The ringmaps we use do not have beam deconvolution applied. There are small artifacts in the image resulting from this which we describe in Section 4.5, however, their presence is not detrimental to studying structures on the scale of several degrees, such as the tadpole. In this analysis, we use the 400 − 729 MHz subset of the full CHIME band, as the highest frequencies are contaminated by aliasing, which makes the maps unreliable in the region of interest.
\ 3.2.1. Polarization angle calibration
\
\
\
\ Stokes U and V are measured from the crosscorrelation products. We assume that ⟨V ⟩ = 0 from the sky in diffuse emission because synchrotron emission in low-density astrophysical environments does not produce circular polarization. Leakage between V and U arises from phase offsets. We measure a mean phase shift ⟨ψ⟩(δ, ν) at each declination and frequency assuming that ⟨V ⟩ = 0 and calculate
\
\ The ⟨V ⟩ = 0 assumption leads to high-quality fits even in fast radio burst (FRB) observations, where the assumption has less clear physical justification than in the diffuse polarized emission we investigate (Mckinven et al. 2023). We find that the phase shift is linear in frequency, consistent with a cable delay τ = ⟨ψ⟩/2πν ∼ 1 ns for the diffuse emission, as Mckinven et al. (2021, their Appendix A) found in CHIME/FRB data.
\ In Figure 1, we compare the calibrated data to the Dwingeloo telescope survey at 610 MHz in the Fan region (Brouw & Spoelstra 1976). There is a strong correlation between Dwingeloo U and CHIME U and Dwingeloo Q and CHIME Q in those directions for which there is Dwingeloo data, with correlation coefficient R values of 0.91 for U − U and 0.89 for Q − Q comparisons. This is a significant improvement from the uncalibrated correlation coefficients of 0.76 and 0.59 respectively. We find a remaining leakage of up to 20% in Stokes Q based on unresolved point source measurements. Using the mean orthogonal distance between each point and the fitted line, we find that noise from CHIME and Dwingeloo data describe ≈ 70% of the scatter in Figure 1. The polarization angle correlation, also shown in Figure 1, is also improved through calibration, and most outliers are points with low polarized intensity (yellow dots), where the uncertainty in derived χ is high.
\ We show the resulting CHIME Q and U maps, with the χ = 0 reference axis rotated to the north Galactic pole, in Figure 2. While Stokes I to Q leakage does exist in our data, the tadpole structure cannot simply be the result of leakage. Although there is total intensity emission over the entire Fan Region, including the tadpole, this emission is featureless on small scales and thus cannot produce spurious polarization matching the tadpole in morphology. Furthermore, the tadpole cannot be the product of Stokes I emission originating at large angular distances (such as the Galactic plane) and seen in far sidelobes. While the far sidelobes have poor polarization properties, their polarization averages to low values over sizable areas. Moreover, with linear feeds, leakage from I is primarily into Q, not U (in the native equatorial coordinates of CHIME), but the tadpole is already evident in Stokes U in equatorial coordinates (not shown).
\
\
\ 3.2.2. Faraday synthesis on CHIME data
\
\
\ Using the rmtools_peakfitcube algorithm in RM-Tools, we obtain the peak Faraday depth and its
\
\ associated error for every spectrum along all lines of sight. The resulting map is shown in Figure 3b. We use peak Faraday depths rather than a first moment (Dickey et al. 2019) to focus on the Faraday depth of the brightest feature in each LOS rather than a weighted mean Faraday depth in Faraday complex regions.
\ We show the integrated polarized intensity across the Faraday depth spectra as a zero moment map in Figure 3a. A polarization angle map derotated to χ0 by the peak Faraday depth at each pixel is shown in Figure 3c.
\
:::info Authors:
(1) Nasser Mohammed, Department of Computer Science, Math, Physics, & Statistics, University of British Columbia, Okanagan Campus, Kelowna, BC V1V 1V7, Canada and Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada;
(2) Anna Ordog, Department of Computer Science, Math, Physics, & Statistics, University of British Columbia, Okanagan Campus, Kelowna, BC V1V 1V7, Canada and Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada;
(3) Rebecca A. Booth, Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4, Canada;
(4) Andrea Bracco, INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze, Italy and Laboratoire de Physique de l’Ecole Normale Superieure, ENS, Universit´e PSL, CNRS, Sorbonne Universite, Universite de Paris, F-75005 Paris, France;
(5) Jo-Anne C. Brown, Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4, Canada;
(6) Ettore Carretti, INAF-Istituto di Radioastronomia, Via Gobetti 101, 40129 Bologna, Italy;
(7) John M. Dickey, School of Natural Sciences, University of Tasmania, Hobart, Tas 7000 Australia;
(8) Simon Foreman, Department of Physics, Arizona State University, Tempe, AZ 85287, USA;
(9) Mark Halpern, Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1 Canada;
(10) Marijke Haverkorn, Department of Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands;
(11) Alex S. Hill, Department of Computer Science, Math, Physics, & Statistics, University of British Columbia, Okanagan Campus, Kelowna, BC V1V 1V7, Canada and Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada;
(12) Gary Hinshaw, Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1 Canada;
(13) Joseph W. Kania, Department of Physics and Astronomy, West Virginia University, P.O. Box 6315, Morgantown, WV 26506, USA and Center for Gravitational Waves and Cosmology, West Virginia University, Chestnut Ridge Research Building, Morgantown, WV 26505, USA;
(14) Roland Kothes, Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada;
(15) T.L. Landecker, Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada;
(16) Joshua MacEachern, Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1 Canada;
(17) Kiyoshi W. Masui, MIT Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA and Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA;
(18) Aimee Menard, Department of Computer Science, Math, Physics, & Statistics, University of British Columbia, Okanagan Campus, Kelowna, BC V1V 1V7, Canada and Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada;
(19) Ryan R. Ransom, Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada and Department of Physics and Astronomy, Okanagan College, Kelowna, BC V1Y 4X8, Canada;
(20) Wolfgang Reich, Max-Planck-Institut fur Radioastronomie, Auf dem Hugel 69, 53121 Bonn, Germany;(21) Patricia Reich, 16
(22) J. Richard Shaw, Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1 Canada
(23) Seth R. Siegel, Perimeter Institute for Theoretical Physics, 31 Caroline Street N, Waterloo, ON N25 2YL, Canada, Department of Physics, McGill University, 3600 rue University, Montreal, QC H3A 2T8, Canada, and Trottier Space Institute, McGill University, 3550 rue University, Montreal, QC H3A 2A7, Canada;
(24) Mehrnoosh Tahani, Banting and KIPAC Fellowships: Kavli Institute for Particle Astrophysics & Cosmology (KIPAC), Stanford University, Stanford, CA 94305, USA;
(25) Alec J. M. Thomson, ATNF, CSIRO Space & Astronomy, Bentley, WA, Australia;
(26) Tristan Pinsonneault-Marotte, Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1 Canada;
(27) Haochen Wang, MIT Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA and Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA;
(28) Jennifer L. West, Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada;
(29) Maik Wolleben, Skaha Remote Sensing Ltd., 3165 Juniper Drive, Naramata, BC V0H 1N0, Canada.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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